I have a spark v.1.4.1 on YARN job where the first stage has ~149,000 tasks (it’s reading a few TB of data). The job itself is fairly simple - it’s just getting a list of distinct values:
val days = spark
.sequenceFile(inputDir, classOf[KeyClass], classOf[ValueClass])
.sample(withReplacement = false, fraction = 0.01)
.map(row => row._1.getTimestamp.toString("yyyy-MM-dd"))
.distinct()
.collect()
The cardinality of the ‘day’ is quite small - there’s only a handful. However,
I’m frequently running into OutOfMemory issues on the driver. I’ve had it fail
with 24GB RAM, and am currently nudging it upwards to find out where it works.
The ratio between input and shuffle write in the distinct stage is about
3TB:7MB. On a smaller dataset, it works without issue on a smaller (4GB) heap.
In YARN cluster mode, I get a failure message similar to:
Container
[pid=36844,containerID=container_e15_1438040390147_4982_01_000001] is running
beyond physical memory limits. Current usage: 27.6 GB of 27 GB physical memory
used; 29.5 GB of 56.7 GB virtual memory used. Killing container.
Is the driver running out of memory simply due to the number of tasks, or is
there something about the job program that’s causing it to put a lot of data
into the driver heap and go oom? If the former, is there any general guidance
about the amount of memory to give to the driver as a function of how many
tasks there are?
Andrew
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