Mixing
Unusual amount of data pulled into the driver when calling dataframe.collect in Spark
In my spark code, I am collecting a small object on the driver from a Dataframe. I see the following error message on the console. I am calling dataframe.take(1) in my program.
Total size of serialized results of 13 tasks (1827.6 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
This know that this can be resolved by setting spark.driver.maxResultSize param. But my question is, Why is so much of data being pulled into the driver when the object that I am collecting is less than an MB in size. Is it the case that all the objects are first serialized and pulled into the driver and then the driver selects one of them (take(1)) for the output.
apache-spark apache-spark-sql
asked Nov 20 '18 at 2:04
devjdevj
241315
add a comment |
In my spark code, I am collecting a small object on the driver from a Dataframe. I see the following error message on the console. I am calling dataframe.take(1) in my program.
Total size of serialized results of 13 tasks (1827.6 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
This know that this can be resolved by setting spark.driver.maxResultSize param. But my question is, Why is so much of data being pulled into the driver when the object that I am collecting is less than an MB in size. Is it the case that all the objects are first serialized and pulled into the driver and then the driver selects one of them (take(1)) for the output.
apache-spark apache-spark-sql
asked Nov 20 '18 at 2:04
devjdevj
241315
add a comment |
In my spark code, I am collecting a small object on the driver from a Dataframe. I see the following error message on the console. I am calling dataframe.take(1) in my program.
Total size of serialized results of 13 tasks (1827.6 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
This know that this can be resolved by setting spark.driver.maxResultSize param. But my question is, Why is so much of data being pulled into the driver when the object that I am collecting is less than an MB in size. Is it the case that all the objects are first serialized and pulled into the driver and then the driver selects one of them (take(1)) for the output.
apache-spark apache-spark-sql
asked Nov 20 '18 at 2:04
devjdevj
241315
In my spark code, I am collecting a small object on the driver from a Dataframe. I see the following error message on the console. I am calling dataframe.take(1) in my program.
Total size of serialized results of 13 tasks (1827.6 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
This know that this can be resolved by setting spark.driver.maxResultSize param. But my question is, Why is so much of data being pulled into the driver when the object that I am collecting is less than an MB in size. Is it the case that all the objects are first serialized and pulled into the driver and then the driver selects one of them (take(1)) for the output.
apache-spark apache-spark-sql
apache-spark apache-spark-sql
asked Nov 20 '18 at 2:04
devjdevj
241315
asked Nov 20 '18 at 2:04
devjdevj
241315
edited Nov 20 '18 at 15:11
devj
asked Nov 20 '18 at 2:04
devjdevj
241315
asked Nov 20 '18 at 2:04
devjdevj
241315
asked Nov 20 '18 at 2:04
devjdevj
241315
241315
add a comment |
add a comment |
1 Answer
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From the above question it seems like you would like to take 1 row from your dataframe which can be acheived using below code.
df.take(1)
However, When you will perform df.take(1).collect() in that case collect will be applied on the result of take(1) which is another collection in scala or python (depending on which language you are using.)
Also, why you would like to perform collect on take(1)?
Regards,
Neeraj
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
From the above question it seems like you would like to take 1 row from your dataframe which can be acheived using below code.
df.take(1)
However, When you will perform df.take(1).collect() in that case collect will be applied on the result of take(1) which is another collection in scala or python (depending on which language you are using.)
Also, why you would like to perform collect on take(1)?
Regards,
Neeraj
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
add a comment |
From the above question it seems like you would like to take 1 row from your dataframe which can be acheived using below code.
df.take(1)
However, When you will perform df.take(1).collect() in that case collect will be applied on the result of take(1) which is another collection in scala or python (depending on which language you are using.)
Also, why you would like to perform collect on take(1)?
Regards,
Neeraj
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
add a comment |
From the above question it seems like you would like to take 1 row from your dataframe which can be acheived using below code.
df.take(1)
However, When you will perform df.take(1).collect() in that case collect will be applied on the result of take(1) which is another collection in scala or python (depending on which language you are using.)
Also, why you would like to perform collect on take(1)?
Regards,
Neeraj
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
From the above question it seems like you would like to take 1 row from your dataframe which can be acheived using below code.
df.take(1)
However, When you will perform df.take(1).collect() in that case collect will be applied on the result of take(1) which is another collection in scala or python (depending on which language you are using.)
Also, why you would like to perform collect on take(1)?
Regards,
Neeraj
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
answered Nov 20 '18 at 11:02
neeraj bhadanineeraj bhadani
829212
829212
add a comment |
add a comment |
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