wangbo commented on a change in pull request #3729: URL: https://github.com/apache/incubator-doris/pull/3729#discussion_r435772039
########## File path: fe/src/main/java/org/apache/doris/load/loadv2/dpp/SparkDpp.java ########## @@ -0,0 +1,851 @@ +// 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.commons.lang3.StringUtils; +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.hadoop.ParquetWriter; +import org.apache.parquet.hadoop.metadata.CompressionCodecName; +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.api.java.function.PairFunction; +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder; +import org.apache.spark.sql.catalyst.encoders.RowEncoder; +import org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport; +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.types.DataTypes; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.util.LongAccumulator; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.TaskContext; + +import scala.Tuple2; +import scala.collection.Seq; + +import java.io.IOException; +import java.math.BigInteger; +import java.net.URI; +import java.net.URISyntaxException; +import java.util.Map; +import java.util.Set; +import java.util.List; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.Iterator; +import java.util.LinkedList; +import java.util.HashSet; +import java.util.Arrays; +import java.util.stream.Collectors; + +import org.apache.hadoop.fs.FileSystem; +import org.apache.hadoop.fs.FSDataOutputStream; +import scala.collection.JavaConverters; +import org.apache.log4j.LogManager; +import org.apache.log4j.Logger; + +// 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 jobs in spark job +// to boost the process of large amount of data load. +// the process steps are as following: +// 1. load data +// 1.1 load data from path/hive table +// 1.2 do the etl process +// 2. repartition data by using doris data model(partition and bucket) +// 3. process aggregation if needed +// 4. write data to parquet file +public final class SparkDpp implements java.io.Serializable { + private static final Logger LOG = LogManager.getLogger(SparkDpp.class); + + 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 SparkSession spark = null; + 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 Map<String, Integer> bucketKeyMap = new HashMap<>(); + // accumulator to collect invalid rows + private StringAccumulator invalidRows = new StringAccumulator(); + + public SparkDpp(SparkSession spark, EtlJobConfig etlJobConfig) { + this.spark = spark; + this.etlJobConfig = etlJobConfig; + } + + public void init() { + abnormalRowAcc = spark.sparkContext().longAccumulator("abnormalRowAcc"); + unselectedRowAcc = spark.sparkContext().longAccumulator("unselectedRowAcc"); + scannedRowsAcc = spark.sparkContext().longAccumulator("scannedRowsAcc"); + fileNumberAcc = spark.sparkContext().longAccumulator("fileNumberAcc"); + fileSizeAcc = spark.sparkContext().longAccumulator("fileSizeAcc"); + spark.sparkContext().register(invalidRows, "InvalidRowsAccumulator"); + } + + private Dataset<Row> processRDDAggAndRepartition(Dataset<Row> dataframe, EtlJobConfig.EtlIndex currentIndexMeta) throws UserException { + final boolean isDuplicateTable = !StringUtils.equalsIgnoreCase(currentIndexMeta.indexType, "AGGREGATE") + && !StringUtils.equalsIgnoreCase(currentIndexMeta.indexType, "UNIQUE"); + + // 1 make metadata for map/reduce + int keyLen = 0; + for (EtlJobConfig.EtlColumn etlColumn : currentIndexMeta.columns) { + keyLen = etlColumn.isKey ? keyLen + 1 : keyLen; + } + + SparkRDDAggregator[] sparkRDDAggregators = new SparkRDDAggregator[currentIndexMeta.columns.size() - keyLen]; + + for (int i = 0 ; i < currentIndexMeta.columns.size(); i++) { + if (!currentIndexMeta.columns.get(i).isKey && !isDuplicateTable) { + sparkRDDAggregators[i - keyLen] = SparkRDDAggregator.buildAggregator(currentIndexMeta.columns.get(i)); + } + } + + PairFunction<Row, List<Object>, Object[]> encodePairFunction = isDuplicateTable ? + // add 1 to include bucketId + new EncodeDuplicateTableFunction(keyLen + 1, currentIndexMeta.columns.size() - keyLen) + : new EncodeAggregateTableFunction(sparkRDDAggregators, keyLen + 1); + + // 2 convert dataframe to rdd + // TODO(wb) use rdd to avoid bitamp/hll serialize + JavaPairRDD<List<Object>, Object[]> currentRollupRDD = dataframe.toJavaRDD().mapToPair(encodePairFunction); + + // 3 do aggregate + // TODO(wb) set the reduce concurrency by statistic instead of hard code 200 + int aggregateConcurrency = 200; + JavaPairRDD<List<Object>, Object[]> reduceResultRDD = isDuplicateTable ? currentRollupRDD + : currentRollupRDD.reduceByKey(new AggregateReduceFunction(sparkRDDAggregators), aggregateConcurrency); + + // 4 repartition + JavaRDD<Row> finalRDD = reduceResultRDD + .repartitionAndSortWithinPartitions(new BucketPartitioner(bucketKeyMap), new BucketComparator()) + .map(record -> { + List<Object> keys = record._1; + Object[] values = record._2; + int size = keys.size() + values.length; + Object[] result = new Object[size]; + + for (int i = 0; i < keys.size(); i++) { + result[i] = keys.get(i); + } + + for (int i = keys.size(); i < size; i++) { + int valueIdx = i - keys.size(); + result[i] = isDuplicateTable ? values[valueIdx] : sparkRDDAggregators[valueIdx].finalize(values[valueIdx]); + } + + return RowFactory.create(result); + }); + + // 4 convert to dataframe + StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(currentIndexMeta.columns, true, true); + dataframe = spark.createDataFrame(finalRDD, tableSchemaWithBucketId); + return dataframe; + + } + + // write data to parquet file by using writing the parquet scheme of spark. + private void writePartitionedAndSortedDataframeToParquet(Dataset<Row> dataframe, + String pathPattern, + long tableId, + EtlJobConfig.EtlIndex indexMeta) throws UserException { + StructType outputSchema = dataframe.schema(); + StructType dstSchema = DataTypes.createStructType( + Arrays.asList(outputSchema.fields()).stream() + .filter(field -> !field.name().equalsIgnoreCase(DppUtils.BUCKET_ID)) + .collect(Collectors.toList())); + ExpressionEncoder encoder = RowEncoder.apply(dstSchema); + 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<InternalRow> parquetWriter = null; + TaskContext taskContext = TaskContext.get(); + long taskAttemptId = taskContext.taskAttemptId(); + String dstPath = ""; + String tmpPath = ""; + + while (t.hasNext()) { + Row row = t.next(); + if (row.length() <= 1) { + LOG.warn("invalid row:" + row); + continue; + } + + + String curBucketKey = row.getString(0); + List<Object> columnObjects = new ArrayList<>(); + for (int i = 1; i < row.length(); ++i) { + Object columnValue = row.get(i); + columnObjects.add(columnValue); + } + System.out.println(); + Row rowWithoutBucketKey = RowFactory.create(columnObjects.toArray()); + // if the bucket key is new, it will belong to a new tablet + if (lastBucketKey == null || !curBucketKey.equals(lastBucketKey)) { + if (parquetWriter != null) { + parquetWriter.close(); + // rename tmpPath to path + try { + fs.rename(new Path(tmpPath), new Path(dstPath)); + } catch (IOException ioe) { + LOG.warn("rename from tmpPath" + tmpPath + " to dstPath:" + dstPath + " failed. exception:" + ioe); + throw ioe; + } + } + // flush current writer and create a new writer + String[] bucketKey = curBucketKey.split("_"); + if (bucketKey.length != 2) { + LOG.warn("invalid bucket key:" + curBucketKey); + continue; + } + int partitionId = Integer.parseInt(bucketKey[0]); + int bucketId = Integer.parseInt(bucketKey[1]); + dstPath = String.format(pathPattern, tableId, partitionId, indexMeta.indexId, + bucketId, indexMeta.schemaHash); + tmpPath = dstPath + "." + taskAttemptId; + conf.setBoolean("spark.sql.parquet.writeLegacyFormat", false); + conf.setBoolean("spark.sql.parquet.int64AsTimestampMillis", false); + conf.setBoolean("spark.sql.parquet.int96AsTimestamp", true); + conf.setBoolean("spark.sql.parquet.binaryAsString", false); + conf.set("spark.sql.parquet.outputTimestampType", "INT96"); + ParquetWriteSupport.setSchema(dstSchema, conf); + ParquetWriteSupport parquetWriteSupport = new ParquetWriteSupport(); + parquetWriter = new ParquetWriter<InternalRow>(new Path(tmpPath), parquetWriteSupport, + CompressionCodecName.SNAPPY, 256 * 1024 * 1024, 16 * 1024, 1024 * 1024, + true, false, + ParquetProperties.WriterVersion.PARQUET_1_0, conf); + if (parquetWriter != null) { + LOG.info("[HdfsOperate]>> initialize writer succeed! path:" + tmpPath); + } + lastBucketKey = curBucketKey; + } + InternalRow internalRow = encoder.toRow(rowWithoutBucketKey); + parquetWriter.write(internalRow); + } + if (parquetWriter != null) { + parquetWriter.close(); + try { + fs.rename(new Path(tmpPath), new Path(dstPath)); + } catch (IOException ioe) { + LOG.warn("rename from tmpPath" + tmpPath + " to dstPath:" + dstPath + " failed. exception:" + ioe); + throw ioe; + } + } + } + }); + } + + private void processRollupTree(RollupTreeNode rootNode, + Dataset<Row> rootDataframe, + long tableId, EtlJobConfig.EtlTable tableMeta, + EtlJobConfig.EtlIndex baseIndex) throws UserException { + 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(); + LOG.info("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) { + for (Dataset<Row> dataframe : parentDataframeMap.values()) { + dataframe.unpersist(); + } + 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); + // aggregate and repartition + curDataFrame = processRDDAggAndRepartition(curDataFrame, curNode.indexMeta); + + 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); + } + } + + // repartition dataframe by partitionid_bucketid + // so data in the same bucket will be consecutive. + 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> validPartitionIds) throws UserException { + List<String> distributeColumns = partitionInfo.distributionColumnRefs; + Partitioner partitioner = new DorisRangePartitioner(partitionInfo, partitionKeyIndex, partitionRangeKeys); + Set<Integer> validPartitionIndex = new HashSet<>(); + if (validPartitionIds == null) { + for (int i = 0; i < partitionInfo.partitions.size(); ++i) { + validPartitionIndex.add(i); + } + } else { + for (int i = 0; i < partitionInfo.partitions.size(); ++i) { + if (validPartitionIds.contains(partitionInfo.partitions.get(i).partitionId)) { + validPartitionIndex.add(i); + } + } + } + // use PairFlatMapFunction instead of PairMapFunction because the there will be + // 0 or 1 output row for 1 input row + 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<Object> keyColumns = new ArrayList<>(); + for (String columnName : keyColumnNames) { + Object columnObject = row.get(row.fieldIndex(columnName)); + columns.add(columnObject); + keyColumns.add(columnObject); + } + + for (String columnName : valueColumnNames) { + columns.add(row.get(row.fieldIndex(columnName))); + } + DppColumns dppColumns = new DppColumns(columns); + DppColumns key = new DppColumns(keyColumns); + List<Tuple2<String, DppColumns>> result = new ArrayList<>(); + int pid = partitioner.getPartition(key); + if (!validPartitionIndex.contains(pid)) { + LOG.warn("invalid partition for row:" + row + ", pid:" + pid); + abnormalRowAcc.add(1); + LOG.info("abnormalRowAcc:" + abnormalRowAcc); + if (abnormalRowAcc.value() < 5) { + LOG.info("add row to invalidRows:" + row.toString()); + invalidRows.add(row.toString()); + LOG.info("invalid rows contents:" + invalidRows.value()); + } + } 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 = partitionId + "_" + 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()); + } + ); + + // TODO(wb): using rdd instead of dataframe from here + StructType tableSchemaWithBucketId = DppUtils.createDstTableSchema(baseIndex.columns, true, false); + dataframe = spark.createDataFrame(resultRdd, tableSchemaWithBucketId); + // use bucket number as the parallel number + int reduceNum = 0; + for (EtlJobConfig.EtlPartition partition : partitionInfo.partitions) { + for (int i = 0; i < partition.bucketNum; i++) { + bucketKeyMap.put(partition.partitionId + "_" + i, reduceNum); + reduceNum++; + } + } + + // print to system.out for easy to find log info + System.out.println("print bucket key map:" + bucketKeyMap.toString()); + + return dataframe; + } + + // do the etl process + private Dataset<Row> convertSrcDataframeToDstDataframe(EtlJobConfig.EtlIndex baseIndex, + Dataset<Row> srcDataframe, + StructType dstTableSchema, + EtlJobConfig.EtlFileGroup fileGroup) throws UserException { + 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("DATE")) { + dataframe = dataframe.withColumn(dstField.name(), dataframe.col(dstField.name()).cast("date")); + } else if (column.columnType.equalsIgnoreCase("BOOLEAN")) { + dataframe = dataframe.withColumn(dstField.name(), + functions.when(dataframe.col(dstField.name()).equalTo("true"), "1") + .otherwise("0")); + } else 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(); + if (mappingDescription.toLowerCase().contains("hll_hash")) { + continue; + } + // 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, + List<EtlJobConfig.EtlColumn> columns) 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(); + List<ColumnParser> parsers = new ArrayList<>(); + for (EtlJobConfig.EtlColumn column : baseIndex.columns) { + parsers.add(ColumnParser.create(column.columnType)); + } + // 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<>(); + boolean validRow = true; + if (attributes.length != columnSize) { + LOG.warn("invalid src schema, data columns:" + + attributes.length + ", file group columns:" + + columnSize + ", row:" + record); + validRow = false; + } else { + for (int i = 0; i < attributes.length; ++i) { + if (attributes[i].equals(NULL_FLAG)) { + if (baseIndex.columns.get(i).isAllowNull) { + attributes[i] = null; + } else { + LOG.warn("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())) { + String type = columns.get(i).columnType; + if (type.equalsIgnoreCase("CHAR") + || type.equalsIgnoreCase("VARCHAR")) { + continue; + } + ColumnParser parser = parsers.get(i); + boolean valid = parser.parse(attributes[i]); + if (!valid) { + validRow = false; + LOG.warn("invalid row:" + record + + ", attribute " + i + ": " + attributes[i] + " parsed failed"); + 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); + } else { + abnormalRowAcc.add(1); + // at most add 5 rows to invalidRows + if (abnormalRowAcc.value() <= 5) { + invalidRows.add(record); + } + } + 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 + private Object convertPartitionKey(Object srcValue, Class dstClass) throws UserException { + 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 { + LOG.warn("unsupport partition key:" + srcValue); + throw new UserException("unsupport partition key:" + srcValue); + } + } + + private List<DorisRangePartitioner.PartitionRangeKey> createPartitionRangeKeys( + EtlJobConfig.EtlPartitionInfo partitionInfo, List<Class> partitionKeySchema) throws UserException { + 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); + ; Review comment: 👌 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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