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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.
*/
package org.apache.hadoop.hbase.spark
import java.util
import org.apache.hadoop.hbase.{HConstants, TableName}
import org.apache.hadoop.hbase.classification.InterfaceAudience;
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.spark.rdd.RDD
import scala.reflect.ClassTag
/**
* HBaseRDDFunctions contains a set of implicit functions that can be
* applied to a Spark RDD so that we can easily interact with HBase
*/
@InterfaceAudience.Public
object HBaseRDDFunctions
{
/**
* These are implicit methods for a RDD that contains any type of
* data.
*
* @param rdd This is for rdd of any type
* @tparam T This is any type
*/
implicit class GenericHBaseRDDFunctions[T](val rdd: RDD[T]) {
/**
* Implicit method that gives easy access to HBaseContext's bulk
* put. This will not return a new RDD. Think of it like a foreach
*
* @param hc The hbaseContext object to identify which
* HBase cluster connection to use
* @param tableName The tableName that the put will be sent to
* @param f The function that will turn the RDD values
* into HBase Put objects.
*/
def hbaseBulkPut(hc: HBaseContext,
tableName: TableName,
f: (T) => Put): Unit = {
hc.bulkPut(rdd, tableName, f)
}
/**
* Implicit method that gives easy access to HBaseContext's bulk
* get. This will return a new RDD. Think about it as a RDD map
* function. In that every RDD value will get a new value out of
* HBase. That new value will populate the newly generated RDD.
*
* @param hc The hbaseContext object to identify which
* HBase cluster connection to use
* @param tableName The tableName that the put will be sent to
* @param batchSize How many gets to execute in a single batch
* @param f The function that will turn the RDD values
* in HBase Get objects
* @param convertResult The function that will convert a HBase
* Result object into a value that will go
* into the resulting RDD
* @tparam R The type of Object that will be coming
* out of the resulting RDD
* @return A resulting RDD with type R objects
*/
def hbaseBulkGet[R: ClassTag](hc: HBaseContext,
tableName: TableName, batchSize:Int,
f: (T) => Get, convertResult: (Result) => R): RDD[R] = {
hc.bulkGet[T, R](tableName, batchSize, rdd, f, convertResult)
}
/**
* Implicit method that gives easy access to HBaseContext's bulk
* get. This will return a new RDD. Think about it as a RDD map
* function. In that every RDD value will get a new value out of
* HBase. That new value will populate the newly generated RDD.
*
* @param hc The hbaseContext object to identify which
* HBase cluster connection to use
* @param tableName The tableName that the put will be sent to
* @param batchSize How many gets to execute in a single batch
* @param f The function that will turn the RDD values
* in HBase Get objects
* @return A resulting RDD with type R objects
*/
def hbaseBulkGet(hc: HBaseContext,
tableName: TableName, batchSize:Int,
f: (T) => Get): RDD[(ImmutableBytesWritable, Result)] = {
hc.bulkGet[T, (ImmutableBytesWritable, Result)](tableName,
batchSize, rdd, f,
result => if (result != null && result.getRow != null) {
(new ImmutableBytesWritable(result.getRow), result)
} else {
null
})
}
/**
* Implicit method that gives easy access to HBaseContext's bulk
* Delete. This will not return a new RDD.
*
* @param hc The hbaseContext object to identify which HBase
* cluster connection to use
* @param tableName The tableName that the deletes will be sent to
* @param f The function that will convert the RDD value into
* a HBase Delete Object
* @param batchSize The number of Deletes to be sent in a single batch
*/
def hbaseBulkDelete(hc: HBaseContext,
tableName: TableName, f:(T) => Delete, batchSize:Int): Unit = {
hc.bulkDelete(rdd, tableName, f, batchSize)
}
/**
* Implicit method that gives easy access to HBaseContext's
* foreachPartition method. This will ack very much like a normal RDD
* foreach method but for the fact that you will now have a HBase connection
* while iterating through the values.
*
* @param hc The hbaseContext object to identify which HBase
* cluster connection to use
* @param f This function will get an iterator for a Partition of an
* RDD along with a connection object to HBase
*/
def hbaseForeachPartition(hc: HBaseContext,
f: (Iterator[T], Connection) => Unit): Unit = {
hc.foreachPartition(rdd, f)
}
/**
* Implicit method that gives easy access to HBaseContext's
* mapPartitions method. This will ask very much like a normal RDD
* map partitions method but for the fact that you will now have a
* HBase connection while iterating through the values
*
* @param hc The hbaseContext object to identify which HBase
* cluster connection to use
* @param f This function will get an iterator for a Partition of an
* RDD along with a connection object to HBase
* @tparam R This is the type of objects that will go into the resulting
* RDD
* @return A resulting RDD of type R
*/
def hbaseMapPartitions[R: ClassTag](hc: HBaseContext,
f: (Iterator[T], Connection) => Iterator[R]):
RDD[R] = {
hc.mapPartitions[T,R](rdd, f)
}
/**
* Spark Implementation of HBase Bulk load for wide rows or when
* values are not already combined at the time of the map process
*
* A Spark Implementation of HBase Bulk load
*
* This will take the content from an existing RDD then sort and shuffle
* it with respect to region splits. The result of that sort and shuffle
* will be written to HFiles.
*
* After this function is executed the user will have to call
* LoadIncrementalHFiles.doBulkLoad(...) to move the files into HBase
*
* Also note this version of bulk load is different from past versions in
* that it includes the qualifier as part of the sort process. The
* reason for this is to be able to support rows will very large number
* of columns.
*
* @param tableName The HBase table we are loading into
* @param flatMap A flapMap function that will make every row in the RDD
* into N cells for the bulk load
* @param stagingDir The location on the FileSystem to bulk load into
* @param familyHFileWriteOptionsMap Options that will define how the HFile for a
* column family is written
* @param compactionExclude Compaction excluded for the HFiles
* @param maxSize Max size for the HFiles before they roll
*/
def hbaseBulkLoad(hc: HBaseContext,
tableName: TableName,
flatMap: (T) => Iterator[(KeyFamilyQualifier, Array[Byte])],
stagingDir:String,
familyHFileWriteOptionsMap:
util.Map[Array[Byte], FamilyHFileWriteOptions] =
new util.HashMap[Array[Byte], FamilyHFileWriteOptions](),
compactionExclude: Boolean = false,
maxSize:Long = HConstants.DEFAULT_MAX_FILE_SIZE):Unit = {
hc.bulkLoad(rdd, tableName,
flatMap, stagingDir, familyHFileWriteOptionsMap,
compactionExclude, maxSize)
}
/**
* Implicit method that gives easy access to HBaseContext's
* bulkLoadThinRows method.
*
* Spark Implementation of HBase Bulk load for short rows some where less then
* a 1000 columns. This bulk load should be faster for tables will thinner
* rows then the other spark implementation of bulk load that puts only one
* value into a record going into a shuffle
*
* This will take the content from an existing RDD then sort and shuffle
* it with respect to region splits. The result of that sort and shuffle
* will be written to HFiles.
*
* After this function is executed the user will have to call
* LoadIncrementalHFiles.doBulkLoad(...) to move the files into HBase
*
* In this implementation only the rowKey is given to the shuffle as the key
* and all the columns are already linked to the RowKey before the shuffle
* stage. The sorting of the qualifier is done in memory out side of the
* shuffle stage
*
* @param tableName The HBase table we are loading into
* @param mapFunction A function that will convert the RDD records to
* the key value format used for the shuffle to prep
* for writing to the bulk loaded HFiles
* @param stagingDir The location on the FileSystem to bulk load into
* @param familyHFileWriteOptionsMap Options that will define how the HFile for a
* column family is written
* @param compactionExclude Compaction excluded for the HFiles
* @param maxSize Max size for the HFiles before they roll
*/
def hbaseBulkLoadThinRows(hc: HBaseContext,
tableName: TableName,
mapFunction: (T) =>
(ByteArrayWrapper, FamiliesQualifiersValues),
stagingDir:String,
familyHFileWriteOptionsMap:
util.Map[Array[Byte], FamilyHFileWriteOptions] =
new util.HashMap[Array[Byte], FamilyHFileWriteOptions](),
compactionExclude: Boolean = false,
maxSize:Long = HConstants.DEFAULT_MAX_FILE_SIZE):Unit = {
hc.bulkLoadThinRows(rdd, tableName,
mapFunction, stagingDir, familyHFileWriteOptionsMap,
compactionExclude, maxSize)
}
}
}
