taklwu commented on code in PR #166: URL: https://github.com/apache/hbase-connectors/pull/166#discussion_r4066429031
########## spark4/hbase-spark4/src/main/scala/org/apache/hadoop/hbase/spark/datasources/HBaseDataWriter.scala: ########## @@ -0,0 +1,146 @@ +/* + * 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.hadoop.hbase.spark.datasources + +import java.util.ArrayList +import org.apache.hadoop.hbase.TableName +import org.apache.hadoop.hbase.client.{Put, Table} +import org.apache.hadoop.hbase.spark.{HBaseConnectionCache, Logging, SmartConnection} +import org.apache.hadoop.hbase.util.Bytes +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.connector.write.{DataWriter, WriterCommitMessage} +import org.apache.spark.sql.types._ +import org.apache.yetus.audience.InterfaceAudience + +/** + * This is a new class in the spark4 module. Implements DataWriter[InternalRow] for the DS V2 write path. + * Each instance handles one Spark partition on an executor. Converts InternalRow to HBase Put operations + * and writes them via BufferedMutator for efficient client-side batching. + * + * In the spark 3 DS V1 model, this logic was inside DefaultSource.insert() which used + * rdd.map(convertToPut).saveAsHadoopDataset() with the old mapred TableOutputFormat. + * + * @param schema + * @param properties + * @param catalog + * @param wrappedConf + */ [email protected] +class HBaseDataWriter( + schema: StructType, + properties: Map[String, String], + catalog: HBaseTableCatalog, + wrappedConf: SerializableConfiguration) + extends DataWriter[InternalRow] + with Logging { + + private val conf = wrappedConf.value + private val connection: SmartConnection = HBaseConnectionCache.getConnection(conf) + private val tableName = TableName.valueOf(s"${catalog.namespace}:${catalog.name}") + private val table: Table = connection.getTable(tableName) + + private val timestamp = properties.get(HBaseSparkConf.TIMESTAMP).map(_.toLong) + + private val rkFields = catalog.getRowKey + private val rkIdxedFields = rkFields.map { f => + (schema.fieldIndex(f.colName), f) + } + private val colIdxedFields = schema.fieldNames + .filter(name => !rkFields.map(_.colName).contains(name)) + .map(name => (schema.fieldIndex(name), catalog.sMap.getField(name))) + + private val batchSize = properties + .get(HBaseSparkConf.BULKGET_SIZE) + .map(_.toInt) + .getOrElse(HBaseSparkConf.DEFAULT_BULKGET_SIZE) + + private val putBuffer = new ArrayList[Put](batchSize) + + override def write(record: InternalRow): Unit = { + val rowKeyBytes = buildRowKey(record) + val put = timestamp.fold(new Put(rowKeyBytes))(new Put(rowKeyBytes, _)) + + colIdxedFields.foreach { case (idx, field) => + if (!record.isNullAt(idx)) { Review Comment: nit: buildRowKey always reads row-key columns with no null guard, maybe double check if there is null pointer and throws/handle it. ########## spark4/hbase-spark4/src/main/scala/org/apache/hadoop/hbase/spark/datasources/HBaseDataWriter.scala: ########## @@ -0,0 +1,146 @@ +/* + * 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.hadoop.hbase.spark.datasources + +import java.util.ArrayList +import org.apache.hadoop.hbase.TableName +import org.apache.hadoop.hbase.client.{Put, Table} +import org.apache.hadoop.hbase.spark.{HBaseConnectionCache, Logging, SmartConnection} +import org.apache.hadoop.hbase.util.Bytes +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.connector.write.{DataWriter, WriterCommitMessage} +import org.apache.spark.sql.types._ +import org.apache.yetus.audience.InterfaceAudience + +/** + * This is a new class in the spark4 module. Implements DataWriter[InternalRow] for the DS V2 write path. + * Each instance handles one Spark partition on an executor. Converts InternalRow to HBase Put operations + * and writes them via BufferedMutator for efficient client-side batching. Review Comment: nit: Javadoc says BufferedMutator, but below code uses Table.put ########## spark4/hbase-spark4/src/main/scala/org/apache/hadoop/hbase/spark/datasources/HBaseDataWriter.scala: ########## @@ -0,0 +1,146 @@ +/* + * 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.hadoop.hbase.spark.datasources + +import java.util.ArrayList +import org.apache.hadoop.hbase.TableName +import org.apache.hadoop.hbase.client.{Put, Table} +import org.apache.hadoop.hbase.spark.{HBaseConnectionCache, Logging, SmartConnection} +import org.apache.hadoop.hbase.util.Bytes +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.connector.write.{DataWriter, WriterCommitMessage} +import org.apache.spark.sql.types._ +import org.apache.yetus.audience.InterfaceAudience + +/** + * This is a new class in the spark4 module. Implements DataWriter[InternalRow] for the DS V2 write path. + * Each instance handles one Spark partition on an executor. Converts InternalRow to HBase Put operations + * and writes them via BufferedMutator for efficient client-side batching. + * + * In the spark 3 DS V1 model, this logic was inside DefaultSource.insert() which used + * rdd.map(convertToPut).saveAsHadoopDataset() with the old mapred TableOutputFormat. + * + * @param schema + * @param properties + * @param catalog + * @param wrappedConf + */ [email protected] +class HBaseDataWriter( + schema: StructType, + properties: Map[String, String], + catalog: HBaseTableCatalog, + wrappedConf: SerializableConfiguration) + extends DataWriter[InternalRow] + with Logging { + + private val conf = wrappedConf.value + private val connection: SmartConnection = HBaseConnectionCache.getConnection(conf) + private val tableName = TableName.valueOf(s"${catalog.namespace}:${catalog.name}") + private val table: Table = connection.getTable(tableName) + + private val timestamp = properties.get(HBaseSparkConf.TIMESTAMP).map(_.toLong) + + private val rkFields = catalog.getRowKey + private val rkIdxedFields = rkFields.map { f => + (schema.fieldIndex(f.colName), f) + } + private val colIdxedFields = schema.fieldNames + .filter(name => !rkFields.map(_.colName).contains(name)) + .map(name => (schema.fieldIndex(name), catalog.sMap.getField(name))) + + private val batchSize = properties + .get(HBaseSparkConf.BULKGET_SIZE) + .map(_.toInt) + .getOrElse(HBaseSparkConf.DEFAULT_BULKGET_SIZE) + + private val putBuffer = new ArrayList[Put](batchSize) + + override def write(record: InternalRow): Unit = { + val rowKeyBytes = buildRowKey(record) + val put = timestamp.fold(new Put(rowKeyBytes))(new Put(rowKeyBytes, _)) + + colIdxedFields.foreach { case (idx, field) => + if (!record.isNullAt(idx)) { + val valueBytes = getValueBytes(record, idx, field) + put.addColumn(field.cfBytes, field.colBytes, valueBytes) + } + } + + putBuffer.add(put) + if (putBuffer.size() >= batchSize) { + flushPuts() + } + } + + override def commit(): WriterCommitMessage = { + flushPuts() + table.close() + connection.close() + HBaseWriterCommitMessage() + } + + override def abort(): Unit = { + table.close() + connection.close() + } + + override def close(): Unit = {} + + private def flushPuts(): Unit = { + if (!putBuffer.isEmpty) { + table.put(putBuffer) + putBuffer.clear() + } + } + + private def buildRowKey(record: InternalRow): Array[Byte] = { + val rowBytes = rkIdxedFields.map { case (idx, field) => + getValueBytes(record, idx, field) + } + val totalLen = rowBytes.foldLeft(0)(_ + _.length) + val result = new Array[Byte](totalLen) + var offset = 0 + rowBytes.foreach { bytes => + System.arraycopy(bytes, 0, result, offset, bytes.length) + offset += bytes.length + } + result + } + + private val MILLIS_PER_DAY = 86400000L + + private def getValueBytes(row: InternalRow, idx: Int, field: Field): Array[Byte] = { + field.dt match { + case BooleanType => Bytes.toBytes(row.getBoolean(idx)) Review Comment: yup, this is needed, but you can do in the next PR. -- This is an automated message from the Apache Git Service. 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