Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1608#discussion_r15438442
--- Diff:
external/hbase/src/main/scala/org/apache/spark/hbase/HBaseContext.scala ---
@@ -0,0 +1,544 @@
+/*
+ * 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.hbase
+
+import org.apache.hadoop.hbase.HBaseConfiguration
+import org.apache.spark.rdd.RDD
+import java.io.ByteArrayOutputStream
+import java.io.DataOutputStream
+import java.io.ByteArrayInputStream
+import java.io.DataInputStream
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.hbase.client.HConnectionManager
+import org.apache.spark.api.java.JavaPairRDD
+import java.io.OutputStream
+import org.apache.hadoop.hbase.client.HTable
+import org.apache.hadoop.hbase.client.Scan
+import org.apache.hadoop.hbase.client.Get
+import java.util.ArrayList
+import org.apache.hadoop.hbase.client.Result
+import scala.reflect.ClassTag
+import org.apache.hadoop.hbase.client.HConnection
+import org.apache.hadoop.hbase.client.Put
+import org.apache.hadoop.hbase.client.Increment
+import org.apache.hadoop.hbase.client.Delete
+import org.apache.spark.SparkContext
+import org.apache.hadoop.hbase.mapreduce.TableInputFormat
+import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil
+import org.apache.hadoop.hbase.io.ImmutableBytesWritable
+import org.apache.hadoop.hbase.util.Bytes
+import org.apache.hadoop.mapreduce.Job
+import org.apache.hadoop.hbase.mapreduce.TableMapper
+import org.apache.hadoop.hbase.mapreduce.IdentityTableMapper
+import org.apache.hadoop.hbase.protobuf.ProtobufUtil
+import org.apache.hadoop.hbase.util.Base64
+import org.apache.hadoop.hbase.mapreduce.MutationSerialization
+import org.apache.hadoop.hbase.mapreduce.ResultSerialization
+import org.apache.hadoop.hbase.mapreduce.KeyValueSerialization
+import org.apache.spark.rdd.HadoopRDD
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.SerializableWritable
+import java.util.HashMap
+import org.apache.hadoop.hbase.protobuf.generated.MasterProtos
+import org.apache.hadoop.hbase.protobuf.generated.MasterProtos
+import java.util.concurrent.atomic.AtomicInteger
+import org.apache.hadoop.hbase.HConstants
+import java.util.concurrent.atomic.AtomicLong
+import java.util.Timer
+import java.util.TimerTask
+import org.apache.hadoop.hbase.client.Mutation
+import scala.collection.mutable.MutableList
+import org.apache.spark.streaming.dstream.DStream
+
+/**
+ * HBaseContext is a façade of simple and complex HBase operations
+ * like bulk put, get, increment, delete, and scan
+ *
+ * HBase Context will take the responsibilities to happen to
+ * complexity of disseminating the configuration information
+ * to the working and managing the life cycle of HConnections.
+ *
+ * First constructor:
+ * @param sc - active SparkContext
+ * @param broadcastedConf - This is a Broadcast object that holds a
+ * serializable Configuration object
+ *
+ */
+@serializable class HBaseContext(@transient sc: SparkContext,
+ broadcastedConf: Broadcast[SerializableWritable[Configuration]]) {
+
+ /**
+ * Second constructor option:
+ * @param sc active SparkContext
+ * @param config Configuration object to make connection to HBase
+ */
+ def this(@transient sc: SparkContext, @transient config: Configuration) {
+ this(sc, sc.broadcast(new SerializableWritable(config)))
+ }
+
+ /**
+ * A simple enrichment of the traditional Spark RDD foreachPartition.
+ * This function differs from the original in that it offers the
+ * developer access to a already connected HConnection object
+ *
+ * Note: Do not close the HConnection object. All HConnection
+ * management is handled outside this method
+ *
+ * @param RDD[t] Original RDD with data to iterate over
+ * @param f function to be given a iterator to iterate through
+ * the RDD values and a HConnection object to interact
+ * with HBase
+ */
+ def foreachPartition[T](rdd: RDD[T],
+ f: (Iterator[T], HConnection) => Unit) = {
+ rdd.foreachPartition(
+ it => hbaseForeachPartition(broadcastedConf, it, f))
+ }
+
+ /**
+ * A simple enrichment of the traditional Spark Streaming dStream foreach
+ * This function differs from the original in that it offers the
+ * developer access to a already connected HConnection object
+ *
+ * Note: Do not close the HConnection object. All HConnection
+ * management is handled outside this method
+ *
+ * @param DStream[t] Original DStream with data to iterate over
+ * @param f function to be given a iterator to iterate through
+ * the DStream values and a HConnection object to
+ * interact with HBase
+ */
+ def streamForeach[T](dstream: DStream[T],
+ f: (Iterator[T], HConnection) => Unit) = {
+ dstream.foreach((rdd, time) => {
+ foreachPartition(rdd, f)
+ })
+ }
+
+ /**
+ * A simple enrichment of the traditional Spark RDD mapPartition.
+ * This function differs from the original in that it offers the
+ * developer access to a already connected HConnection object
+ *
+ * Note: Do not close the HConnection object. All HConnection
+ * management is handled outside this method
+ *
+ * Note: Make sure to partition correctly to avoid memory issue when
+ * getting data from HBase
+ *
+ * @param RDD[t] Original RDD with data to iterate over
+ * @param mp function to be given a iterator to iterate through
+ * the RDD values and a HConnection object to interact
+ * with HBase
+ * @return Returns a new RDD generated by the user definition
+ * function just like normal mapPartition
+ */
+ def mapPartition[T, U: ClassTag](rdd: RDD[T],
+ mp: (Iterator[T], HConnection) => Iterator[U]): RDD[U] = {
+
+ rdd.mapPartitions[U](it => hbaseMapPartition[T, U](broadcastedConf,
+ it,
+ mp), true)
+ }
+
+ /**
+ * A simple enrichment of the traditional Spark Streaming DStream
+ * mapPartition.
+ *
+ * This function differs from the original in that it offers the
+ * developer access to a already connected HConnection object
+ *
+ * Note: Do not close the HConnection object. All HConnection
+ * management is handled outside this method
+ *
+ * Note: Make sure to partition correctly to avoid memory issue when
+ * getting data from HBase
+ *
+ * @param DStream[t] Original DStream with data to iterate over
+ * @param mp function to be given a iterator to iterate through
+ * the DStream values and a HConnection object to
+ * interact with HBase
+ * @return Returns a new DStream generated by the user
+ * definition function just like normal mapPartition
+ */
+ def streamMap[T, U: ClassTag](dstream: DStream[T],
+ mp: (Iterator[T], HConnection) => Iterator[U]): DStream[U] = {
+
+ dstream.mapPartitions(it => hbaseMapPartition[T, U](broadcastedConf,
+ it,
+ mp), true)
+ }
+
+ /**
+ * A simple abstraction over the HBaseContext.foreachPartition method.
+ *
+ * It allow addition support for a user to take RDD
+ * and generate puts and send them to HBase.
+ * The complexity of even the HConnection is
+ * removed from the developer
+ *
+ * @param RDD[t] Original RDD with data to iterate over
+ * @tableName The name of the table to put into
+ * @param f function to convert a value in the RDD to a HBase Put
+ * @autoFlush if autoFlush should be turned on
+ */
+ def bulkPut[T](rdd: RDD[T], tableName: String, f: (T) => Put, autoFlush:
Boolean) {
+
+ rdd.foreachPartition(
+ it => hbaseForeachPartition[T](
+ broadcastedConf,
+ it,
+ (iterator, hConnection) => {
+ val htable = hConnection.getTable(tableName)
+ htable.setAutoFlush(autoFlush, true)
+ iterator.foreach(T => htable.put(f(T)))
+ htable.flushCommits()
+ htable.close()
+ }))
+ }
+
+ /**
+ * A simple abstraction over the HBaseContext.streamMapPartition method.
+ *
+ * It allow addition support for a user to take a DStream and
+ * generate puts and send them to HBase.
+ *
+ * The complexity of even the HConnection is
+ * removed from the developer
+ *
+ * @param DStream[t] Original DStream with data to iterate over
+ * @tableName The name of the table to put into
+ * @param f function to convert a value in the RDD to a HBase
Put
+ * @autoFlush if autoFlush should be turned on
+ */
+ def streamBulkPut[T](dstream: DStream[T],
+ tableName: String,
+ f: (T) => Put,
+ autoFlush: Boolean) = {
+ dstream.foreach((rdd, time) => {
+ bulkPut(rdd, tableName, f, autoFlush)
+ })
+ }
+
+ /**
+ * A simple abstraction over the HBaseContext.foreachPartition method.
+ *
+ * It allow addition support for a user to take a RDD and
+ * generate increments and send them to HBase.
+ *
+ * The complexity of even the HConnection is
+ * removed from the developer
+ *
+ * @param RDD[t] Original RDD with data to iterate over
+ * @tableName The name of the table to increment to
+ * @param f function to convert a value in the RDD to a
+ * HBase Increments
+ * @batchSize The number of increments to batch before sending to
HBase
+ */
+ def bulkIncrement[T](rdd: RDD[T], tableName:String, f:(T) => Increment,
batchSize: Integer) {
--- End diff --
batchSize is declared as a `java.lang.Integer` instead of a `scala.Int`. I
bet it all kind of works but would `Int` not be more standard?
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