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. 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