[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user asfgit closed the pull request at: https://github.com/apache/carbondata/pull/2971 ---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245260405
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
--- End diff --
yes, we can reuse the conversion step and the final status update part.
but I find it will not easy to read the code flow.
so I try to reuse the final status update part.
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245237625
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide RDD
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245224484
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide RDD
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245224217
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide RDD
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245223174
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessorStepOnSpark.scala
---
@@ -95,6 +96,67 @@ object DataLoadProcessorStepOnSpark {
}
}
+ def internalInputFunc(
+ rows: Iterator[InternalRow],
+ index: Int,
+ modelBroadcast: Broadcast[CarbonLoadModel],
+ rowCounter: Accumulator[Int]): Iterator[CarbonRow] = {
+val model: CarbonLoadModel =
modelBroadcast.value.getCopyWithTaskNo(index.toString)
+val conf = DataLoadProcessBuilder.createConfiguration(model)
+val rowParser = new RowParserImpl(conf.getDataFields, conf)
+val isRawDataRequired = CarbonDataProcessorUtil.isRawDataRequired(conf)
+TaskContext.get().addTaskFailureListener { (t: TaskContext, e:
Throwable) =>
+ wrapException(e, model)
+}
+
+new Iterator[CarbonRow] {
+ override def hasNext: Boolean = rows.hasNext
+
+ override def next(): CarbonRow = {
+var row : CarbonRow = null
+val rawRow =
+
rows.next().asInstanceOf[GenericInternalRow].values.asInstanceOf[Array[Object]]
+if(isRawDataRequired) {
+ row = new CarbonRow(rowParser.parseRow(rawRow), rawRow)
+} else {
+ row = new CarbonRow(rowParser.parseRow(rawRow))
+}
+rowCounter.add(1)
+row
+ }
+}
+ }
+
+ def internalSampleInputFunc(
--- End diff --
ok
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r24542
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessorStepOnSpark.scala
---
@@ -95,6 +96,67 @@ object DataLoadProcessorStepOnSpark {
}
}
+ def internalInputFunc(
+ rows: Iterator[InternalRow],
+ index: Int,
+ modelBroadcast: Broadcast[CarbonLoadModel],
+ rowCounter: Accumulator[Int]): Iterator[CarbonRow] = {
+val model: CarbonLoadModel =
modelBroadcast.value.getCopyWithTaskNo(index.toString)
+val conf = DataLoadProcessBuilder.createConfiguration(model)
+val rowParser = new RowParserImpl(conf.getDataFields, conf)
+val isRawDataRequired = CarbonDataProcessorUtil.isRawDataRequired(conf)
+TaskContext.get().addTaskFailureListener { (t: TaskContext, e:
Throwable) =>
+ wrapException(e, model)
+}
+
+new Iterator[CarbonRow] {
+ override def hasNext: Boolean = rows.hasNext
+
+ override def next(): CarbonRow = {
+var row : CarbonRow = null
+val rawRow =
+
rows.next().asInstanceOf[GenericInternalRow].values.asInstanceOf[Array[Object]]
+if(isRawDataRequired) {
+ row = new CarbonRow(rowParser.parseRow(rawRow), rawRow)
+} else {
+ row = new CarbonRow(rowParser.parseRow(rawRow))
+}
+rowCounter.add(1)
+row
+ }
+}
+ }
+
+ def internalSampleInputFunc(
--- End diff --
the parameter "rows" of them is a different type.
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245198317
--- Diff:
integration/spark-common-test/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestGlobalSortDataLoad.scala
---
@@ -106,6 +106,24 @@ class TestGlobalSortDataLoad extends QueryTest with
BeforeAndAfterEach with Befo
sql("SELECT * FROM carbon_localsort_once ORDER BY name"))
}
+ test("Make sure the result is right and sorted in global level for
range_sort") {
--- End diff --
ok
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user QiangCai commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r245198367
--- Diff:
integration/spark-common/src/main/scala/org/apache/spark/DataSkewRangePartitioner.scala
---
@@ -0,0 +1,319 @@
+/*
+ * 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.spark
+
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+import scala.util.hashing.byteswap32
+
+import org.apache.spark.rdd.{PartitionPruningRDD, RDD}
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.util.{CollectionsUtils, Utils}
+
+/**
+ * support data skew scenario
+ * copy from spark: RangePartiitoner
+ */
+class DataSkewRangePartitioner[K: Ordering : ClassTag, V](
--- End diff --
ok
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244681963
--- Diff:
integration/spark-common/src/main/scala/org/apache/spark/DataSkewRangePartitioner.scala
---
@@ -0,0 +1,319 @@
+/*
+ * 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.spark
+
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+import scala.util.hashing.byteswap32
+
+import org.apache.spark.rdd.{PartitionPruningRDD, RDD}
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.util.{CollectionsUtils, Utils}
+
+/**
+ * support data skew scenario
+ * copy from spark: RangePartiitoner
+ */
+class DataSkewRangePartitioner[K: Ordering : ClassTag, V](
--- End diff --
Please add the comment how it is different from spark's range partitioner
and how you do skew partitioning
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244681720
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244681813
--- Diff:
integration/spark-common-test/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestGlobalSortDataLoad.scala
---
@@ -106,6 +106,24 @@ class TestGlobalSortDataLoad extends QueryTest with
BeforeAndAfterEach with Befo
sql("SELECT * FROM carbon_localsort_once ORDER BY name"))
}
+ test("Make sure the result is right and sorted in global level for
range_sort") {
--- End diff --
Please add some test cases with scalefactor as well.
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244681164
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244681062
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessorStepOnSpark.scala
---
@@ -95,6 +96,67 @@ object DataLoadProcessorStepOnSpark {
}
}
+ def internalInputFunc(
+ rows: Iterator[InternalRow],
+ index: Int,
+ modelBroadcast: Broadcast[CarbonLoadModel],
+ rowCounter: Accumulator[Int]): Iterator[CarbonRow] = {
+val model: CarbonLoadModel =
modelBroadcast.value.getCopyWithTaskNo(index.toString)
+val conf = DataLoadProcessBuilder.createConfiguration(model)
+val rowParser = new RowParserImpl(conf.getDataFields, conf)
+val isRawDataRequired = CarbonDataProcessorUtil.isRawDataRequired(conf)
+TaskContext.get().addTaskFailureListener { (t: TaskContext, e:
Throwable) =>
+ wrapException(e, model)
+}
+
+new Iterator[CarbonRow] {
+ override def hasNext: Boolean = rows.hasNext
+
+ override def next(): CarbonRow = {
+var row : CarbonRow = null
+val rawRow =
+
rows.next().asInstanceOf[GenericInternalRow].values.asInstanceOf[Array[Object]]
+if(isRawDataRequired) {
+ row = new CarbonRow(rowParser.parseRow(rawRow), rawRow)
+} else {
+ row = new CarbonRow(rowParser.parseRow(rawRow))
+}
+rowCounter.add(1)
+row
+ }
+}
+ }
+
+ def internalSampleInputFunc(
--- End diff --
Please unify `internalSampleInputFunc` and `internalInputFunc`
---
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244680876
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
+ sparkSession: SparkSession,
+ model: CarbonLoadModel,
+ hadoopConf: Configuration): Array[(String, (LoadMetadataDetails,
ExecutionErrors))] = {
+// initialize and prepare row counter
+val sc = sparkSession.sparkContext
+val modelBroadcast = sc.broadcast(model)
+val partialSuccessAccum = sc.accumulator(0, "Partial Success
Accumulator")
+val inputStepRowCounter = sc.accumulator(0, "Input Processor
Accumulator")
+val convertStepRowCounter = sc.accumulator(0, "Convert Processor
Accumulator")
+val sortStepRowCounter = sc.accumulator(0, "Sort Processor
Accumulator")
+val writeStepRowCounter = sc.accumulator(0, "Write Processor
Accumulator")
+
+// 1. Input
+hadoopConf
+ .set(CarbonCommonConstants.CARBON_WRITTEN_BY_APPNAME,
sparkSession.sparkContext.appName)
+val inputRDD = CsvRDDHelper
+ .csvFileScanRDD(sparkSession, model, hadoopConf)
+ .mapPartitionsWithIndex { case (index, rows) =>
+DataLoadProcessorStepOnSpark
+ .internalInputFunc(rows, index, modelBroadcast,
inputStepRowCounter)
+ }
+
+// 2. Convert
+val conf = SparkSQLUtil.broadCastHadoopConf(sc, hadoopConf)
+val convertRDD = inputRDD
+ .mapPartitionsWithIndex { case (index, rows) =>
+
ThreadLocalSessionInfo.setConfigurationToCurrentThread(conf.value.value)
+DataLoadProcessorStepOnSpark
+ .convertFunc(rows, index, modelBroadcast, partialSuccessAccum,
convertStepRowCounter)
+ }
+ .filter(_ != null)
+
+// 3. Range partition by range_column
+val configuration = DataLoadProcessBuilder.createConfiguration(model)
+val rangeColumnIndex =
+ indexOfColumn(model.getRangePartitionColumn,
configuration.getDataFields)
+// convert RDD[CarbonRow] to RDD[(rangeColumn, CarbonRow)]
+val keyRDD = convertRDD.keyBy(_.getObject(rangeColumnIndex))
+// range partition by key
+val numPartitions = getNumPartitions(configuration, model, convertRDD)
+val objectOrdering: Ordering[Object] =
createOrderingForColumn(model.getRangePartitionColumn)
+import scala.reflect.classTag
+val sampleRDD = getSampleRDD(sparkSession, model, hadoopConf,
configuration, modelBroadcast)
+val rangeRDD = keyRDD
+ .partitionBy(
+new DataSkewRangePartitioner(numPartitions,
sampleRDD)(objectOrdering, classTag[Object]))
+ .map(_._2)
+
+// 4. Sort and Write data
+sc.runJob(rangeRDD, (context: TaskContext, rows: Iterator[CarbonRow])
=>
+ DataLoadProcessorStepOnSpark.sortAndWriteFunc(rows,
context.partitionId, modelBroadcast,
+writeStepRowCounter, conf.value.value))
+
+// Log the number of rows in each step
+LOGGER.info("Total rows processed in step Input Processor: " +
inputStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Converter: " +
convertStepRowCounter.value)
+LOGGER.info("Total rows processed in step Sort Processor: " +
sortStepRowCounter.value)
+LOGGER.info("Total rows processed in step Data Writer: " +
writeStepRowCounter.value)
+
+// Update status
+if (partialSuccessAccum.value != 0) {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE +
+ "Partial_Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+
loadMetadataDetails.setSegmentStatus(SegmentStatus.LOAD_PARTIAL_SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ executionErrors.failureCauses = FailureCauses.BAD_RECORDS
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+} else {
+ val uniqueLoadStatusId = model.getTableName +
CarbonCommonConstants.UNDERSCORE + "Success"
+ val loadMetadataDetails = new LoadMetadataDetails()
+ loadMetadataDetails.setSegmentStatus(SegmentStatus.SUCCESS)
+ val executionErrors = new ExecutionErrors(FailureCauses.NONE, "")
+ Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
+}
+ }
+
+ /**
+ * provide
[GitHub] carbondata pull request #2971: [CARBONDATA-3219] Support range partition the...
Github user ravipesala commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2971#discussion_r244670461
--- Diff:
integration/spark-common/src/main/scala/org/apache/carbondata/spark/load/DataLoadProcessBuilderOnSpark.scala
---
@@ -156,4 +161,206 @@ object DataLoadProcessBuilderOnSpark {
Array((uniqueLoadStatusId, (loadMetadataDetails, executionErrors)))
}
}
+
+ /**
+ * 1. range partition the whole input data
+ * 2. for each range, sort the data and writ it to CarbonData files
+ */
+ def loadDataUsingRangeSort(
--- End diff --
Code is duplicated with method `loadDataUsingGlobalSort` , please try to
extract common code and reuse here.
---
