http://www.jianshu.com/p/cccc56e39429/comments/2022782 和 https://github.com/elastic/elasticsearch-hadoop/issues/745 都有提到通过自定义Spark Partitioner提升es-hadoop Bulk效率,但是无可运行代码,自己针对其思路在spark-shell里实现了一份。
思路:
spark streming监控/tmp/data下的新文件,并将文中每行内容存储到ES的web/blog索引里!
注意:代码里使用了doc ID来定制路由,该id为自动生成的uuid!因此在启动ES后,需要:
curl -s -XPUT localhost:9200/web -d '
{
"mappings": {
"blog": {
"_id": {
"path": "uuid"
},
"properties": {
"title": {
"type": "string",
"index": "analyzed"
}
}
}
}
}'
告诉ES使用blog document中的uuid字段作为_id。ES 2.0以后见 http://stackoverflow.com/questions/32334709/how-to-set-id-in-elasticsearch-2-0
下面是spark-shell代码:
import org.apache.spark._
import org.apache.spark.streaming._
import org.elasticsearch.spark._
import org.apache.spark.Partitioner
import org.elasticsearch.hadoop.cfg.PropertiesSettings
import org.elasticsearch.spark.cfg.SparkSettingsManager
import org.elasticsearch.hadoop.cfg.Settings
import org.elasticsearch.hadoop.rest.RestRepository
import scala.collection.JavaConversions._
// 为方便测试,下面是自己用scala实现的es hash函数
// 尤其注意:在生产环境下,使用ES jar包里的函数,位置为:
// https://github.com/elastic/elasticsearch/blob/master/core/src/main/java/org/elasticsearch/cluster/routing/Murmur3HashFunction.java
object Murmur3HashFunction {
def hash(routing: String): Int = {
val bytesToHash = Array.ofDim[Byte](routing.length * 2)
for (i <- 0 until routing.length) {
val c = routing.charAt(i)
val b1 = c.toByte
val b2 = (c >>> 8).toByte
assert(((b1 & 0xFF) | ((b2 & 0xFF) << 8)) == c)
bytesToHash(i * 2) = b1
bytesToHash(i * 2 + 1) = b2
}
hash(bytesToHash, 0, bytesToHash.length)
}
def hash(bytes: Array[Byte], offset: Int, length: Int): Int = {
murmurhash3_x86_32(bytes, offset, length, 0)
}
def murmurhash3_x86_32(data: Array[Byte],
offset: Int,
len: Int,
seed: Int): Int = {
val c1 = 0xcc9e2d51
val c2 = 0x1b873593
var h1 = seed
val roundedEnd = offset + (len & 0xfffffffc)
var i = offset
while (i < roundedEnd) {
var k1 = (data(i) & 0xff) | ((data(i + 1) & 0xff) << 8) | ((data(i + 2) & 0xff) << 16) |
(data(i + 3) << 24)
k1 *= c1
k1 = (k1 << 15) | (k1 >>> 17)
k1 *= c2
h1 ^= k1
h1 = (h1 << 13) | (h1 >>> 19)
h1 = h1 * 5 + 0xe6546b64
i += 4
}
var k1 = 0
len & 0x03 match {
case 3 => k1 = (data(roundedEnd + 2) & 0xff) << 16
case 2 => k1 |= (data(roundedEnd + 1) & 0xff) << 8
case 1 =>
k1 |= (data(roundedEnd) & 0xff)
k1 *= c1
k1 = (k1 << 15) | (k1 >>> 17)
k1 *= c2
h1 ^= k1
case _ => //break
}
h1 ^= len
h1 ^= h1 >>> 16
h1 *= 0x85ebca6b
h1 ^= h1 >>> 13
h1 *= 0xc2b2ae35
h1 ^= h1 >>> 16
h1
}
}
// 自定义Partitioner
class ESShardPartitioner(settings: String) extends Partitioner {
protected var _numPartitions = -1
override def numPartitions: Int = {
val newSettings = new PropertiesSettings().load(settings)
// 生产环境下,需要自行设置索引的 index/type,我是以web/blog作为实验的index
newSettings.setResourceRead("web/blog") // ******************** !!! modify it !!! ********************
newSettings.setResourceWrite("web/blog") // ******************** !!! modify it !!! ********************
val repository = new RestRepository(newSettings)
val targetShards = repository.getWriteTargetPrimaryShards(newSettings.getNodesClientOnly())
repository.close()
_numPartitions = targetShards.size()
_numPartitions
}
override def getPartition(docID: Any): Int = {
var shardId = Murmur3HashFunction.hash(docID.toString()) % _numPartitions;
if (shardId < 0) {
shardId += _numPartitions;
}
shardId
}
}
sc.getConf.setMaster("local").setAppName("RDDTest").set("es.nodes", "127.0.0.1").set("spark.serializer", "org.apache.spark.serializer.KryoSerializer").set("es.index.auto.create", "true");
val ssc = new StreamingContext(sc, Seconds(2));
val fileStream = ssc.textFileStream("/tmp/data");
fileStream.foreachRDD { rdd => {
def makeItem(content: String) : (String, Map[String,String]) = {
val uuid = java.util.UUID.randomUUID.toString();
(uuid, Map("content"->content, "uuid"->uuid))
}
println("********************start*************************");
var r2 = rdd.map(makeItem);
val sparkCfg = new SparkSettingsManager().load(rdd.sparkContext.getConf)
val settings = sparkCfg.save();
var r3 = r2.partitionBy(new ESShardPartitioner(settings));
r3.map(x=>x._2).saveToEs("web/blog")
println("data count: " + rdd.count.toString);
println("*********************end************************");
}};
ssc.start();
ssc.awaitTermination();
运行方法:
./spark-shell --jars ../lib/elasticsearch-spark-1.2_2.10-2.1.2.jar
然后在spark shell里运行上述代码。
通过shell 伪造数据:
mkdir /mmp/data
#rm -rf /tmp/ ata"
rm -f "/tmp/data/*"
for ((j=0;j<30;j++)); do
{
for ((i=0;i<20;i++)); do
file_name=`python -c 'import random;print random.random()'`
echo "$j $i is sad story." >"/tmp/data/$file_name.log"
done
sleep 1
}
done
echo "OK, waiting..."
echo "done"
运行上述脚本,看到spark shell里显示: