Spark cores & tasks concurrency - apache-spark

Question

I've a very basic question about spark. I usually run spark jobs using 50 cores. While viewing the job progress, most of the times it shows 50 processes running in parallel (as it is supposed to do), but sometimes it shows only 2 or 4 spark processes running in parallel. Like this:
[Stage 8:================================> (297 + 2) / 500]
The RDD's being processed are repartitioned on more than 100 partitions. So that shouldn't be an issue.
I have an observations though. I've seen the pattern that most of the time it happens, the data locality in SparkUI shows NODE_LOCAL, while other times when all 50 processes are running, some of the processes show RACK_LOCAL.
This makes me doubt that, maybe this happens because the data is cached before processing in the same node to avoid network overhead, and this slows down the further processing.
If this is the case, what's the way to avoid it. And if this isn't the case, what's going on here?

Answer 1

After a week or more of struggling with the issue, I think I've found what was causing the problem.
If you are struggling with the same issue, the good point to start would be to check if the Spark instance is configured fine. There is a great cloudera blog post about it.
However, if the problem isn't with configuration (as was the case with me), then the problem is somewhere within your code. The issue is that sometimes due to different reasons (skewed joins, uneven partitions in data sources etc) the RDD you are working on gets a lot of data on 2-3 partitions and the rest of the partitions have very few data.
In order to reduce the data shuffle across the network, Spark tries that each executor processes the data residing locally on that node. So, 2-3 executors are working for a long time, and the rest of the executors are just done with the data in few milliseconds. That's why I was experiencing the issue I described in the question above.
The way to debug this problem is to first of all check the partition sizes of your RDD. If one or few partitions are very big in comparison to others, then the next step would be to find the records in the large partitions, so that you could know, especially in the case of skewed joins, that what key is getting skewed. I've wrote a small function to debug this:
from itertools import islice
def check_skewness(df):
sampled_rdd = df.sample(False,0.01).rdd.cache() # Taking just 1% sample for fast processing
l = sampled_rdd.mapPartitionsWithIndex(lambda x,it: [(x,sum(1 for _ in it))]).collect()
max_part = max(l,key=lambda item:item[1])
min_part = min(l,key=lambda item:item[1])
if max_part[1]/min_part[1] > 5: #if difference is greater than 5 times
print 'Partitions Skewed: Largest Partition',max_part,'Smallest Partition',min_part,'\nSample Content of the largest Partition: \n'
print (sampled_rdd.mapPartitionsWithIndex(lambda i, it: islice(it, 0, 5) if i == max_part[0] else []).take(5))
else:
print 'No Skewness: Largest Partition',max_part,'Smallest Partition',min_part
It gives me the smallest and largest partition size, and if the difference between these two is more than 5 times, it prints 5 elements of the largest partition, to should give you a rough idea on what's going on.
Once you have figured out that the problem is skewed partition, you can find a way to get rid of that skewed key, or you can re-partition your dataframe, which will force it to get equally distributed, and you'll see now all the executors will be working for equal time and you'll see far less dreaded OOM errors and processing will be significantly fast too.
These are just my two cents as a Spark novice, I hope Spark experts can add some more to this issue, as I think a lot of newbies in Spark world face similar kind of problems far too often.