Declares its compatibility with Spark's dynamic
output partitioning by having the stream capability
"mapreduce.job.committer.dynamic.partitioning"
Requires a Spark release with SPARK-40034, which
does the probing before deciding whether to
accept/rejecting instantiation with
dynamic partition overwrite set
This feature can be declared as supported by
any other PathOutputCommitter implementations
whose algorithm and destination filesystem
are compatible.
None of the S3A committers are compatible.
The classic FileOutputCommitter is, but it
does not declare itself as such out of our fear
of changing that code. The Spark-side code
will automatically infer compatibility if
the created committer is of that class or
a subclass.
Contributed by Steve Loughran.
* HADOOP-18321.Fix when to read an additional record from a BZip2 text file split
Co-authored-by: Ashutosh Gupta <ashugpt@amazon.com> and Reviewed by Akira Ajisaka.
Speed up the magic committer with key changes being
* Writes under __magic always retain directory markers
* File creation under __magic skips all overwrite checks,
including the LIST call intended to stop files being
created over dirs.
* mkdirs under __magic probes the path for existence
but does not look any further.
Extra parallelism in task and job commit directory scanning
Use of createFile and openFile with parameters which all for
HEAD checks to be skipped.
The committer can write the summary _SUCCESS file to the path
`fs.s3a.committer.summary.report.directory`, which can be in a
different file system/bucket if desired, using the job id as
the filename.
Also: HADOOP-15460. S3A FS to add `fs.s3a.create.performance`
Application code can set the createFile() option
fs.s3a.create.performance to true to disable the same
safety checks when writing under magic directories.
Use with care.
The createFile option prefix `fs.s3a.create.header.`
can be used to add custom headers to S3 objects when
created.
Contributed by Steve Loughran.
These changes ensure that sequential files are opened with the
right read policy, and split start/end is passed in.
As well as offering opportunities for filesystem clients to
choose fetch/cache/seek policies, the settings ensure that
processing text files on an s3 bucket where the default policy
is "random" will still be processed efficiently.
This commit depends on the associated hadoop-common patch,
which must be committed first.
Contributed by Steve Loughran.
Change-Id: Ic6713fd752441cf42ebe8739d05c2293a5db9f94
This is a mapreduce/spark output committer optimized for
performance and correctness on Azure ADLS Gen 2 storage
(via the abfs connector) and Google Cloud Storage
(via the external gcs connector library).
* It is safe to use with HDFS, however it has not been optimized
for that use.
* It is *not* safe for use with S3, and will fail if an attempt
is made to do so.
Contributed by Steve Loughran
Change-Id: I6f3502e79c578b9fd1a8c1485f826784b5421fca
Part of the HADOOP-16830 IOStatistics API feature.
If the source FileSystem's listing RemoteIterators
implement IOStatisticsSource, these are collected and served through
the IOStatisticsSource API. If they are not: getIOStatistics() returns
null.
Only the listing statistics are collected; FileSystem.globStatus() doesn't
provide any, so IO use there is not included in the aggregate results.
Contributed by Steve Loughran.
