[HUDI-3549] Removing dependency on "spark-avro" (#4955)
Hudi will be taking on promise for it bundles to stay compatible with Spark minor versions (for ex 2.4, 3.1, 3.2): meaning that single build of Hudi (for ex "hudi-spark3.2-bundle") will be compatible with ALL patch versions in that minor branch (in that case 3.2.1, 3.2.0, etc) To achieve that we'll have to remove (and ban) "spark-avro" as a dependency, which on a few occasions was the root-cause of incompatibility b/w consecutive Spark patch versions (most recently 3.2.1 and 3.2.0, due to this PR). Instead of bundling "spark-avro" as dependency, we will be copying over some of the classes Hudi depends on and maintain them along the Hudi code-base to make sure we're able to provide for the aforementioned guarantee. To workaround arising compatibility issues we will be applying local patches to guarantee compatibility of Hudi bundles w/in the Spark minor version branches. Following Hudi modules to Spark minor branches is currently maintained: "hudi-spark3" -> 3.2.x "hudi-spark3.1.x" -> 3.1.x "hudi-spark2" -> 2.4.x Following classes hierarchies (borrowed from "spark-avro") are maintained w/in these Spark-specific modules to guarantee compatibility with respective minor version branches: AvroSerializer AvroDeserializer AvroUtils Each of these classes has been correspondingly copied from Spark 3.2.1 (for 3.2.x branch), 3.1.2 (for 3.1.x branch), 2.4.4 (for 2.4.x branch) into their respective modules. SchemaConverters class in turn is shared across all those modules given its relative stability (there're only cosmetical changes from 2.4.4 to 3.2.1). All of the aforementioned classes have their corresponding scope of visibility limited to corresponding packages (org.apache.spark.sql.avro, org.apache.spark.sql) to make sure broader code-base does not become dependent on them and instead relies on facades abstracting them. Additionally, given that Hudi plans on supporting all the patch versions of Spark w/in aforementioned minor versions branches of Spark, additional build steps were added to validate that Hudi could be properly compiled against those versions. Testing, however, is performed against the most recent patch versions of Spark with the help of Azure CI. Brief change log: - Removing spark-avro bundling from Hudi by default - Scaffolded Spark 3.2.x hierarchy - Bootstrapped Spark 3.1.x Avro serializer/deserializer hierarchy - Bootstrapped Spark 2.4.x Avro serializer/deserializer hierarchy - Moved ExpressionCodeGen,ExpressionPayload into hudi-spark module - Fixed AvroDeserializer to stay compatible w/ both Spark 3.2.1 and 3.2.0 - Modified bot.yml to build full matrix of support Spark versions - Removed "spark-avro" dependency from all modules - Fixed relocation of spark-avro classes in bundles to assist in running integ-tests.
This commit is contained in:
Alexey Kudinkin
@@ -389,9 +389,6 @@ public class SparkRDDWriteClient<T extends HoodieRecordPayload> extends
|
||||
finalizeWrite(table, clusteringCommitTime, writeStats);
|
||||
// Update table's metadata (table)
|
||||
updateTableMetadata(table, metadata, clusteringInstant);
|
||||
// Update tables' metadata indexes
|
||||
// NOTE: This overlaps w/ metadata table (above) and will be reconciled in the future
|
||||
table.updateMetadataIndexes(context, writeStats, clusteringCommitTime);
|
||||
|
||||
LOG.info("Committing Clustering " + clusteringCommitTime + ". Finished with result " + metadata);
|
||||
|
||||
|
||||
@@ -18,8 +18,6 @@
|
||||
|
||||
package org.apache.hudi.table;
|
||||
|
||||
import org.apache.hudi.AvroConversionUtils;
|
||||
import org.apache.hudi.avro.HoodieAvroUtils;
|
||||
import org.apache.hudi.avro.model.HoodieCleanMetadata;
|
||||
import org.apache.hudi.avro.model.HoodieCleanerPlan;
|
||||
import org.apache.hudi.avro.model.HoodieClusteringPlan;
|
||||
@@ -38,18 +36,14 @@ import org.apache.hudi.common.model.HoodieBaseFile;
|
||||
import org.apache.hudi.common.model.HoodieKey;
|
||||
import org.apache.hudi.common.model.HoodieRecord;
|
||||
import org.apache.hudi.common.model.HoodieRecordPayload;
|
||||
import org.apache.hudi.common.model.HoodieWriteStat;
|
||||
import org.apache.hudi.common.table.HoodieTableMetaClient;
|
||||
import org.apache.hudi.common.table.TableSchemaResolver;
|
||||
import org.apache.hudi.common.table.timeline.HoodieInstant;
|
||||
import org.apache.hudi.common.table.timeline.HoodieTimeline;
|
||||
import org.apache.hudi.common.util.Option;
|
||||
import org.apache.hudi.common.util.StringUtils;
|
||||
import org.apache.hudi.config.HoodieWriteConfig;
|
||||
import org.apache.hudi.exception.HoodieIOException;
|
||||
import org.apache.hudi.exception.HoodieNotSupportedException;
|
||||
import org.apache.hudi.exception.HoodieUpsertException;
|
||||
import org.apache.hudi.index.columnstats.ColumnStatsIndexHelper;
|
||||
import org.apache.hudi.io.HoodieCreateHandle;
|
||||
import org.apache.hudi.io.HoodieMergeHandle;
|
||||
import org.apache.hudi.io.HoodieSortedMergeHandle;
|
||||
@@ -78,21 +72,14 @@ import org.apache.hudi.table.action.rollback.BaseRollbackPlanActionExecutor;
|
||||
import org.apache.hudi.table.action.rollback.CopyOnWriteRollbackActionExecutor;
|
||||
import org.apache.hudi.table.action.rollback.RestorePlanActionExecutor;
|
||||
import org.apache.hudi.table.action.savepoint.SavepointActionExecutor;
|
||||
|
||||
import org.apache.avro.Schema;
|
||||
import org.apache.hadoop.fs.Path;
|
||||
import org.apache.log4j.LogManager;
|
||||
import org.apache.log4j.Logger;
|
||||
|
||||
import javax.annotation.Nonnull;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.util.Arrays;
|
||||
import java.util.Collections;
|
||||
import java.util.Iterator;
|
||||
import java.util.List;
|
||||
import java.util.Map;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* Implementation of a very heavily read-optimized Hoodie Table where, all data is stored in base files, with
|
||||
@@ -172,63 +159,6 @@ public class HoodieSparkCopyOnWriteTable<T extends HoodieRecordPayload>
|
||||
return new SparkInsertOverwriteTableCommitActionExecutor(context, config, this, instantTime, records).execute();
|
||||
}
|
||||
|
||||
@Override
|
||||
public void updateMetadataIndexes(@Nonnull HoodieEngineContext context, @Nonnull List<HoodieWriteStat> stats, @Nonnull String instantTime) throws Exception {
|
||||
updateColumnsStatsIndex(context, stats, instantTime);
|
||||
}
|
||||
|
||||
private void updateColumnsStatsIndex(
|
||||
@Nonnull HoodieEngineContext context,
|
||||
@Nonnull List<HoodieWriteStat> updatedFilesStats,
|
||||
@Nonnull String instantTime
|
||||
) throws Exception {
|
||||
String sortColsList = config.getClusteringSortColumns();
|
||||
String basePath = metaClient.getBasePath();
|
||||
String indexPath = metaClient.getColumnStatsIndexPath();
|
||||
|
||||
List<String> touchedFiles =
|
||||
updatedFilesStats.stream()
|
||||
.map(s -> new Path(basePath, s.getPath()).toString())
|
||||
.collect(Collectors.toList());
|
||||
|
||||
if (touchedFiles.isEmpty() || StringUtils.isNullOrEmpty(sortColsList) || StringUtils.isNullOrEmpty(indexPath)) {
|
||||
return;
|
||||
}
|
||||
|
||||
LOG.info(String.format("Updating column-statistics index table (%s)", indexPath));
|
||||
|
||||
List<String> sortCols = Arrays.stream(sortColsList.split(","))
|
||||
.map(String::trim)
|
||||
.collect(Collectors.toList());
|
||||
|
||||
HoodieSparkEngineContext sparkEngineContext = (HoodieSparkEngineContext)context;
|
||||
|
||||
// Fetch table schema to appropriately construct col-stats index schema
|
||||
Schema tableWriteSchema =
|
||||
HoodieAvroUtils.createHoodieWriteSchema(
|
||||
new TableSchemaResolver(metaClient).getTableAvroSchemaWithoutMetadataFields()
|
||||
);
|
||||
|
||||
List<String> completedCommits =
|
||||
metaClient.getCommitsTimeline()
|
||||
.filterCompletedInstants()
|
||||
.getInstants()
|
||||
.map(HoodieInstant::getTimestamp)
|
||||
.collect(Collectors.toList());
|
||||
|
||||
ColumnStatsIndexHelper.updateColumnStatsIndexFor(
|
||||
sparkEngineContext.getSqlContext().sparkSession(),
|
||||
AvroConversionUtils.convertAvroSchemaToStructType(tableWriteSchema),
|
||||
touchedFiles,
|
||||
sortCols,
|
||||
indexPath,
|
||||
instantTime,
|
||||
completedCommits
|
||||
);
|
||||
|
||||
LOG.info(String.format("Successfully updated column-statistics index at instant (%s)", instantTime));
|
||||
}
|
||||
|
||||
@Override
|
||||
public Option<HoodieCompactionPlan> scheduleCompaction(HoodieEngineContext context, String instantTime, Option<Map<String, String>> extraMetadata) {
|
||||
throw new HoodieNotSupportedException("Compaction is not supported on a CopyOnWrite table");
|
||||
|
||||
@@ -17,13 +17,13 @@
|
||||
*/
|
||||
|
||||
package org.apache.hudi
|
||||
|
||||
import org.apache.avro.Schema.Type
|
||||
import org.apache.avro.generic.{GenericRecord, GenericRecordBuilder, IndexedRecord}
|
||||
import org.apache.avro.{AvroRuntimeException, JsonProperties, Schema}
|
||||
import org.apache.hudi.HoodieSparkUtils.sparkAdapter
|
||||
import org.apache.hudi.avro.HoodieAvroUtils
|
||||
import org.apache.spark.rdd.RDD
|
||||
import org.apache.spark.sql.avro.SchemaConverters
|
||||
import org.apache.spark.sql.catalyst.InternalRow
|
||||
import org.apache.spark.sql.catalyst.encoders.RowEncoder
|
||||
import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructType}
|
||||
@@ -136,27 +136,36 @@ object AvroConversionUtils {
|
||||
}
|
||||
|
||||
/**
|
||||
*
|
||||
* Returns avro schema from spark StructType.
|
||||
*
|
||||
* @param structType Dataframe Struct Type.
|
||||
* @param structName Avro record name.
|
||||
* @param recordNamespace Avro record namespace.
|
||||
* @return Avro schema corresponding to given struct type.
|
||||
*/
|
||||
*
|
||||
* Returns avro schema from spark StructType.
|
||||
*
|
||||
* @param structType Dataframe Struct Type.
|
||||
* @param structName Avro record name.
|
||||
* @param recordNamespace Avro record namespace.
|
||||
* @return Avro schema corresponding to given struct type.
|
||||
*/
|
||||
def convertStructTypeToAvroSchema(structType: DataType,
|
||||
structName: String,
|
||||
recordNamespace: String): Schema = {
|
||||
getAvroSchemaWithDefaults(SchemaConverters.toAvroType(structType, nullable = false, structName, recordNamespace), structType)
|
||||
val schemaConverters = sparkAdapter.getAvroSchemaConverters
|
||||
val avroSchema = schemaConverters.toAvroType(structType, nullable = false, structName, recordNamespace)
|
||||
getAvroSchemaWithDefaults(avroSchema, structType)
|
||||
}
|
||||
|
||||
def convertAvroSchemaToStructType(avroSchema: Schema): StructType = {
|
||||
val schemaConverters = sparkAdapter.getAvroSchemaConverters
|
||||
schemaConverters.toSqlType(avroSchema) match {
|
||||
case (dataType, _) => dataType.asInstanceOf[StructType]
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
*
|
||||
* Method to add default value of null to nullable fields in given avro schema
|
||||
*
|
||||
* @param schema input avro schema
|
||||
* @return Avro schema with null default set to nullable fields
|
||||
*/
|
||||
*
|
||||
* Method to add default value of null to nullable fields in given avro schema
|
||||
*
|
||||
* @param schema input avro schema
|
||||
* @return Avro schema with null default set to nullable fields
|
||||
*/
|
||||
def getAvroSchemaWithDefaults(schema: Schema, dataType: DataType): Schema = {
|
||||
|
||||
schema.getType match {
|
||||
@@ -205,10 +214,6 @@ object AvroConversionUtils {
|
||||
}
|
||||
}
|
||||
|
||||
def convertAvroSchemaToStructType(avroSchema: Schema): StructType = {
|
||||
SchemaConverters.toSqlType(avroSchema).dataType.asInstanceOf[StructType]
|
||||
}
|
||||
|
||||
def getAvroRecordNameAndNamespace(tableName: String): (String, String) = {
|
||||
val name = HoodieAvroUtils.sanitizeName(tableName)
|
||||
(s"${name}_record", s"hoodie.${name}")
|
||||
|
||||
@@ -0,0 +1,33 @@
|
||||
/*
|
||||
* 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.avro
|
||||
|
||||
import org.apache.avro.Schema
|
||||
import org.apache.spark.sql.types.DataType
|
||||
|
||||
/**
|
||||
* Allows to convert Avro schema into Spark's Catalyst one
|
||||
*/
|
||||
trait HoodieAvroSchemaConverters {
|
||||
|
||||
def toSqlType(avroSchema: Schema): (DataType, Boolean)
|
||||
|
||||
def toAvroType(catalystType: DataType, nullable: Boolean, recordName: String, nameSpace: String = ""): Schema
|
||||
|
||||
}
|
||||
@@ -20,7 +20,7 @@ package org.apache.spark.sql.hudi
|
||||
|
||||
import org.apache.avro.Schema
|
||||
import org.apache.hudi.client.utils.SparkRowSerDe
|
||||
import org.apache.spark.sql.avro.{HoodieAvroDeserializer, HoodieAvroSerializer}
|
||||
import org.apache.spark.sql.avro.{HoodieAvroDeserializer, HoodieAvroSchemaConverters, HoodieAvroSerializer}
|
||||
import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
|
||||
import org.apache.spark.sql.catalyst.catalog.CatalogTable
|
||||
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
|
||||
@@ -59,6 +59,11 @@ trait SparkAdapter extends Serializable {
|
||||
*/
|
||||
def createAvroDeserializer(rootAvroType: Schema, rootCatalystType: DataType): HoodieAvroDeserializer
|
||||
|
||||
/**
|
||||
* Creates instance of [[HoodieAvroSchemaConverters]] allowing to convert b/w Avro and Catalyst schemas
|
||||
*/
|
||||
def getAvroSchemaConverters: HoodieAvroSchemaConverters
|
||||
|
||||
/**
|
||||
* Create the SparkRowSerDe.
|
||||
*/
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
/*
|
||||
* 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.hudi.index.bucket;
|
||||
|
||||
import org.apache.hudi.common.model.HoodieAvroRecord;
|
||||
import org.apache.hudi.common.model.HoodieKey;
|
||||
import org.apache.hudi.common.model.HoodieRecord;
|
||||
import org.apache.hudi.keygen.KeyGenUtils;
|
||||
import org.apache.hudi.testutils.KeyGeneratorTestUtilities;
|
||||
|
||||
import org.apache.avro.generic.GenericRecord;
|
||||
import org.junit.jupiter.api.Test;
|
||||
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
|
||||
public class TestBucketIdentifier {
|
||||
|
||||
@Test
|
||||
public void testBucketFileId() {
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
String bucketId = BucketIdentifier.bucketIdStr(i);
|
||||
String fileId = BucketIdentifier.newBucketFileIdPrefix(bucketId);
|
||||
assert BucketIdentifier.bucketIdFromFileId(fileId) == i;
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testBucketIdWithSimpleRecordKey() {
|
||||
String recordKeyField = "_row_key";
|
||||
String indexKeyField = "_row_key";
|
||||
GenericRecord record = KeyGeneratorTestUtilities.getRecord();
|
||||
HoodieRecord hoodieRecord = new HoodieAvroRecord(
|
||||
new HoodieKey(KeyGenUtils.getRecordKey(record, recordKeyField, false), ""), null);
|
||||
int bucketId = BucketIdentifier.getBucketId(hoodieRecord, indexKeyField, 8);
|
||||
assert bucketId == BucketIdentifier.getBucketId(
|
||||
Arrays.asList(record.get(indexKeyField).toString()), 8);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testBucketIdWithComplexRecordKey() {
|
||||
List<String> recordKeyField = Arrays.asList("_row_key","ts_ms");
|
||||
String indexKeyField = "_row_key";
|
||||
GenericRecord record = KeyGeneratorTestUtilities.getRecord();
|
||||
HoodieRecord hoodieRecord = new HoodieAvroRecord(
|
||||
new HoodieKey(KeyGenUtils.getRecordKey(record, recordKeyField, false), ""), null);
|
||||
int bucketId = BucketIdentifier.getBucketId(hoodieRecord, indexKeyField, 8);
|
||||
assert bucketId == BucketIdentifier.getBucketId(
|
||||
Arrays.asList(record.get(indexKeyField).toString()), 8);
|
||||
}
|
||||
}
|
||||
@@ -1,129 +0,0 @@
|
||||
/*
|
||||
* 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.hudi.testutils;
|
||||
|
||||
import org.apache.avro.Schema;
|
||||
import org.apache.avro.generic.GenericData;
|
||||
import org.apache.avro.generic.GenericRecord;
|
||||
import org.apache.hudi.AvroConversionUtils;
|
||||
import org.apache.spark.package$;
|
||||
import org.apache.spark.sql.Row;
|
||||
import org.apache.spark.sql.catalyst.InternalRow;
|
||||
import org.apache.spark.sql.catalyst.analysis.SimpleAnalyzer$;
|
||||
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder;
|
||||
import org.apache.spark.sql.catalyst.encoders.RowEncoder;
|
||||
import org.apache.spark.sql.catalyst.expressions.Attribute;
|
||||
import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema;
|
||||
import org.apache.spark.sql.types.StructType;
|
||||
import scala.Function1;
|
||||
import scala.collection.JavaConversions;
|
||||
import scala.collection.JavaConverters;
|
||||
|
||||
import java.lang.reflect.InvocationTargetException;
|
||||
import java.lang.reflect.Method;
|
||||
import java.util.List;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
public class KeyGeneratorTestUtilities {
|
||||
|
||||
public static final String NESTED_COL_SCHEMA = "{\"type\":\"record\", \"name\":\"nested_col\",\"fields\": ["
|
||||
+ "{\"name\": \"prop1\",\"type\": \"string\"},{\"name\": \"prop2\", \"type\": \"long\"}]}";
|
||||
public static final String EXAMPLE_SCHEMA = "{\"type\": \"record\",\"name\": \"testrec\",\"fields\": [ "
|
||||
+ "{\"name\": \"timestamp\",\"type\": \"long\"},{\"name\": \"_row_key\", \"type\": \"string\"},"
|
||||
+ "{\"name\": \"ts_ms\", \"type\": \"string\"},"
|
||||
+ "{\"name\": \"pii_col\", \"type\": \"string\"},"
|
||||
+ "{\"name\": \"nested_col\",\"type\": [\"null\", " + NESTED_COL_SCHEMA + "]}"
|
||||
+ "]}";
|
||||
|
||||
public static final String TEST_STRUCTNAME = "test_struct_name";
|
||||
public static final String TEST_RECORD_NAMESPACE = "test_record_namespace";
|
||||
public static Schema schema = new Schema.Parser().parse(EXAMPLE_SCHEMA);
|
||||
public static StructType structType = AvroConversionUtils.convertAvroSchemaToStructType(schema);
|
||||
|
||||
public static GenericRecord getRecord() {
|
||||
return getRecord(getNestedColRecord("val1", 10L));
|
||||
}
|
||||
|
||||
public static GenericRecord getNestedColRecord(String prop1Value, Long prop2Value) {
|
||||
GenericRecord nestedColRecord = new GenericData.Record(new Schema.Parser().parse(NESTED_COL_SCHEMA));
|
||||
nestedColRecord.put("prop1", prop1Value);
|
||||
nestedColRecord.put("prop2", prop2Value);
|
||||
return nestedColRecord;
|
||||
}
|
||||
|
||||
public static GenericRecord getRecord(GenericRecord nestedColRecord) {
|
||||
GenericRecord record = new GenericData.Record(new Schema.Parser().parse(EXAMPLE_SCHEMA));
|
||||
record.put("timestamp", 4357686L);
|
||||
record.put("_row_key", "key1");
|
||||
record.put("ts_ms", "2020-03-21");
|
||||
record.put("pii_col", "pi");
|
||||
record.put("nested_col", nestedColRecord);
|
||||
return record;
|
||||
}
|
||||
|
||||
public static Row getRow(GenericRecord record) {
|
||||
return getRow(record, schema, structType);
|
||||
}
|
||||
|
||||
public static Row getRow(GenericRecord record, Schema schema, StructType structType) {
|
||||
Function1<GenericRecord, Row> converterFn = AvroConversionUtils.createConverterToRow(schema, structType);
|
||||
Row row = converterFn.apply(record);
|
||||
int fieldCount = structType.fieldNames().length;
|
||||
Object[] values = new Object[fieldCount];
|
||||
for (int i = 0; i < fieldCount; i++) {
|
||||
values[i] = row.get(i);
|
||||
}
|
||||
return new GenericRowWithSchema(values, structType);
|
||||
}
|
||||
|
||||
public static InternalRow getInternalRow(Row row) {
|
||||
try {
|
||||
return getInternalRow(row, getEncoder(row.schema()));
|
||||
} catch (Exception e) {
|
||||
throw new IllegalStateException("Exception thrown while converting Row to InternalRow", e);
|
||||
}
|
||||
}
|
||||
|
||||
private static ExpressionEncoder getEncoder(StructType schema) {
|
||||
List<Attribute> attributes = JavaConversions.asJavaCollection(schema.toAttributes()).stream()
|
||||
.map(Attribute::toAttribute).collect(Collectors.toList());
|
||||
return RowEncoder.apply(schema)
|
||||
.resolveAndBind(JavaConverters.asScalaBufferConverter(attributes).asScala().toSeq(),
|
||||
SimpleAnalyzer$.MODULE$);
|
||||
}
|
||||
|
||||
public static InternalRow getInternalRow(Row row, ExpressionEncoder<Row> encoder) throws ClassNotFoundException, InvocationTargetException, IllegalAccessException, NoSuchMethodException {
|
||||
return serializeRow(encoder, row);
|
||||
}
|
||||
|
||||
private static InternalRow serializeRow(ExpressionEncoder encoder, Row row)
|
||||
throws InvocationTargetException, IllegalAccessException, NoSuchMethodException, ClassNotFoundException {
|
||||
// TODO remove reflection if Spark 2.x support is dropped
|
||||
if (package$.MODULE$.SPARK_VERSION().startsWith("2.")) {
|
||||
Method spark2method = encoder.getClass().getMethod("toRow", Object.class);
|
||||
return (InternalRow) spark2method.invoke(encoder, row);
|
||||
} else {
|
||||
Class<?> serializerClass = Class.forName("org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$Serializer");
|
||||
Object serializer = encoder.getClass().getMethod("createSerializer").invoke(encoder);
|
||||
Method aboveSpark2method = serializerClass.getMethod("apply", Object.class);
|
||||
return (InternalRow) aboveSpark2method.invoke(serializer, row);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
Reference in New Issue
Block a user