Creating parquet Petastorm dataset through Spark fails with Overflow error (larger than 4GB)
I'm trying to implement Uber's Petastorm dataset creation which utilizes Spark to create a parquet file following the tutorial on their Github page.
The code:
spark = SparkSession.builder.config('spark.driver.memory', '10g').master('local[4]').getOrCreate()
sc = spark.sparkContext
with materialize_dataset(spark=spark, dataset_url='file:///opt/data/hello_world_dataset',
schema=MySchema, row_group_size_mb=256):
logging.info('Building RDD...')
rows_rdd = sc.parallelize(ids)
.map(row_generator) # Generator that yields lists of examples
.flatMap(lambda x: dict_to_spark_row(MySchema, x))
logging.info('Creating DataFrame...')
spark.createDataFrame(rows_rdd, MySchema.as_spark_schema())
.coalesce(10)
.write
.mode('overwrite')
.parquet('file:///opt/data/hello_world_dataset')
Now the RDD code executes successfully but fails only the .createDataFrame call with the following error:
_pickle.PicklingError: Could not serialize broadcast: OverflowError: cannot serialize a string larger than 4GiB
This is my first experience with Spark, so I can't really tell if this error originates in Spark or Petastorm.
Looking through other solutions to this error (in respect to Spark, not Petastorm) I saw that it might have to do with the pickling protocol, but I can't confirm that, neither did I find a way of altering the pickling protocol.
How could I avoid this error?
python pyspark petastorm
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
add a comment |
I'm trying to implement Uber's Petastorm dataset creation which utilizes Spark to create a parquet file following the tutorial on their Github page.
The code:
spark = SparkSession.builder.config('spark.driver.memory', '10g').master('local[4]').getOrCreate()
sc = spark.sparkContext
with materialize_dataset(spark=spark, dataset_url='file:///opt/data/hello_world_dataset',
schema=MySchema, row_group_size_mb=256):
logging.info('Building RDD...')
rows_rdd = sc.parallelize(ids)
.map(row_generator) # Generator that yields lists of examples
.flatMap(lambda x: dict_to_spark_row(MySchema, x))
logging.info('Creating DataFrame...')
spark.createDataFrame(rows_rdd, MySchema.as_spark_schema())
.coalesce(10)
.write
.mode('overwrite')
.parquet('file:///opt/data/hello_world_dataset')
Now the RDD code executes successfully but fails only the .createDataFrame call with the following error:
_pickle.PicklingError: Could not serialize broadcast: OverflowError: cannot serialize a string larger than 4GiB
This is my first experience with Spark, so I can't really tell if this error originates in Spark or Petastorm.
Looking through other solutions to this error (in respect to Spark, not Petastorm) I saw that it might have to do with the pickling protocol, but I can't confirm that, neither did I find a way of altering the pickling protocol.
How could I avoid this error?
python pyspark petastorm
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
add a comment |
I'm trying to implement Uber's Petastorm dataset creation which utilizes Spark to create a parquet file following the tutorial on their Github page.
The code:
spark = SparkSession.builder.config('spark.driver.memory', '10g').master('local[4]').getOrCreate()
sc = spark.sparkContext
with materialize_dataset(spark=spark, dataset_url='file:///opt/data/hello_world_dataset',
schema=MySchema, row_group_size_mb=256):
logging.info('Building RDD...')
rows_rdd = sc.parallelize(ids)
.map(row_generator) # Generator that yields lists of examples
.flatMap(lambda x: dict_to_spark_row(MySchema, x))
logging.info('Creating DataFrame...')
spark.createDataFrame(rows_rdd, MySchema.as_spark_schema())
.coalesce(10)
.write
.mode('overwrite')
.parquet('file:///opt/data/hello_world_dataset')
Now the RDD code executes successfully but fails only the .createDataFrame call with the following error:
_pickle.PicklingError: Could not serialize broadcast: OverflowError: cannot serialize a string larger than 4GiB
This is my first experience with Spark, so I can't really tell if this error originates in Spark or Petastorm.
Looking through other solutions to this error (in respect to Spark, not Petastorm) I saw that it might have to do with the pickling protocol, but I can't confirm that, neither did I find a way of altering the pickling protocol.
How could I avoid this error?
python pyspark petastorm
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
I'm trying to implement Uber's Petastorm dataset creation which utilizes Spark to create a parquet file following the tutorial on their Github page.
The code:
spark = SparkSession.builder.config('spark.driver.memory', '10g').master('local[4]').getOrCreate()
sc = spark.sparkContext
with materialize_dataset(spark=spark, dataset_url='file:///opt/data/hello_world_dataset',
schema=MySchema, row_group_size_mb=256):
logging.info('Building RDD...')
rows_rdd = sc.parallelize(ids)
.map(row_generator) # Generator that yields lists of examples
.flatMap(lambda x: dict_to_spark_row(MySchema, x))
logging.info('Creating DataFrame...')
spark.createDataFrame(rows_rdd, MySchema.as_spark_schema())
.coalesce(10)
.write
.mode('overwrite')
.parquet('file:///opt/data/hello_world_dataset')
Now the RDD code executes successfully but fails only the .createDataFrame call with the following error:
_pickle.PicklingError: Could not serialize broadcast: OverflowError: cannot serialize a string larger than 4GiB
This is my first experience with Spark, so I can't really tell if this error originates in Spark or Petastorm.
Looking through other solutions to this error (in respect to Spark, not Petastorm) I saw that it might have to do with the pickling protocol, but I can't confirm that, neither did I find a way of altering the pickling protocol.
How could I avoid this error?
python pyspark petastorm
python pyspark petastorm
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
edited Nov 21 '18 at 10:04
bluesummers
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
asked Nov 19 '18 at 8:51
bluesummersbluesummers
2,23612043
2,23612043
add a comment |
add a comment |
1 Answer
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votes
The problem lies in the pickling that is done to pass the data between the different processes, the default pickling protocol is 2, and we need to use 4 in order to pass objects larger than 4GB.
To change the pickling protocol, before creation a Spark session, use the following code
from pyspark import broadcast
import pickle
def broadcast_dump(self, value, f):
pickle.dump(value, f, 4) # was 2, 4 is first protocol supporting >4GB
f.close()
return f.name
broadcast.Broadcast.dump = broadcast_dump
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
add a comment |
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1 Answer
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1 Answer
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active
oldest
votes
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oldest
votes
active
oldest
votes
The problem lies in the pickling that is done to pass the data between the different processes, the default pickling protocol is 2, and we need to use 4 in order to pass objects larger than 4GB.
To change the pickling protocol, before creation a Spark session, use the following code
from pyspark import broadcast
import pickle
def broadcast_dump(self, value, f):
pickle.dump(value, f, 4) # was 2, 4 is first protocol supporting >4GB
f.close()
return f.name
broadcast.Broadcast.dump = broadcast_dump
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
add a comment |
The problem lies in the pickling that is done to pass the data between the different processes, the default pickling protocol is 2, and we need to use 4 in order to pass objects larger than 4GB.
To change the pickling protocol, before creation a Spark session, use the following code
from pyspark import broadcast
import pickle
def broadcast_dump(self, value, f):
pickle.dump(value, f, 4) # was 2, 4 is first protocol supporting >4GB
f.close()
return f.name
broadcast.Broadcast.dump = broadcast_dump
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
add a comment |
The problem lies in the pickling that is done to pass the data between the different processes, the default pickling protocol is 2, and we need to use 4 in order to pass objects larger than 4GB.
To change the pickling protocol, before creation a Spark session, use the following code
from pyspark import broadcast
import pickle
def broadcast_dump(self, value, f):
pickle.dump(value, f, 4) # was 2, 4 is first protocol supporting >4GB
f.close()
return f.name
broadcast.Broadcast.dump = broadcast_dump
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
The problem lies in the pickling that is done to pass the data between the different processes, the default pickling protocol is 2, and we need to use 4 in order to pass objects larger than 4GB.
To change the pickling protocol, before creation a Spark session, use the following code
from pyspark import broadcast
import pickle
def broadcast_dump(self, value, f):
pickle.dump(value, f, 4) # was 2, 4 is first protocol supporting >4GB
f.close()
return f.name
broadcast.Broadcast.dump = broadcast_dump
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
answered Nov 21 '18 at 10:05
bluesummersbluesummers
2,23612043
2,23612043
add a comment |
add a comment |
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