Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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.common.util;
import org.apache.hudi.avro.HoodieAvroUtils;
import org.apache.hudi.common.fs.FSUtils;
import org.apache.hudi.common.model.HoodieColumnRangeMetadata;
import org.apache.hudi.common.model.HoodieFileFormat;
import org.apache.hudi.common.model.HoodieKey;
import org.apache.hudi.common.model.HoodieRecord;
import org.apache.hudi.common.util.collection.ClosableIterator;
import org.apache.hudi.exception.HoodieIOException;
import org.apache.hudi.exception.MetadataNotFoundException;
import org.apache.hudi.keygen.BaseKeyGenerator;
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.parquet.avro.AvroParquetReader;
import org.apache.parquet.avro.AvroReadSupport;
import org.apache.parquet.avro.AvroSchemaConverter;
import org.apache.parquet.column.statistics.Statistics;
import org.apache.parquet.hadoop.ParquetFileReader;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.metadata.BlockMetaData;
import org.apache.parquet.hadoop.metadata.ParquetMetadata;
import org.apache.parquet.io.api.Binary;
import org.apache.parquet.schema.DecimalMetadata;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.OriginalType;
import org.apache.parquet.schema.PrimitiveType;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import java.io.IOException;
import java.math.BigDecimal;
import java.math.BigInteger;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.function.Function;
import java.util.stream.Collector;
import java.util.stream.Collectors;
import java.util.stream.Stream;
/**
* Utility functions involving with parquet.
*/
public class ParquetUtils extends BaseFileUtils {
private static final Logger LOG = LoggerFactory.getLogger(ParquetUtils.class);
/**
* Read the rowKey list matching the given filter, from the given parquet file. If the filter is empty, then this will
* return all the rowkeys.
*
* @param filePath The parquet file path.
* @param configuration configuration to build fs object
* @param filter record keys filter
* @return Set Set of row keys matching candidateRecordKeys
*/
@Override
public Set filterRowKeys(Configuration configuration, Path filePath, Set filter) {
return filterParquetRowKeys(configuration, filePath, filter, HoodieAvroUtils.getRecordKeySchema());
}
public static ParquetMetadata readMetadata(Configuration conf, Path parquetFilePath) {
ParquetMetadata footer;
try {
// TODO(vc): Should we use the parallel reading version here?
footer = ParquetFileReader.readFooter(FSUtils.getFs(parquetFilePath.toString(), conf).getConf(), parquetFilePath);
} catch (IOException e) {
throw new HoodieIOException("Failed to read footer for parquet " + parquetFilePath, e);
}
return footer;
}
/**
* Read the rowKey list matching the given filter, from the given parquet file. If the filter is empty, then this will
* return all the rowkeys.
*
* @param filePath The parquet file path.
* @param configuration configuration to build fs object
* @param filter record keys filter
* @param readSchema schema of columns to be read
* @return Set Set of row keys matching candidateRecordKeys
*/
private static Set filterParquetRowKeys(Configuration configuration, Path filePath, Set filter,
Schema readSchema) {
Option filterFunction = Option.empty();
if (filter != null && !filter.isEmpty()) {
filterFunction = Option.of(new RecordKeysFilterFunction(filter));
}
Configuration conf = new Configuration(configuration);
conf.addResource(FSUtils.getFs(filePath.toString(), conf).getConf());
AvroReadSupport.setAvroReadSchema(conf, readSchema);
AvroReadSupport.setRequestedProjection(conf, readSchema);
Set rowKeys = new HashSet<>();
try (ParquetReader reader = AvroParquetReader.builder(filePath).withConf(conf).build()) {
Object obj = reader.read();
while (obj != null) {
if (obj instanceof GenericRecord) {
String recordKey = ((GenericRecord) obj).get(HoodieRecord.RECORD_KEY_METADATA_FIELD).toString();
if (!filterFunction.isPresent() || filterFunction.get().apply(recordKey)) {
rowKeys.add(recordKey);
}
}
obj = reader.read();
}
} catch (IOException e) {
throw new HoodieIOException("Failed to read row keys from Parquet " + filePath, e);
}
// ignore
return rowKeys;
}
/**
* Fetch {@link HoodieKey}s from the given parquet file.
*
* @param filePath The parquet file path.
* @param configuration configuration to build fs object
* @return {@link List} of {@link HoodieKey}s fetched from the parquet file
*/
@Override
public List fetchHoodieKeys(Configuration configuration, Path filePath) {
return fetchHoodieKeys(configuration, filePath, Option.empty());
}
@Override
public ClosableIterator getHoodieKeyIterator(Configuration configuration, Path filePath) {
return getHoodieKeyIterator(configuration, filePath, Option.empty());
}
/**
* Returns a closable iterator for reading the given parquet file.
*
* @param configuration configuration to build fs object
* @param filePath The parquet file path
* @param keyGeneratorOpt instance of KeyGenerator
*
* @return {@link ClosableIterator} of {@link HoodieKey}s for reading the parquet file
*/
@Override
public ClosableIterator getHoodieKeyIterator(Configuration configuration, Path filePath, Option keyGeneratorOpt) {
try {
Configuration conf = new Configuration(configuration);
conf.addResource(FSUtils.getFs(filePath.toString(), conf).getConf());
Schema readSchema = keyGeneratorOpt.map(keyGenerator -> {
List fields = new ArrayList<>();
fields.addAll(keyGenerator.getRecordKeyFieldNames());
fields.addAll(keyGenerator.getPartitionPathFields());
return HoodieAvroUtils.getSchemaForFields(readAvroSchema(conf, filePath), fields);
})
.orElse(HoodieAvroUtils.getRecordKeyPartitionPathSchema());
AvroReadSupport.setAvroReadSchema(conf, readSchema);
AvroReadSupport.setRequestedProjection(conf, readSchema);
ParquetReader reader = AvroParquetReader.builder(filePath).withConf(conf).build();
return HoodieKeyIterator.getInstance(new ParquetReaderIterator<>(reader), keyGeneratorOpt);
} catch (IOException e) {
throw new HoodieIOException("Failed to read from Parquet file " + filePath, e);
}
}
/**
* Fetch {@link HoodieKey}s from the given parquet file.
*
* @param configuration configuration to build fs object
* @param filePath The parquet file path.
* @param keyGeneratorOpt instance of KeyGenerator.
* @return {@link List} of {@link HoodieKey}s fetched from the parquet file
*/
@Override
public List fetchHoodieKeys(Configuration configuration, Path filePath, Option keyGeneratorOpt) {
List hoodieKeys = new ArrayList<>();
try (ClosableIterator iterator = getHoodieKeyIterator(configuration, filePath, keyGeneratorOpt)) {
iterator.forEachRemaining(hoodieKeys::add);
return hoodieKeys;
}
}
/**
* Get the schema of the given parquet file.
*/
public MessageType readSchema(Configuration configuration, Path parquetFilePath) {
return readMetadata(configuration, parquetFilePath).getFileMetaData().getSchema();
}
@Override
public Map readFooter(Configuration configuration, boolean required,
Path parquetFilePath, String... footerNames) {
Map footerVals = new HashMap<>();
ParquetMetadata footer = readMetadata(configuration, parquetFilePath);
Map metadata = footer.getFileMetaData().getKeyValueMetaData();
for (String footerName : footerNames) {
if (metadata.containsKey(footerName)) {
footerVals.put(footerName, metadata.get(footerName));
} else if (required) {
throw new MetadataNotFoundException(
"Could not find index in Parquet footer. Looked for key " + footerName + " in " + parquetFilePath);
}
}
return footerVals;
}
@Override
public Schema readAvroSchema(Configuration conf, Path parquetFilePath) {
MessageType parquetSchema = readSchema(conf, parquetFilePath);
return new AvroSchemaConverter(conf).convert(parquetSchema);
}
@Override
public HoodieFileFormat getFormat() {
return HoodieFileFormat.PARQUET;
}
/**
* NOTE: This literally reads the entire file contents, thus should be used with caution.
*/
@Override
public List readAvroRecords(Configuration configuration, Path filePath) {
List records = new ArrayList<>();
try (ParquetReader reader = AvroParquetReader.builder(filePath).withConf(configuration).build()) {
Object obj = reader.read();
while (obj != null) {
if (obj instanceof GenericRecord) {
records.add(((GenericRecord) obj));
}
obj = reader.read();
}
} catch (IOException e) {
throw new HoodieIOException("Failed to read avro records from Parquet " + filePath, e);
}
return records;
}
@Override
public List readAvroRecords(Configuration configuration, Path filePath, Schema schema) {
AvroReadSupport.setAvroReadSchema(configuration, schema);
return readAvroRecords(configuration, filePath);
}
/**
* Returns the number of records in the parquet file.
*
* @param conf Configuration
* @param parquetFilePath path of the file
*/
@Override
public long getRowCount(Configuration conf, Path parquetFilePath) {
ParquetMetadata footer;
long rowCount = 0;
footer = readMetadata(conf, parquetFilePath);
for (BlockMetaData b : footer.getBlocks()) {
rowCount += b.getRowCount();
}
return rowCount;
}
static class RecordKeysFilterFunction implements Function {
private final Set candidateKeys;
RecordKeysFilterFunction(Set candidateKeys) {
this.candidateKeys = candidateKeys;
}
@Override
public Boolean apply(String recordKey) {
return candidateKeys.contains(recordKey);
}
}
/**
* Parse min/max statistics stored in parquet footers for all columns.
*/
@SuppressWarnings("rawtype")
public List> readRangeFromParquetMetadata(
@Nonnull Configuration conf,
@Nonnull Path parquetFilePath,
@Nonnull List cols
) {
ParquetMetadata metadata = readMetadata(conf, parquetFilePath);
// NOTE: This collector has to have fully specialized generic type params since
// Java 1.8 struggles to infer them
Collector, ?, Map>>> groupingByCollector =
Collectors.groupingBy(HoodieColumnRangeMetadata::getColumnName);
// Collect stats from all individual Parquet blocks
Map>> columnToStatsListMap =
(Map>>) metadata.getBlocks().stream().sequential()
.flatMap(blockMetaData ->
blockMetaData.getColumns().stream()
.filter(f -> cols.contains(f.getPath().toDotString()))
.map(columnChunkMetaData -> {
Statistics stats = columnChunkMetaData.getStatistics();
return HoodieColumnRangeMetadata.create(
parquetFilePath.getName(),
columnChunkMetaData.getPath().toDotString(),
convertToNativeJavaType(
columnChunkMetaData.getPrimitiveType(),
stats.genericGetMin()),
convertToNativeJavaType(
columnChunkMetaData.getPrimitiveType(),
stats.genericGetMax()),
// NOTE: In case when column contains only nulls Parquet won't be creating
// stats for it instead returning stubbed (empty) object. In that case
// we have to equate number of nulls to the value count ourselves
stats.isEmpty() ? columnChunkMetaData.getValueCount() : stats.getNumNulls(),
columnChunkMetaData.getValueCount(),
columnChunkMetaData.getTotalSize(),
columnChunkMetaData.getTotalUncompressedSize());
})
)
.collect(groupingByCollector);
// Combine those into file-level statistics
// NOTE: Inlining this var makes javac (1.8) upset (due to its inability to infer
// expression type correctly)
Stream> stream = columnToStatsListMap.values()
.stream()
.map(this::getColumnRangeInFile);
return stream.collect(Collectors.toList());
}
private > HoodieColumnRangeMetadata getColumnRangeInFile(
@Nonnull List> blockRanges
) {
if (blockRanges.size() == 1) {
// only one block in parquet file. we can just return that range.
return blockRanges.get(0);
}
// there are multiple blocks. Compute min(block_mins) and max(block_maxs)
return blockRanges.stream()
.sequential()
.reduce(this::combineRanges).get();
}
private > HoodieColumnRangeMetadata combineRanges(
HoodieColumnRangeMetadata one,
HoodieColumnRangeMetadata another
) {
final T minValue;
final T maxValue;
if (one.getMinValue() != null && another.getMinValue() != null) {
minValue = one.getMinValue().compareTo(another.getMinValue()) < 0 ? one.getMinValue() : another.getMinValue();
} else if (one.getMinValue() == null) {
minValue = another.getMinValue();
} else {
minValue = one.getMinValue();
}
if (one.getMaxValue() != null && another.getMaxValue() != null) {
maxValue = one.getMaxValue().compareTo(another.getMaxValue()) < 0 ? another.getMaxValue() : one.getMaxValue();
} else if (one.getMaxValue() == null) {
maxValue = another.getMaxValue();
} else {
maxValue = one.getMaxValue();
}
return HoodieColumnRangeMetadata.create(
one.getFilePath(),
one.getColumnName(), minValue, maxValue,
one.getNullCount() + another.getNullCount(),
one.getValueCount() + another.getValueCount(),
one.getTotalSize() + another.getTotalSize(),
one.getTotalUncompressedSize() + another.getTotalUncompressedSize());
}
private static Comparable convertToNativeJavaType(PrimitiveType primitiveType, Comparable val) {
if (val == null) {
return null;
}
if (primitiveType.getOriginalType() == OriginalType.DECIMAL) {
return extractDecimal(val, primitiveType.getDecimalMetadata());
} else if (primitiveType.getOriginalType() == OriginalType.DATE) {
// NOTE: This is a workaround to address race-condition in using
// {@code SimpleDataFormat} concurrently (w/in {@code DateStringifier})
// TODO cleanup after Parquet upgrade to 1.12
synchronized (primitiveType.stringifier()) {
// Date logical type is implemented as a signed INT32
// REF: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md
return java.sql.Date.valueOf(
primitiveType.stringifier().stringify((Integer) val)
);
}
} else if (primitiveType.getOriginalType() == OriginalType.UTF8) {
// NOTE: UTF8 type designates a byte array that should be interpreted as a
// UTF-8 encoded character string
// REF: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md
return ((Binary) val).toStringUsingUTF8();
} else if (primitiveType.getPrimitiveTypeName() == PrimitiveType.PrimitiveTypeName.BINARY) {
// NOTE: `getBytes` access makes a copy of the underlying byte buffer
return ((Binary) val).toByteBuffer();
}
return val;
}
@Nonnull
private static BigDecimal extractDecimal(Object val, DecimalMetadata decimalMetadata) {
// In Parquet, Decimal could be represented as either of
// 1. INT32 (for 1 <= precision <= 9)
// 2. INT64 (for 1 <= precision <= 18)
// 3. FIXED_LEN_BYTE_ARRAY (precision is limited by the array size. Length n can store <= floor(log_10(2^(8*n - 1) - 1)) base-10 digits)
// 4. BINARY (precision is not limited)
// REF: https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#DECIMAL
int scale = decimalMetadata.getScale();
if (val == null) {
return null;
} else if (val instanceof Integer) {
return BigDecimal.valueOf((Integer) val, scale);
} else if (val instanceof Long) {
return BigDecimal.valueOf((Long) val, scale);
} else if (val instanceof Binary) {
// NOTE: Unscaled number is stored in BE format (most significant byte is 0th)
return new BigDecimal(new BigInteger(((Binary) val).getBytesUnsafe()), scale);
} else {
throw new UnsupportedOperationException(String.format("Unsupported value type (%s)", val.getClass().getName()));
}
}
// -------------------------------------------------------------------------
// Inner Class
// -------------------------------------------------------------------------
/**
* An iterator that can apply the given function {@code func} to transform records
* from the underneath record iterator to hoodie keys.
*/
private static class HoodieKeyIterator implements ClosableIterator {
private final ClosableIterator nestedItr;
private final Function func;
public static HoodieKeyIterator getInstance(ClosableIterator nestedItr, Option keyGenerator) {
return new HoodieKeyIterator(nestedItr, keyGenerator);
}
private HoodieKeyIterator(ClosableIterator nestedItr, Option keyGenerator) {
this.nestedItr = nestedItr;
if (keyGenerator.isPresent()) {
this.func = retVal -> {
String recordKey = keyGenerator.get().getRecordKey(retVal);
String partitionPath = keyGenerator.get().getPartitionPath(retVal);
return new HoodieKey(recordKey, partitionPath);
};
} else {
this.func = retVal -> {
String recordKey = retVal.get(HoodieRecord.RECORD_KEY_METADATA_FIELD).toString();
String partitionPath = retVal.get(HoodieRecord.PARTITION_PATH_METADATA_FIELD).toString();
return new HoodieKey(recordKey, partitionPath);
};
}
}
@Override
public void close() {
if (this.nestedItr != null) {
this.nestedItr.close();
}
}
@Override
public boolean hasNext() {
return this.nestedItr.hasNext();
}
@Override
public HoodieKey next() {
return this.func.apply(this.nestedItr.next());
}
}
}
