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[HUDI-4992] Fixing invalid min/max record key stats in Parquet metada…
…ta (#6883)
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118 changes: 118 additions & 0 deletions
118
...nt/hudi-client-common/src/test/java/org/apache/hudi/avro/TestHoodieAvroParquetWriter.java
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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 | ||
| * | ||
| * 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. | ||
| */ | ||
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| package org.apache.hudi.avro; | ||
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| import org.apache.avro.Schema; | ||
| import org.apache.avro.generic.GenericRecord; | ||
| import org.apache.hadoop.conf.Configuration; | ||
| import org.apache.hadoop.fs.Path; | ||
| import org.apache.hudi.DummyTaskContextSupplier; | ||
| import org.apache.hudi.common.bloom.BloomFilter; | ||
| import org.apache.hudi.common.bloom.BloomFilterFactory; | ||
| import org.apache.hudi.common.bloom.BloomFilterTypeCode; | ||
| import org.apache.hudi.common.testutils.HoodieTestDataGenerator; | ||
| import org.apache.hudi.common.util.Option; | ||
| import org.apache.hudi.common.util.ParquetUtils; | ||
| import org.apache.hudi.io.storage.HoodieAvroParquetWriter; | ||
| import org.apache.hudi.io.storage.HoodieParquetConfig; | ||
| import org.apache.parquet.avro.AvroSchemaConverter; | ||
| import org.apache.parquet.hadoop.ParquetWriter; | ||
| import org.apache.parquet.hadoop.metadata.CompressionCodecName; | ||
| import org.apache.parquet.hadoop.metadata.FileMetaData; | ||
| import org.junit.jupiter.api.Test; | ||
| import org.junit.jupiter.api.io.TempDir; | ||
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| import java.io.IOException; | ||
| import java.util.Comparator; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
| import java.util.stream.Collectors; | ||
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| import static org.junit.jupiter.api.Assertions.assertEquals; | ||
| import static org.junit.jupiter.api.Assertions.assertTrue; | ||
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| public class TestHoodieAvroParquetWriter { | ||
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| @TempDir java.nio.file.Path tmpDir; | ||
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| @Test | ||
| public void testProperWriting() throws IOException { | ||
| Configuration hadoopConf = new Configuration(); | ||
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| HoodieTestDataGenerator dataGen = new HoodieTestDataGenerator(0xDEED); | ||
| List<GenericRecord> records = dataGen.generateGenericRecords(10); | ||
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| Schema schema = records.get(0).getSchema(); | ||
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| BloomFilter filter = BloomFilterFactory.createBloomFilter(1000, 0.0001, 10000, | ||
| BloomFilterTypeCode.DYNAMIC_V0.name()); | ||
| HoodieAvroWriteSupport writeSupport = new HoodieAvroWriteSupport(new AvroSchemaConverter().convert(schema), | ||
| schema, Option.of(filter)); | ||
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| HoodieParquetConfig<HoodieAvroWriteSupport> parquetConfig = | ||
| new HoodieParquetConfig(writeSupport, CompressionCodecName.GZIP, ParquetWriter.DEFAULT_BLOCK_SIZE, | ||
| ParquetWriter.DEFAULT_PAGE_SIZE, 1024 * 1024 * 1024, hadoopConf, 0.1); | ||
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| Path filePath = new Path(tmpDir.resolve("test.parquet").toAbsolutePath().toString()); | ||
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| try (HoodieAvroParquetWriter<GenericRecord> writer = | ||
| new HoodieAvroParquetWriter<>(filePath, parquetConfig, "001", new DummyTaskContextSupplier(), true)) { | ||
| for (GenericRecord record : records) { | ||
| writer.writeAvro((String) record.get("_row_key"), record); | ||
| } | ||
| } | ||
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| ParquetUtils utils = new ParquetUtils(); | ||
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| // Step 1: Make sure records are written appropriately | ||
| List<GenericRecord> readRecords = utils.readAvroRecords(hadoopConf, filePath); | ||
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| assertEquals(toJson(records), toJson(readRecords)); | ||
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| // Step 2: Assert Parquet metadata was written appropriately | ||
| List<String> recordKeys = records.stream().map(r -> (String) r.get("_row_key")).collect(Collectors.toList()); | ||
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| String minKey = recordKeys.stream().min(Comparator.naturalOrder()).get(); | ||
| String maxKey = recordKeys.stream().max(Comparator.naturalOrder()).get(); | ||
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| FileMetaData parquetMetadata = ParquetUtils.readMetadata(hadoopConf, filePath).getFileMetaData(); | ||
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| Map<String, String> extraMetadata = parquetMetadata.getKeyValueMetaData(); | ||
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| assertEquals(extraMetadata.get(HoodieBloomFilterWriteSupport.HOODIE_MIN_RECORD_KEY_FOOTER), minKey); | ||
| assertEquals(extraMetadata.get(HoodieBloomFilterWriteSupport.HOODIE_MAX_RECORD_KEY_FOOTER), maxKey); | ||
| assertEquals(extraMetadata.get(HoodieBloomFilterWriteSupport.HOODIE_BLOOM_FILTER_TYPE_CODE), BloomFilterTypeCode.DYNAMIC_V0.name()); | ||
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| // Step 3: Make sure Bloom Filter contains all the record keys | ||
| BloomFilter bloomFilter = utils.readBloomFilterFromMetadata(hadoopConf, filePath); | ||
| recordKeys.forEach(recordKey -> { | ||
| assertTrue(bloomFilter.mightContain(recordKey)); | ||
| }); | ||
| } | ||
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| private static List<String> toJson(List<GenericRecord> records) { | ||
| return records.stream().map(r -> { | ||
| try { | ||
| return new String(HoodieAvroUtils.avroToJson(r, true)); | ||
| } catch (IOException e) { | ||
| throw new RuntimeException(e); | ||
| } | ||
| }).collect(Collectors.toList()); | ||
| } | ||
| } |
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