[RFC-33] [HUDI-2429][Stacked on HUDI-2560] Support full Schema evolution for Spark (#4910)
* [HUDI-2560] introduce id_based schema to support full schema evolution. * add test for FileBasedInternalSchemaStorageManger and rebase code * add support for change column type and fix some test case * fix some bugs encountered in the production env and delete useless code * fix test error * rebase code * fixed some nested schema change bugs * [HUDI-2429][Stacked On HUDI-2560]Support full schema evolution for spark * [use dummyInternalSchema instead of null] * add support for spark3.1.x * remove support for spark3.1.x , sicne some compile fail * support spark3.1.x * rebase and prepare solve all comments * address all comments * rebase code * fixed the count(*) bug * try to get internalSchema by parser commit file/history file directly, not use metaclient which is time cost address some comments * fixed all comments * fix new comments * rebase code,fix UT failed * fixed mistake * rebase code ,fixed new comments * rebase code , and prepare for address new comments * address commits * address new comments * fix new issues * control fallback original write logical
This commit is contained in:
xiarixiaoyao
@@ -0,0 +1,191 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
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* this work for additional information regarding copyright ownership.
|
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* The ASF licenses this file to You under the Apache License, Version 2.0
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||||
* (the "License"); you may not use this file except in compliance with
|
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
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*/
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package org.apache.spark.sql.execution.datasources.parquet;
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import org.apache.hadoop.mapreduce.InputSplit;
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import org.apache.hadoop.mapreduce.TaskAttemptContext;
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import org.apache.hudi.client.utils.SparkInternalSchemaConverter;
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import org.apache.hudi.common.util.collection.Pair;
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import org.apache.spark.memory.MemoryMode;
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import org.apache.spark.sql.catalyst.InternalRow;
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import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
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import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
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import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
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import org.apache.spark.sql.types.DataType;
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import org.apache.spark.sql.types.StructType;
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import org.apache.spark.sql.vectorized.ColumnarBatch;
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import java.io.IOException;
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import java.time.ZoneId;
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import java.util.HashMap;
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import java.util.Map;
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public class Spark32HoodieVectorizedParquetRecordReader extends VectorizedParquetRecordReader {
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// save the col type change info.
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private Map<Integer, Pair<DataType, DataType>> typeChangeInfos;
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private ColumnarBatch columnarBatch;
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private Map<Integer, WritableColumnVector> idToColumnVectors;
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private WritableColumnVector[] columnVectors;
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// The capacity of vectorized batch.
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private int capacity;
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// If true, this class returns batches instead of rows.
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private boolean returnColumnarBatch;
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// The memory mode of the columnarBatch.
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private final MemoryMode memoryMode;
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/**
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* Batch of rows that we assemble and the current index we've returned. Every time this
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* batch is used up (batchIdx == numBatched), we populated the batch.
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*/
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private int batchIdx = 0;
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private int numBatched = 0;
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public Spark32HoodieVectorizedParquetRecordReader(
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ZoneId convertTz,
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String datetimeRebaseMode,
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String datetimeRebaseTz,
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String int96RebaseMode,
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String int96RebaseTz,
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boolean useOffHeap,
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int capacity,
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Map<Integer, Pair<DataType, DataType>> typeChangeInfos) {
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super(convertTz, datetimeRebaseMode, datetimeRebaseTz, int96RebaseMode, int96RebaseTz, useOffHeap, capacity);
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memoryMode = useOffHeap ? MemoryMode.OFF_HEAP : MemoryMode.ON_HEAP;
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this.typeChangeInfos = typeChangeInfos;
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this.capacity = capacity;
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}
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@Override
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public void initBatch(StructType partitionColumns, InternalRow partitionValues) {
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super.initBatch(partitionColumns, partitionValues);
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if (columnVectors == null) {
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columnVectors = new WritableColumnVector[sparkSchema.length() + partitionColumns.length()];
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}
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if (idToColumnVectors == null) {
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idToColumnVectors = new HashMap<>();
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typeChangeInfos.entrySet()
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.stream()
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.forEach(f -> {
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WritableColumnVector vector =
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memoryMode == MemoryMode.OFF_HEAP ? new OffHeapColumnVector(capacity, f.getValue().getLeft()) : new OnHeapColumnVector(capacity, f.getValue().getLeft());
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idToColumnVectors.put(f.getKey(), vector);
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});
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}
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}
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@Override
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public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptContext) throws IOException, InterruptedException, UnsupportedOperationException {
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super.initialize(inputSplit, taskAttemptContext);
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}
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@Override
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public void close() throws IOException {
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super.close();
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for (Map.Entry<Integer, WritableColumnVector> e : idToColumnVectors.entrySet()) {
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e.getValue().close();
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}
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idToColumnVectors = null;
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columnarBatch = null;
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columnVectors = null;
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}
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@Override
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public ColumnarBatch resultBatch() {
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ColumnarBatch currentColumnBatch = super.resultBatch();
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boolean changed = false;
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for (Map.Entry<Integer, Pair<DataType, DataType>> entry : typeChangeInfos.entrySet()) {
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boolean rewrite = SparkInternalSchemaConverter
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.convertColumnVectorType((WritableColumnVector) currentColumnBatch.column(entry.getKey()),
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idToColumnVectors.get(entry.getKey()), currentColumnBatch.numRows());
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if (rewrite) {
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changed = true;
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columnVectors[entry.getKey()] = idToColumnVectors.get(entry.getKey());
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}
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}
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if (changed) {
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if (columnarBatch == null) {
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// fill other vector
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for (int i = 0; i < columnVectors.length; i++) {
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if (columnVectors[i] == null) {
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columnVectors[i] = (WritableColumnVector) currentColumnBatch.column(i);
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}
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}
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columnarBatch = new ColumnarBatch(columnVectors);
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}
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columnarBatch.setNumRows(currentColumnBatch.numRows());
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return columnarBatch;
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} else {
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return currentColumnBatch;
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}
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}
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@Override
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public boolean nextBatch() throws IOException {
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boolean result = super.nextBatch();
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if (idToColumnVectors != null) {
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idToColumnVectors.entrySet().stream().forEach(e -> e.getValue().reset());
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}
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numBatched = resultBatch().numRows();
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batchIdx = 0;
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return result;
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}
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@Override
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public void enableReturningBatches() {
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returnColumnarBatch = true;
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super.enableReturningBatches();
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}
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@Override
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public Object getCurrentValue() {
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if (typeChangeInfos == null || typeChangeInfos.isEmpty()) {
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return super.getCurrentValue();
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}
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if (returnColumnarBatch) {
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return columnarBatch == null ? super.getCurrentValue() : columnarBatch;
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}
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return columnarBatch == null ? super.getCurrentValue() : columnarBatch.getRow(batchIdx - 1);
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}
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@Override
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public boolean nextKeyValue() throws IOException {
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resultBatch();
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if (returnColumnarBatch) {
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return nextBatch();
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}
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if (batchIdx >= numBatched) {
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if (!nextBatch()) {
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return false;
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}
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}
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++batchIdx;
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return true;
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}
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}
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@@ -22,6 +22,9 @@ import org.apache.spark.sql.avro._
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import org.apache.spark.sql.catalyst.expressions.Expression
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import org.apache.spark.sql.catalyst.parser.ParserInterface
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import org.apache.spark.sql.catalyst.plans.logical._
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import org.apache.spark.SPARK_VERSION
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import org.apache.spark.sql.catalyst.rules.Rule
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import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
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import org.apache.spark.sql.parser.HoodieSpark3_2ExtendedSqlParser
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import org.apache.spark.sql.types.DataType
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import org.apache.spark.sql.{HoodieCatalystExpressionUtils, HoodieSpark3_2CatalystExpressionUtils, SparkSession}
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@@ -63,4 +66,28 @@ class Spark3_2Adapter extends BaseSpark3Adapter {
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(spark: SparkSession, delegate: ParserInterface) => new HoodieSpark3_2ExtendedSqlParser(spark, delegate)
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)
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}
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override def createResolveHudiAlterTableCommand(sparkSession: SparkSession): Rule[LogicalPlan] = {
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if (SPARK_VERSION.startsWith("3.2")) {
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val loadClassName = "org.apache.spark.sql.hudi.ResolveHudiAlterTableCommandSpark32"
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val clazz = Class.forName(loadClassName, true, Thread.currentThread().getContextClassLoader)
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val ctor = clazz.getConstructors.head
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ctor.newInstance(sparkSession).asInstanceOf[Rule[LogicalPlan]]
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} else {
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new Rule[LogicalPlan] {
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override def apply(plan: LogicalPlan): LogicalPlan = plan
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}
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}
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}
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override def createHoodieParquetFileFormat(): Option[ParquetFileFormat] = {
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if (SPARK_VERSION.startsWith("3.2")) {
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val loadClassName = "org.apache.spark.sql.execution.datasources.parquet.Spark32HoodieParquetFileFormat"
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val clazz = Class.forName(loadClassName, true, Thread.currentThread().getContextClassLoader)
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val ctor = clazz.getConstructors.head
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Some(ctor.newInstance().asInstanceOf[ParquetFileFormat])
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} else {
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None
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}
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}
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}
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@@ -0,0 +1,349 @@
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/*
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* 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
|
||||
*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
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* Unless required by applicable law or agreed to in writing, software
|
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* 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
|
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* limitations under the License.
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*/
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package org.apache.spark.sql.execution.datasources.parquet
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import java.net.URI
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import org.apache.hadoop.conf.Configuration
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import org.apache.hadoop.fs.Path
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import org.apache.hadoop.mapred.FileSplit
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import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
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import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
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import org.apache.hudi.client.utils.SparkInternalSchemaConverter
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import org.apache.hudi.common.fs.FSUtils
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import org.apache.hudi.common.util.InternalSchemaCache
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import org.apache.hudi.common.util.collection.Pair
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import org.apache.hudi.internal.schema.InternalSchema
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import org.apache.hudi.internal.schema.action.InternalSchemaMerger
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import org.apache.hudi.internal.schema.utils.{InternalSchemaUtils, SerDeHelper}
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import org.apache.parquet.filter2.compat.FilterCompat
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import org.apache.parquet.filter2.predicate.FilterApi
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import org.apache.parquet.format.converter.ParquetMetadataConverter.SKIP_ROW_GROUPS
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import org.apache.parquet.hadoop.{ParquetFileReader, ParquetInputFormat, ParquetRecordReader}
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import org.apache.spark.TaskContext
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import org.apache.spark.sql.SparkSession
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import org.apache.spark.sql.catalyst.InternalRow
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import org.apache.spark.sql.catalyst.expressions.{Cast, JoinedRow}
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import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
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import org.apache.spark.sql.catalyst.util.DateTimeUtils
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import org.apache.spark.sql.execution.datasources.{DataSourceUtils, PartitionedFile, RecordReaderIterator}
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import org.apache.spark.sql.internal.SQLConf
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import org.apache.spark.sql.sources._
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import org.apache.spark.sql.types.{AtomicType, DataType, StructField, StructType}
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import org.apache.spark.util.SerializableConfiguration
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class Spark32HoodieParquetFileFormat extends ParquetFileFormat {
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// reference ParquetFileFormat from spark project
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override def buildReaderWithPartitionValues(
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sparkSession: SparkSession,
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dataSchema: StructType,
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partitionSchema: StructType,
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requiredSchema: StructType,
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filters: Seq[Filter],
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options: Map[String, String],
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hadoopConf: Configuration): PartitionedFile => Iterator[InternalRow] = {
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if (hadoopConf.get(SparkInternalSchemaConverter.HOODIE_QUERY_SCHEMA, "").isEmpty) {
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// fallback to origin parquet File read
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super.buildReaderWithPartitionValues(sparkSession, dataSchema, partitionSchema, requiredSchema, filters, options, hadoopConf)
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} else {
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hadoopConf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[ParquetReadSupport].getName)
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hadoopConf.set(
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ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
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requiredSchema.json)
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hadoopConf.set(
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ParquetWriteSupport.SPARK_ROW_SCHEMA,
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requiredSchema.json)
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hadoopConf.set(
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SQLConf.SESSION_LOCAL_TIMEZONE.key,
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sparkSession.sessionState.conf.sessionLocalTimeZone)
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hadoopConf.setBoolean(
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SQLConf.NESTED_SCHEMA_PRUNING_ENABLED.key,
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sparkSession.sessionState.conf.nestedSchemaPruningEnabled)
|
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hadoopConf.setBoolean(
|
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SQLConf.CASE_SENSITIVE.key,
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sparkSession.sessionState.conf.caseSensitiveAnalysis)
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ParquetWriteSupport.setSchema(requiredSchema, hadoopConf)
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// Sets flags for `ParquetToSparkSchemaConverter`
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hadoopConf.setBoolean(
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SQLConf.PARQUET_BINARY_AS_STRING.key,
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sparkSession.sessionState.conf.isParquetBinaryAsString)
|
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hadoopConf.setBoolean(
|
||||
SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
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sparkSession.sessionState.conf.isParquetINT96AsTimestamp)
|
||||
// for dataSource v1, we have no method to do project for spark physical plan.
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// it's safe to do cols project here.
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val internalSchemaString = hadoopConf.get(SparkInternalSchemaConverter.HOODIE_QUERY_SCHEMA)
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val querySchemaOption = SerDeHelper.fromJson(internalSchemaString)
|
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if (querySchemaOption.isPresent && !requiredSchema.isEmpty) {
|
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val prunedSchema = SparkInternalSchemaConverter.convertAndPruneStructTypeToInternalSchema(requiredSchema, querySchemaOption.get())
|
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hadoopConf.set(SparkInternalSchemaConverter.HOODIE_QUERY_SCHEMA, SerDeHelper.toJson(prunedSchema))
|
||||
}
|
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val broadcastedHadoopConf =
|
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sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
|
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|
||||
// TODO: if you move this into the closure it reverts to the default values.
|
||||
// If true, enable using the custom RecordReader for parquet. This only works for
|
||||
// a subset of the types (no complex types).
|
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val resultSchema = StructType(partitionSchema.fields ++ requiredSchema.fields)
|
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val sqlConf = sparkSession.sessionState.conf
|
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val enableOffHeapColumnVector = sqlConf.offHeapColumnVectorEnabled
|
||||
val enableVectorizedReader: Boolean =
|
||||
sqlConf.parquetVectorizedReaderEnabled &&
|
||||
resultSchema.forall(_.dataType.isInstanceOf[AtomicType])
|
||||
val enableRecordFilter: Boolean = sqlConf.parquetRecordFilterEnabled
|
||||
val timestampConversion: Boolean = sqlConf.isParquetINT96TimestampConversion
|
||||
val capacity = sqlConf.parquetVectorizedReaderBatchSize
|
||||
val enableParquetFilterPushDown: Boolean = sqlConf.parquetFilterPushDown
|
||||
// Whole stage codegen (PhysicalRDD) is able to deal with batches directly
|
||||
val returningBatch = supportBatch(sparkSession, resultSchema)
|
||||
val pushDownDate = sqlConf.parquetFilterPushDownDate
|
||||
val pushDownTimestamp = sqlConf.parquetFilterPushDownTimestamp
|
||||
val pushDownDecimal = sqlConf.parquetFilterPushDownDecimal
|
||||
val pushDownStringStartWith = sqlConf.parquetFilterPushDownStringStartWith
|
||||
val pushDownInFilterThreshold = sqlConf.parquetFilterPushDownInFilterThreshold
|
||||
val isCaseSensitive = sqlConf.caseSensitiveAnalysis
|
||||
val parquetOptions = new ParquetOptions(options, sparkSession.sessionState.conf)
|
||||
val datetimeRebaseModeInRead = parquetOptions.datetimeRebaseModeInRead
|
||||
val int96RebaseModeInread = parquetOptions.int96RebaseModeInRead
|
||||
|
||||
(file: PartitionedFile) => {
|
||||
assert(file.partitionValues.numFields == partitionSchema.size)
|
||||
val filePath = new Path(new URI(file.filePath))
|
||||
val split = new FileSplit(filePath, file.start, file.length, Array.empty[String])
|
||||
val sharedConf = broadcastedHadoopConf.value.value
|
||||
// do deal with internalSchema
|
||||
val internalSchemaString = sharedConf.get(SparkInternalSchemaConverter.HOODIE_QUERY_SCHEMA)
|
||||
// querySchema must be a pruned schema.
|
||||
val querySchemaOption = SerDeHelper.fromJson(internalSchemaString)
|
||||
val internalSchemaChangeEnabled = if (internalSchemaString.isEmpty || !querySchemaOption.isPresent) false else true
|
||||
val tablePath = sharedConf.get(SparkInternalSchemaConverter.HOODIE_TABLE_PATH)
|
||||
val commitInstantTime = FSUtils.getCommitTime(filePath.getName).toLong;
|
||||
val fileSchema = if (internalSchemaChangeEnabled) {
|
||||
val validCommits = sharedConf.get(SparkInternalSchemaConverter.HOODIE_VALID_COMMITS_LIST)
|
||||
InternalSchemaCache.getInternalSchemaByVersionId(commitInstantTime, tablePath, sharedConf, if (validCommits == null) "" else validCommits)
|
||||
} else {
|
||||
// this should not happened, searchSchemaAndCache will deal with correctly.
|
||||
null
|
||||
}
|
||||
|
||||
lazy val footerFileMetaData =
|
||||
ParquetFooterReader.readFooter(sharedConf, filePath, SKIP_ROW_GROUPS).getFileMetaData
|
||||
val datetimeRebaseSpec = DataSourceUtils.datetimeRebaseSpec(
|
||||
footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
|
||||
// Try to push down filters when filter push-down is enabled.
|
||||
val pushed = if (enableParquetFilterPushDown) {
|
||||
val parquetSchema = footerFileMetaData.getSchema
|
||||
val parquetFilters = new ParquetFilters(
|
||||
parquetSchema,
|
||||
pushDownDate,
|
||||
pushDownTimestamp,
|
||||
pushDownDecimal,
|
||||
pushDownStringStartWith,
|
||||
pushDownInFilterThreshold,
|
||||
isCaseSensitive,
|
||||
datetimeRebaseSpec)
|
||||
filters.map(Spark32HoodieParquetFileFormat.rebuildFilterFromParquet(_, fileSchema, querySchemaOption.get()))
|
||||
// Collects all converted Parquet filter predicates. Notice that not all predicates can be
|
||||
// converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
|
||||
// is used here.
|
||||
.flatMap(parquetFilters.createFilter(_))
|
||||
.reduceOption(FilterApi.and)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
|
||||
// PARQUET_INT96_TIMESTAMP_CONVERSION says to apply timezone conversions to int96 timestamps'
|
||||
// *only* if the file was created by something other than "parquet-mr", so check the actual
|
||||
// writer here for this file. We have to do this per-file, as each file in the table may
|
||||
// have different writers.
|
||||
// Define isCreatedByParquetMr as function to avoid unnecessary parquet footer reads.
|
||||
def isCreatedByParquetMr: Boolean =
|
||||
footerFileMetaData.getCreatedBy().startsWith("parquet-mr")
|
||||
|
||||
val convertTz =
|
||||
if (timestampConversion && !isCreatedByParquetMr) {
|
||||
Some(DateTimeUtils.getZoneId(sharedConf.get(SQLConf.SESSION_LOCAL_TIMEZONE.key)))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
val int96RebaseSpec = DataSourceUtils.int96RebaseSpec(
|
||||
footerFileMetaData.getKeyValueMetaData.get, int96RebaseModeInread)
|
||||
|
||||
val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
|
||||
// use new conf
|
||||
val hadoopAttempConf = new Configuration(broadcastedHadoopConf.value.value)
|
||||
//
|
||||
// reset request schema
|
||||
var typeChangeInfos: java.util.Map[Integer, Pair[DataType, DataType]] = new java.util.HashMap()
|
||||
if (internalSchemaChangeEnabled) {
|
||||
val mergedInternalSchema = new InternalSchemaMerger(fileSchema, querySchemaOption.get(), true, true).mergeSchema()
|
||||
val mergedSchema = SparkInternalSchemaConverter.constructSparkSchemaFromInternalSchema(mergedInternalSchema)
|
||||
typeChangeInfos = SparkInternalSchemaConverter.collectTypeChangedCols(querySchemaOption.get(), mergedInternalSchema)
|
||||
hadoopAttempConf.set(ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA, mergedSchema.json)
|
||||
}
|
||||
val hadoopAttemptContext =
|
||||
new TaskAttemptContextImpl(hadoopAttempConf, attemptId)
|
||||
|
||||
// Try to push down filters when filter push-down is enabled.
|
||||
// Notice: This push-down is RowGroups level, not individual records.
|
||||
if (pushed.isDefined) {
|
||||
ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get)
|
||||
}
|
||||
val taskContext = Option(TaskContext.get())
|
||||
if (enableVectorizedReader) {
|
||||
val vectorizedReader = new Spark32HoodieVectorizedParquetRecordReader(
|
||||
convertTz.orNull,
|
||||
datetimeRebaseSpec.mode.toString,
|
||||
datetimeRebaseSpec.timeZone,
|
||||
int96RebaseSpec.mode.toString,
|
||||
int96RebaseSpec.timeZone,
|
||||
enableOffHeapColumnVector && taskContext.isDefined,
|
||||
capacity, typeChangeInfos)
|
||||
val iter = new RecordReaderIterator(vectorizedReader)
|
||||
// SPARK-23457 Register a task completion listener before `initialization`.
|
||||
// taskContext.foreach(_.addTaskCompletionListener[Unit](_ => iter.close()))
|
||||
try {
|
||||
vectorizedReader.initialize(split, hadoopAttemptContext)
|
||||
logDebug(s"Appending $partitionSchema ${file.partitionValues}")
|
||||
vectorizedReader.initBatch(partitionSchema, file.partitionValues)
|
||||
if (returningBatch) {
|
||||
vectorizedReader.enableReturningBatches()
|
||||
}
|
||||
|
||||
// UnsafeRowParquetRecordReader appends the columns internally to avoid another copy.
|
||||
iter.asInstanceOf[Iterator[InternalRow]]
|
||||
} catch {
|
||||
case e: Throwable =>
|
||||
// SPARK-23457: In case there is an exception in initialization, close the iterator to
|
||||
// avoid leaking resources.
|
||||
iter.close()
|
||||
throw e
|
||||
}
|
||||
} else {
|
||||
logDebug(s"Falling back to parquet-mr")
|
||||
// ParquetRecordReader returns InternalRow
|
||||
val readSupport = new ParquetReadSupport(
|
||||
convertTz,
|
||||
enableVectorizedReader = false,
|
||||
datetimeRebaseSpec,
|
||||
int96RebaseSpec)
|
||||
val reader = if (pushed.isDefined && enableRecordFilter) {
|
||||
val parquetFilter = FilterCompat.get(pushed.get, null)
|
||||
new ParquetRecordReader[InternalRow](readSupport, parquetFilter)
|
||||
} else {
|
||||
new ParquetRecordReader[InternalRow](readSupport)
|
||||
}
|
||||
val iter = new RecordReaderIterator[InternalRow](reader)
|
||||
// SPARK-23457 Register a task completion listener before `initialization`.
|
||||
taskContext.foreach(_.addTaskCompletionListener[Unit](_ => iter.close()))
|
||||
reader.initialize(split, hadoopAttemptContext)
|
||||
|
||||
val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes
|
||||
val unsafeProjection = if (typeChangeInfos.isEmpty) {
|
||||
GenerateUnsafeProjection.generate(fullSchema, fullSchema)
|
||||
} else {
|
||||
// find type changed.
|
||||
val newFullSchema = new StructType(requiredSchema.fields.zipWithIndex.map { case (f, i) =>
|
||||
if (typeChangeInfos.containsKey(i)) {
|
||||
StructField(f.name, typeChangeInfos.get(i).getRight, f.nullable, f.metadata)
|
||||
} else f
|
||||
}).toAttributes ++ partitionSchema.toAttributes
|
||||
val castSchema = newFullSchema.zipWithIndex.map { case (attr, i) =>
|
||||
if (typeChangeInfos.containsKey(i)) {
|
||||
Cast(attr, typeChangeInfos.get(i).getLeft)
|
||||
} else attr
|
||||
}
|
||||
GenerateUnsafeProjection.generate(castSchema, newFullSchema)
|
||||
}
|
||||
|
||||
if (partitionSchema.length == 0) {
|
||||
// There is no partition columns
|
||||
iter.map(unsafeProjection)
|
||||
} else {
|
||||
val joinedRow = new JoinedRow()
|
||||
iter.map(d => unsafeProjection(joinedRow(d, file.partitionValues)))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
object Spark32HoodieParquetFileFormat {
|
||||
|
||||
private def rebuildFilterFromParquet(oldFilter: Filter, fileSchema: InternalSchema, querySchema: InternalSchema): Filter = {
|
||||
if (fileSchema == null || querySchema == null) {
|
||||
oldFilter
|
||||
} else {
|
||||
oldFilter match {
|
||||
case eq: EqualTo =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(eq.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else eq.copy(attribute = newAttribute)
|
||||
case eqs: EqualNullSafe =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(eqs.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else eqs.copy(attribute = newAttribute)
|
||||
case gt: GreaterThan =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(gt.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else gt.copy(attribute = newAttribute)
|
||||
case gtr: GreaterThanOrEqual =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(gtr.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else gtr.copy(attribute = newAttribute)
|
||||
case lt: LessThan =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(lt.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else lt.copy(attribute = newAttribute)
|
||||
case lte: LessThanOrEqual =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(lte.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else lte.copy(attribute = newAttribute)
|
||||
case i: In =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(i.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else i.copy(attribute = newAttribute)
|
||||
case isn: IsNull =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(isn.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else isn.copy(attribute = newAttribute)
|
||||
case isnn: IsNotNull =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(isnn.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else isnn.copy(attribute = newAttribute)
|
||||
case And(left, right) =>
|
||||
And(rebuildFilterFromParquet(left, fileSchema, querySchema), rebuildFilterFromParquet(right, fileSchema, querySchema))
|
||||
case Or(left, right) =>
|
||||
Or(rebuildFilterFromParquet(left, fileSchema, querySchema), rebuildFilterFromParquet(right, fileSchema, querySchema))
|
||||
case Not(child) =>
|
||||
Not(rebuildFilterFromParquet(child, fileSchema, querySchema))
|
||||
case ssw: StringStartsWith =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(ssw.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else ssw.copy(attribute = newAttribute)
|
||||
case ses: StringEndsWith =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(ses.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else ses.copy(attribute = newAttribute)
|
||||
case sc: StringContains =>
|
||||
val newAttribute = InternalSchemaUtils.reBuildFilterName(sc.attribute, fileSchema, querySchema)
|
||||
if (newAttribute.isEmpty) AlwaysTrue else sc.copy(attribute = newAttribute)
|
||||
case AlwaysTrue =>
|
||||
AlwaysTrue
|
||||
case AlwaysFalse =>
|
||||
AlwaysFalse
|
||||
case _ =>
|
||||
AlwaysTrue
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
/*
|
||||
* 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.sql.hudi
|
||||
|
||||
import org.apache.hudi.config.HoodieWriteConfig
|
||||
import org.apache.hudi.internal.schema.action.TableChange.ColumnChangeID
|
||||
import org.apache.spark.sql.catalyst.analysis.ResolvedTable
|
||||
import org.apache.spark.sql.catalyst.catalog.CatalogTable
|
||||
import org.apache.spark.sql.SparkSession
|
||||
import org.apache.spark.sql.catalyst.plans.logical.{AddColumns, AlterColumn, DropColumns, LogicalPlan, RenameColumn, ReplaceColumns, SetTableProperties, UnsetTableProperties}
|
||||
import org.apache.spark.sql.catalyst.rules.Rule
|
||||
import org.apache.spark.sql.hudi.catalog.HoodieInternalV2Table
|
||||
import org.apache.spark.sql.hudi.command.{AlterTableCommand => HudiAlterTableCommand}
|
||||
|
||||
/**
|
||||
* Rule to mostly resolve, normalize and rewrite column names based on case sensitivity.
|
||||
* for alter table column commands.
|
||||
*/
|
||||
class ResolveHudiAlterTableCommandSpark32(sparkSession: SparkSession) extends Rule[LogicalPlan] {
|
||||
|
||||
def apply(plan: LogicalPlan): LogicalPlan = plan.resolveOperatorsUp {
|
||||
case set @ SetTableProperties(asTable(table), _) if schemaEvolutionEnabled && set.resolved =>
|
||||
HudiAlterTableCommand(table, set.changes, ColumnChangeID.PROPERTY_CHANGE)
|
||||
case unSet @ UnsetTableProperties(asTable(table), _, _) if schemaEvolutionEnabled && unSet.resolved =>
|
||||
HudiAlterTableCommand(table, unSet.changes, ColumnChangeID.PROPERTY_CHANGE)
|
||||
case drop @ DropColumns(asTable(table), _) if schemaEvolutionEnabled && drop.resolved =>
|
||||
HudiAlterTableCommand(table, drop.changes, ColumnChangeID.DELETE)
|
||||
case add @ AddColumns(asTable(table), _) if schemaEvolutionEnabled && add.resolved =>
|
||||
HudiAlterTableCommand(table, add.changes, ColumnChangeID.ADD)
|
||||
case renameColumn @ RenameColumn(asTable(table), _, _) if schemaEvolutionEnabled && renameColumn.resolved=>
|
||||
HudiAlterTableCommand(table, renameColumn.changes, ColumnChangeID.UPDATE)
|
||||
case alter @ AlterColumn(asTable(table), _, _, _, _, _) if schemaEvolutionEnabled && alter.resolved =>
|
||||
HudiAlterTableCommand(table, alter.changes, ColumnChangeID.UPDATE)
|
||||
case replace @ ReplaceColumns(asTable(table), _) if schemaEvolutionEnabled && replace.resolved =>
|
||||
HudiAlterTableCommand(table, replace.changes, ColumnChangeID.REPLACE)
|
||||
}
|
||||
|
||||
private def schemaEvolutionEnabled(): Boolean = sparkSession
|
||||
.sessionState.conf.getConfString(HoodieWriteConfig.SCHEMA_EVOLUTION_ENABLE.key(), "false").toBoolean
|
||||
|
||||
object asTable {
|
||||
def unapply(a: LogicalPlan): Option[CatalogTable] = {
|
||||
a match {
|
||||
case ResolvedTable(_, _, table: HoodieInternalV2Table, _) =>
|
||||
table.catalogTable
|
||||
case _ =>
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,347 @@
|
||||
/*
|
||||
* 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.sql.hudi.command
|
||||
|
||||
import java.net.URI
|
||||
import java.nio.charset.StandardCharsets
|
||||
import java.util
|
||||
import java.util.concurrent.atomic.AtomicInteger
|
||||
import org.apache.hadoop.conf.Configuration
|
||||
import org.apache.hadoop.fs.Path
|
||||
import org.apache.hudi.DataSourceWriteOptions._
|
||||
import org.apache.hudi.client.utils.SparkInternalSchemaConverter
|
||||
import org.apache.hudi.common.model.{HoodieCommitMetadata, WriteOperationType}
|
||||
import org.apache.hudi.{DataSourceOptionsHelper, DataSourceUtils}
|
||||
import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline, HoodieInstant}
|
||||
import org.apache.hudi.common.table.timeline.HoodieInstant.State
|
||||
import org.apache.hudi.common.table.{HoodieTableMetaClient, TableSchemaResolver}
|
||||
import org.apache.hudi.common.util.{CommitUtils, Option}
|
||||
import org.apache.hudi.config.HoodieWriteConfig
|
||||
import org.apache.hudi.internal.schema.InternalSchema
|
||||
import org.apache.hudi.internal.schema.action.TableChange.ColumnChangeID
|
||||
import org.apache.hudi.internal.schema.action.TableChanges
|
||||
import org.apache.hudi.internal.schema.convert.AvroInternalSchemaConverter
|
||||
import org.apache.hudi.internal.schema.utils.{SchemaChangeUtils, SerDeHelper}
|
||||
import org.apache.hudi.internal.schema.io.FileBasedInternalSchemaStorageManager
|
||||
import org.apache.hudi.table.HoodieSparkTable
|
||||
import org.apache.spark.api.java.JavaSparkContext
|
||||
import org.apache.spark.internal.Logging
|
||||
import org.apache.spark.sql.{Row, SparkSession}
|
||||
import org.apache.spark.sql.catalyst.catalog.{CatalogTable, CatalogTableType}
|
||||
import org.apache.spark.sql.connector.catalog.{TableCatalog, TableChange}
|
||||
import org.apache.spark.sql.connector.catalog.TableChange.{AddColumn, DeleteColumn, RemoveProperty, SetProperty}
|
||||
import org.apache.spark.sql.types.StructType
|
||||
|
||||
import scala.collection.JavaConverters._
|
||||
import scala.util.control.NonFatal
|
||||
|
||||
case class AlterTableCommand(table: CatalogTable, changes: Seq[TableChange], changeType: ColumnChangeID) extends HoodieLeafRunnableCommand with Logging {
|
||||
override def run(sparkSession: SparkSession): Seq[Row] = {
|
||||
changeType match {
|
||||
case ColumnChangeID.ADD => applyAddAction(sparkSession)
|
||||
case ColumnChangeID.DELETE => applyDeleteAction(sparkSession)
|
||||
case ColumnChangeID.UPDATE => applyUpdateAction(sparkSession)
|
||||
case ColumnChangeID.PROPERTY_CHANGE if (changes.filter(_.isInstanceOf[SetProperty]).size == changes.size) =>
|
||||
applyPropertySet(sparkSession)
|
||||
case ColumnChangeID.PROPERTY_CHANGE if (changes.filter(_.isInstanceOf[RemoveProperty]).size == changes.size) =>
|
||||
applyPropertyUnset(sparkSession)
|
||||
case ColumnChangeID.REPLACE => applyReplaceAction(sparkSession)
|
||||
case other => throw new RuntimeException(s"find unsupported alter command type: ${other}")
|
||||
}
|
||||
Seq.empty[Row]
|
||||
}
|
||||
|
||||
def applyReplaceAction(sparkSession: SparkSession): Unit = {
|
||||
// convert to delete first then add again
|
||||
val deleteChanges = changes.filter(p => p.isInstanceOf[DeleteColumn]).map(_.asInstanceOf[DeleteColumn])
|
||||
val addChanges = changes.filter(p => p.isInstanceOf[AddColumn]).map(_.asInstanceOf[AddColumn])
|
||||
val (oldSchema, historySchema) = getInternalSchemaAndHistorySchemaStr(sparkSession)
|
||||
val newSchema = applyAddAction2Schema(sparkSession, applyDeleteAction2Schema(sparkSession, oldSchema, deleteChanges), addChanges)
|
||||
val verifiedHistorySchema = if (historySchema == null || historySchema.isEmpty) {
|
||||
SerDeHelper.inheritSchemas(oldSchema, "")
|
||||
} else {
|
||||
historySchema
|
||||
}
|
||||
AlterTableCommand.commitWithSchema(newSchema, verifiedHistorySchema, table, sparkSession)
|
||||
logInfo("column replace finished")
|
||||
}
|
||||
|
||||
def applyAddAction2Schema(sparkSession: SparkSession, oldSchema: InternalSchema, addChanges: Seq[AddColumn]): InternalSchema = {
|
||||
val addChange = TableChanges.ColumnAddChange.get(oldSchema)
|
||||
addChanges.foreach { addColumn =>
|
||||
val names = addColumn.fieldNames()
|
||||
val parentName = AlterTableCommand.getParentName(names)
|
||||
// add col change
|
||||
val colType = SparkInternalSchemaConverter.buildTypeFromStructType(addColumn.dataType(), true, new AtomicInteger(0))
|
||||
addChange.addColumns(parentName, names.last, colType, addColumn.comment())
|
||||
// add position change
|
||||
addColumn.position() match {
|
||||
case after: TableChange.After =>
|
||||
addChange.addPositionChange(names.mkString("."),
|
||||
if (parentName.isEmpty) after.column() else parentName + "." + after.column(), "after")
|
||||
case _: TableChange.First =>
|
||||
addChange.addPositionChange(names.mkString("."), "", "first")
|
||||
case _ =>
|
||||
}
|
||||
}
|
||||
SchemaChangeUtils.applyTableChanges2Schema(oldSchema, addChange)
|
||||
}
|
||||
|
||||
def applyDeleteAction2Schema(sparkSession: SparkSession, oldSchema: InternalSchema, deleteChanges: Seq[DeleteColumn]): InternalSchema = {
|
||||
val deleteChange = TableChanges.ColumnDeleteChange.get(oldSchema)
|
||||
deleteChanges.foreach { c =>
|
||||
val originalColName = c.fieldNames().mkString(".")
|
||||
checkSchemaChange(Seq(originalColName), table)
|
||||
deleteChange.deleteColumn(originalColName)
|
||||
}
|
||||
SchemaChangeUtils.applyTableChanges2Schema(oldSchema, deleteChange).setSchemaId(oldSchema.getMaxColumnId)
|
||||
}
|
||||
|
||||
|
||||
def applyAddAction(sparkSession: SparkSession): Unit = {
|
||||
val (oldSchema, historySchema) = getInternalSchemaAndHistorySchemaStr(sparkSession)
|
||||
val newSchema = applyAddAction2Schema(sparkSession, oldSchema, changes.map(_.asInstanceOf[AddColumn]))
|
||||
val verifiedHistorySchema = if (historySchema == null || historySchema.isEmpty) {
|
||||
SerDeHelper.inheritSchemas(oldSchema, "")
|
||||
} else {
|
||||
historySchema
|
||||
}
|
||||
AlterTableCommand.commitWithSchema(newSchema, verifiedHistorySchema, table, sparkSession)
|
||||
logInfo("column add finished")
|
||||
}
|
||||
|
||||
def applyDeleteAction(sparkSession: SparkSession): Unit = {
|
||||
val (oldSchema, historySchema) = getInternalSchemaAndHistorySchemaStr(sparkSession)
|
||||
val newSchema = applyDeleteAction2Schema(sparkSession, oldSchema, changes.map(_.asInstanceOf[DeleteColumn]))
|
||||
// delete action should not change the getMaxColumnId field.
|
||||
newSchema.setMaxColumnId(oldSchema.getMaxColumnId)
|
||||
val verifiedHistorySchema = if (historySchema == null || historySchema.isEmpty) {
|
||||
SerDeHelper.inheritSchemas(oldSchema, "")
|
||||
} else {
|
||||
historySchema
|
||||
}
|
||||
AlterTableCommand.commitWithSchema(newSchema, verifiedHistorySchema, table, sparkSession)
|
||||
logInfo("column delete finished")
|
||||
}
|
||||
|
||||
def applyUpdateAction(sparkSession: SparkSession): Unit = {
|
||||
val (oldSchema, historySchema) = getInternalSchemaAndHistorySchemaStr(sparkSession)
|
||||
val updateChange = TableChanges.ColumnUpdateChange.get(oldSchema)
|
||||
changes.foreach { change =>
|
||||
change match {
|
||||
case updateType: TableChange.UpdateColumnType =>
|
||||
val newType = SparkInternalSchemaConverter.buildTypeFromStructType(updateType.newDataType(), true, new AtomicInteger(0))
|
||||
updateChange.updateColumnType(updateType.fieldNames().mkString("."), newType)
|
||||
case updateComment: TableChange.UpdateColumnComment =>
|
||||
updateChange.updateColumnComment(updateComment.fieldNames().mkString("."), updateComment.newComment())
|
||||
case updateName: TableChange.RenameColumn =>
|
||||
val originalColName = updateName.fieldNames().mkString(".")
|
||||
checkSchemaChange(Seq(originalColName), table)
|
||||
updateChange.renameColumn(originalColName, updateName.newName())
|
||||
case updateNullAbility: TableChange.UpdateColumnNullability =>
|
||||
updateChange.updateColumnNullability(updateNullAbility.fieldNames().mkString("."), updateNullAbility.nullable())
|
||||
case updatePosition: TableChange.UpdateColumnPosition =>
|
||||
val names = updatePosition.fieldNames()
|
||||
val parentName = AlterTableCommand.getParentName(names)
|
||||
updatePosition.position() match {
|
||||
case after: TableChange.After =>
|
||||
updateChange.addPositionChange(names.mkString("."),
|
||||
if (parentName.isEmpty) after.column() else parentName + "." + after.column(), "after")
|
||||
case _: TableChange.First =>
|
||||
updateChange.addPositionChange(names.mkString("."), "", "first")
|
||||
case _ =>
|
||||
}
|
||||
}
|
||||
}
|
||||
val newSchema = SchemaChangeUtils.applyTableChanges2Schema(oldSchema, updateChange)
|
||||
val verifiedHistorySchema = if (historySchema == null || historySchema.isEmpty) {
|
||||
SerDeHelper.inheritSchemas(oldSchema, "")
|
||||
} else {
|
||||
historySchema
|
||||
}
|
||||
AlterTableCommand.commitWithSchema(newSchema, verifiedHistorySchema, table, sparkSession)
|
||||
logInfo("column update finished")
|
||||
}
|
||||
|
||||
// to do support unset default value to columns, and apply them to internalSchema
|
||||
def applyPropertyUnset(sparkSession: SparkSession): Unit = {
|
||||
val catalog = sparkSession.sessionState.catalog
|
||||
val propKeys = changes.map(_.asInstanceOf[RemoveProperty]).map(_.property())
|
||||
// ignore NonExist unset
|
||||
propKeys.foreach { k =>
|
||||
if (!table.properties.contains(k) && k != TableCatalog.PROP_COMMENT) {
|
||||
logWarning(s"find non exist unset property: ${k} , ignore it")
|
||||
}
|
||||
}
|
||||
val tableComment = if (propKeys.contains(TableCatalog.PROP_COMMENT)) None else table.comment
|
||||
val newProperties = table.properties.filter { case (k, _) => !propKeys.contains(k) }
|
||||
val newTable = table.copy(properties = newProperties, comment = tableComment)
|
||||
catalog.alterTable(newTable)
|
||||
logInfo("table properties change finished")
|
||||
}
|
||||
|
||||
// to do support set default value to columns, and apply them to internalSchema
|
||||
def applyPropertySet(sparkSession: SparkSession): Unit = {
|
||||
val catalog = sparkSession.sessionState.catalog
|
||||
val properties = changes.map(_.asInstanceOf[SetProperty]).map(f => f.property -> f.value).toMap
|
||||
// This overrides old properties and update the comment parameter of CatalogTable
|
||||
// with the newly added/modified comment since CatalogTable also holds comment as its
|
||||
// direct property.
|
||||
val newTable = table.copy(
|
||||
properties = table.properties ++ properties,
|
||||
comment = properties.get(TableCatalog.PROP_COMMENT).orElse(table.comment))
|
||||
catalog.alterTable(newTable)
|
||||
logInfo("table properties change finished")
|
||||
}
|
||||
|
||||
def getInternalSchemaAndHistorySchemaStr(sparkSession: SparkSession): (InternalSchema, String) = {
|
||||
val path = AlterTableCommand.getTableLocation(table, sparkSession)
|
||||
val hadoopConf = sparkSession.sessionState.newHadoopConf()
|
||||
val metaClient = HoodieTableMetaClient.builder().setBasePath(path)
|
||||
.setConf(hadoopConf).build()
|
||||
val schemaUtil = new TableSchemaResolver(metaClient)
|
||||
|
||||
val schema = schemaUtil.getTableInternalSchemaFromCommitMetadata().orElse {
|
||||
AvroInternalSchemaConverter.convert(schemaUtil.getTableAvroSchema)
|
||||
}
|
||||
|
||||
val historySchemaStr = schemaUtil.getTableHistorySchemaStrFromCommitMetadata.orElse("")
|
||||
(schema, historySchemaStr)
|
||||
}
|
||||
|
||||
def checkSchemaChange(colNames: Seq[String], catalogTable: CatalogTable): Unit = {
|
||||
val primaryKeys = catalogTable.storage.properties.getOrElse("primaryKey", catalogTable.properties.getOrElse("primaryKey", "keyid")).split(",").map(_.trim)
|
||||
val preCombineKey = Seq(catalogTable.storage.properties.getOrElse("preCombineField", catalogTable.properties.getOrElse("preCombineField", "ts"))).map(_.trim)
|
||||
val partitionKey = catalogTable.partitionColumnNames.map(_.trim)
|
||||
val checkNames = primaryKeys ++ preCombineKey ++ partitionKey
|
||||
colNames.foreach { col =>
|
||||
if (checkNames.contains(col)) {
|
||||
throw new UnsupportedOperationException("cannot support apply changes for primaryKey/CombineKey/partitionKey")
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
object AlterTableCommand extends Logging {
|
||||
|
||||
/**
|
||||
* Generate an commit with new schema to change the table's schema.
|
||||
*
|
||||
* @param internalSchema new schema after change
|
||||
* @param historySchemaStr history schemas
|
||||
* @param table The hoodie table.
|
||||
* @param sparkSession The spark session.
|
||||
*/
|
||||
def commitWithSchema(internalSchema: InternalSchema, historySchemaStr: String, table: CatalogTable, sparkSession: SparkSession): Unit = {
|
||||
val schema = AvroInternalSchemaConverter.convert(internalSchema, table.identifier.table)
|
||||
val path = getTableLocation(table, sparkSession)
|
||||
val jsc = new JavaSparkContext(sparkSession.sparkContext)
|
||||
val client = DataSourceUtils.createHoodieClient(jsc, schema.toString,
|
||||
path, table.identifier.table, parametersWithWriteDefaults(table.storage.properties).asJava)
|
||||
|
||||
val hadoopConf = sparkSession.sessionState.newHadoopConf()
|
||||
val metaClient = HoodieTableMetaClient.builder().setBasePath(path).setConf(hadoopConf).build()
|
||||
|
||||
val commitActionType = CommitUtils.getCommitActionType(WriteOperationType.ALTER_SCHEMA, metaClient.getTableType)
|
||||
val instantTime = HoodieActiveTimeline.createNewInstantTime
|
||||
client.startCommitWithTime(instantTime, commitActionType)
|
||||
|
||||
val hoodieTable = HoodieSparkTable.create(client.getConfig, client.getEngineContext)
|
||||
val timeLine = hoodieTable.getActiveTimeline
|
||||
val requested = new HoodieInstant(State.REQUESTED, commitActionType, instantTime)
|
||||
val metadata = new HoodieCommitMetadata
|
||||
metadata.setOperationType(WriteOperationType.ALTER_SCHEMA)
|
||||
timeLine.transitionRequestedToInflight(requested, Option.of(metadata.toJsonString.getBytes(StandardCharsets.UTF_8)))
|
||||
val extraMeta = new util.HashMap[String, String]()
|
||||
extraMeta.put(SerDeHelper.LATEST_SCHEMA, SerDeHelper.toJson(internalSchema.setSchemaId(instantTime.toLong)))
|
||||
val schemaManager = new FileBasedInternalSchemaStorageManager(metaClient)
|
||||
schemaManager.persistHistorySchemaStr(instantTime, SerDeHelper.inheritSchemas(internalSchema, historySchemaStr))
|
||||
client.commit(instantTime, jsc.emptyRDD, Option.of(extraMeta))
|
||||
val existRoTable = sparkSession.catalog.tableExists(table.identifier.unquotedString + "_ro")
|
||||
val existRtTable = sparkSession.catalog.tableExists(table.identifier.unquotedString + "_rt")
|
||||
try {
|
||||
sparkSession.catalog.refreshTable(table.identifier.unquotedString)
|
||||
// try to refresh ro/rt table
|
||||
if (existRoTable) sparkSession.catalog.refreshTable(table.identifier.unquotedString + "_ro")
|
||||
if (existRoTable) sparkSession.catalog.refreshTable(table.identifier.unquotedString + "_rt")
|
||||
} catch {
|
||||
case NonFatal(e) =>
|
||||
log.error(s"Exception when attempting to refresh table ${table.identifier.quotedString}", e)
|
||||
}
|
||||
// try to sync to hive
|
||||
// drop partition field before call alter table
|
||||
val fullSparkSchema = SparkInternalSchemaConverter.constructSparkSchemaFromInternalSchema(internalSchema)
|
||||
val dataSparkSchema = new StructType(fullSparkSchema.fields.filter(p => !table.partitionColumnNames.exists(f => sparkSession.sessionState.conf.resolver(f, p.name))))
|
||||
alterTableDataSchema(sparkSession, table.identifier.database.getOrElse("default"), table.identifier.table, dataSparkSchema)
|
||||
if (existRoTable) alterTableDataSchema(sparkSession, table.identifier.database.getOrElse("default"), table.identifier.table + "_ro", dataSparkSchema)
|
||||
if (existRtTable) alterTableDataSchema(sparkSession, table.identifier.database.getOrElse("default"), table.identifier.table + "_rt", dataSparkSchema)
|
||||
}
|
||||
|
||||
def alterTableDataSchema(sparkSession: SparkSession, db: String, tableName: String, dataSparkSchema: StructType): Unit = {
|
||||
sparkSession.sessionState.catalog
|
||||
.externalCatalog
|
||||
.alterTableDataSchema(db, tableName, dataSparkSchema)
|
||||
}
|
||||
|
||||
def getTableLocation(table: CatalogTable, sparkSession: SparkSession): String = {
|
||||
val uri = if (table.tableType == CatalogTableType.MANAGED) {
|
||||
Some(sparkSession.sessionState.catalog.defaultTablePath(table.identifier))
|
||||
} else {
|
||||
table.storage.locationUri
|
||||
}
|
||||
val conf = sparkSession.sessionState.newHadoopConf()
|
||||
uri.map(makePathQualified(_, conf))
|
||||
.map(removePlaceHolder)
|
||||
.getOrElse(throw new IllegalArgumentException(s"Missing location for ${table.identifier}"))
|
||||
}
|
||||
|
||||
private def removePlaceHolder(path: String): String = {
|
||||
if (path == null || path.length == 0) {
|
||||
path
|
||||
} else if (path.endsWith("-PLACEHOLDER")) {
|
||||
path.substring(0, path.length() - 16)
|
||||
} else {
|
||||
path
|
||||
}
|
||||
}
|
||||
|
||||
def makePathQualified(path: URI, hadoopConf: Configuration): String = {
|
||||
val hadoopPath = new Path(path)
|
||||
val fs = hadoopPath.getFileSystem(hadoopConf)
|
||||
fs.makeQualified(hadoopPath).toUri.toString
|
||||
}
|
||||
|
||||
def getParentName(names: Array[String]): String = {
|
||||
if (names.size > 1) {
|
||||
names.dropRight(1).mkString(".")
|
||||
} else ""
|
||||
}
|
||||
|
||||
def parametersWithWriteDefaults(parameters: Map[String, String]): Map[String, String] = {
|
||||
Map(OPERATION.key -> OPERATION.defaultValue,
|
||||
TABLE_TYPE.key -> TABLE_TYPE.defaultValue,
|
||||
PRECOMBINE_FIELD.key -> PRECOMBINE_FIELD.defaultValue,
|
||||
HoodieWriteConfig.WRITE_PAYLOAD_CLASS_NAME.key -> HoodieWriteConfig.DEFAULT_WRITE_PAYLOAD_CLASS,
|
||||
INSERT_DROP_DUPS.key -> INSERT_DROP_DUPS.defaultValue,
|
||||
ASYNC_COMPACT_ENABLE.key -> ASYNC_COMPACT_ENABLE.defaultValue,
|
||||
INLINE_CLUSTERING_ENABLE.key -> INLINE_CLUSTERING_ENABLE.defaultValue,
|
||||
ASYNC_CLUSTERING_ENABLE.key -> ASYNC_CLUSTERING_ENABLE.defaultValue
|
||||
) ++ DataSourceOptionsHelper.translateConfigurations(parameters)
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user