I'm running a 5 node Spark cluster on AWS EMR each sized m3.xlarge (1 master 4 slaves). I successfully ran through a 146Mb bzip2 compressed CSV file and ended up with a perfectly aggregated result.
Now I'm trying to process a ~5GB bzip2 CSV file on this cluster but I'm receiving this error:
16/11/23 17:29:53 WARN TaskSetManager: Lost task 49.2 in stage 6.0 (TID xxx, xxx.xxx.xxx.compute.internal): ExecutorLostFailure (executor 16 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.4 GB of 10.4 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.
I'm confused as to why I'm getting a ~10.5GB memory limit on a ~75GB cluster (15GB per 3m.xlarge instance)...
Here is my EMR config:
[
{
"classification":"spark-env",
"properties":{
},
"configurations":[
{
"classification":"export",
"properties":{
"PYSPARK_PYTHON":"python34"
},
"configurations":[
]
}
]
},
{
"classification":"spark",
"properties":{
"maximizeResourceAllocation":"true"
},
"configurations":[
]
}
]
From what I've read, setting the maximizeResourceAllocation property should tell EMR to configure Spark to fully utilize all resources available on the cluster. Ie, I should have ~75GB of memory available... So why am I getting a ~10.5GB memory limit error?
Here is the code I'm running:
def sessionize(raw_data, timeout):
# https://www.dataiku.com/learn/guide/code/reshaping_data/sessionization.html
window = (pyspark.sql.Window.partitionBy("user_id", "site_id")
.orderBy("timestamp"))
diff = (pyspark.sql.functions.lag(raw_data.timestamp, 1)
.over(window))
time_diff = (raw_data.withColumn("time_diff", raw_data.timestamp - diff)
.withColumn("new_session", pyspark.sql.functions.when(pyspark.sql.functions.col("time_diff") >= timeout.seconds, 1).otherwise(0)))
window = (pyspark.sql.Window.partitionBy("user_id", "site_id")
.orderBy("timestamp")
.rowsBetween(-1, 0))
sessions = (time_diff.withColumn("session_id", pyspark.sql.functions.concat_ws("_", "user_id", "site_id", pyspark.sql.functions.sum("new_session").over(window))))
return sessions
def aggregate_sessions(sessions):
median = pyspark.sql.functions.udf(lambda x: statistics.median(x))
aggregated = sessions.groupBy(pyspark.sql.functions.col("session_id")).agg(
pyspark.sql.functions.first("site_id").alias("site_id"),
pyspark.sql.functions.first("user_id").alias("user_id"),
pyspark.sql.functions.count("id").alias("hits"),
pyspark.sql.functions.min("timestamp").alias("start"),
pyspark.sql.functions.max("timestamp").alias("finish"),
median(pyspark.sql.functions.collect_list("foo")).alias("foo"),
)
return aggregated
spark_context = pyspark.SparkContext(appName="process-raw-data")
spark_session = pyspark.sql.SparkSession(spark_context)
raw_data = spark_session.read.csv(sys.argv[1],
header=True,
inferSchema=True)
# Windowing doesn't seem to play nicely with TimestampTypes.
#
# Should be able to do this within the ``spark.read.csv`` call, I'd
# think. Need to look into it.
convert_to_unix = pyspark.sql.functions.udf(lambda s: arrow.get(s).timestamp)
raw_data = raw_data.withColumn("timestamp",
convert_to_unix(pyspark.sql.functions.col("timestamp")))
sessions = sessionize(raw_data, SESSION_TIMEOUT)
aggregated = aggregate_sessions(sessions)
aggregated.foreach(save_session)
Basically, nothing more than windowing and a groupBy to aggregate the data.
It starts with a few of those errors, and towards halting increases in the amount of the same error.
I've tried running spark-submit with --conf spark.yarn.executor.memoryOverhead but that doesn't seem to solve the problem either.
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