How to transform structured streams with PySpark?
This seems like it should be obvious, but in reviewing the docs and examples, I'm not sure I can find a way to take a structured stream and transform using PySpark.
For example:
from pyspark.sql import SparkSession
spark = (
SparkSession
.builder
.appName('StreamingWordCount')
.getOrCreate()
)
raw_records = (
spark
.readStream
.format('socket')
.option('host', 'localhost')
.option('port', 9999)
.load()
)
# I realize there's a SQL function for upper-case, just illustrating a sample
# use of an arbitrary map function
records = raw_records.rdd.map(lambda w: w.upper()).toDF()
counts = (
records
.groupBy(records.value)
.count()
)
query = (
counts
.writeStream
.outputMode('complete')
.format('console')
.start()
)
query.awaitTermination()
This will throw the following exception:
Queries with streaming sources must be executed with writeStream.start
However, if I remove the call to rdd.map(...).toDF() things seem to work fine.
Seems as though the call to rdd.map branched execution from the streaming context and causes Spark to warn that it was never started?
Is there a "right" way to apply map or mapPartition style transformations using Structured Streaming and PySpark?
apache-spark pyspark spark-structured-streaming
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
add a comment |
This seems like it should be obvious, but in reviewing the docs and examples, I'm not sure I can find a way to take a structured stream and transform using PySpark.
For example:
from pyspark.sql import SparkSession
spark = (
SparkSession
.builder
.appName('StreamingWordCount')
.getOrCreate()
)
raw_records = (
spark
.readStream
.format('socket')
.option('host', 'localhost')
.option('port', 9999)
.load()
)
# I realize there's a SQL function for upper-case, just illustrating a sample
# use of an arbitrary map function
records = raw_records.rdd.map(lambda w: w.upper()).toDF()
counts = (
records
.groupBy(records.value)
.count()
)
query = (
counts
.writeStream
.outputMode('complete')
.format('console')
.start()
)
query.awaitTermination()
This will throw the following exception:
Queries with streaming sources must be executed with writeStream.start
However, if I remove the call to rdd.map(...).toDF() things seem to work fine.
Seems as though the call to rdd.map branched execution from the streaming context and causes Spark to warn that it was never started?
Is there a "right" way to apply map or mapPartition style transformations using Structured Streaming and PySpark?
apache-spark pyspark spark-structured-streaming
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
add a comment |
This seems like it should be obvious, but in reviewing the docs and examples, I'm not sure I can find a way to take a structured stream and transform using PySpark.
For example:
from pyspark.sql import SparkSession
spark = (
SparkSession
.builder
.appName('StreamingWordCount')
.getOrCreate()
)
raw_records = (
spark
.readStream
.format('socket')
.option('host', 'localhost')
.option('port', 9999)
.load()
)
# I realize there's a SQL function for upper-case, just illustrating a sample
# use of an arbitrary map function
records = raw_records.rdd.map(lambda w: w.upper()).toDF()
counts = (
records
.groupBy(records.value)
.count()
)
query = (
counts
.writeStream
.outputMode('complete')
.format('console')
.start()
)
query.awaitTermination()
This will throw the following exception:
Queries with streaming sources must be executed with writeStream.start
However, if I remove the call to rdd.map(...).toDF() things seem to work fine.
Seems as though the call to rdd.map branched execution from the streaming context and causes Spark to warn that it was never started?
Is there a "right" way to apply map or mapPartition style transformations using Structured Streaming and PySpark?
apache-spark pyspark spark-structured-streaming
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
This seems like it should be obvious, but in reviewing the docs and examples, I'm not sure I can find a way to take a structured stream and transform using PySpark.
For example:
from pyspark.sql import SparkSession
spark = (
SparkSession
.builder
.appName('StreamingWordCount')
.getOrCreate()
)
raw_records = (
spark
.readStream
.format('socket')
.option('host', 'localhost')
.option('port', 9999)
.load()
)
# I realize there's a SQL function for upper-case, just illustrating a sample
# use of an arbitrary map function
records = raw_records.rdd.map(lambda w: w.upper()).toDF()
counts = (
records
.groupBy(records.value)
.count()
)
query = (
counts
.writeStream
.outputMode('complete')
.format('console')
.start()
)
query.awaitTermination()
This will throw the following exception:
Queries with streaming sources must be executed with writeStream.start
However, if I remove the call to rdd.map(...).toDF() things seem to work fine.
Seems as though the call to rdd.map branched execution from the streaming context and causes Spark to warn that it was never started?
Is there a "right" way to apply map or mapPartition style transformations using Structured Streaming and PySpark?
apache-spark pyspark spark-structured-streaming
apache-spark pyspark spark-structured-streaming
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
edited Oct 28 '18 at 11:33
user6910411
32.8k86995
32.8k86995
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
asked Jul 25 '18 at 17:56
Mike Sukmanowsky
5532820
5532820
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Every transformation that is applied in Structured Streaming has to be fully contained in Dataset world - in case of PySpark it means you can use only DataFrame or SQL and conversion to RDD (or DStream or local collections) are not supported.
If you want to use plain Python code you have to use UserDefinedFunction.
from pyspark.sql.functions import udf
@udf
def to_upper(s)
return s.upper()
raw_records.select(to_upper("value"))
See also Spark Structured Streaming and Spark-Ml Regression
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
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
Every transformation that is applied in Structured Streaming has to be fully contained in Dataset world - in case of PySpark it means you can use only DataFrame or SQL and conversion to RDD (or DStream or local collections) are not supported.
If you want to use plain Python code you have to use UserDefinedFunction.
from pyspark.sql.functions import udf
@udf
def to_upper(s)
return s.upper()
raw_records.select(to_upper("value"))
See also Spark Structured Streaming and Spark-Ml Regression
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
add a comment |
Every transformation that is applied in Structured Streaming has to be fully contained in Dataset world - in case of PySpark it means you can use only DataFrame or SQL and conversion to RDD (or DStream or local collections) are not supported.
If you want to use plain Python code you have to use UserDefinedFunction.
from pyspark.sql.functions import udf
@udf
def to_upper(s)
return s.upper()
raw_records.select(to_upper("value"))
See also Spark Structured Streaming and Spark-Ml Regression
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
add a comment |
Every transformation that is applied in Structured Streaming has to be fully contained in Dataset world - in case of PySpark it means you can use only DataFrame or SQL and conversion to RDD (or DStream or local collections) are not supported.
If you want to use plain Python code you have to use UserDefinedFunction.
from pyspark.sql.functions import udf
@udf
def to_upper(s)
return s.upper()
raw_records.select(to_upper("value"))
See also Spark Structured Streaming and Spark-Ml Regression
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
Every transformation that is applied in Structured Streaming has to be fully contained in Dataset world - in case of PySpark it means you can use only DataFrame or SQL and conversion to RDD (or DStream or local collections) are not supported.
If you want to use plain Python code you have to use UserDefinedFunction.
from pyspark.sql.functions import udf
@udf
def to_upper(s)
return s.upper()
raw_records.select(to_upper("value"))
See also Spark Structured Streaming and Spark-Ml Regression
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
edited Nov 23 '18 at 17:22
user6910411
32.8k86995
32.8k86995
answered Jul 25 '18 at 21:36
user10135885
561
answered Jul 25 '18 at 21:36
user10135885
561
answered Jul 25 '18 at 21:36
user10135885
561
561
add a comment |
add a comment |
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