You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@doris.apache.org by GitBox <gi...@apache.org> on 2020/04/10 12:28:04 UTC
[GitHub] [incubator-doris] kangpinghuang opened a new pull request #3296:
spark data preparation process, issue #3295
kangpinghuang opened a new pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296
To resolve issue #3295, Now this pr accomplish the following tasks:
1. data etl
2. data repartition and bucket
3. rollup data build
4. write data to parquet
5. write job statistics
what are todos:
1. to support all udf that hadoop load support
2. optimize performance
3. more test cases
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407069068
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
Review comment:
```suggestion
public List<Object> columns =Lists.newArrayList();
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407070222
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
Review comment:
This todo should fix.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407317908
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DorisRangePartitioner.java
##########
@@ -0,0 +1,87 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.spark.Partitioner;
+
+import java.io.Serializable;
+import java.util.List;
+
+public class DorisRangePartitioner extends Partitioner {
+ private static final String UNPARTITIONED_TYPE = "UNPARTITIONED";
+ private EtlJobConfig.EtlPartitionInfo partitionInfo;
+ private List<PartitionRangeKey> partitionRangeKeys;
+ List<Integer> partitionKeyIndexes;
+ public DorisRangePartitioner(EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndexes,
+ List<PartitionRangeKey> partitionRangeKeys) {
+ this.partitionInfo = partitionInfo;
+ this.partitionKeyIndexes = partitionKeyIndexes;
+ this.partitionRangeKeys = partitionRangeKeys;
+ }
+
+ public int numPartitions() {
+ if (partitionInfo == null) {
+ return 0;
+ }
+ if (partitionInfo.partitionType.equalsIgnoreCase(UNPARTITIONED_TYPE)) {
+ return 1;
+ }
+ return partitionInfo.partitions.size();
+ }
+
+ public int getPartition(Object var1) {
+ if (partitionInfo.partitionType != null
+ && partitionInfo.partitionType.equalsIgnoreCase(UNPARTITIONED_TYPE)) {
+ return 0;
+ }
+ DppColumns key = (DppColumns)var1;
+ // get the partition columns from key as partition key
+ DppColumns partitionKey = new DppColumns(key, partitionKeyIndexes);
+ // TODO: optimize this by use binary search
Review comment:
maybe use a map instead of a list to avoid search is better?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407068924
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
Review comment:
Would better init columns and schema directly to new ArrayList, not null.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407318979
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
+ schema = null;
+ }
+
+ public DppColumns(List<Object> keys, List<Class> schema){
+ this.columns = keys;
+ this.schema = schema;
+
+ }
+
+ public DppColumns(DppColumns key, List<Integer> indexes){
+ columns = new ArrayList<Object>();
+ schema = new ArrayList<Class>();
+ for (int i = 0; i < indexes.size(); ++i) {
+ columns.add(key.columns.get(indexes.get(i)));
+ schema.add(key.schema.get(indexes.get(i)));
+ }
+ }
+
+ @Override
+ public int compareTo(DppColumns other) {
+ assert (columns.size() == other.columns.size());
+ assert (schema.size() == other.schema.size());
+
+ int cmp = 0;
+ for (int i = 0; i < columns.size(); i++) {
+ Object columnObj = columns.get(i);
+ Object otherColumn = other.columns.get(i);
+ if (columnObj == null && otherColumn == null) {
+ return 0;
+ } else if (columnObj == null || otherColumn == null) {
+ if (columnObj == null) {
+ return -1;
+ } else {
+ return 1;
+ }
+ }
+ if (columns.get(i) instanceof Integer) {
+ cmp = ((Integer)(columns.get(i))).compareTo((Integer)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Long) {
+ cmp = ((Long)(columns.get(i))).compareTo((Long)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Boolean) {
+ cmp = ((Boolean)(columns.get(i))).compareTo((Boolean) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Short) {
+ cmp = ((Short)(columns.get(i))).compareTo((Short)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Float) {
+ cmp = ((Float)(columns.get(i))).compareTo((Float) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Double) {
+ cmp = ((Double)(columns.get(i))).compareTo((Double) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Date) {
+ cmp = ((Date)(columns.get(i))).compareTo((Date) (other.columns.get(i)));
+ } else {
+ cmp = ((String)(columns.get(i))).compareTo((String) (other.columns.get(i)));
Review comment:
dealing binary for bitmap and hll here
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407317908
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DorisRangePartitioner.java
##########
@@ -0,0 +1,87 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.spark.Partitioner;
+
+import java.io.Serializable;
+import java.util.List;
+
+public class DorisRangePartitioner extends Partitioner {
+ private static final String UNPARTITIONED_TYPE = "UNPARTITIONED";
+ private EtlJobConfig.EtlPartitionInfo partitionInfo;
+ private List<PartitionRangeKey> partitionRangeKeys;
+ List<Integer> partitionKeyIndexes;
+ public DorisRangePartitioner(EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndexes,
+ List<PartitionRangeKey> partitionRangeKeys) {
+ this.partitionInfo = partitionInfo;
+ this.partitionKeyIndexes = partitionKeyIndexes;
+ this.partitionRangeKeys = partitionRangeKeys;
+ }
+
+ public int numPartitions() {
+ if (partitionInfo == null) {
+ return 0;
+ }
+ if (partitionInfo.partitionType.equalsIgnoreCase(UNPARTITIONED_TYPE)) {
+ return 1;
+ }
+ return partitionInfo.partitions.size();
+ }
+
+ public int getPartition(Object var1) {
+ if (partitionInfo.partitionType != null
+ && partitionInfo.partitionType.equalsIgnoreCase(UNPARTITIONED_TYPE)) {
+ return 0;
+ }
+ DppColumns key = (DppColumns)var1;
+ // get the partition columns from key as partition key
+ DppColumns partitionKey = new DppColumns(key, partitionKeyIndexes);
+ // TODO: optimize this by use binary search
Review comment:
maybe use a map instead of a list to avoid search is better?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407312053
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
Review comment:
1. For HLL and BITMAP,output schema is Binary instead of String
2. A better understand method name is,```columnTypeToClassForInputSchema``` and ```columnTypeToDataTypeForOutputSchema```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407069506
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + columnType);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
+ buffer = ByteBuffer.wrap(str.getBytes("UTF-8"));
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ }
+ return buffer;
+ }
+
+ public static long getHashValue(Row row, List<String> distributeColumns, StructType dstTableSchema) {
+ CRC32 hashValue = new CRC32();
+ for (String distColumn : distributeColumns) {
+ Object columnObject = row.get(row.fieldIndex(distColumn));
+ ByteBuffer buffer = getHashValue(columnObject, dstTableSchema.apply(distColumn).dataType());
+ hashValue.update(buffer.array(), 0, buffer.limit());
+ }
+ return hashValue.getValue();
+ }
+
+ public static StructType createDstTableSchema(List<EtlJobConfig.EtlColumn> columns, boolean addBucketIdColumn) throws UserException {
+ List<StructField> fields = new ArrayList<>();
+ if (addBucketIdColumn) {
+ StructField bucketIdField = DataTypes.createStructField(BUCKET_ID, DataTypes.StringType, true);
+ fields.add(bucketIdField);
+ }
+ for (EtlJobConfig.EtlColumn column : columns) {
+ String columnName = column.columnName;
+ String columnType = column.columnType;
+ DataType structColumnType = columnTypeToDataType(columnType);
+ StructField field = DataTypes.createStructField(columnName, structColumnType, column.isAllowNull);
+ fields.add(field);
+ }
+ StructType dstSchema = DataTypes.createStructType(fields);
+ return dstSchema;
+ }
+
+ public static List<String> parseColumnsFromPath(String filePath, List<String> columnsFromPath) throws UserException {
+ if (columnsFromPath == null || columnsFromPath.isEmpty()) {
+ return Collections.emptyList();
+ }
+ String[] strings = filePath.split("/");
+ if (strings.length < 2) {
+ System.err.println("Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
+ throw new UserException("Reason: Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
+ }
+ String[] columns = new String[columnsFromPath.size()];
+ int size = 0;
+ for (int i = strings.length - 2; i >= 0; i--) {
+ String str = strings[i];
+ if (str != null && str.isEmpty()) {
+ continue;
+ }
+ if (str == null || !str.contains("=")) {
+ System.err.println("Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
Review comment:
Please use Log. not `System.err.println`
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407069586
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/MinimalCoverRollupTreeBuilder.java
##########
@@ -0,0 +1,107 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+// Build RollupTree by using minimal cover strategy
Review comment:
What's the `minimal cover strategy` meaning?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407071305
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
Review comment:
Please add a detail comment to describe the work flow,
Such as:
step1 : xx
step2: xxx
step3:xxx
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407070343
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ }
+ }
+ MessageType index_schema = builder.named("index_" + indexMeta.indexId);
+ while (t.hasNext()) {
+ Row row = t.next();
+ if (row.length() <= 1) {
+ System.err.println("invalid row:" + row);
+ continue;
+ }
+ String curBucketKey = row.getString(0);
+ if (lastBucketKey == null || !curBucketKey.equals(lastBucketKey)) {
+ if (writer != null) {
+ System.out.println("close writer");
+ writer.close();
+ }
+ // flush current writer and create a new writer
+ String[] bucketKey = curBucketKey.split("_");
+ if (bucketKey.length != 2) {
+ System.err.println("invalid bucket key:" + curBucketKey);
+ continue;
+ }
+ int partitionId = Integer.parseInt(bucketKey[0]);
+ int bucketId = Integer.parseInt(bucketKey[1]);
+ String path = String.format(pathPattern, tableId, partitionId, indexMeta.indexId,
+ bucketId, indexMeta.schemaHash);
+ GroupWriteSupport.setSchema(index_schema, conf);
+ writer = new ParquetWriter<Group>(new Path(path), new GroupWriteSupport(),
+ CompressionCodecName.SNAPPY, 1024, 1024, 512,
+ true, false,
+ ParquetProperties.WriterVersion.PARQUET_1_0, conf);
+ if(writer != null){
+ System.out.println("[HdfsOperate]>> initialize writer succeed! path:" + path);
+ }
+ lastBucketKey = curBucketKey;
+ }
+ SimpleGroupFactory groupFactory = new SimpleGroupFactory(index_schema);
+ Group group = groupFactory.newGroup();
+ for (int i = 1; i < row.length(); i++) {
+ Object columnObject = row.get(i);
+ if (columnObject instanceof Short) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getShort(i));
+ } else if (columnObject instanceof Integer) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getInt(i));
+ } else if (columnObject instanceof String) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getString(i));
+ } else if (columnObject instanceof Long) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getLong(i));
+ }
+ }
+ try {
+ writer.write(group);
+ } catch (Exception e) {
+ System.err.println("exception caught:" + e);
+ e.printStackTrace();
+ }
+ }
+ if (writer != null) {
+ writer.close();
+ }
+ }
+ });
+ }
+
+ private void processRollupTree(RollupTreeNode rootNode,
+ Dataset<Row> rootDataframe,
+ long tableId, EtlJobConfig.EtlTable tableMeta, EtlJobConfig.EtlIndex baseIndex) {
+ Queue<RollupTreeNode> nodeQueue = new LinkedList<>();
+ nodeQueue.offer(rootNode);
+ int currentLevel = 0;
+ // level travel the tree
+ Map<Long, Dataset<Row>> parentDataframeMap = new HashMap<>();
+ parentDataframeMap.put(baseIndex.indexId, rootDataframe);
+ Map<Long, Dataset<Row>> childrenDataframeMap = new HashMap<>();
+ String pathPattern = etlJobConfig.outputPath + "/" + etlJobConfig.outputFilePattern;
+ while (!nodeQueue.isEmpty()) {
+ RollupTreeNode curNode = nodeQueue.poll();
+ System.err.println("start to process index:" + curNode.indexId);
+ if (curNode.children != null) {
+ for (RollupTreeNode child : curNode.children) {
+ nodeQueue.offer(child);
+ }
+ }
+ Dataset<Row> curDataFrame = null;
+ // column select for rollup
+ if (curNode.level != currentLevel) {
+ // need to unpersist parent dataframe
+ currentLevel = curNode.level;
+ parentDataframeMap.clear();
+ parentDataframeMap = childrenDataframeMap;
+ childrenDataframeMap = new HashMap<>();
+ }
+
+ long parentIndexId = baseIndex.indexId;
+ if (curNode.parent != null) {
+ parentIndexId = curNode.parent.indexId;
+ }
+
+ Dataset<Row> parentDataframe = parentDataframeMap.get(parentIndexId);
+ List<Column> columns = new ArrayList<>();
+ List<Column> keyColumns = new ArrayList<>();
+ Column bucketIdColumn = new Column(DppUtils.BUCKET_ID);
+ keyColumns.add(bucketIdColumn);
+ columns.add(bucketIdColumn);
+ for (String keyName : curNode.keyColumnNames) {
+ columns.add(new Column(keyName));
+ keyColumns.add(new Column(keyName));
+ }
+ for (String valueName : curNode.valueColumnNames) {
+ columns.add(new Column(valueName));
+ }
+ Seq<Column> columnSeq = JavaConverters.asScalaIteratorConverter(columns.iterator()).asScala().toSeq();
+ curDataFrame = parentDataframe.select(columnSeq);
+ if (curNode.indexMeta.indexType.equals("AGGREGATE")) {
+ // do aggregation
+ curDataFrame = processDataframeAgg(curDataFrame, curNode.indexMeta);
+ }
+ Seq<Column> keyColumnSeq = JavaConverters.asScalaIteratorConverter(keyColumns.iterator()).asScala().toSeq();
+ // should use sortWithinPartitions, not sort
+ // because sort will modify the partition number which will lead to bug
+ curDataFrame = curDataFrame.sortWithinPartitions(keyColumnSeq);
+ childrenDataframeMap.put(curNode.indexId, curDataFrame);
+
+ if (curNode.children != null && curNode.children.size() > 1) {
+ // if the children number larger than 1, persist the dataframe for performance
+ curDataFrame.persist();
+ }
+ writePartitionedAndSortedDataframeToParquet(curDataFrame, pathPattern, tableId, curNode.indexMeta);
+ }
+ }
+
+ private Dataset<Row> repartitionDataframeByBucketId(SparkSession spark, Dataset<Row> dataframe,
+ EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndex,
+ List<Class> partitionKeySchema,
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys,
+ List<String> keyColumnNames,
+ List<String> valueColumnNames,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<Long> validPartitionId) throws UserException {
+ List<String> distributeColumns = partitionInfo.distributionColumnRefs;
+ Partitioner partitioner = new DorisRangePartitioner(partitionInfo, partitionKeyIndex, partitionRangeKeys);
+ Set<Integer> validPartitionIndex = new HashSet<>();
+ if (validPartitionId == null) {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ validPartitionIndex.add(i);
+ }
+ } else {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ if (validPartitionId.contains(partitionInfo.partitions.get(i).partitionId)) {
+ validPartitionIndex.add(i);
+ }
+ }
+ }
+ JavaPairRDD<String, DppColumns> pairRDD = dataframe.javaRDD().flatMapToPair(
+ new PairFlatMapFunction<Row, String, DppColumns>() {
+ @Override
+ public Iterator<Tuple2<String, DppColumns>> call(Row row) {
+ List<Object> columns = new ArrayList<>();
+ List<Class> classes = new ArrayList<>();
+ List<Object> keyColumns = new ArrayList<>();
+ List<Class> keyClasses = new ArrayList<>();
+ for (String columnName : keyColumnNames) {
+ Object columnObject = row.get(row.fieldIndex(columnName));
+ columns.add(columnObject);
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ keyColumns.add(columnObject);
+ keyClasses.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+
+ for (String columnName : valueColumnNames) {
+ columns.add(row.get(row.fieldIndex(columnName)));
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+ DppColumns dppColumns = new DppColumns(columns, classes);
+ DppColumns key = new DppColumns(keyColumns, keyClasses);
+ List<Tuple2<String, DppColumns>> result = new ArrayList<>();
+ int pid = partitioner.getPartition(key);
+ if (!validPartitionIndex.contains(pid)) {
+ System.err.println("invalid partition for row:" + row + ", pid:" + pid);
+ abnormalRowAcc.add(1);
+ } else {
+ long hashValue = DppUtils.getHashValue(row, distributeColumns, dstTableSchema);
+ int bucketId = (int) ((hashValue & 0xffffffff) % partitionInfo.partitions.get(pid).bucketNum);
+ long partitionId = partitionInfo.partitions.get(pid).partitionId;
+ // bucketKey is partitionId_bucketId
+ String bucketKey = Long.toString(partitionId) + "_" + Integer.toString(bucketId);
+ Tuple2<String, DppColumns> newTuple = new Tuple2<String, DppColumns>(bucketKey, dppColumns);
+ result.add(newTuple);
+ }
+ return result.iterator();
+ }
+ });
+
+ JavaRDD<Row> resultRdd = pairRDD.map( record -> {
+ String bucketKey = record._1;
+ List<Object> row = new ArrayList<>();
+ // bucketKey as the first key
+ row.add(bucketKey);
+ row.addAll(record._2.columns);
+ return RowFactory.create(row.toArray());
+ }
+ );
+
+ StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(baseIndex.columns, true);
+ dataframe = spark.createDataFrame(resultRdd, tableSchemaWithBucketId);
+ // use bucket number as the parallel number
+ int reduceNum = 0;
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ reduceNum += partition.bucketNum;
+ }
+ dataframe = dataframe.repartition(reduceNum, new Column(DppUtils.BUCKET_ID));
+ return dataframe;
+ }
+
+ private Dataset<Row> convertSrcDataframeToDstDataframe(EtlJobConfig.EtlIndex baseIndex,
+ Dataset<Row> srcDataframe,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlFileGroup fileGroup) throws UserException {
+ System.out.println("convertSrcDataframeToDstDataframe for index:" + baseIndex);
Review comment:
Use Log
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407312053
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
Review comment:
1. For HLL and BITMAP,output schema is Binary instead of String
2. A better understand method name is,```columnTypeToClass ForInputSchema``` and ```columnTypeToDataType for output schema```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407068566
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
+ }
+ buffer.update(0, bos.toByteArray());
+ }
+
+ @Override
+ public void update(MutableAggregationBuffer buffer, Row input) {
+ // here maybe there is performance problems
+ if (!input.isNullAt(0)) {
Review comment:
This performance is ensure bad, not maybe.
You could refer to `DistinctCountBufferAggregator` in Apache Druid.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407071143
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ }
+ }
+ MessageType index_schema = builder.named("index_" + indexMeta.indexId);
+ while (t.hasNext()) {
+ Row row = t.next();
+ if (row.length() <= 1) {
+ System.err.println("invalid row:" + row);
+ continue;
+ }
+ String curBucketKey = row.getString(0);
+ if (lastBucketKey == null || !curBucketKey.equals(lastBucketKey)) {
+ if (writer != null) {
+ System.out.println("close writer");
+ writer.close();
+ }
+ // flush current writer and create a new writer
+ String[] bucketKey = curBucketKey.split("_");
+ if (bucketKey.length != 2) {
+ System.err.println("invalid bucket key:" + curBucketKey);
+ continue;
+ }
+ int partitionId = Integer.parseInt(bucketKey[0]);
+ int bucketId = Integer.parseInt(bucketKey[1]);
+ String path = String.format(pathPattern, tableId, partitionId, indexMeta.indexId,
+ bucketId, indexMeta.schemaHash);
+ GroupWriteSupport.setSchema(index_schema, conf);
+ writer = new ParquetWriter<Group>(new Path(path), new GroupWriteSupport(),
+ CompressionCodecName.SNAPPY, 1024, 1024, 512,
+ true, false,
+ ParquetProperties.WriterVersion.PARQUET_1_0, conf);
+ if(writer != null){
+ System.out.println("[HdfsOperate]>> initialize writer succeed! path:" + path);
+ }
+ lastBucketKey = curBucketKey;
+ }
+ SimpleGroupFactory groupFactory = new SimpleGroupFactory(index_schema);
+ Group group = groupFactory.newGroup();
+ for (int i = 1; i < row.length(); i++) {
+ Object columnObject = row.get(i);
+ if (columnObject instanceof Short) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getShort(i));
+ } else if (columnObject instanceof Integer) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getInt(i));
+ } else if (columnObject instanceof String) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getString(i));
+ } else if (columnObject instanceof Long) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getLong(i));
+ }
+ }
+ try {
+ writer.write(group);
+ } catch (Exception e) {
+ System.err.println("exception caught:" + e);
+ e.printStackTrace();
+ }
+ }
+ if (writer != null) {
+ writer.close();
+ }
+ }
+ });
+ }
+
+ private void processRollupTree(RollupTreeNode rootNode,
+ Dataset<Row> rootDataframe,
+ long tableId, EtlJobConfig.EtlTable tableMeta, EtlJobConfig.EtlIndex baseIndex) {
+ Queue<RollupTreeNode> nodeQueue = new LinkedList<>();
+ nodeQueue.offer(rootNode);
+ int currentLevel = 0;
+ // level travel the tree
+ Map<Long, Dataset<Row>> parentDataframeMap = new HashMap<>();
+ parentDataframeMap.put(baseIndex.indexId, rootDataframe);
+ Map<Long, Dataset<Row>> childrenDataframeMap = new HashMap<>();
+ String pathPattern = etlJobConfig.outputPath + "/" + etlJobConfig.outputFilePattern;
+ while (!nodeQueue.isEmpty()) {
+ RollupTreeNode curNode = nodeQueue.poll();
+ System.err.println("start to process index:" + curNode.indexId);
+ if (curNode.children != null) {
+ for (RollupTreeNode child : curNode.children) {
+ nodeQueue.offer(child);
+ }
+ }
+ Dataset<Row> curDataFrame = null;
+ // column select for rollup
+ if (curNode.level != currentLevel) {
+ // need to unpersist parent dataframe
+ currentLevel = curNode.level;
+ parentDataframeMap.clear();
+ parentDataframeMap = childrenDataframeMap;
+ childrenDataframeMap = new HashMap<>();
+ }
+
+ long parentIndexId = baseIndex.indexId;
+ if (curNode.parent != null) {
+ parentIndexId = curNode.parent.indexId;
+ }
+
+ Dataset<Row> parentDataframe = parentDataframeMap.get(parentIndexId);
+ List<Column> columns = new ArrayList<>();
+ List<Column> keyColumns = new ArrayList<>();
+ Column bucketIdColumn = new Column(DppUtils.BUCKET_ID);
+ keyColumns.add(bucketIdColumn);
+ columns.add(bucketIdColumn);
+ for (String keyName : curNode.keyColumnNames) {
+ columns.add(new Column(keyName));
+ keyColumns.add(new Column(keyName));
+ }
+ for (String valueName : curNode.valueColumnNames) {
+ columns.add(new Column(valueName));
+ }
+ Seq<Column> columnSeq = JavaConverters.asScalaIteratorConverter(columns.iterator()).asScala().toSeq();
+ curDataFrame = parentDataframe.select(columnSeq);
+ if (curNode.indexMeta.indexType.equals("AGGREGATE")) {
+ // do aggregation
+ curDataFrame = processDataframeAgg(curDataFrame, curNode.indexMeta);
+ }
+ Seq<Column> keyColumnSeq = JavaConverters.asScalaIteratorConverter(keyColumns.iterator()).asScala().toSeq();
+ // should use sortWithinPartitions, not sort
+ // because sort will modify the partition number which will lead to bug
+ curDataFrame = curDataFrame.sortWithinPartitions(keyColumnSeq);
+ childrenDataframeMap.put(curNode.indexId, curDataFrame);
+
+ if (curNode.children != null && curNode.children.size() > 1) {
+ // if the children number larger than 1, persist the dataframe for performance
+ curDataFrame.persist();
+ }
+ writePartitionedAndSortedDataframeToParquet(curDataFrame, pathPattern, tableId, curNode.indexMeta);
+ }
+ }
+
+ private Dataset<Row> repartitionDataframeByBucketId(SparkSession spark, Dataset<Row> dataframe,
+ EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndex,
+ List<Class> partitionKeySchema,
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys,
+ List<String> keyColumnNames,
+ List<String> valueColumnNames,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<Long> validPartitionId) throws UserException {
+ List<String> distributeColumns = partitionInfo.distributionColumnRefs;
+ Partitioner partitioner = new DorisRangePartitioner(partitionInfo, partitionKeyIndex, partitionRangeKeys);
+ Set<Integer> validPartitionIndex = new HashSet<>();
+ if (validPartitionId == null) {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ validPartitionIndex.add(i);
+ }
+ } else {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ if (validPartitionId.contains(partitionInfo.partitions.get(i).partitionId)) {
+ validPartitionIndex.add(i);
+ }
+ }
+ }
+ JavaPairRDD<String, DppColumns> pairRDD = dataframe.javaRDD().flatMapToPair(
+ new PairFlatMapFunction<Row, String, DppColumns>() {
+ @Override
+ public Iterator<Tuple2<String, DppColumns>> call(Row row) {
+ List<Object> columns = new ArrayList<>();
+ List<Class> classes = new ArrayList<>();
+ List<Object> keyColumns = new ArrayList<>();
+ List<Class> keyClasses = new ArrayList<>();
+ for (String columnName : keyColumnNames) {
+ Object columnObject = row.get(row.fieldIndex(columnName));
+ columns.add(columnObject);
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ keyColumns.add(columnObject);
+ keyClasses.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+
+ for (String columnName : valueColumnNames) {
+ columns.add(row.get(row.fieldIndex(columnName)));
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+ DppColumns dppColumns = new DppColumns(columns, classes);
+ DppColumns key = new DppColumns(keyColumns, keyClasses);
+ List<Tuple2<String, DppColumns>> result = new ArrayList<>();
+ int pid = partitioner.getPartition(key);
+ if (!validPartitionIndex.contains(pid)) {
+ System.err.println("invalid partition for row:" + row + ", pid:" + pid);
+ abnormalRowAcc.add(1);
+ } else {
+ long hashValue = DppUtils.getHashValue(row, distributeColumns, dstTableSchema);
+ int bucketId = (int) ((hashValue & 0xffffffff) % partitionInfo.partitions.get(pid).bucketNum);
+ long partitionId = partitionInfo.partitions.get(pid).partitionId;
+ // bucketKey is partitionId_bucketId
+ String bucketKey = Long.toString(partitionId) + "_" + Integer.toString(bucketId);
+ Tuple2<String, DppColumns> newTuple = new Tuple2<String, DppColumns>(bucketKey, dppColumns);
+ result.add(newTuple);
+ }
+ return result.iterator();
+ }
+ });
+
+ JavaRDD<Row> resultRdd = pairRDD.map( record -> {
+ String bucketKey = record._1;
+ List<Object> row = new ArrayList<>();
+ // bucketKey as the first key
+ row.add(bucketKey);
+ row.addAll(record._2.columns);
+ return RowFactory.create(row.toArray());
+ }
+ );
+
+ StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(baseIndex.columns, true);
+ dataframe = spark.createDataFrame(resultRdd, tableSchemaWithBucketId);
+ // use bucket number as the parallel number
+ int reduceNum = 0;
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ reduceNum += partition.bucketNum;
+ }
+ dataframe = dataframe.repartition(reduceNum, new Column(DppUtils.BUCKET_ID));
+ return dataframe;
+ }
+
+ private Dataset<Row> convertSrcDataframeToDstDataframe(EtlJobConfig.EtlIndex baseIndex,
+ Dataset<Row> srcDataframe,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlFileGroup fileGroup) throws UserException {
+ System.out.println("convertSrcDataframeToDstDataframe for index:" + baseIndex);
+ Dataset<Row> dataframe = srcDataframe;
+ StructType srcSchema = dataframe.schema();
+ Set<String> srcColumnNames = new HashSet<>();
+ for (StructField field : srcSchema.fields()) {
+ srcColumnNames.add(field.name());
+ }
+ Map<String, EtlJobConfig.EtlColumnMapping> columnMappings = fileGroup.columnMappings;
+ // 1. process simple columns
+ Set<String> mappingColumns = null;
+ if (columnMappings != null) {
+ mappingColumns = columnMappings.keySet();
+ }
+ List<String> dstColumnNames = new ArrayList<>();
+ for (StructField dstField : dstTableSchema.fields()) {
+ dstColumnNames.add(dstField.name());
+ EtlJobConfig.EtlColumn column = baseIndex.getColumn(dstField.name());
+ if (!srcColumnNames.contains(dstField.name())) {
+ if (mappingColumns != null && mappingColumns.contains(dstField.name())) {
+ // mapping columns will be processed in next step
+ continue;
+ }
+ if (column.defaultValue != null) {
+ if (column.defaultValue.equals(NULL_FLAG)) {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(null));
+ } else {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(column.defaultValue));
+ }
+ } else if (column.isAllowNull) {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(null));
+ } else {
+ throw new UserException("Reason: no data for column:" + dstField.name());
+ }
+ }
+ if (!column.columnType.equalsIgnoreCase(BITMAP_TYPE) && !dstField.dataType().equals(DataTypes.StringType)) {
+ dataframe = dataframe.withColumn(dstField.name(), dataframe.col(dstField.name()).cast(dstField.dataType()));
+ if (fileGroup.isNegative && !column.isKey) {
+ // negative load
+ // value will be convert te -1 * value
+ dataframe = dataframe.withColumn(dstField.name(), functions.expr("-1 *" + dstField.name()));
+ }
+ }
+ }
+ // 2. process the mapping columns
+ for (String mappingColumn : mappingColumns) {
+ String mappingDescription = columnMappings.get(mappingColumn).toDescription();
+ // here should cast data type to dst column type
+ dataframe = dataframe.withColumn(mappingColumn,
+ functions.expr(mappingDescription).cast(dstTableSchema.apply(mappingColumn).dataType()));
+ }
+ // projection and reorder the columns
+ dataframe.createOrReplaceTempView("src_table");
+ StringBuilder selectSqlBuilder = new StringBuilder();
+ selectSqlBuilder.append("select ");
+ for (String name : dstColumnNames) {
+ selectSqlBuilder.append(name + ",");
+ }
+ selectSqlBuilder.deleteCharAt(selectSqlBuilder.length() - 1);
+ selectSqlBuilder.append(" from src_table");
+ String selectSql = selectSqlBuilder.toString();
+ dataframe = spark.sql(selectSql);
+ return dataframe;
+ }
+
+ private Dataset<Row> loadDataFromPath(SparkSession spark,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ String fileUrl,
+ EtlJobConfig.EtlIndex baseIndex,
+ StructType dstTableSchema) throws UserException {
+ List<String> columnValueFromPath = DppUtils.parseColumnsFromPath(fileUrl, fileGroup.columnsFromPath);
+ List<String> dataSrcColumns = fileGroup.fileFieldNames;
+ if (dataSrcColumns == null) {
+ // if there is no source columns info
+ // use base index columns as source columns
+ dataSrcColumns = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn column : baseIndex.columns) {
+ dataSrcColumns.add(column.columnName);
+ }
+ }
+ List<String> dstTableNames = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn column : baseIndex.columns) {
+ dstTableNames.add(column.columnName);
+ }
+ List<String> srcColumnsWithColumnsFromPath = new ArrayList<>();
+ srcColumnsWithColumnsFromPath.addAll(dataSrcColumns);
+ if (fileGroup.columnsFromPath != null) {
+ srcColumnsWithColumnsFromPath.addAll(fileGroup.columnsFromPath);
+ }
+ StructType srcSchema = createScrSchema(srcColumnsWithColumnsFromPath);
+ JavaRDD<String> sourceDataRdd = spark.read().textFile(fileUrl).toJavaRDD();
+ int columnSize = dataSrcColumns.size();
+ // now we first support csv file
+ // TODO: support parquet file and orc file
+ JavaRDD<Row> rowRDD = sourceDataRdd.flatMap(
+ record -> {
+ scannedRowsAcc.add(1);
+ String[] attributes = record.split(fileGroup.columnSeparator);
+ List<Row> result = new ArrayList<>();
+ if (attributes.length != columnSize) {
+ abnormalRowAcc.add(1);
+ System.err.println("invalid src schema, data columns:"
+ + attributes.length + ", file group columns:"
+ + columnSize + ", row:" + record);
+ } else {
+ boolean validRow = true;
+ for (int i = 0; i < attributes.length; ++i) {
+ if (attributes[i].equals(NULL_FLAG)) {
+ if (baseIndex.columns.get(i).isAllowNull) {
+ attributes[i] = null;
+ } else {
+ abnormalRowAcc.add(1);
+ System.err.println("colunm:" + i + " can not be null. row:" + record);
+ validRow = false;
+ break;
+ }
+ }
+ boolean isStrictMode = (boolean)etlJobConfig.properties.strictMode;
+ if (isStrictMode) {
+ StructField field = srcSchema.apply(i);
+ if (dstTableNames.contains(field.name())) {
+ DataType type = dstTableSchema.apply(field.name()).dataType();
+ if (!type.equals(DataTypes.StringType)) {
+ if (type.equals(DataTypes.ShortType)) {
+ try{
+ Short value = Short.parseShort(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.IntegerType)) {
+ try {
+ Integer value = Integer.parseInt(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.LongType)) {
+ try {
+ Long value = Long.parseLong(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.FloatType)) {
+ try {
+ Float value = Float.parseFloat(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.DoubleType)) {
+ try {
+ Double value = Double.parseDouble(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.DateType)) {
+ try {
+ long value = Date.parse(attributes[i]);
+ } catch (IllegalArgumentException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ }
+ }
+ }
+ }
+ }
+ if (validRow) {
+ Row row = null;
+ if (fileGroup.columnsFromPath == null) {
+ row = RowFactory.create(attributes);
+ } else {
+ // process columns from path
+ // append columns from path to the tail
+ List<String> columnAttributes = new ArrayList<>();
+ columnAttributes.addAll(Arrays.asList(attributes));
+ columnAttributes.addAll(columnValueFromPath);
+ row = RowFactory.create(columnAttributes.toArray());
+ }
+ result.add(row);
+ }
+ }
+ return result.iterator();
+ }
+ );
+
+ Dataset<Row> dataframe = spark.createDataFrame(rowRDD, srcSchema);
+ return dataframe;
+ }
+
+ private StructType createScrSchema(List<String> srcColumns) {
+ List<StructField> fields = new ArrayList<>();
+ for (String srcColumn : srcColumns) {
+ // user StringType to load source data
+ StructField field = DataTypes.createStructField(srcColumn, DataTypes.StringType, true);
+ fields.add(field);
+ }
+ StructType srcSchema = DataTypes.createStructType(fields);
+ return srcSchema;
+ }
+
+ // partition keys will be parsed into double from json
+ // so need to convert it to partition columns' type
+ Object convertPartitionKey(Object srcValue, Class dstClass) {
+ if (dstClass.equals(Float.class) || dstClass.equals(Double.class)) {
+ return null;
+ }
+ if (srcValue instanceof Double) {
+ if (dstClass.equals(Short.class)) {
+ return ((Double)srcValue).shortValue();
+ } else if (dstClass.equals(Integer.class)) {
+ return ((Double)srcValue).intValue();
+ } else if (dstClass.equals(Long.class)) {
+ return ((Double)srcValue).longValue();
+ } else if (dstClass.equals(BigInteger.class)) {
+ return new BigInteger(((Double)srcValue).toString());
+ } else {
+ // dst type is string
+ return srcValue.toString();
+ }
+ } else if (srcValue instanceof String) {
+ // TODO: support src value is string type
+ return null;
+ } else {
+ System.out.println("unsupport partition key:" + srcValue);
+ return null;
+ }
+ }
+
+ private List<DorisRangePartitioner.PartitionRangeKey> createPartitionRangeKeys(
+ EtlJobConfig.EtlPartitionInfo partitionInfo, List<Class> partitionKeySchema) {
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys = new ArrayList<>();
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ DorisRangePartitioner.PartitionRangeKey partitionRangeKey = new DorisRangePartitioner.PartitionRangeKey();
+ List<Object> startKeyColumns = new ArrayList<>();
+ for (int i = 0; i < partition.startKeys.size(); i++) {
+ Object value = partition.startKeys.get(i);
+ startKeyColumns.add(convertPartitionKey(value, partitionKeySchema.get(i)));
+ }
+ partitionRangeKey.startKeys = new DppColumns(startKeyColumns, partitionKeySchema);;
+ if (!partition.isMaxPartition) {
+ partitionRangeKey.isMaxPartition = false;
+ List<Object> endKeyColumns = new ArrayList<>();
+ for (int i = 0; i < partition.endKeys.size(); i++) {
+ Object value = partition.endKeys.get(i);
+ endKeyColumns.add(convertPartitionKey(value, partitionKeySchema.get(i)));
+ }
+ partitionRangeKey.endKeys = new DppColumns(endKeyColumns, partitionKeySchema);
+ } else {
+ partitionRangeKey.isMaxPartition = false;
+ }
+ partitionRangeKeys.add(partitionRangeKey);
+ }
+ return partitionRangeKeys;
+ }
+
+ private Dataset<Row> loadDataFromFilePaths(SparkSession spark,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<String> filePaths,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ StructType dstTableSchema) throws UserException {
+ Dataset<Row> fileGroupDataframe = null;
+ for (String filePath : filePaths) {
+ fileNumberAcc.add(1);
+ try {
+ Configuration conf = new Configuration();
+ URI uri = new URI(filePath);
+ FileSystem fs = FileSystem.get(uri, conf);
+ FileStatus fileStatus = fs.getFileStatus(new Path(filePath));
+ fileSizeAcc.add(fileStatus.getLen());
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+ if (fileGroup.columnSeparator == null) {
+ System.err.println("invalid null column separator!");
+ throw new UserException("Reason: invalid null column separator!");
+ }
+ Dataset<Row> dataframe = null;
+
+ dataframe = loadDataFromPath(spark, fileGroup, filePath, baseIndex, dstTableSchema);
+ dataframe = convertSrcDataframeToDstDataframe(baseIndex, dataframe, dstTableSchema, fileGroup);
+ if (fileGroupDataframe == null) {
+ fileGroupDataframe = dataframe;
+ } else {
+ fileGroupDataframe.union(dataframe);
+ }
+ }
+ fileGroupDataframe.show();
+ return fileGroupDataframe;
+ }
+
+ private Dataset<Row> loadDataFromHiveTable(SparkSession spark,
+ String hiveTableName,
+ EtlJobConfig.EtlIndex baseIndex,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ StructType dstTableSchema) throws UserException {
+ Dataset<Row> dataframe = spark.sql("select * from " + hiveTableName);
+ dataframe = convertSrcDataframeToDstDataframe(baseIndex, dataframe, dstTableSchema, fileGroup);
+ return dataframe;
+ }
+
+ private DppResult process() throws Exception {
+ dppResult = new DppResult();
+ try {
+ for (Map.Entry<Long, EtlJobConfig.EtlTable> entry : etlJobConfig.tables.entrySet()) {
+ Long tableId = entry.getKey();
+ EtlJobConfig.EtlTable etlTable = entry.getValue();
+
+ // get the base index meta
+ EtlJobConfig.EtlIndex baseIndex = null;
+ for (EtlJobConfig.EtlIndex indexMeta : etlTable.indexes) {
+ if (indexMeta.isBaseIndex) {
+ baseIndex = indexMeta;
+ break;
+ }
+ }
+
+ // get key column names and value column names seperately
+ List<String> keyColumnNames = new ArrayList<>();
+ List<String> valueColumnNames = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn etlColumn : baseIndex.columns) {
+ if (etlColumn.isKey) {
+ keyColumnNames.add(etlColumn.columnName);
+ } else {
+ valueColumnNames.add(etlColumn.columnName);
+ }
+ }
+
+ EtlJobConfig.EtlPartitionInfo partitionInfo = etlTable.partitionInfo;
+ List<Integer> partitionKeyIndex = new ArrayList<Integer>();
+ List<Class> partitionKeySchema = new ArrayList<>();
+ for (String key : partitionInfo.partitionColumnRefs) {
+ for (int i = 0; i < baseIndex.columns.size(); ++i) {
+ EtlJobConfig.EtlColumn column = baseIndex.columns.get(i);
+ if (column.columnName.equals(key)) {
+ partitionKeyIndex.add(i);
+ partitionKeySchema.add(DppUtils.columnTypeToClass(column.columnType));
+ break;
+ }
+ }
+ }
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys = createPartitionRangeKeys(partitionInfo, partitionKeySchema);
+ StructType dstTableSchema = DppUtils.createDstTableSchema(baseIndex.columns, false);
+ RollupTreeBuilder rollupTreeParser = new MinimalCoverRollupTreeBuilder();
+ RollupTreeNode rootNode = rollupTreeParser.build(etlTable);
+ System.out.println("Rollup Tree:" + rootNode);
+
+ for (EtlJobConfig.EtlFileGroup fileGroup : etlTable.fileGroups) {
+ List<String> filePaths = fileGroup.filePaths;
+ Dataset<Row> fileGroupDataframe = null;
+ if (Strings.isNullOrEmpty(fileGroup.hiveTableName)) {
+ fileGroupDataframe = loadDataFromFilePaths(spark, baseIndex, filePaths, fileGroup, dstTableSchema);
+ } else {
+ String taskId = etlJobConfig.outputPath.substring(etlJobConfig.outputPath.lastIndexOf("/") + 1);
+ String dorisIntermediateHiveTable = String.format(EtlJobConfig.DORIS_INTERMEDIATE_HIVE_TABLE_NAME,
+ tableId, taskId);
+ fileGroupDataframe = loadDataFromHiveTable(spark, dorisIntermediateHiveTable, baseIndex, fileGroup, dstTableSchema);
+ }
+ if (fileGroupDataframe == null) {
+ System.err.println("no data for file file group:" + fileGroup);
+ continue;
+ }
+ if (!Strings.isNullOrEmpty(fileGroup.where)) {
+ long originalSize = fileGroupDataframe.count();
+ fileGroupDataframe = fileGroupDataframe.filter(fileGroup.where);
+ long currentSize = fileGroupDataframe.count();
+ unselectedRowAcc.add(currentSize - originalSize);
+ }
+
+ fileGroupDataframe = repartitionDataframeByBucketId(spark, fileGroupDataframe,
+ partitionInfo, partitionKeyIndex,
+ partitionKeySchema, partitionRangeKeys,
+ keyColumnNames, valueColumnNames,
+ dstTableSchema, baseIndex, fileGroup.partitions);
+ processRollupTree(rootNode, fileGroupDataframe, tableId, etlTable, baseIndex);
+ }
+ }
+ spark.stop();
+ } catch (Exception exception) {
+ exception.printStackTrace();
+ System.err.println("spark dpp failed for exception:" + exception);
+ dppResult.isSuccess = false;
+ dppResult.failedReason = exception.getMessage();
+ dppResult.normalRows = scannedRowsAcc.value() - abnormalRowAcc.value();
+ dppResult.scannedRows = scannedRowsAcc.value();
+ dppResult.fileNumber = fileNumberAcc.value();
+ dppResult.fileSize = fileSizeAcc.value();
+ dppResult.abnormalRows = abnormalRowAcc.value();
+ throw exception;
Review comment:
Are you sure you want to throw exception?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417815823
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
+ }
+ buffer.update(0, bos.toByteArray());
+ }
+
+ @Override
+ public void update(MutableAggregationBuffer buffer, Row input) {
+ // here maybe there is performance problems
+ if (!input.isNullAt(0)) {
Review comment:
The performance optimization will be done in next step.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r415601679
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + columnType);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
+ buffer = ByteBuffer.wrap(str.getBytes("UTF-8"));
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ }
+ return buffer;
+ }
+
+ public static long getHashValue(Row row, List<String> distributeColumns, StructType dstTableSchema) {
+ CRC32 hashValue = new CRC32();
+ for (String distColumn : distributeColumns) {
+ Object columnObject = row.get(row.fieldIndex(distColumn));
+ ByteBuffer buffer = getHashValue(columnObject, dstTableSchema.apply(distColumn).dataType());
+ hashValue.update(buffer.array(), 0, buffer.limit());
+ }
+ return hashValue.getValue();
+ }
+
+ public static StructType createDstTableSchema(List<EtlJobConfig.EtlColumn> columns, boolean addBucketIdColumn) throws UserException {
+ List<StructField> fields = new ArrayList<>();
+ if (addBucketIdColumn) {
+ StructField bucketIdField = DataTypes.createStructField(BUCKET_ID, DataTypes.StringType, true);
+ fields.add(bucketIdField);
+ }
+ for (EtlJobConfig.EtlColumn column : columns) {
+ String columnName = column.columnName;
+ String columnType = column.columnType;
Review comment:
ok, I will remove them
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] yangzhg commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
yangzhg commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r408560620
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + columnType);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
+ buffer = ByteBuffer.wrap(str.getBytes("UTF-8"));
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ }
+ return buffer;
+ }
+
+ public static long getHashValue(Row row, List<String> distributeColumns, StructType dstTableSchema) {
+ CRC32 hashValue = new CRC32();
+ for (String distColumn : distributeColumns) {
+ Object columnObject = row.get(row.fieldIndex(distColumn));
+ ByteBuffer buffer = getHashValue(columnObject, dstTableSchema.apply(distColumn).dataType());
+ hashValue.update(buffer.array(), 0, buffer.limit());
+ }
+ return hashValue.getValue();
+ }
+
+ public static StructType createDstTableSchema(List<EtlJobConfig.EtlColumn> columns, boolean addBucketIdColumn) throws UserException {
+ List<StructField> fields = new ArrayList<>();
+ if (addBucketIdColumn) {
+ StructField bucketIdField = DataTypes.createStructField(BUCKET_ID, DataTypes.StringType, true);
+ fields.add(bucketIdField);
+ }
+ for (EtlJobConfig.EtlColumn column : columns) {
+ String columnName = column.columnName;
+ String columnType = column.columnType;
Review comment:
seems not necessary to define columnName and columnType
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407312053
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
Review comment:
1. For HLL and BITMAP,output schema is Binary instead of String
2. A better understand method name is,```columnTypeToClassForInputSchema``` and ```columnTypeToDataType for output schema```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangkaisen commented on a change in pull request
#3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangkaisen commented on a change in pull request #3296: spark data preparation process, issue #3295
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r407069192
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
+ schema = null;
+ }
+
+ public DppColumns(List<Object> keys, List<Class> schema){
+ this.columns = keys;
+ this.schema = schema;
+
+ }
+
+ public DppColumns(DppColumns key, List<Integer> indexes){
+ columns = new ArrayList<Object>();
+ schema = new ArrayList<Class>();
+ for (int i = 0; i < indexes.size(); ++i) {
+ columns.add(key.columns.get(indexes.get(i)));
+ schema.add(key.schema.get(indexes.get(i)));
+ }
+ }
+
+ @Override
+ public int compareTo(DppColumns other) {
+ assert (columns.size() == other.columns.size());
Review comment:
Prefer `Preconditions.checkState`
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] yangzhg commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
yangzhg commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r414438775
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
Review comment:
better use log
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417816377
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + columnType);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
+ buffer = ByteBuffer.wrap(str.getBytes("UTF-8"));
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ }
+ return buffer;
+ }
+
+ public static long getHashValue(Row row, List<String> distributeColumns, StructType dstTableSchema) {
+ CRC32 hashValue = new CRC32();
+ for (String distColumn : distributeColumns) {
+ Object columnObject = row.get(row.fieldIndex(distColumn));
+ ByteBuffer buffer = getHashValue(columnObject, dstTableSchema.apply(distColumn).dataType());
+ hashValue.update(buffer.array(), 0, buffer.limit());
+ }
+ return hashValue.getValue();
+ }
+
+ public static StructType createDstTableSchema(List<EtlJobConfig.EtlColumn> columns, boolean addBucketIdColumn) throws UserException {
+ List<StructField> fields = new ArrayList<>();
+ if (addBucketIdColumn) {
+ StructField bucketIdField = DataTypes.createStructField(BUCKET_ID, DataTypes.StringType, true);
+ fields.add(bucketIdField);
+ }
+ for (EtlJobConfig.EtlColumn column : columns) {
+ String columnName = column.columnName;
+ String columnType = column.columnType;
+ DataType structColumnType = columnTypeToDataType(columnType);
+ StructField field = DataTypes.createStructField(columnName, structColumnType, column.isAllowNull);
+ fields.add(field);
+ }
+ StructType dstSchema = DataTypes.createStructType(fields);
+ return dstSchema;
+ }
+
+ public static List<String> parseColumnsFromPath(String filePath, List<String> columnsFromPath) throws UserException {
+ if (columnsFromPath == null || columnsFromPath.isEmpty()) {
+ return Collections.emptyList();
+ }
+ String[] strings = filePath.split("/");
+ if (strings.length < 2) {
+ System.err.println("Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
+ throw new UserException("Reason: Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
+ }
+ String[] columns = new String[columnsFromPath.size()];
+ int size = 0;
+ for (int i = strings.length - 2; i >= 0; i--) {
+ String str = strings[i];
+ if (str != null && str.isEmpty()) {
+ continue;
+ }
+ if (str == null || !str.contains("=")) {
+ System.err.println("Fail to parse columnsFromPath, expected: " + columnsFromPath + ", filePath: " + filePath);
Review comment:
done
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417817943
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ }
+ }
+ MessageType index_schema = builder.named("index_" + indexMeta.indexId);
+ while (t.hasNext()) {
+ Row row = t.next();
+ if (row.length() <= 1) {
+ System.err.println("invalid row:" + row);
+ continue;
+ }
+ String curBucketKey = row.getString(0);
+ if (lastBucketKey == null || !curBucketKey.equals(lastBucketKey)) {
+ if (writer != null) {
+ System.out.println("close writer");
+ writer.close();
+ }
+ // flush current writer and create a new writer
+ String[] bucketKey = curBucketKey.split("_");
+ if (bucketKey.length != 2) {
+ System.err.println("invalid bucket key:" + curBucketKey);
+ continue;
+ }
+ int partitionId = Integer.parseInt(bucketKey[0]);
+ int bucketId = Integer.parseInt(bucketKey[1]);
+ String path = String.format(pathPattern, tableId, partitionId, indexMeta.indexId,
+ bucketId, indexMeta.schemaHash);
+ GroupWriteSupport.setSchema(index_schema, conf);
+ writer = new ParquetWriter<Group>(new Path(path), new GroupWriteSupport(),
+ CompressionCodecName.SNAPPY, 1024, 1024, 512,
+ true, false,
+ ParquetProperties.WriterVersion.PARQUET_1_0, conf);
+ if(writer != null){
+ System.out.println("[HdfsOperate]>> initialize writer succeed! path:" + path);
+ }
+ lastBucketKey = curBucketKey;
+ }
+ SimpleGroupFactory groupFactory = new SimpleGroupFactory(index_schema);
+ Group group = groupFactory.newGroup();
+ for (int i = 1; i < row.length(); i++) {
+ Object columnObject = row.get(i);
+ if (columnObject instanceof Short) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getShort(i));
+ } else if (columnObject instanceof Integer) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getInt(i));
+ } else if (columnObject instanceof String) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getString(i));
+ } else if (columnObject instanceof Long) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getLong(i));
+ }
+ }
+ try {
+ writer.write(group);
+ } catch (Exception e) {
+ System.err.println("exception caught:" + e);
+ e.printStackTrace();
+ }
+ }
+ if (writer != null) {
+ writer.close();
+ }
+ }
+ });
+ }
+
+ private void processRollupTree(RollupTreeNode rootNode,
+ Dataset<Row> rootDataframe,
+ long tableId, EtlJobConfig.EtlTable tableMeta, EtlJobConfig.EtlIndex baseIndex) {
+ Queue<RollupTreeNode> nodeQueue = new LinkedList<>();
+ nodeQueue.offer(rootNode);
+ int currentLevel = 0;
+ // level travel the tree
+ Map<Long, Dataset<Row>> parentDataframeMap = new HashMap<>();
+ parentDataframeMap.put(baseIndex.indexId, rootDataframe);
+ Map<Long, Dataset<Row>> childrenDataframeMap = new HashMap<>();
+ String pathPattern = etlJobConfig.outputPath + "/" + etlJobConfig.outputFilePattern;
+ while (!nodeQueue.isEmpty()) {
+ RollupTreeNode curNode = nodeQueue.poll();
+ System.err.println("start to process index:" + curNode.indexId);
+ if (curNode.children != null) {
+ for (RollupTreeNode child : curNode.children) {
+ nodeQueue.offer(child);
+ }
+ }
+ Dataset<Row> curDataFrame = null;
+ // column select for rollup
+ if (curNode.level != currentLevel) {
+ // need to unpersist parent dataframe
+ currentLevel = curNode.level;
+ parentDataframeMap.clear();
+ parentDataframeMap = childrenDataframeMap;
+ childrenDataframeMap = new HashMap<>();
+ }
+
+ long parentIndexId = baseIndex.indexId;
+ if (curNode.parent != null) {
+ parentIndexId = curNode.parent.indexId;
+ }
+
+ Dataset<Row> parentDataframe = parentDataframeMap.get(parentIndexId);
+ List<Column> columns = new ArrayList<>();
+ List<Column> keyColumns = new ArrayList<>();
+ Column bucketIdColumn = new Column(DppUtils.BUCKET_ID);
+ keyColumns.add(bucketIdColumn);
+ columns.add(bucketIdColumn);
+ for (String keyName : curNode.keyColumnNames) {
+ columns.add(new Column(keyName));
+ keyColumns.add(new Column(keyName));
+ }
+ for (String valueName : curNode.valueColumnNames) {
+ columns.add(new Column(valueName));
+ }
+ Seq<Column> columnSeq = JavaConverters.asScalaIteratorConverter(columns.iterator()).asScala().toSeq();
+ curDataFrame = parentDataframe.select(columnSeq);
+ if (curNode.indexMeta.indexType.equals("AGGREGATE")) {
+ // do aggregation
+ curDataFrame = processDataframeAgg(curDataFrame, curNode.indexMeta);
+ }
+ Seq<Column> keyColumnSeq = JavaConverters.asScalaIteratorConverter(keyColumns.iterator()).asScala().toSeq();
+ // should use sortWithinPartitions, not sort
+ // because sort will modify the partition number which will lead to bug
+ curDataFrame = curDataFrame.sortWithinPartitions(keyColumnSeq);
+ childrenDataframeMap.put(curNode.indexId, curDataFrame);
+
+ if (curNode.children != null && curNode.children.size() > 1) {
+ // if the children number larger than 1, persist the dataframe for performance
+ curDataFrame.persist();
+ }
+ writePartitionedAndSortedDataframeToParquet(curDataFrame, pathPattern, tableId, curNode.indexMeta);
+ }
+ }
+
+ private Dataset<Row> repartitionDataframeByBucketId(SparkSession spark, Dataset<Row> dataframe,
+ EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndex,
+ List<Class> partitionKeySchema,
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys,
+ List<String> keyColumnNames,
+ List<String> valueColumnNames,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<Long> validPartitionId) throws UserException {
+ List<String> distributeColumns = partitionInfo.distributionColumnRefs;
+ Partitioner partitioner = new DorisRangePartitioner(partitionInfo, partitionKeyIndex, partitionRangeKeys);
+ Set<Integer> validPartitionIndex = new HashSet<>();
+ if (validPartitionId == null) {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ validPartitionIndex.add(i);
+ }
+ } else {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ if (validPartitionId.contains(partitionInfo.partitions.get(i).partitionId)) {
+ validPartitionIndex.add(i);
+ }
+ }
+ }
+ JavaPairRDD<String, DppColumns> pairRDD = dataframe.javaRDD().flatMapToPair(
+ new PairFlatMapFunction<Row, String, DppColumns>() {
+ @Override
+ public Iterator<Tuple2<String, DppColumns>> call(Row row) {
+ List<Object> columns = new ArrayList<>();
+ List<Class> classes = new ArrayList<>();
+ List<Object> keyColumns = new ArrayList<>();
+ List<Class> keyClasses = new ArrayList<>();
+ for (String columnName : keyColumnNames) {
+ Object columnObject = row.get(row.fieldIndex(columnName));
+ columns.add(columnObject);
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ keyColumns.add(columnObject);
+ keyClasses.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+
+ for (String columnName : valueColumnNames) {
+ columns.add(row.get(row.fieldIndex(columnName)));
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+ DppColumns dppColumns = new DppColumns(columns, classes);
+ DppColumns key = new DppColumns(keyColumns, keyClasses);
+ List<Tuple2<String, DppColumns>> result = new ArrayList<>();
+ int pid = partitioner.getPartition(key);
+ if (!validPartitionIndex.contains(pid)) {
+ System.err.println("invalid partition for row:" + row + ", pid:" + pid);
+ abnormalRowAcc.add(1);
+ } else {
+ long hashValue = DppUtils.getHashValue(row, distributeColumns, dstTableSchema);
+ int bucketId = (int) ((hashValue & 0xffffffff) % partitionInfo.partitions.get(pid).bucketNum);
+ long partitionId = partitionInfo.partitions.get(pid).partitionId;
+ // bucketKey is partitionId_bucketId
+ String bucketKey = Long.toString(partitionId) + "_" + Integer.toString(bucketId);
+ Tuple2<String, DppColumns> newTuple = new Tuple2<String, DppColumns>(bucketKey, dppColumns);
+ result.add(newTuple);
+ }
+ return result.iterator();
+ }
+ });
+
+ JavaRDD<Row> resultRdd = pairRDD.map( record -> {
+ String bucketKey = record._1;
+ List<Object> row = new ArrayList<>();
+ // bucketKey as the first key
+ row.add(bucketKey);
+ row.addAll(record._2.columns);
+ return RowFactory.create(row.toArray());
+ }
+ );
+
+ StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(baseIndex.columns, true);
+ dataframe = spark.createDataFrame(resultRdd, tableSchemaWithBucketId);
+ // use bucket number as the parallel number
+ int reduceNum = 0;
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ reduceNum += partition.bucketNum;
+ }
+ dataframe = dataframe.repartition(reduceNum, new Column(DppUtils.BUCKET_ID));
+ return dataframe;
+ }
+
+ private Dataset<Row> convertSrcDataframeToDstDataframe(EtlJobConfig.EtlIndex baseIndex,
+ Dataset<Row> srcDataframe,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlFileGroup fileGroup) throws UserException {
+ System.out.println("convertSrcDataframeToDstDataframe for index:" + baseIndex);
Review comment:
done
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] wangbo commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
wangbo commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417925871
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,252 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import io.fabric8.openshift.api.model.User;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class getClassFromDataType(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ } else if (dataType instanceof DecimalType) {
+ DecimalType decimalType = (DecimalType)dataType;
+ return BigDecimal.valueOf(decimalType.precision(), decimalType.scale()).getClass();
+ } else if (dataType.equals(DataTypes.TimestampType)) {
+ return Long.class;
+ }
+ return null;
+ }
+
+ public static Class getClassFromColumn(EtlJobConfig.EtlColumn column) throws UserException {
+ switch (column.columnType) {
+ case "BOOLEAN":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ throw new UserException("LARGEINT is not supported now");
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ case "DECIMALV2":
+ return BigDecimal.valueOf(column.precision, column.scale).getClass();
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType getDataTypeFromColumn(EtlJobConfig.EtlColumn column) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (column.columnType) {
+ case "BOOLEAN":
+ dataType = DataTypes.StringType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ dataType = DataTypes.TimestampType;
+ break;
+ case "BIGINT":
+ dataType = DataTypes.LongType;
+ break;
+ case "LARGEINT":
+ dataType = DataTypes.StringType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "CHAR":
+ case "VARCHAR":
+ case "HLL":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ case "DECIMALV2":
+ dataType = DecimalType.apply(column.precision, column.scale);
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + column);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
Review comment:
```suggestion
String str = String.valueOf(o);
```
For the case，a column whose type is largeint in doris, but in source hive table,its type is bigint, type cast failed will happened here.
Or you can change the logic in ```convertSrcDataframeToDstDataframe```.
Cast source hive table column type to string when doris column type is largeint
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] yangzhg commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
yangzhg commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r414439660
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
+ }
+ buffer.update(0, bos.toByteArray());
+ }
+
+ @Override
+ public void update(MutableAggregationBuffer buffer, Row input) {
+ // here maybe there is performance problems
+ if (!input.isNullAt(0)) {
+ String value = input.getString(0);
+ int id = Integer.parseInt(value);
+ Object bitmapObj = buffer.get(0);
+ byte[] bitmapBytes = (byte[])bitmapObj;
+ RoaringBitmap bitmap = new RoaringBitmap();
+ try {
+ bitmap.deserialize(new DataInputStream(new ByteArrayInputStream(bitmapBytes)));
+ bitmap.add(id);
+ bitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ bitmap.serialize(new DataOutputStream(bos));
+ buffer.update(0, bos.toByteArray());
+ } catch (IOException e) {
+ e.printStackTrace();
Review comment:
prefer using log instead of print to std
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r415665773
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ }
+ }
+ MessageType index_schema = builder.named("index_" + indexMeta.indexId);
+ while (t.hasNext()) {
+ Row row = t.next();
+ if (row.length() <= 1) {
+ System.err.println("invalid row:" + row);
+ continue;
+ }
+ String curBucketKey = row.getString(0);
+ if (lastBucketKey == null || !curBucketKey.equals(lastBucketKey)) {
+ if (writer != null) {
+ System.out.println("close writer");
+ writer.close();
+ }
+ // flush current writer and create a new writer
+ String[] bucketKey = curBucketKey.split("_");
+ if (bucketKey.length != 2) {
+ System.err.println("invalid bucket key:" + curBucketKey);
+ continue;
+ }
+ int partitionId = Integer.parseInt(bucketKey[0]);
+ int bucketId = Integer.parseInt(bucketKey[1]);
+ String path = String.format(pathPattern, tableId, partitionId, indexMeta.indexId,
+ bucketId, indexMeta.schemaHash);
+ GroupWriteSupport.setSchema(index_schema, conf);
+ writer = new ParquetWriter<Group>(new Path(path), new GroupWriteSupport(),
+ CompressionCodecName.SNAPPY, 1024, 1024, 512,
+ true, false,
+ ParquetProperties.WriterVersion.PARQUET_1_0, conf);
+ if(writer != null){
+ System.out.println("[HdfsOperate]>> initialize writer succeed! path:" + path);
+ }
+ lastBucketKey = curBucketKey;
+ }
+ SimpleGroupFactory groupFactory = new SimpleGroupFactory(index_schema);
+ Group group = groupFactory.newGroup();
+ for (int i = 1; i < row.length(); i++) {
+ Object columnObject = row.get(i);
+ if (columnObject instanceof Short) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getShort(i));
+ } else if (columnObject instanceof Integer) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getInt(i));
+ } else if (columnObject instanceof String) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getString(i));
+ } else if (columnObject instanceof Long) {
+ group.add(indexMeta.columns.get(i - 1).columnName, row.getLong(i));
+ }
+ }
+ try {
+ writer.write(group);
+ } catch (Exception e) {
+ System.err.println("exception caught:" + e);
+ e.printStackTrace();
+ }
+ }
+ if (writer != null) {
+ writer.close();
+ }
+ }
+ });
+ }
+
+ private void processRollupTree(RollupTreeNode rootNode,
+ Dataset<Row> rootDataframe,
+ long tableId, EtlJobConfig.EtlTable tableMeta, EtlJobConfig.EtlIndex baseIndex) {
+ Queue<RollupTreeNode> nodeQueue = new LinkedList<>();
+ nodeQueue.offer(rootNode);
+ int currentLevel = 0;
+ // level travel the tree
+ Map<Long, Dataset<Row>> parentDataframeMap = new HashMap<>();
+ parentDataframeMap.put(baseIndex.indexId, rootDataframe);
+ Map<Long, Dataset<Row>> childrenDataframeMap = new HashMap<>();
+ String pathPattern = etlJobConfig.outputPath + "/" + etlJobConfig.outputFilePattern;
+ while (!nodeQueue.isEmpty()) {
+ RollupTreeNode curNode = nodeQueue.poll();
+ System.err.println("start to process index:" + curNode.indexId);
+ if (curNode.children != null) {
+ for (RollupTreeNode child : curNode.children) {
+ nodeQueue.offer(child);
+ }
+ }
+ Dataset<Row> curDataFrame = null;
+ // column select for rollup
+ if (curNode.level != currentLevel) {
+ // need to unpersist parent dataframe
+ currentLevel = curNode.level;
+ parentDataframeMap.clear();
+ parentDataframeMap = childrenDataframeMap;
+ childrenDataframeMap = new HashMap<>();
+ }
+
+ long parentIndexId = baseIndex.indexId;
+ if (curNode.parent != null) {
+ parentIndexId = curNode.parent.indexId;
+ }
+
+ Dataset<Row> parentDataframe = parentDataframeMap.get(parentIndexId);
+ List<Column> columns = new ArrayList<>();
+ List<Column> keyColumns = new ArrayList<>();
+ Column bucketIdColumn = new Column(DppUtils.BUCKET_ID);
+ keyColumns.add(bucketIdColumn);
+ columns.add(bucketIdColumn);
+ for (String keyName : curNode.keyColumnNames) {
+ columns.add(new Column(keyName));
+ keyColumns.add(new Column(keyName));
+ }
+ for (String valueName : curNode.valueColumnNames) {
+ columns.add(new Column(valueName));
+ }
+ Seq<Column> columnSeq = JavaConverters.asScalaIteratorConverter(columns.iterator()).asScala().toSeq();
+ curDataFrame = parentDataframe.select(columnSeq);
+ if (curNode.indexMeta.indexType.equals("AGGREGATE")) {
+ // do aggregation
+ curDataFrame = processDataframeAgg(curDataFrame, curNode.indexMeta);
+ }
+ Seq<Column> keyColumnSeq = JavaConverters.asScalaIteratorConverter(keyColumns.iterator()).asScala().toSeq();
+ // should use sortWithinPartitions, not sort
+ // because sort will modify the partition number which will lead to bug
+ curDataFrame = curDataFrame.sortWithinPartitions(keyColumnSeq);
+ childrenDataframeMap.put(curNode.indexId, curDataFrame);
+
+ if (curNode.children != null && curNode.children.size() > 1) {
+ // if the children number larger than 1, persist the dataframe for performance
+ curDataFrame.persist();
+ }
+ writePartitionedAndSortedDataframeToParquet(curDataFrame, pathPattern, tableId, curNode.indexMeta);
+ }
+ }
+
+ private Dataset<Row> repartitionDataframeByBucketId(SparkSession spark, Dataset<Row> dataframe,
+ EtlJobConfig.EtlPartitionInfo partitionInfo,
+ List<Integer> partitionKeyIndex,
+ List<Class> partitionKeySchema,
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys,
+ List<String> keyColumnNames,
+ List<String> valueColumnNames,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<Long> validPartitionId) throws UserException {
+ List<String> distributeColumns = partitionInfo.distributionColumnRefs;
+ Partitioner partitioner = new DorisRangePartitioner(partitionInfo, partitionKeyIndex, partitionRangeKeys);
+ Set<Integer> validPartitionIndex = new HashSet<>();
+ if (validPartitionId == null) {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ validPartitionIndex.add(i);
+ }
+ } else {
+ for (int i = 0; i < partitionInfo.partitions.size(); ++i) {
+ if (validPartitionId.contains(partitionInfo.partitions.get(i).partitionId)) {
+ validPartitionIndex.add(i);
+ }
+ }
+ }
+ JavaPairRDD<String, DppColumns> pairRDD = dataframe.javaRDD().flatMapToPair(
+ new PairFlatMapFunction<Row, String, DppColumns>() {
+ @Override
+ public Iterator<Tuple2<String, DppColumns>> call(Row row) {
+ List<Object> columns = new ArrayList<>();
+ List<Class> classes = new ArrayList<>();
+ List<Object> keyColumns = new ArrayList<>();
+ List<Class> keyClasses = new ArrayList<>();
+ for (String columnName : keyColumnNames) {
+ Object columnObject = row.get(row.fieldIndex(columnName));
+ columns.add(columnObject);
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ keyColumns.add(columnObject);
+ keyClasses.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+
+ for (String columnName : valueColumnNames) {
+ columns.add(row.get(row.fieldIndex(columnName)));
+ classes.add(DppUtils.dataTypeToClass(dstTableSchema.apply(dstTableSchema.fieldIndex(columnName)).dataType()));
+ }
+ DppColumns dppColumns = new DppColumns(columns, classes);
+ DppColumns key = new DppColumns(keyColumns, keyClasses);
+ List<Tuple2<String, DppColumns>> result = new ArrayList<>();
+ int pid = partitioner.getPartition(key);
+ if (!validPartitionIndex.contains(pid)) {
+ System.err.println("invalid partition for row:" + row + ", pid:" + pid);
+ abnormalRowAcc.add(1);
+ } else {
+ long hashValue = DppUtils.getHashValue(row, distributeColumns, dstTableSchema);
+ int bucketId = (int) ((hashValue & 0xffffffff) % partitionInfo.partitions.get(pid).bucketNum);
+ long partitionId = partitionInfo.partitions.get(pid).partitionId;
+ // bucketKey is partitionId_bucketId
+ String bucketKey = Long.toString(partitionId) + "_" + Integer.toString(bucketId);
+ Tuple2<String, DppColumns> newTuple = new Tuple2<String, DppColumns>(bucketKey, dppColumns);
+ result.add(newTuple);
+ }
+ return result.iterator();
+ }
+ });
+
+ JavaRDD<Row> resultRdd = pairRDD.map( record -> {
+ String bucketKey = record._1;
+ List<Object> row = new ArrayList<>();
+ // bucketKey as the first key
+ row.add(bucketKey);
+ row.addAll(record._2.columns);
+ return RowFactory.create(row.toArray());
+ }
+ );
+
+ StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(baseIndex.columns, true);
+ dataframe = spark.createDataFrame(resultRdd, tableSchemaWithBucketId);
+ // use bucket number as the parallel number
+ int reduceNum = 0;
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ reduceNum += partition.bucketNum;
+ }
+ dataframe = dataframe.repartition(reduceNum, new Column(DppUtils.BUCKET_ID));
+ return dataframe;
+ }
+
+ private Dataset<Row> convertSrcDataframeToDstDataframe(EtlJobConfig.EtlIndex baseIndex,
+ Dataset<Row> srcDataframe,
+ StructType dstTableSchema,
+ EtlJobConfig.EtlFileGroup fileGroup) throws UserException {
+ System.out.println("convertSrcDataframeToDstDataframe for index:" + baseIndex);
+ Dataset<Row> dataframe = srcDataframe;
+ StructType srcSchema = dataframe.schema();
+ Set<String> srcColumnNames = new HashSet<>();
+ for (StructField field : srcSchema.fields()) {
+ srcColumnNames.add(field.name());
+ }
+ Map<String, EtlJobConfig.EtlColumnMapping> columnMappings = fileGroup.columnMappings;
+ // 1. process simple columns
+ Set<String> mappingColumns = null;
+ if (columnMappings != null) {
+ mappingColumns = columnMappings.keySet();
+ }
+ List<String> dstColumnNames = new ArrayList<>();
+ for (StructField dstField : dstTableSchema.fields()) {
+ dstColumnNames.add(dstField.name());
+ EtlJobConfig.EtlColumn column = baseIndex.getColumn(dstField.name());
+ if (!srcColumnNames.contains(dstField.name())) {
+ if (mappingColumns != null && mappingColumns.contains(dstField.name())) {
+ // mapping columns will be processed in next step
+ continue;
+ }
+ if (column.defaultValue != null) {
+ if (column.defaultValue.equals(NULL_FLAG)) {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(null));
+ } else {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(column.defaultValue));
+ }
+ } else if (column.isAllowNull) {
+ dataframe = dataframe.withColumn(dstField.name(), functions.lit(null));
+ } else {
+ throw new UserException("Reason: no data for column:" + dstField.name());
+ }
+ }
+ if (!column.columnType.equalsIgnoreCase(BITMAP_TYPE) && !dstField.dataType().equals(DataTypes.StringType)) {
+ dataframe = dataframe.withColumn(dstField.name(), dataframe.col(dstField.name()).cast(dstField.dataType()));
+ if (fileGroup.isNegative && !column.isKey) {
+ // negative load
+ // value will be convert te -1 * value
+ dataframe = dataframe.withColumn(dstField.name(), functions.expr("-1 *" + dstField.name()));
+ }
+ }
+ }
+ // 2. process the mapping columns
+ for (String mappingColumn : mappingColumns) {
+ String mappingDescription = columnMappings.get(mappingColumn).toDescription();
+ // here should cast data type to dst column type
+ dataframe = dataframe.withColumn(mappingColumn,
+ functions.expr(mappingDescription).cast(dstTableSchema.apply(mappingColumn).dataType()));
+ }
+ // projection and reorder the columns
+ dataframe.createOrReplaceTempView("src_table");
+ StringBuilder selectSqlBuilder = new StringBuilder();
+ selectSqlBuilder.append("select ");
+ for (String name : dstColumnNames) {
+ selectSqlBuilder.append(name + ",");
+ }
+ selectSqlBuilder.deleteCharAt(selectSqlBuilder.length() - 1);
+ selectSqlBuilder.append(" from src_table");
+ String selectSql = selectSqlBuilder.toString();
+ dataframe = spark.sql(selectSql);
+ return dataframe;
+ }
+
+ private Dataset<Row> loadDataFromPath(SparkSession spark,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ String fileUrl,
+ EtlJobConfig.EtlIndex baseIndex,
+ StructType dstTableSchema) throws UserException {
+ List<String> columnValueFromPath = DppUtils.parseColumnsFromPath(fileUrl, fileGroup.columnsFromPath);
+ List<String> dataSrcColumns = fileGroup.fileFieldNames;
+ if (dataSrcColumns == null) {
+ // if there is no source columns info
+ // use base index columns as source columns
+ dataSrcColumns = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn column : baseIndex.columns) {
+ dataSrcColumns.add(column.columnName);
+ }
+ }
+ List<String> dstTableNames = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn column : baseIndex.columns) {
+ dstTableNames.add(column.columnName);
+ }
+ List<String> srcColumnsWithColumnsFromPath = new ArrayList<>();
+ srcColumnsWithColumnsFromPath.addAll(dataSrcColumns);
+ if (fileGroup.columnsFromPath != null) {
+ srcColumnsWithColumnsFromPath.addAll(fileGroup.columnsFromPath);
+ }
+ StructType srcSchema = createScrSchema(srcColumnsWithColumnsFromPath);
+ JavaRDD<String> sourceDataRdd = spark.read().textFile(fileUrl).toJavaRDD();
+ int columnSize = dataSrcColumns.size();
+ // now we first support csv file
+ // TODO: support parquet file and orc file
+ JavaRDD<Row> rowRDD = sourceDataRdd.flatMap(
+ record -> {
+ scannedRowsAcc.add(1);
+ String[] attributes = record.split(fileGroup.columnSeparator);
+ List<Row> result = new ArrayList<>();
+ if (attributes.length != columnSize) {
+ abnormalRowAcc.add(1);
+ System.err.println("invalid src schema, data columns:"
+ + attributes.length + ", file group columns:"
+ + columnSize + ", row:" + record);
+ } else {
+ boolean validRow = true;
+ for (int i = 0; i < attributes.length; ++i) {
+ if (attributes[i].equals(NULL_FLAG)) {
+ if (baseIndex.columns.get(i).isAllowNull) {
+ attributes[i] = null;
+ } else {
+ abnormalRowAcc.add(1);
+ System.err.println("colunm:" + i + " can not be null. row:" + record);
+ validRow = false;
+ break;
+ }
+ }
+ boolean isStrictMode = (boolean)etlJobConfig.properties.strictMode;
+ if (isStrictMode) {
+ StructField field = srcSchema.apply(i);
+ if (dstTableNames.contains(field.name())) {
+ DataType type = dstTableSchema.apply(field.name()).dataType();
+ if (!type.equals(DataTypes.StringType)) {
+ if (type.equals(DataTypes.ShortType)) {
+ try{
+ Short value = Short.parseShort(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.IntegerType)) {
+ try {
+ Integer value = Integer.parseInt(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.LongType)) {
+ try {
+ Long value = Long.parseLong(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.FloatType)) {
+ try {
+ Float value = Float.parseFloat(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.DoubleType)) {
+ try {
+ Double value = Double.parseDouble(attributes[i]);
+ } catch (NumberFormatException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ } else if (type.equals(DataTypes.DateType)) {
+ try {
+ long value = Date.parse(attributes[i]);
+ } catch (IllegalArgumentException e) {
+ abnormalRowAcc.add(1);
+ validRow = false;
+ break;
+ }
+ }
+ }
+ }
+ }
+ }
+ if (validRow) {
+ Row row = null;
+ if (fileGroup.columnsFromPath == null) {
+ row = RowFactory.create(attributes);
+ } else {
+ // process columns from path
+ // append columns from path to the tail
+ List<String> columnAttributes = new ArrayList<>();
+ columnAttributes.addAll(Arrays.asList(attributes));
+ columnAttributes.addAll(columnValueFromPath);
+ row = RowFactory.create(columnAttributes.toArray());
+ }
+ result.add(row);
+ }
+ }
+ return result.iterator();
+ }
+ );
+
+ Dataset<Row> dataframe = spark.createDataFrame(rowRDD, srcSchema);
+ return dataframe;
+ }
+
+ private StructType createScrSchema(List<String> srcColumns) {
+ List<StructField> fields = new ArrayList<>();
+ for (String srcColumn : srcColumns) {
+ // user StringType to load source data
+ StructField field = DataTypes.createStructField(srcColumn, DataTypes.StringType, true);
+ fields.add(field);
+ }
+ StructType srcSchema = DataTypes.createStructType(fields);
+ return srcSchema;
+ }
+
+ // partition keys will be parsed into double from json
+ // so need to convert it to partition columns' type
+ Object convertPartitionKey(Object srcValue, Class dstClass) {
+ if (dstClass.equals(Float.class) || dstClass.equals(Double.class)) {
+ return null;
+ }
+ if (srcValue instanceof Double) {
+ if (dstClass.equals(Short.class)) {
+ return ((Double)srcValue).shortValue();
+ } else if (dstClass.equals(Integer.class)) {
+ return ((Double)srcValue).intValue();
+ } else if (dstClass.equals(Long.class)) {
+ return ((Double)srcValue).longValue();
+ } else if (dstClass.equals(BigInteger.class)) {
+ return new BigInteger(((Double)srcValue).toString());
+ } else {
+ // dst type is string
+ return srcValue.toString();
+ }
+ } else if (srcValue instanceof String) {
+ // TODO: support src value is string type
+ return null;
+ } else {
+ System.out.println("unsupport partition key:" + srcValue);
+ return null;
+ }
+ }
+
+ private List<DorisRangePartitioner.PartitionRangeKey> createPartitionRangeKeys(
+ EtlJobConfig.EtlPartitionInfo partitionInfo, List<Class> partitionKeySchema) {
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys = new ArrayList<>();
+ for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) {
+ DorisRangePartitioner.PartitionRangeKey partitionRangeKey = new DorisRangePartitioner.PartitionRangeKey();
+ List<Object> startKeyColumns = new ArrayList<>();
+ for (int i = 0; i < partition.startKeys.size(); i++) {
+ Object value = partition.startKeys.get(i);
+ startKeyColumns.add(convertPartitionKey(value, partitionKeySchema.get(i)));
+ }
+ partitionRangeKey.startKeys = new DppColumns(startKeyColumns, partitionKeySchema);;
+ if (!partition.isMaxPartition) {
+ partitionRangeKey.isMaxPartition = false;
+ List<Object> endKeyColumns = new ArrayList<>();
+ for (int i = 0; i < partition.endKeys.size(); i++) {
+ Object value = partition.endKeys.get(i);
+ endKeyColumns.add(convertPartitionKey(value, partitionKeySchema.get(i)));
+ }
+ partitionRangeKey.endKeys = new DppColumns(endKeyColumns, partitionKeySchema);
+ } else {
+ partitionRangeKey.isMaxPartition = false;
+ }
+ partitionRangeKeys.add(partitionRangeKey);
+ }
+ return partitionRangeKeys;
+ }
+
+ private Dataset<Row> loadDataFromFilePaths(SparkSession spark,
+ EtlJobConfig.EtlIndex baseIndex,
+ List<String> filePaths,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ StructType dstTableSchema) throws UserException {
+ Dataset<Row> fileGroupDataframe = null;
+ for (String filePath : filePaths) {
+ fileNumberAcc.add(1);
+ try {
+ Configuration conf = new Configuration();
+ URI uri = new URI(filePath);
+ FileSystem fs = FileSystem.get(uri, conf);
+ FileStatus fileStatus = fs.getFileStatus(new Path(filePath));
+ fileSizeAcc.add(fileStatus.getLen());
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+ if (fileGroup.columnSeparator == null) {
+ System.err.println("invalid null column separator!");
+ throw new UserException("Reason: invalid null column separator!");
+ }
+ Dataset<Row> dataframe = null;
+
+ dataframe = loadDataFromPath(spark, fileGroup, filePath, baseIndex, dstTableSchema);
+ dataframe = convertSrcDataframeToDstDataframe(baseIndex, dataframe, dstTableSchema, fileGroup);
+ if (fileGroupDataframe == null) {
+ fileGroupDataframe = dataframe;
+ } else {
+ fileGroupDataframe.union(dataframe);
+ }
+ }
+ fileGroupDataframe.show();
+ return fileGroupDataframe;
+ }
+
+ private Dataset<Row> loadDataFromHiveTable(SparkSession spark,
+ String hiveTableName,
+ EtlJobConfig.EtlIndex baseIndex,
+ EtlJobConfig.EtlFileGroup fileGroup,
+ StructType dstTableSchema) throws UserException {
+ Dataset<Row> dataframe = spark.sql("select * from " + hiveTableName);
+ dataframe = convertSrcDataframeToDstDataframe(baseIndex, dataframe, dstTableSchema, fileGroup);
+ return dataframe;
+ }
+
+ private DppResult process() throws Exception {
+ dppResult = new DppResult();
+ try {
+ for (Map.Entry<Long, EtlJobConfig.EtlTable> entry : etlJobConfig.tables.entrySet()) {
+ Long tableId = entry.getKey();
+ EtlJobConfig.EtlTable etlTable = entry.getValue();
+
+ // get the base index meta
+ EtlJobConfig.EtlIndex baseIndex = null;
+ for (EtlJobConfig.EtlIndex indexMeta : etlTable.indexes) {
+ if (indexMeta.isBaseIndex) {
+ baseIndex = indexMeta;
+ break;
+ }
+ }
+
+ // get key column names and value column names seperately
+ List<String> keyColumnNames = new ArrayList<>();
+ List<String> valueColumnNames = new ArrayList<>();
+ for (EtlJobConfig.EtlColumn etlColumn : baseIndex.columns) {
+ if (etlColumn.isKey) {
+ keyColumnNames.add(etlColumn.columnName);
+ } else {
+ valueColumnNames.add(etlColumn.columnName);
+ }
+ }
+
+ EtlJobConfig.EtlPartitionInfo partitionInfo = etlTable.partitionInfo;
+ List<Integer> partitionKeyIndex = new ArrayList<Integer>();
+ List<Class> partitionKeySchema = new ArrayList<>();
+ for (String key : partitionInfo.partitionColumnRefs) {
+ for (int i = 0; i < baseIndex.columns.size(); ++i) {
+ EtlJobConfig.EtlColumn column = baseIndex.columns.get(i);
+ if (column.columnName.equals(key)) {
+ partitionKeyIndex.add(i);
+ partitionKeySchema.add(DppUtils.columnTypeToClass(column.columnType));
+ break;
+ }
+ }
+ }
+ List<DorisRangePartitioner.PartitionRangeKey> partitionRangeKeys = createPartitionRangeKeys(partitionInfo, partitionKeySchema);
+ StructType dstTableSchema = DppUtils.createDstTableSchema(baseIndex.columns, false);
+ RollupTreeBuilder rollupTreeParser = new MinimalCoverRollupTreeBuilder();
+ RollupTreeNode rootNode = rollupTreeParser.build(etlTable);
+ System.out.println("Rollup Tree:" + rootNode);
+
+ for (EtlJobConfig.EtlFileGroup fileGroup : etlTable.fileGroups) {
+ List<String> filePaths = fileGroup.filePaths;
+ Dataset<Row> fileGroupDataframe = null;
+ if (Strings.isNullOrEmpty(fileGroup.hiveTableName)) {
+ fileGroupDataframe = loadDataFromFilePaths(spark, baseIndex, filePaths, fileGroup, dstTableSchema);
+ } else {
+ String taskId = etlJobConfig.outputPath.substring(etlJobConfig.outputPath.lastIndexOf("/") + 1);
+ String dorisIntermediateHiveTable = String.format(EtlJobConfig.DORIS_INTERMEDIATE_HIVE_TABLE_NAME,
+ tableId, taskId);
+ fileGroupDataframe = loadDataFromHiveTable(spark, dorisIntermediateHiveTable, baseIndex, fileGroup, dstTableSchema);
+ }
+ if (fileGroupDataframe == null) {
+ System.err.println("no data for file file group:" + fileGroup);
+ continue;
+ }
+ if (!Strings.isNullOrEmpty(fileGroup.where)) {
+ long originalSize = fileGroupDataframe.count();
+ fileGroupDataframe = fileGroupDataframe.filter(fileGroup.where);
+ long currentSize = fileGroupDataframe.count();
+ unselectedRowAcc.add(currentSize - originalSize);
+ }
+
+ fileGroupDataframe = repartitionDataframeByBucketId(spark, fileGroupDataframe,
+ partitionInfo, partitionKeyIndex,
+ partitionKeySchema, partitionRangeKeys,
+ keyColumnNames, valueColumnNames,
+ dstTableSchema, baseIndex, fileGroup.partitions);
+ processRollupTree(rootNode, fileGroupDataframe, tableId, etlTable, baseIndex);
+ }
+ }
+ spark.stop();
+ } catch (Exception exception) {
+ exception.printStackTrace();
+ System.err.println("spark dpp failed for exception:" + exception);
+ dppResult.isSuccess = false;
+ dppResult.failedReason = exception.getMessage();
+ dppResult.normalRows = scannedRowsAcc.value() - abnormalRowAcc.value();
+ dppResult.scannedRows = scannedRowsAcc.value();
+ dppResult.fileNumber = fileNumberAcc.value();
+ dppResult.fileSize = fileSizeAcc.value();
+ dppResult.abnormalRows = abnormalRowAcc.value();
+ throw exception;
Review comment:
yes, use exception to indicate the spark app failed.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417818167
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
Review comment:
done
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r421972254
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,252 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import io.fabric8.openshift.api.model.User;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class getClassFromDataType(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ } else if (dataType instanceof DecimalType) {
+ DecimalType decimalType = (DecimalType)dataType;
+ return BigDecimal.valueOf(decimalType.precision(), decimalType.scale()).getClass();
+ } else if (dataType.equals(DataTypes.TimestampType)) {
+ return Long.class;
+ }
+ return null;
+ }
+
+ public static Class getClassFromColumn(EtlJobConfig.EtlColumn column) throws UserException {
+ switch (column.columnType) {
+ case "BOOLEAN":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ throw new UserException("LARGEINT is not supported now");
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ case "DECIMALV2":
+ return BigDecimal.valueOf(column.precision, column.scale).getClass();
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType getDataTypeFromColumn(EtlJobConfig.EtlColumn column) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (column.columnType) {
+ case "BOOLEAN":
+ dataType = DataTypes.StringType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ dataType = DataTypes.TimestampType;
+ break;
+ case "BIGINT":
+ dataType = DataTypes.LongType;
+ break;
+ case "LARGEINT":
+ dataType = DataTypes.StringType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "CHAR":
+ case "VARCHAR":
+ case "HLL":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
+ break;
+ case "DECIMALV2":
+ dataType = DecimalType.apply(column.precision, column.scale);
+ break;
+ default:
+ throw new UserException("Reason: invalid column type:" + column);
+ }
+ return dataType;
+ }
+
+ public static ByteBuffer getHashValue(Object o, DataType type) {
+ ByteBuffer buffer = ByteBuffer.allocate(8);
+ buffer.order(ByteOrder.LITTLE_ENDIAN);
+ if (o == null) {
+ buffer.putInt(0);
+ return buffer;
+ }
+ if (type.equals(DataTypes.ShortType)) {
+ buffer.putShort((Short)o);
+ } else if (type.equals(DataTypes.IntegerType)) {
+ buffer.putInt((Integer) o);
+ } else if (type.equals(DataTypes.LongType)) {
+ buffer.putLong((Long)o);
+ } else if (type.equals(DataTypes.StringType)) {
+ try {
+ String str = (String)o;
Review comment:
now largeint is not supported in spark dpp.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417817067
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/MinimalCoverRollupTreeBuilder.java
##########
@@ -0,0 +1,107 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+// Build RollupTree by using minimal cover strategy
Review comment:
I add some comment and example
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java
##########
@@ -0,0 +1,848 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import com.google.common.base.Strings;
+import com.google.gson.Gson;
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+import org.apache.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.column.ParquetProperties;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.GroupWriteSupport;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Types;
+import org.apache.spark.Partitioner;
+import org.apache.spark.api.java.function.ForeachPartitionFunction;
+import org.apache.spark.api.java.function.PairFlatMapFunction;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.util.LongAccumulator;
+import scala.Tuple2;
+import scala.collection.Seq;
+
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+
+import java.math.BigInteger;
+import java.net.URI;
+import java.util.Map;
+import java.util.Set;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Queue;
+import java.util.LinkedList;
+import java.util.HashSet;
+import java.util.Arrays;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import scala.collection.JavaConverters;
+
+// This class is a Spark-based data preprocessing program,
+// which will make use of the distributed compute framework of spark to
+// do ETL job/sort/preaggregate/convert data format job in spark job
+// to boost the process of large amount of data load.
+public final class SparkDpp implements java.io.Serializable {
+ private static final String NULL_FLAG = "\\N";
+ private static final String DPP_RESULT_FILE = "dpp_result.json";
+ private static final String BITMAP_TYPE = "bitmap";
+ private EtlJobConfig etlJobConfig = null;
+ private LongAccumulator abnormalRowAcc = null;
+ private LongAccumulator unselectedRowAcc = null;
+ private LongAccumulator scannedRowsAcc = null;
+ private LongAccumulator fileNumberAcc = null;
+ private LongAccumulator fileSizeAcc = null;
+ private DppResult dppResult;
+ private SparkSession spark = null;
+ private UserDefinedAggregateFunction bitmap_union = null;
+
+ public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) {
+ this.spark = spark;
+ this.etlJobConfig = etlJobConfig;
+ }
+
+ public void init() {
+ bitmap_union = spark.udf().register("bitmap_union", new BitmapUnion());
+ abnormalRowAcc = spark.sparkContext().longAccumulator();
+ unselectedRowAcc = spark.sparkContext().longAccumulator();
+ scannedRowsAcc = spark.sparkContext().longAccumulator();
+ fileNumberAcc = spark.sparkContext().longAccumulator();
+ fileSizeAcc = spark.sparkContext().longAccumulator();
+ }
+
+ private Dataset<Row> processDataframeAgg(Dataset<Row> dataframe, EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.createOrReplaceTempView("base_table");
+ StringBuilder sb = new StringBuilder();
+ sb.append("select ");
+ sb.append(DppUtils.BUCKET_ID + ",");
+
+ // assume that keys are all before values
+ StringBuilder groupBySb = new StringBuilder();
+ groupBySb.append(DppUtils.BUCKET_ID + ",");
+ Map<String, DataType> valueColumnsOriginalType = new HashMap<>();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isKey) {
+ sb.append(column.columnName + ",");
+ groupBySb.append(column.columnName + ",");
+ } else {
+ // get the value columns's original type
+ DataType originalType = dataframe.schema().apply(column.columnName).dataType();
+ valueColumnsOriginalType.put(column.columnName, originalType);
+ if (column.aggregationType.equalsIgnoreCase("MAX")) {
+ sb.append("max(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("MIN")) {
+ sb.append("min(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("SUM")) {
+ sb.append("sum(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ } else if (column.aggregationType.equalsIgnoreCase("BITMAP_UNION")) {
+ sb.append("bitmap_union(" + column.columnName + ") as " + column.columnName);
+ sb.append(",");
+ }
+ }
+ }
+ sb.deleteCharAt(sb.length() - 1);
+ groupBySb.deleteCharAt(groupBySb.length() - 1);
+ sb.append(" from base_table ");
+ sb.append(" group by ");
+ sb.append(groupBySb.toString());
+ String aggSql = sb.toString();
+ Dataset<Row> aggDataFrame = spark.sql(aggSql);
+ // after agg, the type of sum column maybe be changed, so should add type cast for value column
+ for (Map.Entry<String, DataType> entry : valueColumnsOriginalType.entrySet()) {
+ DataType currentType = aggDataFrame.schema().apply(entry.getKey()).dataType();
+ if (!currentType.equals(entry.getValue())) {
+ aggDataFrame = aggDataFrame.withColumn(entry.getKey(), aggDataFrame.col(entry.getKey()).cast(entry.getValue()));
+ }
+ }
+ return aggDataFrame;
+ }
+
+ private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, String pathPattern, long tableId,
+ EtlJobConfig.EtlIndex indexMeta) {
+ dataframe.foreachPartition(new ForeachPartitionFunction<Row>() {
+ @Override
+ public void call(Iterator<Row> t) throws Exception {
+ // write the data to dst file
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(URI.create(etlJobConfig.outputPath), conf);
+ String lastBucketKey = null;
+ ParquetWriter<Group> writer = null;
+ Types.MessageTypeBuilder builder = Types.buildMessage();
+ for (EtlJobConfig.EtlColumn column : indexMeta.columns) {
+ if (column.isAllowNull) {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.optional(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.optional(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
+ System.err.println("invalid column type:" + column);
+ }
+ } else {
+ if (column.columnType.equals("SMALLINT") ||column.columnType.equals("INT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT32).named(column.columnName);
+ } else if (column.columnType.equals("BIGINT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.INT64).named(column.columnName);
+ } else if (column.columnType.equals("BOOL")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.BOOLEAN).named(column.columnName);
+ } else if (column.columnType.equals("VARCHAR")) {
+ // should use as(OriginalType.UTF8), or result will be binary
+ builder.required(PrimitiveType.PrimitiveTypeName.BINARY).as(OriginalType.UTF8).named(column.columnName);
+ } else if (column.columnType.equals("FLOAT")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.FLOAT).named(column.columnName);
+ } else if (column.columnType.equals("DOUBLE")) {
+ builder.required(PrimitiveType.PrimitiveTypeName.DOUBLE).named(column.columnName);
+ } else {
+ // TODO: exception handle
Review comment:
done
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r415671288
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
+ schema = null;
+ }
+
+ public DppColumns(List<Object> keys, List<Class> schema){
+ this.columns = keys;
+ this.schema = schema;
+
+ }
+
+ public DppColumns(DppColumns key, List<Integer> indexes){
+ columns = new ArrayList<Object>();
+ schema = new ArrayList<Class>();
+ for (int i = 0; i < indexes.size(); ++i) {
+ columns.add(key.columns.get(indexes.get(i)));
+ schema.add(key.schema.get(indexes.get(i)));
+ }
+ }
+
+ @Override
+ public int compareTo(DppColumns other) {
+ assert (columns.size() == other.columns.size());
+ assert (schema.size() == other.schema.size());
+
+ int cmp = 0;
+ for (int i = 0; i < columns.size(); i++) {
+ Object columnObj = columns.get(i);
+ Object otherColumn = other.columns.get(i);
+ if (columnObj == null && otherColumn == null) {
+ return 0;
+ } else if (columnObj == null || otherColumn == null) {
+ if (columnObj == null) {
+ return -1;
+ } else {
+ return 1;
+ }
+ }
+ if (columns.get(i) instanceof Integer) {
+ cmp = ((Integer)(columns.get(i))).compareTo((Integer)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Long) {
+ cmp = ((Long)(columns.get(i))).compareTo((Long)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Boolean) {
+ cmp = ((Boolean)(columns.get(i))).compareTo((Boolean) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Short) {
+ cmp = ((Short)(columns.get(i))).compareTo((Short)(other.columns.get(i)));
+ } else if (columns.get(i) instanceof Float) {
+ cmp = ((Float)(columns.get(i))).compareTo((Float) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Double) {
+ cmp = ((Double)(columns.get(i))).compareTo((Double) (other.columns.get(i)));
+ } else if (columns.get(i) instanceof Date) {
+ cmp = ((Date)(columns.get(i))).compareTo((Date) (other.columns.get(i)));
+ } else {
+ cmp = ((String)(columns.get(i))).compareTo((String) (other.columns.get(i)));
Review comment:
will treat binary as string
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r415667297
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppUtils.java
##########
@@ -0,0 +1,230 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.doris.common.UserException;
+import org.apache.doris.load.loadv2.etl.EtlJobConfig;
+
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.Row;
+
+import com.google.common.collect.Lists;
+
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.List;
+import java.util.zip.CRC32;
+
+public class DppUtils {
+ public static final String BUCKET_ID = "__bucketId__";
+ public static Class dataTypeToClass(DataType dataType) {
+ if (dataType == null) {
+ return null;
+ }
+ if (dataType.equals(DataTypes.BooleanType)) {
+ return Boolean.class;
+ } else if (dataType.equals(DataTypes.ShortType)) {
+ return Short.class;
+ } else if (dataType.equals(DataTypes.IntegerType)) {
+ return Integer.class;
+ } else if (dataType.equals(DataTypes.LongType)) {
+ return Long.class;
+ } else if (dataType.equals(DataTypes.FloatType)) {
+ return Float.class;
+ } else if (dataType.equals(DataTypes.DoubleType)) {
+ return Double.class;
+ } else if (dataType.equals(DataTypes.DateType)) {
+ return Date.class;
+ } else if (dataType.equals(DataTypes.StringType)) {
+ return String.class;
+ }
+ return null;
+ }
+
+ public static Class columnTypeToClass(String columnType) {
+ switch (columnType) {
+ case "BOOL":
+ return Boolean.class;
+ case "TINYINT":
+ case "SMALLINT":
+ return Short.class;
+ case "INT":
+ return Integer.class;
+ case "DATETIME":
+ case "BIGINT":
+ return Long.class;
+ case "LARGEINT":
+ return BigInteger.class;
+ case "FLOAT":
+ return Float.class;
+ case "DOUBLE":
+ return Double.class;
+ case "DATE":
+ return Date.class;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ return String.class;
+ default:
+ return String.class;
+ }
+ }
+
+ public static DataType columnTypeToDataType(String columnType) throws UserException {
+ DataType dataType = DataTypes.StringType;
+ switch (columnType) {
+ case "BOOL":
+ dataType = DataTypes.BooleanType;
+ break;
+ case "TINYINT":
+ case "SMALLINT":
+ dataType = DataTypes.ShortType;
+ break;
+ case "INT":
+ dataType = DataTypes.IntegerType;
+ break;
+ case "DATETIME":
+ case "BIGINT":
+ case "LARGEINT":
+ // todo: special treat LARGEINT because spark do not support int128
+ dataType = DataTypes.LongType;
+ break;
+ case "FLOAT":
+ dataType = DataTypes.FloatType;
+ break;
+ case "DOUBLE":
+ dataType = DataTypes.DoubleType;
+ break;
+ case "DATE":
+ dataType = DataTypes.DateType;
+ break;
+ case "HLL":
+ case "CHAR":
+ case "VARCHAR":
+ case "BITMAP":
+ case "OBJECT":
+ dataType = DataTypes.StringType;
Review comment:
the input type for hll and bitmap is string.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417818340
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
+ }
+ buffer.update(0, bos.toByteArray());
+ }
+
+ @Override
+ public void update(MutableAggregationBuffer buffer, Row input) {
+ // here maybe there is performance problems
+ if (!input.isNullAt(0)) {
+ String value = input.getString(0);
+ int id = Integer.parseInt(value);
+ Object bitmapObj = buffer.get(0);
+ byte[] bitmapBytes = (byte[])bitmapObj;
+ RoaringBitmap bitmap = new RoaringBitmap();
+ try {
+ bitmap.deserialize(new DataInputStream(new ByteArrayInputStream(bitmapBytes)));
+ bitmap.add(id);
+ bitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ bitmap.serialize(new DataOutputStream(bos));
+ buffer.update(0, bos.toByteArray());
+ } catch (IOException e) {
+ e.printStackTrace();
Review comment:
done
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r417816035
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
Review comment:
ok
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
Review comment:
ok
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/DppColumns.java
##########
@@ -0,0 +1,119 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.Objects;
+import java.util.Comparator;
+
+class DppColumns implements Comparable<DppColumns>, Serializable {
+ public List<Object> columns;
+ public List<Class> schema;
+
+ public DppColumns() {
+ columns = null;
+ schema = null;
+ }
+
+ public DppColumns(List<Object> keys, List<Class> schema){
+ this.columns = keys;
+ this.schema = schema;
+
+ }
+
+ public DppColumns(DppColumns key, List<Integer> indexes){
+ columns = new ArrayList<Object>();
+ schema = new ArrayList<Class>();
+ for (int i = 0; i < indexes.size(); ++i) {
+ columns.add(key.columns.get(indexes.get(i)));
+ schema.add(key.schema.get(indexes.get(i)));
+ }
+ }
+
+ @Override
+ public int compareTo(DppColumns other) {
+ assert (columns.size() == other.columns.size());
Review comment:
ok
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] kangpinghuang closed pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
kangpinghuang closed pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org
[GitHub] [incubator-doris] yangzhg commented on a change in pull request #3296: spark data preparation process, issue #3295
Posted by GitBox <gi...@apache.org>.
yangzhg commented on a change in pull request #3296:
URL: https://github.com/apache/incubator-doris/pull/3296#discussion_r414438775
##########
File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/BitmapUnion.java
##########
@@ -0,0 +1,129 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+package org.apache.doris.load.loadv2.dpp;
+
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.expressions.MutableAggregationBuffer;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import org.roaringbitmap.RoaringBitmap;
+
+import java.io.IOException;
+import java.io.ByteArrayOutputStream;
+import java.io.DataOutputStream;
+import java.io.DataInputStream;
+import java.io.ByteArrayInputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class BitmapUnion extends UserDefinedAggregateFunction {
+ private StructType inputSchema;
+ private StructType bufferSchema;
+
+ public BitmapUnion() {
+ List<StructField> inputFields = new ArrayList<>();
+ inputFields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
+ inputSchema = DataTypes.createStructType(inputFields);
+
+ List<StructField> bufferFields = new ArrayList<>();
+ bufferFields.add(DataTypes.createStructField("bitmap", DataTypes.BinaryType,true));
+ bufferSchema = DataTypes.createStructType(bufferFields);
+ }
+
+ @Override
+ public StructType inputSchema() {
+ return inputSchema;
+ }
+
+ @Override
+ public StructType bufferSchema() {
+ return bufferSchema;
+ }
+
+ @Override
+ public DataType dataType() {
+ return DataTypes.StringType;
+ }
+
+ @Override
+ public boolean deterministic() {
+ return true;
+ }
+
+ @Override
+ public void initialize(MutableAggregationBuffer buffer) {
+ RoaringBitmap roaringBitmap = new RoaringBitmap();
+ roaringBitmap.runOptimize();
+ ByteArrayOutputStream bos = new ByteArrayOutputStream();
+ try {
+ roaringBitmap.serialize(new DataOutputStream(bos));
+ } catch (IOException ioException) {
+ ioException.printStackTrace();
Review comment:
prefer using log instead of print to std
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@doris.apache.org
For additional commands, e-mail: commits-help@doris.apache.org