Imran, thanks for your explaining about the parallelism. That is very helpful.
In my test case, I am only use one box cluster, with one executor. So if I put
10 cores, then 10 concurrent task will be run within this one executor, which
will handle more data than 4 core case, then leaded to OOM.
I haven't setup Spark on our production cluster yet, but assume that we have
100 nodes cluster, if I guess right, set up to 1000 cores mean that on
average, each box's executor will run 10 threads to process data. So lowering
core will reduce the speed of spark, but can help to avoid the OOM, as less
data to be processed in the memory.
My another guess is that each partition will be processed by one core
eventually. So make bigger partition count can decrease partition size, which
should help the memory footprint. In my case, I guess that Spark SQL in fact
doesn't use the "spark.default.parallelism" setting, or at least in my query,
it is not used. So no matter what I changed, it doesn't matter. The reason I
said that is that there is always 200 tasks in stage 2 and 3 of my query job,
no matter what I set the "spark.default.parallelism".
I think lowering the core is to exchange lower memory usage vs speed. Hope my
understanding is correct.
Thanks
Yong
Date: Thu, 26 Feb 2015 17:03:20 -0500
Subject: Re: Help me understand the partition, parallelism in Spark
From: yana.kadiy...@gmail.com
To: iras...@cloudera.com
CC: java8...@hotmail.com; user@spark.apache.org
Imran, I have also observed the phenomenon of reducing the cores helping with
OOM. I wanted to ask this (hopefully without straying off topic): we can
specify the number of cores and the executor memory. But we don't get to
specify _how_ the cores are spread among executors.
Is it possible that with 24G memory and 4 cores we get a spread of 1 core per
executor thus ending up with 24G for the task, but with 24G memory and 10 cores
some executor ends up with 3 cores on the same machine and thus we have only 8G
per task?
On Thu, Feb 26, 2015 at 4:42 PM, Imran Rashid <iras...@cloudera.com> wrote:
Hi Yong,
mostly correct except for:Since we are doing reduceByKey, shuffling will
happen. Data will be shuffled into 1000 partitions, as we have 1000 unique
keys.no, you will not get 1000 partitions. Spark has to decide how many
partitions to use before it even knows how many unique keys there are. If you
have 200 as the default parallelism (or you just explicitly make it the second
parameter to reduceByKey()), then you will get 200 partitions. The 1000 unique
keys will be distributed across the 200 partitions. ideally they will be
distributed pretty equally, but how they get distributed depends on the
partitioner (by default you will have a HashPartitioner, so it depends on the
hash of your keys).
Note that this is more or less the same as in Hadoop MapReduce.
the amount of parallelism matters b/c there are various places in spark where
there is some overhead proportional to the size of a partition. So in your
example, if you have 1000 unique keys in 200 partitions, you expect about 5
unique keys per partitions -- if instead you had 10 partitions, you'd expect
100 unique keys per partitions, and thus more data and you'd be more likely to
hit an OOM. But there are many other possible sources of OOM, so this is
definitely not the *only* solution.
Sorry I can't comment in particular about Spark SQL -- hopefully somebody more
knowledgeable can comment on that.
On Wed, Feb 25, 2015 at 8:58 PM, java8964 <java8...@hotmail.com> wrote:
Hi, Sparkers:
I come from the Hadoop MapReducer world, and try to understand some internal
information of spark. From the web and this list, I keep seeing people talking
about increase the parallelism if you get the OOM error. I tried to read
document as much as possible to understand the RDD partition, and parallelism
usage in the spark.
I understand that for RDD from HDFS, by default, one partition will be one HDFS
block, pretty straightforward. I saw that lots of RDD operations support 2nd
parameter of parallelism. This is the part confuse me. From my understand, the
parallelism is totally controlled by how many cores you give to your job.
Adjust that parameter, or "spark.default.parallelism" shouldn't have any impact.
For example, if I have a 10G data in HDFS, and assume the block size is 128M,
so we get 100 blocks/partitions in RDD. Now if I transfer that RDD to a Pair
RDD, with 1000 unique keys in the pair RDD, and doing reduceByKey action, using
200 as the default parallelism. Here is what I assume:
We have 100 partitions, as the data comes from 100 blocks. Most likely the
spark will generate 100 tasks to read and shuffle them?The 1000 unique keys
mean the 1000 reducer group, like in MRIf I set the max core to be 50, so there
will be up to 50 tasks can be run concurrently. The rest tasks just have to
wait for the core, if there are 50 tasks are running.Since we are doing
reduceByKey, shuffling will happen. Data will be shuffled into 1000 partitions,
as we have 1000 unique keys.I don't know these 1000 partitions will be
processed by how many tasks, maybe this is the parallelism parameter comes
in?No matter what parallelism this will be, there are ONLY 50 task can be run
concurrently. So if we set more cores, more partitions' data will be processed
in the executor (which runs more thread in this case), so more memory needs. I
don't see how increasing parallelism could help the OOM in this case.In my test
case of Spark SQL, I gave 24G as the executor heap, my join between 2 big
datasets keeps getting OOM. I keep increasing the "spark.default.parallelism",
from 200 to 400, to 2000, even to 4000, no help. What really makes the query
finish finally without OOM is after I change the "--total-executor-cores" from
10 to 4.
So my questions are:1) What is the parallelism really mean in the Spark? In the
simple example above, for reduceByKey, what difference it is between
parallelism change from 10 to 20?2) When we talk about partition in the spark,
for the data coming from HDFS, I can understand the partition clearly. For the
intermediate data, the partition will be same as key, right? For group,
reducing, join action, uniqueness of the keys will be partition. Is that
correct?3) Why increasing parallelism could help OOM? I don't get this part.
From my limited experience, adjusting the core count really matters for memory.
Thanks
Yong
