You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@druid.apache.org by GitBox <gi...@apache.org> on 2021/05/18 18:40:05 UTC
[GitHub] [druid] samarthjain commented on a change in pull request #10920: Spark Direct Readers and Writers for Druid.
samarthjain commented on a change in pull request #10920:
URL: https://github.com/apache/druid/pull/10920#discussion_r626086242
##########
File path: extensions-core/spark-extensions/src/main/scala/org/apache/druid/spark/clients/DruidClient.scala
##########
@@ -0,0 +1,188 @@
+/*
+ * 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.druid.spark.clients
+
+import com.fasterxml.jackson.core.`type`.TypeReference
+import com.fasterxml.jackson.databind.ObjectMapper
+import com.google.common.net.HostAndPort
+import org.apache.druid.java.util.common.{ISE, Intervals, JodaUtils, StringUtils}
+import org.apache.druid.java.util.http.client.response.{StringFullResponseHandler,
+ StringFullResponseHolder}
+import org.apache.druid.java.util.http.client.{HttpClient, Request}
+import org.apache.druid.query.Druids
+import org.apache.druid.query.metadata.metadata.{ColumnAnalysis, SegmentAnalysis,
+ SegmentMetadataQuery}
+import org.apache.druid.spark.MAPPER
+import org.apache.druid.spark.configuration.{Configuration, DruidConfigurationKeys}
+import org.apache.druid.spark.mixins.Logging
+import org.jboss.netty.handler.codec.http.{HttpMethod, HttpResponseStatus}
+import org.joda.time.{Duration, Interval}
+
+import java.net.URL
+import java.util.{List => JList}
+import javax.ws.rs.core.MediaType
+import scala.collection.JavaConverters.{asScalaBufferConverter, mapAsJavaMapConverter,
+ mapAsScalaMapConverter, seqAsJavaListConverter}
+
+/**
+ * Going with SegmentMetadataQueries despite the significant overhead because there's no other way
+ * to get accurate information about Druid columns.
+ *
+ * This is pulled pretty directly from the druid-hadoop-input-format project on GitHub. There is
+ * likely substantial room for improvement.
+ *
+ * @param httpClient
+ * @param hostAndPort
+ */
+class DruidClient(
+ val httpClient: HttpClient,
+ val hostAndPort: HostAndPort
+ ) extends Logging {
+ private val RetryCount = 5
Review comment:
Does it make sense to have these settings configurable?
##########
File path: extensions-core/spark-extensions/src/main/scala/org/apache/druid/spark/configuration/DruidConfigurationKeys.scala
##########
@@ -0,0 +1,130 @@
+/*
+ * 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.druid.spark.configuration
+
+import org.apache.spark.sql.sources.v2.DataSourceOptions
+
+object DruidConfigurationKeys {
+ // Shadowing DataSourceOptions.TABLE_KEY here so other classes won't need unnecessary imports
+ val tableKey: String = DataSourceOptions.TABLE_KEY
+
+ // Metadata Client Configs
+ val metadataPrefix: String = "metadata"
+ val metadataDbTypeKey: String = "dbType"
+ val metadataHostKey: String = "host"
+ val metadataPortKey: String = "port"
+ val metadataConnectUriKey: String = "connectUri"
+ val metadataUserKey: String = "user"
+ val metadataPasswordKey: String = "password"
+ val metadataDbcpPropertiesKey: String = "dbcpProperties"
+ val metadataBaseNameKey: String = "baseName"
+ private[spark] val metadataHostDefaultKey: (String, String) = (metadataHostKey, "localhost")
+ private[spark] val metadataPortDefaultKey: (String, Int) = (metadataPortKey, 1527)
+ private[spark] val metadataBaseNameDefaultKey: (String, String) = (metadataBaseNameKey, "druid")
+
+ // Druid Client Configs
+ val brokerPrefix: String = "broker"
+ val brokerHostKey: String = "host"
+ val brokerPortKey: String = "port"
+ private[spark] val brokerHostDefaultKey: (String, String) = (brokerHostKey, "localhost")
+ private[spark] val brokerPortDefaultKey: (String, Int) = (brokerPortKey, 8082)
+
+ // Common configs
+ val useCompactSketchesKey: String = "useCompactSketches" // Default: false
Review comment:
What is the purpose of this config?
##########
File path: extensions-core/spark-extensions/src/main/scala/org/apache/druid/spark/v2/writer/DruidDataWriter.scala
##########
@@ -0,0 +1,304 @@
+/*
+ * 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.druid.spark.v2.writer
+
+import com.fasterxml.jackson.core.`type`.TypeReference
+import org.apache.druid.data.input.MapBasedInputRow
+import org.apache.druid.java.util.common.io.Closer
+import org.apache.druid.java.util.common.parsers.TimestampParser
+import org.apache.druid.java.util.common.{DateTimes, FileUtils, Intervals}
+import org.apache.druid.segment.column.ValueType
+import org.apache.druid.segment.data.{BitmapSerdeFactory, CompressionFactory, CompressionStrategy,
+ ConciseBitmapSerdeFactory, RoaringBitmapSerdeFactory}
+import org.apache.druid.segment.incremental.{IncrementalIndex, IncrementalIndexSchema,
+ OnheapIncrementalIndex}
+import org.apache.druid.segment.indexing.DataSchema
+import org.apache.druid.segment.loading.DataSegmentPusher
+import org.apache.druid.segment.writeout.OnHeapMemorySegmentWriteOutMediumFactory
+import org.apache.druid.segment.{IndexSpec, IndexableAdapter, QueryableIndexIndexableAdapter}
+import org.apache.druid.spark.MAPPER
+import org.apache.druid.spark.configuration.{DruidConfigurationKeys, DruidDataWriterConfig}
+import org.apache.druid.spark.mixins.Logging
+import org.apache.druid.spark.registries.{ComplexMetricRegistry, SegmentWriterRegistry,
+ ShardSpecRegistry}
+import org.apache.druid.spark.utils.NullHandlingUtils
+import org.apache.druid.spark.v2.{INDEX_IO, INDEX_MERGER_V9}
+import org.apache.druid.timeline.DataSegment
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.UnsafeArrayData
+import org.apache.spark.sql.sources.v2.writer.{DataWriter, WriterCommitMessage}
+import org.apache.spark.sql.types.ArrayType
+
+import java.io.Closeable
+import java.util.{List => JList}
+import scala.collection.JavaConverters.{asScalaBufferConverter, mapAsJavaMapConverter,
+ seqAsJavaListConverter}
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * A DruidDataWriter does the actual work of writing a partition of a dataframe to files Druid
+ * knows how to read. Users should not use this class directly but should instead call write() on their dataframe
+ * (e.g. df.write.format("druid").options(Map[String, String](...)).save()).
+ *
+ * TODO: Describe the writing logic (creating the map from bucket to flushed indexers (i.e. adapters
+ * and the current open index), how indices are merged and pushed to deep storage, partition number
+ * /counts concerns, etc.
+ *
+ * @param config An object holding necessary configuration settings for the writer passed along
+ * from the driver.
+ */
+class DruidDataWriter(config: DruidDataWriterConfig) extends DataWriter[InternalRow] with Logging {
+ NullHandlingUtils.initializeDruidNullHandling(config.useDefaultNullHandling)
+
+ private val tmpPersistDir = FileUtils.createTempDir("persist")
+ private val tmpMergeDir = FileUtils.createTempDir("merge")
+ private val closer = Closer.create()
+ closer.register(
+ new Closeable {
+ override def close(): Unit = {
+ FileUtils.deleteDirectory(tmpMergeDir)
+ FileUtils.deleteDirectory(tmpPersistDir)
+ }
+ }
+ )
+
+ private val dataSchema: DataSchema =
+ MAPPER.readValue[DataSchema](config.dataSchemaSerialized,
+ new TypeReference[DataSchema] {})
+ private val dimensions: JList[String] =
+ dataSchema.getDimensionsSpec.getDimensions.asScala.map(_.getName).asJava
+ private val tsColumn: String = dataSchema.getTimestampSpec.getTimestampColumn
+ private val tsColumnIndex = config.schema.fieldIndex(tsColumn)
+ private val timestampParser = TimestampParser
+ .createObjectTimestampParser(dataSchema.getTimestampSpec.getTimestampFormat)
+
+ private val partitionMap = config.partitionMap.map(_(config.partitionId))
+ .getOrElse(Map[String, String]("partitionId" -> config.partitionId.toString))
+
+ private val indexSpecConf = config.properties.dive(DruidConfigurationKeys.indexSpecPrefix)
+ private val indexSpec: IndexSpec = new IndexSpec(
+ DruidDataWriter.getBitmapSerde(
+ indexSpecConf.get(DruidConfigurationKeys.bitmapTypeDefaultKey),
+ indexSpecConf.getBoolean(
+ DruidConfigurationKeys.compressRunOnSerializationKey,
+ RoaringBitmapSerdeFactory.DEFAULT_COMPRESS_RUN_ON_SERIALIZATION)
+ ),
+ DruidDataWriter.getCompressionStrategy(
+ indexSpecConf.get(
+ DruidConfigurationKeys.dimensionCompressionKey,
+ CompressionStrategy.DEFAULT_COMPRESSION_STRATEGY.toString)
+ ),
+ DruidDataWriter.getCompressionStrategy(
+ indexSpecConf.get(
+ DruidConfigurationKeys.metricCompressionKey,
+ CompressionStrategy.DEFAULT_COMPRESSION_STRATEGY.toString)
+ ),
+ DruidDataWriter.getLongEncodingStrategy(
+ indexSpecConf.get(
+ DruidConfigurationKeys.longEncodingKey,
+ CompressionFactory.DEFAULT_LONG_ENCODING_STRATEGY.toString
+ )
+ )
+ )
+
+ private val complexColumnTypes = {
+ dataSchema.getAggregators.toSeq.filter(_.getType == ValueType.COMPLEX).map(_.getComplexTypeName)
+ }
+
+ if (complexColumnTypes.nonEmpty) {
+ // Callers will need to explicitly register any complex metrics not known to ComplexMetricRegistry by default
+ complexColumnTypes.foreach {
+ ComplexMetricRegistry.registerByName(_, config.writeCompactSketches)
+ }
+ }
+ ComplexMetricRegistry.registerSerdes()
+
+ private val pusher: DataSegmentPusher = SegmentWriterRegistry.getSegmentPusher(
+ config.deepStorageType, config.properties
+ )
+
+ // TODO: rewrite this without using IncrementalIndex, because IncrementalIndex bears a lot of overhead
+ // to support concurrent querying, that is not needed in Spark
+ // We may have rows for multiple intervals here, so we need to keep a map of segment start time to
+ // flushed adapters and currently incrementing indexers.
+ private val bucketToIndexMap: mutable.Map[Long,
+ (ArrayBuffer[IndexableAdapter], IncrementalIndex[_])
+ ] = mutable.HashMap[Long, (ArrayBuffer[IndexableAdapter], IncrementalIndex[_])]()
+ .withDefault(bucket => (ArrayBuffer.empty[IndexableAdapter], createInterval(bucket)))
+
+ override def write(row: InternalRow): Unit = {
+ val timestamp = timestampParser
+ .apply(row.get(tsColumnIndex, config.schema(tsColumnIndex).dataType)).getMillis
+ val bucket = getBucketForRow(timestamp)
+ // Scala MutableMaps don't have a computeIfAbsent equivalent, so this is either a logical no-op or it creates the
+ // entry following the function defined in the map's .withDefault declaration
+ bucketToIndexMap(bucket) = bucketToIndexMap(bucket)
+ var index = bucketToIndexMap(bucket)._2
+
+ // Check index, flush if too many rows in memory and recreate
+ if (index.size() == config.rowsPerPersist) {
+ val adapters = bucketToIndexMap(bucket)._1 :+ flushIndex(index)
+ index.close()
+ bucketToIndexMap(bucket) = (adapters, createInterval(bucket))
+ index = bucketToIndexMap(bucket)._2
+ }
+ index.add(
+ index.formatRow(
+ new MapBasedInputRow(
+ timestamp,
+ dimensions,
+ // Convert to Java types that Druid knows how to handle
+ config.schema
+ .map(field => field.name -> (field.dataType, row.get(config.schema.indexOf(field), field.dataType))).toMap
+ .mapValues {
+ case (_, traversable: Traversable[_]) => traversable.toSeq.asJava
+ case (dataType, unsafe: UnsafeArrayData) =>
+ (0 until unsafe.numElements()).map(unsafe.get(_, dataType.asInstanceOf[ArrayType].elementType)).asJava
+ case (_, x) => x
+ }.asJava
+ )
+ )
+ )
+ }
+
+ private[v2] def createInterval(startMillis: Long): IncrementalIndex[_] = {
+ // Using OnHeapIncrementalIndex to minimize changes when migrating from IncrementalIndex. In the future, this should
+ // be optimized further. See https://github.com/apache/druid/issues/10321 for more information.
+ new OnheapIncrementalIndex.Builder()
+ .setIndexSchema(
+ new IncrementalIndexSchema.Builder()
+ .withDimensionsSpec(dataSchema.getDimensionsSpec)
+ .withQueryGranularity(
+ dataSchema.getGranularitySpec.getQueryGranularity
+ )
+ .withMetrics(dataSchema.getAggregators: _*)
+ .withTimestampSpec(dataSchema.getTimestampSpec)
+ .withMinTimestamp(startMillis)
+ .withRollup(dataSchema.getGranularitySpec.isRollup)
+ .build()
+ )
+ .setMaxRowCount(config.rowsPerPersist)
+ .build()
+ }
+
+ private[v2] def flushIndex(index: IncrementalIndex[_]): IndexableAdapter = {
+ new QueryableIndexIndexableAdapter(
+ closer.register(
+ INDEX_IO.loadIndex(
+ INDEX_MERGER_V9
+ .persist(
+ index,
+ index.getInterval,
+ tmpPersistDir,
+ indexSpec,
+ OnHeapMemorySegmentWriteOutMediumFactory.instance()
+ )
+ )
+ )
+ )
+ }
+
+ private[v2] def getBucketForRow(ts: Long): Long = {
+ dataSchema.getGranularitySpec.getSegmentGranularity.bucketStart(DateTimes.utc(ts)).getMillis
+ }
+
+ override def commit(): WriterCommitMessage = {
+ // Return segment locations on deep storage
+ val specs = bucketToIndexMap.values.map { case (adapters, index) =>
+ if (!index.isEmpty) {
+ adapters += flushIndex(index)
+ index.close()
+ }
+ if (adapters.nonEmpty) {
+ // TODO: Merge adapters up to a total number of rows, and then split into new segments.
+ // The tricky piece will be determining the partition number for multiple segments (interpolate 0?)
+ val finalStaticIndexer = INDEX_MERGER_V9
+ val file = finalStaticIndexer.merge(
+ adapters.asJava,
+ true,
+ dataSchema.getAggregators,
+ tmpMergeDir,
+ indexSpec,
+ -1 // TODO: Make maxColumnsToMerge configurable
+ )
+ val allDimensions: JList[String] = adapters
+ .map(_.getDimensionNames)
+ .foldLeft(Set[String]())(_ ++ _.asScala)
+ .toList
+ .asJava
+ val interval = adapters.map(_.getDataInterval)
+ .reduce((l, r) => Intervals.utc(
+ Math.min(l.getStartMillis, r.getStartMillis), Math.max(l.getEndMillis, r.getEndMillis)
+ ))
+ val shardSpec = ShardSpecRegistry.createShardSpec(
+ config.shardSpec,
+ partitionMap)
+ val dataSegmentTemplate = new DataSegment(
+ config.dataSource,
+ dataSchema.getGranularitySpec.getSegmentGranularity.bucket(DateTimes.utc(interval.getStartMillis)),
+ config.version,
+ null, // scalastyle:ignore null
+ allDimensions,
+ dataSchema.getAggregators.map(_.getName).toList.asJava,
+ shardSpec,
+ -1,
+ 0L
+ )
+ val finalDataSegment = pusher.push(file, dataSegmentTemplate, true)
+ Seq(MAPPER.writeValueAsString(finalDataSegment))
+ } else {
+ Seq.empty
+ }
+ }.toSeq.flatten
+ closer.close()
Review comment:
Should this be in a try-with-resources like block you have added in this PR?
##########
File path: extensions-core/spark-extensions/src/main/scala/org/apache/druid/spark/clients/DruidClient.scala
##########
@@ -0,0 +1,180 @@
+/*
+ * 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.druid.spark.clients
+
+import com.fasterxml.jackson.core.`type`.TypeReference
+import com.fasterxml.jackson.databind.ObjectMapper
+import com.google.common.net.HostAndPort
+import org.apache.druid.java.util.common.{ISE, StringUtils}
+import org.apache.druid.java.util.http.client.response.{StringFullResponseHandler,
+ StringFullResponseHolder}
+import org.apache.druid.java.util.http.client.{HttpClient, Request}
+import org.apache.druid.query.Druids
+import org.apache.druid.query.metadata.metadata.{ColumnAnalysis, SegmentAnalysis,
+ SegmentMetadataQuery}
+import org.apache.druid.spark.MAPPER
+import org.apache.druid.spark.configuration.{Configuration, DruidConfigurationKeys}
+import org.apache.druid.spark.mixins.Logging
+import org.jboss.netty.handler.codec.http.{HttpMethod, HttpResponseStatus}
+import org.joda.time.{Duration, Interval}
+
+import java.net.URL
+import java.util.{List => JList}
+import javax.ws.rs.core.MediaType
+import scala.collection.JavaConverters.{asScalaBufferConverter, mapAsJavaMapConverter,
+ mapAsScalaMapConverter, seqAsJavaListConverter}
+
+/**
+ * Going with SegmentMetadataQueries despite the significant overhead because there's no other way
+ * to get accurate information about Druid columns.
+ *
+ * This is pulled pretty directly from the druid-hadoop-input-format project on GitHub. There is
+ * likely substantial room for improvement.
+ *
+ * @param httpClient
+ * @param objectMapper
+ * @param hostAndPort
+ */
+class DruidClient(
+ val httpClient: HttpClient,
+ val objectMapper: ObjectMapper,
+ val hostAndPort: HostAndPort
+ ) extends Logging {
+ private val RETRY_COUNT = 5
+ private val RETRY_WAIT_SECONDS = 5
+ private val TIMEOUT_MILLISECONDS = 300000
+ private val druidBaseQueryURL: HostAndPort => String =
+ (hostAndPort: HostAndPort) => s"http://$hostAndPort/druid/v2/"
+
+ /**
+ * The SQL system catalog tables are incorrect for multivalue columns and don't have accurate
+ * type info, so we need to fall back to segmentMetadataQueries. Given a DATASOURCE and a range
+ * of INTERVALS to query over, return a map from column name to a tuple of
+ * (columnType, hasMultipleValues). Note that this is a very expensive operation over large
+ * numbers of segments. If possible, this method should only be called with the most granular
+ * starting and ending intervals instead of over a larger interval.
+ *
+ * @param dataSource The Druid dataSource to fetch the schema for.
+ * @param intervals The intervals to return the schema for.
+ * @return A map from column name to data type and whether or not the column is multi-value
+ * for the schema of DATASOURCE.
+ */
+ def getSchema(dataSource: String, intervals: List[Interval]): Map[String, (String, Boolean)] = {
+ val body = Druids.newSegmentMetadataQueryBuilder()
+ .dataSource(dataSource)
+ .intervals(intervals.asJava)
+ .analysisTypes(SegmentMetadataQuery.AnalysisType.SIZE)
+ .merge(true)
+ .context(Map[String, AnyRef](
+ "timeout" -> Int.box(TIMEOUT_MILLISECONDS)
+ ).asJava)
+ .build()
+ val response = sendRequestWithRetry(
+ druidBaseQueryURL(hostAndPort), RETRY_COUNT, Option(objectMapper.writeValueAsBytes(body))
+ )
+ val segments =
+ objectMapper.readValue[JList[SegmentAnalysis]](
+ response.getContent, new TypeReference[JList[SegmentAnalysis]]() {})
+ if (segments.size() == 0) {
+ throw new ISE(
+ s"No segments found for intervals [${intervals.mkString(",")}] on $dataSource"
+ )
+ }
+ // Since we're setting merge to true, there should only be one item in the list
+ if (segments.size() > 1) {
+ logWarn("Segment metadata response had more than one row, problem?")
+ }
+ segments.asScala.head.getColumns.asScala.map{ case (name: String, col: ColumnAnalysis) =>
Review comment:
Does it make sense to fail the operation if there is an error reported in the response? At a minimum, we should log a warning to let the user know that we are falling back to using "String" as datatype for a column since, possibly, data types are different for a column across segments in the requested interval.
--
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@druid.apache.org
For additional commands, e-mail: commits-help@druid.apache.org