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https://issues.apache.org/jira/browse/SPARK-29280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nicholas Chammas updated SPARK-29280:
-
Description:
[DataFrameWriter|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter]
supports a {{compression}} option, but
[DataFrameReader|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader]
doesn't. The lack of a {{compression}} option in the reader causes some
friction in the following cases:
# You want to read some data compressed with a codec that Spark does not [load
by
default|http://spark.apache.org/docs/latest/configuration.html#compression-and-serialization].
# You want to read some data with a codec that overrides one of the built-in
codecs that Spark supports.
# You want to explicitly instruct Spark on what codec to use on read when it
will not be able to correctly auto-detect it (e.g. because the file extension
is [missing,|https://stackoverflow.com/q/52011697/877069]
[non-standard|https://stackoverflow.com/q/44372995/877069], or
[incorrect|https://stackoverflow.com/q/49110384/877069]).
Case #2 came up in SPARK-29102. There is a very handy library called
[SplittableGzip|https://github.com/nielsbasjes/splittablegzip] that lets you
load a single gzipped file using multiple concurrent tasks. (You can see the
details of how it works and why it's useful in the project README and in
SPARK-29102.)
To use this codec, I had to set {{io.compression.codecs}}. I guess this is a
Hadoop filesystem API setting, since it [doesn't appear to be documented by
Spark|http://spark.apache.org/docs/latest/configuration.html]. Confusingly,
there is also a setting called {{spark.io.compression.codec}}, which seems to
be for a different purpose.
It would be much clearer for the user and more consistent with the writer
interface if the reader let you directly specify the codec.
For example, I think all of the following should be possible:
{code:python}
spark.read.option('compression', 'lz4').csv(...)
spark.read.csv(...,
compression='nl.basjes.hadoop.io.compress.SplittableGzipCodec')
spark.read.json(..., compression='none')
{code}
was:
[DataFrameWriter|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter]
supports a {{compression}} option, but
[DataFrameReader|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader]
doesn't. The lack of a {{compression}} option in the reader causes some
friction in the following cases:
# You want to read some data compressed with a codec that Spark does not [load
by
default|http://spark.apache.org/docs/latest/configuration.html#compression-and-serialization].
# You want to read some data with a codec that overrides one of the built-in
codecs that Spark supports.
# You want to explicitly instruct Spark on what codec to use on read when it
will not be able to correctly auto-detect it (e.g. because the file extension
is [missing,|https://stackoverflow.com/q/52011697/877069]
[non-standard|https://stackoverflow.com/q/44372995/877069], or
[incorrect|https://stackoverflow.com/q/49110384/877069]).
Case #2 came up in SPARK-29102. There is a very handy library called
[SplittableGzip|https://github.com/nielsbasjes/splittablegzip] that lets you
load a single gzipped file using multiple concurrent tasks. (You can see the
details of how it works and why it's useful in the project README and in
SPARK-29102.)
To use this codec, I had to set {{io.compression.codecs}}. I guess this is a
Hadoop filesystem API setting, since it [doesn't appear to be documented by
Spark|http://spark.apache.org/docs/latest/configuration.html]. Confusingly,
there is also a setting called {{spark.io.compression.codec}}, which seems to
be for a different purpose.
It would be much clearer for the user and more consistent with the writer
interface if the reader let you directly specify the codec.
For example:
{code:java}
spark.read.option('compression', 'lz4').csv(...)
spark.read.csv(...,
compression='nl.basjes.hadoop.io.compress.SplittableGzipCodec') {code}
> DataFrameReader should support a compression option
> ---
>
> Key: SPARK-29280
> URL: https://issues.apache.org/jira/browse/SPARK-29280
> Project: Spark
> Issue Type: Improvement
> Components: Input/Output
>Affects Versions: 2.4.4
>Reporter: Nicholas Chammas
>Priority: Minor
>
> [DataFrameWriter|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter]
> supports a {{compression}} option, but
> [DataFrameReader|http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader]
> doesn't. The lack of a {{compression}} option in the reader causes some
>
