I want to carry out a join of a large Spark dataframe with a comparatively small dataframe
0
I am joining a Spark dataframe with 23 Million records with a dataframe having 0.5 Million records. The Broadcast join doesn't seem feasible as the smaller table won't fit into the memory to be distributed over all workers. Whenever I carry out the join, Spark halts at the shuffle task and doesn't continue. How should I go on with the join?
apache-spark apache-spark-sql left-join apache-spark-2.1
asked Nov 22 at 9:39
Anand Nautiyal
336
add a comment |
0
I am joining a Spark dataframe with 23 Million records with a dataframe having 0.5 Million records. The Broadcast join doesn't seem feasible as the smaller table won't fit into the memory to be distributed over all workers. Whenever I carry out the join, Spark halts at the shuffle task and doesn't continue. How should I go on with the join?
apache-spark apache-spark-sql left-join apache-spark-2.1
asked Nov 22 at 9:39
Anand Nautiyal
336
How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00
add a comment |
0
0
0
I am joining a Spark dataframe with 23 Million records with a dataframe having 0.5 Million records. The Broadcast join doesn't seem feasible as the smaller table won't fit into the memory to be distributed over all workers. Whenever I carry out the join, Spark halts at the shuffle task and doesn't continue. How should I go on with the join?
apache-spark apache-spark-sql left-join apache-spark-2.1
asked Nov 22 at 9:39
Anand Nautiyal
336
I am joining a Spark dataframe with 23 Million records with a dataframe having 0.5 Million records. The Broadcast join doesn't seem feasible as the smaller table won't fit into the memory to be distributed over all workers. Whenever I carry out the join, Spark halts at the shuffle task and doesn't continue. How should I go on with the join?
apache-spark apache-spark-sql left-join apache-spark-2.1
apache-spark apache-spark-sql left-join apache-spark-2.1
asked Nov 22 at 9:39
Anand Nautiyal
336
asked Nov 22 at 9:39
Anand Nautiyal
336
asked Nov 22 at 9:39
Anand Nautiyal
336
asked Nov 22 at 9:39
Anand Nautiyal
336
asked Nov 22 at 9:39
Anand Nautiyal
336
336
How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00
add a comment |
How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00
How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00
add a comment |
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How many partitions do you have for your data? How long did you wait (as in an hour or so)? Just want to make sure it really halts and it's not just really slow :)
– Frank
Nov 22 at 10:56
@Frank - The bigger dataframe had 400 partitions and the smaller one has 40 partitions. I waited for 2 hours or more but after that it threw an error that "Not able to write rows" and RemoteException. The error was that the data was not able to be written to datanodes.
– Anand Nautiyal
Nov 22 at 12:43
@Frank - Can Repartitioning help with this case ?
– Anand Nautiyal
Nov 23 at 4:34
How many CPU cores do you have where you execute your Spark program on? Also see stackoverflow.com/questions/35800795/… and spark.apache.org/docs/latest/tuning.html on this.
– Frank
Nov 23 at 19:00