Details
-
Bug
-
Status: Resolved
-
Major
-
Resolution: Incomplete
-
1.6.1
-
None
-
None
Description
Before running an intensive iterative job (in this case a distributed topic model training), we need to load a dataset and persist it across executors.
After loading from HDFS and persisting, the partitions are spread unevenly across executors (based on the initial scheduling of the reads which are not data locale sensitive). The partition sizes are even, just not their distribution over executors. We currently have no way to force the partitions to spread evenly, and as the iterative algorithm begins, tasks are distributed to executors based on this initial load, forcing some very unbalanced work.
This has been mentioned a number of times in various user/dev group threads.
None of the discussions I could find had solutions that worked for me. Here are examples of things I have tried. All resulted in partitions in memory that were NOT evenly distributed to executors, causing future tasks to be imbalanced across executors as well.
Reduce Locality
spark.shuffle.reduceLocality.enabled=false/true
"Legacy" memory mode
spark.memory.useLegacyMode = true/false
Basic load and repartition
val numPartitions = 48*16
val df = sqlContext.read.
parquet("/data/folder_to_load").
repartition(numPartitions).
persist
df.count
Load and repartition to 2x partitions, then shuffle repartition down to desired partitions
val numPartitions = 48*16
val df2 = sqlContext.read.
parquet("/data/folder_to_load").
repartition(numPartitions*2)
val df = df2.repartition(numPartitions).
persist
df.count
It would be great if when persisting an RDD/DataFrame, if we could request that those partitions be stored evenly across executors in preparation for future tasks.
I'm not sure if this is a more general issue (I.E. not just involving persisting RDDs), but for the persisted in-memory case, it can make a HUGE difference in the over-all running time of the remaining work.
