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Reformatting code per Google Code Style all over

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This commit is contained in:
Vinoth Chandar
2017-11-12 22:54:56 -08:00
committed by vinoth chandar
parent 5a62480a92
commit e45679f5e2
254 changed files with 21580 additions and 21108 deletions
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232
hoodie-client/src/main/java/com/uber/hoodie/HoodieReadClient.java
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@@ -17,25 +17,19 @@
package com.uber.hoodie;
import com.google.common.base.Optional;
import com.uber.hoodie.common.model.HoodieCommitMetadata;
import com.uber.hoodie.common.model.HoodieDataFile;
import com.uber.hoodie.common.model.HoodieKey;
import com.uber.hoodie.common.model.HoodieRecord;
import com.uber.hoodie.common.table.HoodieTableMetaClient;
import com.uber.hoodie.common.table.HoodieTimeline;
import com.uber.hoodie.common.table.TableFileSystemView;
import com.uber.hoodie.common.table.timeline.HoodieInstant;
import com.uber.hoodie.common.table.view.HoodieTableFileSystemView;
import com.uber.hoodie.common.util.FSUtils;
import com.uber.hoodie.config.HoodieWriteConfig;
import com.uber.hoodie.exception.HoodieException;
import com.uber.hoodie.index.bloom.HoodieBloomIndex;
import com.uber.hoodie.table.HoodieTable;
import java.io.Serializable;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.log4j.LogManager;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
@@ -46,136 +40,126 @@ import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.types.StructType;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.stream.Collectors;
import scala.Tuple2;
/**
* Provides an RDD based API for accessing/filtering Hoodie tables, based on keys.
*
*/
public class HoodieReadClient implements Serializable {
private static Logger logger = LogManager.getLogger(HoodieReadClient.class);
private static Logger logger = LogManager.getLogger(HoodieReadClient.class);
private transient final JavaSparkContext jsc;
private transient final JavaSparkContext jsc;
private transient final FileSystem fs;
/**
* TODO: We need to persist the index type into hoodie.properties and be able to access the
* index just with a simple basepath pointing to the dataset. Until, then just always assume a
* BloomIndex
*/
private transient final HoodieBloomIndex index;
private final HoodieTimeline commitTimeline;
private HoodieTable hoodieTable;
private transient Optional<SQLContext> sqlContextOpt;
private transient final FileSystem fs;
/**
* TODO: We need to persist the index type into hoodie.properties and be able to access the index
* just with a simple basepath pointing to the dataset. Until, then just always assume a
* BloomIndex
*/
private transient final HoodieBloomIndex index;
private final HoodieTimeline commitTimeline;
private HoodieTable hoodieTable;
private transient Optional<SQLContext> sqlContextOpt;
/**
* @param basePath path to Hoodie dataset
*/
public HoodieReadClient(JavaSparkContext jsc, String basePath) {
this.jsc = jsc;
this.fs = FSUtils.getFs();
// Create a Hoodie table which encapsulated the commits and files visible
this.hoodieTable = HoodieTable
.getHoodieTable(new HoodieTableMetaClient(fs, basePath, true), null);
this.commitTimeline = hoodieTable.getCompletedCompactionCommitTimeline();
this.index =
new HoodieBloomIndex(HoodieWriteConfig.newBuilder().withPath(basePath).build(), jsc);
this.sqlContextOpt = Optional.absent();
/**
* @param basePath path to Hoodie dataset
*/
public HoodieReadClient(JavaSparkContext jsc, String basePath) {
this.jsc = jsc;
this.fs = FSUtils.getFs();
// Create a Hoodie table which encapsulated the commits and files visible
this.hoodieTable = HoodieTable
.getHoodieTable(new HoodieTableMetaClient(fs, basePath, true), null);
this.commitTimeline = hoodieTable.getCompletedCompactionCommitTimeline();
this.index =
new HoodieBloomIndex(HoodieWriteConfig.newBuilder().withPath(basePath).build(), jsc);
this.sqlContextOpt = Optional.absent();
}
/**
*
* @param jsc
* @param basePath
* @param sqlContext
*/
public HoodieReadClient(JavaSparkContext jsc, String basePath, SQLContext sqlContext) {
this(jsc, basePath);
this.sqlContextOpt = Optional.of(sqlContext);
}
/**
* Adds support for accessing Hoodie built tables from SparkSQL, as you normally would.
*
* @return SparkConf object to be used to construct the SparkContext by caller
*/
public static SparkConf addHoodieSupport(SparkConf conf) {
conf.set("spark.sql.hive.convertMetastoreParquet", "false");
return conf;
}
private void assertSqlContext() {
if (!sqlContextOpt.isPresent()) {
throw new IllegalStateException(
"SQLContext must be set, when performing dataframe operations");
}
}
/**
*
* @param jsc
* @param basePath
* @param sqlContext
*/
public HoodieReadClient(JavaSparkContext jsc, String basePath, SQLContext sqlContext) {
this(jsc, basePath);
this.sqlContextOpt = Optional.of(sqlContext);
}
/**
* Given a bunch of hoodie keys, fetches all the individual records out as a data frame
*
* @return a dataframe
*/
public Dataset<Row> read(JavaRDD<HoodieKey> hoodieKeys, int parallelism)
throws Exception {
/**
* Adds support for accessing Hoodie built tables from SparkSQL, as you normally would.
*
* @return SparkConf object to be used to construct the SparkContext by caller
*/
public static SparkConf addHoodieSupport(SparkConf conf) {
conf.set("spark.sql.hive.convertMetastoreParquet", "false");
return conf;
}
assertSqlContext();
JavaPairRDD<HoodieKey, Optional<String>> keyToFileRDD =
index.fetchRecordLocation(hoodieKeys, hoodieTable);
List<String> paths = keyToFileRDD
.filter(keyFileTuple -> keyFileTuple._2().isPresent())
.map(keyFileTuple -> keyFileTuple._2().get())
.collect();
private void assertSqlContext() {
if (!sqlContextOpt.isPresent()) {
throw new IllegalStateException("SQLContext must be set, when performing dataframe operations");
}
}
// record locations might be same for multiple keys, so need a unique list
Set<String> uniquePaths = new HashSet<>(paths);
Dataset<Row> originalDF = sqlContextOpt.get().read()
.parquet(uniquePaths.toArray(new String[uniquePaths.size()]));
StructType schema = originalDF.schema();
JavaPairRDD<HoodieKey, Row> keyRowRDD = originalDF.javaRDD()
.mapToPair(row -> {
HoodieKey key = new HoodieKey(
row.<String>getAs(HoodieRecord.RECORD_KEY_METADATA_FIELD),
row.<String>getAs(HoodieRecord.PARTITION_PATH_METADATA_FIELD));
return new Tuple2<>(key, row);
});
/**
* Given a bunch of hoodie keys, fetches all the individual records out as a data frame
*
* @return a dataframe
*/
public Dataset<Row> read(JavaRDD<HoodieKey> hoodieKeys, int parallelism)
throws Exception {
// Now, we need to further filter out, for only rows that match the supplied hoodie keys
JavaRDD<Row> rowRDD = keyRowRDD.join(keyToFileRDD, parallelism)
.map(tuple -> tuple._2()._1());
assertSqlContext();
JavaPairRDD<HoodieKey, Optional<String>> keyToFileRDD =
index.fetchRecordLocation(hoodieKeys, hoodieTable);
List<String> paths = keyToFileRDD
.filter(keyFileTuple -> keyFileTuple._2().isPresent())
.map(keyFileTuple -> keyFileTuple._2().get())
.collect();
return sqlContextOpt.get().createDataFrame(rowRDD, schema);
}
// record locations might be same for multiple keys, so need a unique list
Set<String> uniquePaths = new HashSet<>(paths);
Dataset<Row> originalDF = sqlContextOpt.get().read()
.parquet(uniquePaths.toArray(new String[uniquePaths.size()]));
StructType schema = originalDF.schema();
JavaPairRDD<HoodieKey, Row> keyRowRDD = originalDF.javaRDD()
.mapToPair(row -> {
HoodieKey key = new HoodieKey(
row.<String>getAs(HoodieRecord.RECORD_KEY_METADATA_FIELD),
row.<String>getAs(HoodieRecord.PARTITION_PATH_METADATA_FIELD));
return new Tuple2<>(key, row);
});
/**
* Checks if the given [Keys] exists in the hoodie table and returns [Key, Optional[FullFilePath]]
* If the optional FullFilePath value is not present, then the key is not found. If the
* FullFilePath value is present, it is the path component (without scheme) of the URI underlying
* file
*/
public JavaPairRDD<HoodieKey, Optional<String>> checkExists(JavaRDD<HoodieKey> hoodieKeys) {
return index.fetchRecordLocation(hoodieKeys, hoodieTable);
}
// Now, we need to further filter out, for only rows that match the supplied hoodie keys
JavaRDD<Row> rowRDD = keyRowRDD.join(keyToFileRDD, parallelism)
.map(tuple -> tuple._2()._1());
return sqlContextOpt.get().createDataFrame(rowRDD, schema);
}
/**
* Checks if the given [Keys] exists in the hoodie table and returns [Key,
* Optional[FullFilePath]] If the optional FullFilePath value is not present, then the key is
* not found. If the FullFilePath value is present, it is the path component (without scheme) of
* the URI underlying file
*/
public JavaPairRDD<HoodieKey, Optional<String>> checkExists(JavaRDD<HoodieKey> hoodieKeys) {
return index.fetchRecordLocation(hoodieKeys, hoodieTable);
}
/**
* Filter out HoodieRecords that already exists in the output folder. This is useful in
* deduplication.
*
* @param hoodieRecords Input RDD of Hoodie records.
* @return A subset of hoodieRecords RDD, with existing records filtered out.
*/
public JavaRDD<HoodieRecord> filterExists(JavaRDD<HoodieRecord> hoodieRecords) {
JavaRDD<HoodieRecord> recordsWithLocation = index.tagLocation(hoodieRecords, hoodieTable);
return recordsWithLocation.filter(v1 -> !v1.isCurrentLocationKnown());
}
/**
* Filter out HoodieRecords that already exists in the output folder. This is useful in
* deduplication.
*
* @param hoodieRecords Input RDD of Hoodie records.
* @return A subset of hoodieRecords RDD, with existing records filtered out.
*/
public JavaRDD<HoodieRecord> filterExists(JavaRDD<HoodieRecord> hoodieRecords) {
JavaRDD<HoodieRecord> recordsWithLocation = index.tagLocation(hoodieRecords, hoodieTable);
return recordsWithLocation.filter(v1 -> !v1.isCurrentLocationKnown());
}
}