[GitHub] [iceberg] openinx commented on a change in pull request #1185: Flink: Add the iceberg files committer to collect data files and commit to iceberg table.

[GitBox <gi...@apache.org>]

openinx commented on a change in pull request #1185:
URL: https://github.com/apache/iceberg/pull/1185#discussion_r480625043



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File path: flink/src/main/java/org/apache/iceberg/flink/sink/FlinkSink.java
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@@ -0,0 +1,220 @@
+/*
+ * 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.iceberg.flink.sink;
+
+import java.util.Locale;
+import java.util.Map;
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.datastream.DataStreamSink;
+import org.apache.flink.streaming.api.functions.sink.DiscardingSink;
+import org.apache.flink.table.api.TableSchema;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.util.DataFormatConverters;
+import org.apache.flink.table.runtime.typeutils.RowDataTypeInfo;
+import org.apache.flink.table.types.DataType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.types.Row;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.DataFile;
+import org.apache.iceberg.FileFormat;
+import org.apache.iceberg.Schema;
+import org.apache.iceberg.Table;
+import org.apache.iceberg.flink.FlinkSchemaUtil;
+import org.apache.iceberg.flink.TableLoader;
+import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
+import org.apache.iceberg.types.TypeUtil;
+import org.apache.iceberg.util.PropertyUtil;
+
+import static org.apache.iceberg.TableProperties.DEFAULT_FILE_FORMAT;
+import static org.apache.iceberg.TableProperties.DEFAULT_FILE_FORMAT_DEFAULT;
+import static org.apache.iceberg.TableProperties.WRITE_TARGET_FILE_SIZE_BYTES;
+import static org.apache.iceberg.TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT;
+
+public class FlinkSink {
+
+  private static final String ICEBERG_STREAM_WRITER_NAME = IcebergStreamWriter.class.getSimpleName();
+  private static final String ICEBERG_FILES_COMMITTER_NAME = IcebergFilesCommitter.class.getSimpleName();
+
+  private FlinkSink() {
+  }
+
+  /**
+   * Initialize a {@link Builder} to export the data from generic input data stream into iceberg table. We use
+   * {@link RowData} inside the sink connector, so users need to provide a mapper function and a
+   * {@link TypeInformation} to convert those generic records to a RowData DataStream.
+   *
+   * @param input      the generic source input data stream.
+   * @param mapper     function to convert the generic data to {@link RowData}
+   * @param outputType to define the {@link TypeInformation} for the input data.
+   * @param <T>        the data type of records.
+   * @return {@link Builder} to connect the iceberg table.
+   */
+  public static <T> Builder builderFor(DataStream<T> input,
+                                       MapFunction<T, RowData> mapper,
+                                       TypeInformation<RowData> outputType) {
+    DataStream<RowData> dataStream = input.map(mapper, outputType);
+    return forRowData(dataStream);
+  }
+
+  /**
+   * Initialize a {@link Builder} to export the data from input data stream with {@link Row}s into iceberg table. We use
+   * {@link RowData} inside the sink connector, so users need to provide a {@link TableSchema} for builder to convert
+   * those {@link Row}s to a {@link RowData} DataStream.
+   *
+   * @param input       the source input data stream with {@link Row}s.
+   * @param tableSchema defines the {@link TypeInformation} for input data.
+   * @return {@link Builder} to connect the iceberg table.
+   */
+  public static Builder forRow(DataStream<Row> input, TableSchema tableSchema) {
+    RowType rowType = (RowType) tableSchema.toRowDataType().getLogicalType();
+    DataType[] fieldDataTypes = tableSchema.getFieldDataTypes();
+
+    DataFormatConverters.RowConverter rowConverter = new DataFormatConverters.RowConverter(fieldDataTypes);
+    return builderFor(input, rowConverter::toInternal, RowDataTypeInfo.of(rowType))
+        .tableSchema(tableSchema);
+  }
+
+  /**
+   * Initialize a {@link Builder} to export the data from input data stream with {@link RowData}s into iceberg table.
+   *
+   * @param input the source input data stream with {@link RowData}s.
+   * @return {@link Builder} to connect the iceberg table.
+   */
+  public static Builder forRowData(DataStream<RowData> input) {
+    return new Builder().forRowData(input);
+  }
+
+  public static class Builder {
+    private DataStream<RowData> rowDataInput = null;
+    private TableLoader tableLoader;
+    private Configuration hadoopConf;
+    private Table table;
+    private TableSchema tableSchema;
+
+    private Builder() {
+    }
+
+    private Builder forRowData(DataStream<RowData> newRowDataInput) {
+      this.rowDataInput = newRowDataInput;
+      return this;
+    }
+
+    /**
+     * This iceberg {@link Table} instance is used for initializing {@link IcebergStreamWriter} which will write all
+     * the records into {@link DataFile}s and emit them to downstream operator. Providing a table would avoid so many
+     * table loading from each separate task.
+     *
+     * @param newTable the loaded iceberg table instance.
+     * @return {@link Builder} to connect the iceberg table.
+     */
+    public Builder table(Table newTable) {
+      this.table = newTable;
+      return this;
+    }
+
+    /**
+     * The table loader is used for loading tables in {@link IcebergFilesCommitter} lazily, we need this loader because
+     * {@link Table} is not serializable and could not just use the loaded table from Builder#table in the remote task
+     * manager.
+     *
+     * @param newTableLoader to load iceberg table inside tasks.
+     * @return {@link Builder} to connect the iceberg table.
+     */
+    public Builder tableLoader(TableLoader newTableLoader) {
+      this.tableLoader = newTableLoader;
+      return this;
+    }
+
+    public Builder hadoopConf(Configuration newHadoopConf) {
+      this.hadoopConf = newHadoopConf;
+      return this;
+    }
+
+    public Builder tableSchema(TableSchema newTableSchema) {
+      this.tableSchema = newTableSchema;
+      return this;
+    }
+
+    @SuppressWarnings("unchecked")
+    public DataStreamSink<RowData> build() {
+      Preconditions.checkArgument(rowDataInput != null,
+          "Please use forRowData() to initialize the input DataStream.");
+      Preconditions.checkNotNull(table, "Table shouldn't be null");
+      Preconditions.checkNotNull(tableLoader, "Table loader shouldn't be null");
+      Preconditions.checkNotNull(hadoopConf, "Hadoop configuration shouldn't be null");
+
+      IcebergStreamWriter<RowData> streamWriter = createStreamWriter(table, tableSchema);
+      IcebergFilesCommitter filesCommitter = new IcebergFilesCommitter(tableLoader, hadoopConf);
+
+      DataStream<Void> returnStream = rowDataInput
+          .transform(ICEBERG_STREAM_WRITER_NAME, TypeInformation.of(DataFile.class), streamWriter)
+          .setParallelism(rowDataInput.getParallelism())
+          .transform(ICEBERG_FILES_COMMITTER_NAME, Types.VOID, filesCommitter)
+          .setParallelism(1)
+          .setMaxParallelism(1);
+
+      return returnStream.addSink(new DiscardingSink())

Review comment:
       There're three cases: 1>  unbounded streaming job; 2> bounded streaming job;  3> batch job.  If users only need the `unbounded streaming` ability, then only need to implement the `SinkFunction`,  otherwise if need both `unbounded streaming` and `bounded streaming`  ability,  then we need to extend/implement the `AbstractStreamOperator` & `BoundedOneInput`  and add the `DiscardingSink` to the tail.  If want to `batch` ability, then  need to provide an `OutputFormat` implementation.  In future flink,  we will unify the case#2 and case#3 in one sink interface,  but for now we have to implement separately for the bounded streaming and batch cases. 
   
   Flink hive connector is a good case, which have support case1, case2 and case3.  It also use the similar way to the current iceberg sink connector now. 
   




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