writing rdd from spark to Elastic Search fails - hadoop

Question

I am trying to write a pair rdd to Elastic Search on Elastic Cloud on version 2.4.0.
I am using elasticsearch-spark_2.10-2.4.0 plugin to write to ES.
Here is the code I am using to write to ES:
def predict_imgs(r):
import json
out_d = {}
out_d["pid"] = r["pid"]
out_d["other_stuff"] = r["other_stuff"]
return (r["pid"], json.dumps(out_d))
res2 = res1.map(predict_imgs)
es_write_conf = {
"es.nodes" : image_es,
#"es.port" : "9243",
"es.resource" : "index/type",
"es.nodes.wan.only":"True",
"es.write.operation":"upsert",
"es.mapping.id":"product_id",
"es.nodes.discovery" : "false",
"es.net.http.auth.user": "username",
"es.net.http.auth.pass": "pass",
"es.input.json": "true",
"es.http.timeout":"1m",
"es.scroll.size":"10",
"es.batch.size.bytes":"1mb",
"es.http.retries":"1",
"es.batch.size.entries":"5",
"es.batch.write.refresh":"False",
"es.batch.write.retry.count":"1",
"es.batch.write.retry.wait":"10s"}
res2.saveAsNewAPIHadoopFile(
path='-',
outputFormatClass="org.elasticsearch.hadoop.mr.EsOutputFormat",
keyClass="org.apache.hadoop.io.NullWritable",
valueClass="org.elasticsearch.hadoop.mr.LinkedMapWritable",
conf=es_write_conf)
The Error I get is as follows:
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 744 in stage 26.0 failed 4 times, most recent failure: Lost task 744.3 in stage 26.0 (TID 2841, 10.181.252.29): org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
The interesting part is this works when I do a take on the first few elements on rdd2 and then make a new rdd out of it and write it to ES, it works flawlessly:
x = sc.parallelize([res2.take(1)])
x.saveAsNewAPIHadoopFile(
path='-',
outputFormatClass="org.elasticsearch.hadoop.mr.EsOutputFormat",
keyClass="org.apache.hadoop.io.NullWritable",
valueClass="org.elasticsearch.hadoop.mr.LinkedMapWritable",
conf=es_write_conf)
I am using Elastic Cloud (cloud offering of Elastic Search) and Databricks (cloud offering of Apache Spark)
Could it be that ES is not able to keep up with the through put of Spark writing to ES ?
I increased our Elastic Cloud size from 2GB RAM to 8GB RAM.
Are there any recommended configs for the es_write_conf I used above? Any other confs that you can think of?
Does updating to ES 5.0 help?
Any help is appreciated. Have been struggling with this for a few days now. Thank you.

Answer 1

It looks like problem with pyspark calculations, not necessarly elasticsearch saving process. Ensure your RDDs are OK by:
Performing count() on rdd1 (to "materialize" results)
Performing count() on rdd2
If counts are OK, try with caching results before saving into ES:
res2.cache()
res2.count() # to fill the cache
res2.saveAsNewAPIHadoopFile(...
It the problem still appears, try to look at dead executors stderr and stdout (you can find them on Executors tab in SparkUI).
I also noticed the very small batch size in es_write_conf, try increasing it to 500 or 1000 to get better performance.