Problem:

The only committer of work from Spark to Azure ADLS Gen 2 “abfs://” storage which is safe to use is the “v1 file committer”.

This is “correct” in that if a task attempt fails, its output is guaranteed not to be included in the final out. The “v2” commit algorithm cannot meet that guarantee, which is why it is no longer the default.

But: it is slow, especially on jobs where deep directory trees of output are used. Why is it slow? It’s hard to point at a particular cause, primarily because of the lack of any instrumentation in the FileOutputCommitter. Stack traces of running jobs generally show rename(), though list operations do surface too.

On Google GCS, neither the v1 nor v2 algorithm are safe because the google filesystem doesn’t have the atomic directory rename which the v1 algorithm requires.

A further issue is that both Azure and GCS storage may encounter scale issues with deleting directories with many descendants. This can trigger timeouts because the FileOutputCommitter assumes that cleaning up after the job is a fast call to delete("_temporary", true).

Solution.

The Intermediate Manifest committer is a new committer for work which should deliver performance on ABFS for “real world” queries, and performance and correctness on GCS.

This committer uses the extension point which came in for the S3A committers. Users can declare a new committer factory for abfs:// and gcs:// URLs. A suitably configured spark deployment will pick up the new committer.

Directory performance issues in job cleanup can be addressed by some options 1. The committer will parallelize deletion of task attempt directories before deleting the _temporary directory. 2. An initial attempt to delete the _temporary directory before the parallel attempt is made. 3. Exceptions can be supressed, so that cleanup failures do not fail the job 4. Cleanup can be disabled.

The committer can be used with any filesystem client which has a “real” file rename() operation. It has been optimised for remote object stores where listing and file probes are expensive -the design is less likely to offer such signifcant speedup on HDFS -though the parallel renaming operations will speed up jobs there compared to the classic v1 algorithm.

How it works

The full details are covered in Manifest Committer Architecture.

Using the committer

The hooks put in to support the S3A committers were designed to allow every filesystem schema to provide their own committer. See Switching To an S3A Committer

A factory for the abfs schema would be defined in mapreduce.outputcommitter.factory.scheme.abfs ; and a similar one for gcs.

Some matching spark configuration changes, especially for parquet binding, will be required. These can be done in core-site.xml, if it is not defined in the mapred-default.xml JAR.

<property>
  <name>mapreduce.outputcommitter.factory.scheme.abfs</name>
  <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value>
</property>
<property>
  <name>mapreduce.outputcommitter.factory.scheme.gs</name>
  <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory</value>
</property>

Binding to the manifest committer in Spark.

In Apache Spark, the configuration can be done either with command line options (after the --conf) or by using the spark-defaults.conf file. The following is an example of using spark-defaults.conf also including the configuration for Parquet with a subclass of the parquet committer which uses the factory mechanism internally.

spark.hadoop.mapreduce.outputcommitter.factory.scheme.abfs org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory
spark.hadoop.mapreduce.outputcommitter.factory.scheme.gs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory
spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter
spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol

hadoop jar cloudstore-1.0.jar committerinfo abfs://testing@ukwest.dfs.core.windows.net/

2021-09-16 19:42:59,731 [main] INFO  commands.CommitterInfo (StoreDurationInfo.java:<init>(53)) - Starting: Create committer
Committer factory for path abfs://testing@ukwest.dfs.core.windows.net/ is
 org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory@3315d2d7
  (classname org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory)
2021-09-16 19:43:00,897 [main] INFO  manifest.ManifestCommitter (ManifestCommitter.java:<init>(144)) - Created ManifestCommitter with
   JobID job__0000, Task Attempt attempt__0000_r_000000_1 and destination abfs://testing@ukwest.dfs.core.windows.net/
Created committer of class org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitter:
 ManifestCommitter{ManifestCommitterConfig{destinationDir=abfs://testing@ukwest.dfs.core.windows.net/,
   role='task committer',
   taskAttemptDir=abfs://testing@ukwest.dfs.core.windows.net/_temporary/manifest_job__0000/0/_temporary/attempt__0000_r_000000_1,
   createJobMarker=true,
   jobUniqueId='job__0000',
   jobUniqueIdSource='JobID',
   jobAttemptNumber=0,
   jobAttemptId='job__0000_0',
   taskId='task__0000_r_000000',
   taskAttemptId='attempt__0000_r_000000_1'},
   iostatistics=counters=();

gauges=();

minimums=();

maximums=();

means=();
}

Verifying that the committer was used

The new committer will write a JSON summary of the operation, including statistics, in the _SUCCESS file.

If this file exists and is zero bytes long: the classic FileOutputCommitter was used.

If this file exists and is greater than zero bytes long, either the manifest committer was used, or in the case of S3A filesystems, one of the S3A committers. They all use the same JSON format.

Configuration options

Here are the main configuration options of the committer.

There are some more, as covered in the (Advanced)[#advanced] section.

Scaling jobs mapreduce.manifest.committer.io.threads

The core reason that this committer is faster than the classic FileOutputCommitter is that it tries to parallelize as much file IO as it can during job commit, specifically:

• task manifest loading
• deletion of files where directories will be created
• directory creation
• file-by-file renaming
• deletion of task attempt directories in job cleanup

These operations are all performed in the same thread pool, whose size is set in the option mapreduce.manifest.committer.io.threads.

Larger values may be used.

Hadoop XML configuration

<property>
  <name>mapreduce.manifest.committer.io.threads</name>
  <value>32</value>
</property>

In spark-defaults.conf

spark.hadoop.mapreduce.manifest.committer.io.threads 32

A larger value than that of the number of cores allocated to the MapReduce AM or Spark Driver does not directly overload the CPUs, as the threads are normally waiting for (slow) IO against the object store/filesystem to complete.

Manifest loading in job commit may be memory intensive; the larger the number of threads, the more manifests which will be loaded simultaneously.

Caveats * In Spark, multiple jobs may be committed in the same process, each of which will create their own thread pool during job commit or cleanup. * Azure rate throttling may be triggered if too many IO requests are made against the store. The rate throttling option mapreduce.manifest.committer.io.rate can help avoid this.

Optional: deleting target files in Job Commit

The classic FileOutputCommitter deletes files at the destination paths before renaming the job’s files into place.

This is optional in the manifest committers, set in the option mapreduce.manifest.committer.delete.target.files with a default value of false.

This increases performance and is safe to use when all files created by a job have unique filenames.

Apache Spark does generate unique filenames for ORC and Parquet since SPARK-8406 Adding UUID to output file name to avoid accidental overwriting

Avoiding checks for/deleting target files saves one delete call per file being committed, so can save a significant amount of store IO.

When appending to existing tables, using formats other than ORC and parquet, unless confident that unique identifiers are added to each filename, enable deletion of the target files.

spark.hadoop.mapreduce.manifest.committer.delete.target.files true

Note 1: the committer will skip deletion operations when it created the directory into which a file is to be renamed. This makes it slightly more efficient, at least if jobs appending data are creating and writing into new partitions.

Note 2: the committer still requires tasks within a single job to create unique files. This is foundational for any job to generate correct data.

Spark Dynamic Partition overwriting

Spark has a feature called “Dynamic Partition Overwrites”,

This can be initiated in SQL

INSERT OVERWRITE TABLE ...

Or through DataSet writes where the mode is overwrite and the partitioning matches that of the existing table

sparkConf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
// followed by an overwrite of a Dataset into an existing partitioned table.
eventData2
  .write
  .mode("overwrite")
  .partitionBy("year", "month")
  .format("parquet")
  .save(existingDir)

This feature is implemented in Spark, which 1. Directs the job to write its new data to a temporary directory 1. After job commit completes, scans the output to identify the leaf directories “partitions” into which data was written. 1. Deletes the content of those directories in the destination table 1. Renames the new files into the partitions.

This is all done in spark, which takes over the tasks of scanning the intermediate output tree, deleting partitions and of renaming the new files.

This feature also adds the ability for a job to write data entirely outside the destination table, which is done by 1. writing new files into the working directory 1. spark moving them to the final destination in job commit

The manifest committer is compatible with dynamic partition overwrites on Azure and Google cloud storage as together they meet the core requirements of the extension: 1. The working directory returned in getWorkPath() is in the same filesystem as the final output. 2. rename() is an O(1) operation which is safe and fast to use when committing a job.

None of the S3A committers support this. Condition (1) is not met by the staging committers, while (2) is not met by S3 itself.

To use the manifest committer with dynamic partition overwrites, the spark version must contain SPARK-40034 PathOutputCommitters to work with dynamic partition overwrite.

Be aware that the rename phase of the operation will be slow if many files are renamed -this is done sequentially. Parallel renaming would speed this up, but could trigger the abfs overload problems the manifest committer is designed to both minimize the risk of and support recovery from

The spark side of the commit operation will be listing/treewalking the temporary output directory (some overhead), followed by the file promotion, done with a classic filesystem rename() call. There will be no explicit rate limiting here.

What does this mean?

It means that _dynamic partitioning should not be used on Azure Storage for SQL queries/Spark DataSet operations where many thousands of files are created. The fact that these will suffer from performance problems before throttling scale issues surface, should be considered a warning.

Job Summaries in _SUCCESS files

The original hadoop committer creates a zero byte _SUCCESS file in the root of the output directory unless disabled.

This committer writes a JSON summary which includes * The name of the committer. * Diagnostics information. * A list of some of the files created (for testing; a full list is excluded as it can get big). * IO Statistics.

If, after running a query, this _SUCCESS file is zero bytes long, the new committer has not been used

If it is not empty, then it can be examined.

Viewing _SUCCESS file files through the ManifestPrinter tool.

The summary files are JSON, and can be viewed in any text editor.

For a more succinct summary, including better display of statistics, use the ManifestPrinter tool.

hadoop org.apache.hadoop.mapreduce.lib.output.committer.manifest.files.ManifestPrinter <path>

This works for the files saved at the base of an output directory, and any reports saved to a report directory.

Example from a run of the ITestAbfsTerasort MapReduce terasort.

bin/mapred successfile abfs://testing@ukwest.dfs.core.windows.net/terasort/_SUCCESS

Manifest file: abfs://testing@ukwest.dfs.core.windows.net/terasort/_SUCCESS
succeeded: true
created: 2024-04-18T18:34:34.003+01:00[Europe/London]
committer: org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitter
hostname: pi5
jobId: job_1713461587013_0003
jobIdSource: JobID
Diagnostics
  mapreduce.manifest.committer.io.threads = 192
  principal = alice
  stage = committer_commit_job

Statistics:
counters=((commit_file_rename=1)
(committer_bytes_committed=21)
(committer_commit_job=1)
(committer_files_committed=1)
(committer_task_directory_depth=2)
(committer_task_file_count=2)
(committer_task_file_size=21)
(committer_task_manifest_file_size=37157)
(job_stage_cleanup=1)
(job_stage_create_target_dirs=1)
(job_stage_load_manifests=1)
(job_stage_optional_validate_output=1)
(job_stage_rename_files=1)
(job_stage_save_success_marker=1)
(job_stage_setup=1)
(op_create_directories=1)
(op_delete=3)
(op_delete_dir=1)
(op_get_file_status=9)
(op_get_file_status.failures=6)
(op_list_status=3)
(op_load_all_manifests=1)
(op_load_manifest=2)
(op_mkdirs=4)
(op_msync=1)
(op_rename=2)
(op_rename.failures=1)
(task_stage_commit=2)
(task_stage_save_task_manifest=1)
(task_stage_scan_directory=2)
(task_stage_setup=2));

gauges=();

minimums=((commit_file_rename.min=141)
(committer_commit_job.min=2306)
(committer_task_directory_count=0)
(committer_task_directory_depth=1)
(committer_task_file_count=0)
(committer_task_file_size=0)
(committer_task_manifest_file_size=18402)
(job_stage_cleanup.min=196)
(job_stage_create_target_dirs.min=2)
(job_stage_load_manifests.min=687)
(job_stage_optional_validate_output.min=66)
(job_stage_rename_files.min=161)
(job_stage_save_success_marker.min=653)
(job_stage_setup.min=571)
(op_create_directories.min=1)
(op_delete.min=57)
(op_delete_dir.min=129)
(op_get_file_status.failures.min=57)
(op_get_file_status.min=55)
(op_list_status.min=202)
(op_load_all_manifests.min=445)
(op_load_manifest.min=171)
(op_mkdirs.min=67)
(op_msync.min=0)
(op_rename.failures.min=266)
(op_rename.min=139)
(task_stage_commit.min=206)
(task_stage_save_task_manifest.min=651)
(task_stage_scan_directory.min=206)
(task_stage_setup.min=127));

maximums=((commit_file_rename.max=141)
(committer_commit_job.max=2306)
(committer_task_directory_count=0)
(committer_task_directory_depth=1)
(committer_task_file_count=1)
(committer_task_file_size=21)
(committer_task_manifest_file_size=18755)
(job_stage_cleanup.max=196)
(job_stage_create_target_dirs.max=2)
(job_stage_load_manifests.max=687)
(job_stage_optional_validate_output.max=66)
(job_stage_rename_files.max=161)
(job_stage_save_success_marker.max=653)
(job_stage_setup.max=571)
(op_create_directories.max=1)
(op_delete.max=113)
(op_delete_dir.max=129)
(op_get_file_status.failures.max=231)
(op_get_file_status.max=61)
(op_list_status.max=300)
(op_load_all_manifests.max=445)
(op_load_manifest.max=436)
(op_mkdirs.max=123)
(op_msync.max=0)
(op_rename.failures.max=266)
(op_rename.max=139)
(task_stage_commit.max=302)
(task_stage_save_task_manifest.max=651)
(task_stage_scan_directory.max=302)
(task_stage_setup.max=157));

means=((commit_file_rename.mean=(samples=1, sum=141, mean=141.0000))
(committer_commit_job.mean=(samples=1, sum=2306, mean=2306.0000))
(committer_task_directory_count=(samples=4, sum=0, mean=0.0000))
(committer_task_directory_depth=(samples=2, sum=2, mean=1.0000))
(committer_task_file_count=(samples=4, sum=2, mean=0.5000))
(committer_task_file_size=(samples=2, sum=21, mean=10.5000))
(committer_task_manifest_file_size=(samples=2, sum=37157, mean=18578.5000))
(job_stage_cleanup.mean=(samples=1, sum=196, mean=196.0000))
(job_stage_create_target_dirs.mean=(samples=1, sum=2, mean=2.0000))
(job_stage_load_manifests.mean=(samples=1, sum=687, mean=687.0000))
(job_stage_optional_validate_output.mean=(samples=1, sum=66, mean=66.0000))
(job_stage_rename_files.mean=(samples=1, sum=161, mean=161.0000))
(job_stage_save_success_marker.mean=(samples=1, sum=653, mean=653.0000))
(job_stage_setup.mean=(samples=1, sum=571, mean=571.0000))
(op_create_directories.mean=(samples=1, sum=1, mean=1.0000))
(op_delete.mean=(samples=3, sum=240, mean=80.0000))
(op_delete_dir.mean=(samples=1, sum=129, mean=129.0000))
(op_get_file_status.failures.mean=(samples=6, sum=614, mean=102.3333))
(op_get_file_status.mean=(samples=3, sum=175, mean=58.3333))
(op_list_status.mean=(samples=3, sum=671, mean=223.6667))
(op_load_all_manifests.mean=(samples=1, sum=445, mean=445.0000))
(op_load_manifest.mean=(samples=2, sum=607, mean=303.5000))
(op_mkdirs.mean=(samples=4, sum=361, mean=90.2500))
(op_msync.mean=(samples=1, sum=0, mean=0.0000))
(op_rename.failures.mean=(samples=1, sum=266, mean=266.0000))
(op_rename.mean=(samples=1, sum=139, mean=139.0000))
(task_stage_commit.mean=(samples=2, sum=508, mean=254.0000))
(task_stage_save_task_manifest.mean=(samples=1, sum=651, mean=651.0000))
(task_stage_scan_directory.mean=(samples=2, sum=508, mean=254.0000))
(task_stage_setup.mean=(samples=2, sum=284, mean=142.0000)));

Collecting Job Summaries mapreduce.manifest.committer.summary.report.directory

The committer can be configured to save the _SUCCESS summary files to a report directory, irrespective of whether the job succeed or failed, by setting a filesystem path in the option mapreduce.manifest.committer.summary.report.directory.

The path does not have to be on the same store/filesystem as the destination of work. For example, a local filesystem could be used.

XML

<property>
  <name>mapreduce.manifest.committer.summary.report.directory</name>
  <value>file:///tmp/reports</value>
</property>

spark-defaults.conf

spark.hadoop.mapreduce.manifest.committer.summary.report.directory file:///tmp/reports

This allows for the statistics of jobs to be collected irrespective of their outcome, Whether or not saving the _SUCCESS marker is enabled, and without problems caused by a chain of queries overwriting the markers.

The mapred successfile operation can be used to print these reports.

Cleanup

Job cleanup is convoluted as it is designed to address a number of issues which may surface in cloud storage.

• Slow performance for deletion of directories (GCS).
• Timeout when deleting very deep and wide directory trees (Azure).
• General resilience to cleanup issues escalating to job failures.

The algorithm is:

if "mapreduce.fileoutputcommitter.cleanup.skipped":
  return
if "mapreduce.manifest.committer.cleanup.parallel.delete":
  if "mapreduce.manifest.committer.cleanup.parallel.delete.base.first" :
    if delete("_temporary"):
      return
  delete(list("$task-directories")) catch any exception
if not "mapreduce.fileoutputcommitter.cleanup.skipped":
  delete("_temporary"); catch any exception
if caught-exception and not "mapreduce.fileoutputcommitter.cleanup-failures.ignored":
  raise caught-exception

It’s a bit complicated, but the goal is to perform a fast/scalable delete and throw a meaningful exception if that didn’t work.

For ABFS set mapreduce.manifest.committer.cleanup.parallel.delete.base.first to true which should normally result in less network IO and a faster cleanup.

spark.hadoop.mapreduce.manifest.committer.cleanup.parallel.delete.base.first true

For GCS, setting mapreduce.manifest.committer.cleanup.parallel.delete.base.first to false may speed up cleanup.

If somehow errors surface during cleanup, ignoring failures will ensure the job is still considered a success. mapreduce.fileoutputcommitter.cleanup-failures.ignored = true

Disabling cleanup even avoids the overhead of cleanup, but requires a workflow or manual operation to clean up all _temporary directories on a regular basis: mapreduce.fileoutputcommitter.cleanup.skipped = true.

Working with Azure ADLS Gen2 Storage

To switch to the manifest committer, the factory for committers for destinations with abfs:// URLs must be switched to the manifest committer factory, either for the application or the entire cluster.

<property>
  <name>mapreduce.outputcommitter.factory.scheme.abfs</name>
  <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value>
</property>

This allows for ADLS Gen2 -specific performance and consistency logic to be used from within the committer. In particular: * the Etag header can be collected in listings and used in the job commit phase. * IO rename operations are rate limited * recovery is attempted when throttling triggers rename failures.

Warning This committer is not compatible with older Azure storage services (WASB or ADLS Gen 1).

The core set of Azure-optimized options becomes

<property>
  <name>mapreduce.outputcommitter.factory.scheme.abfs</name>
  <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value>
</property>

<property>
  <name>fs.azure.io.rate.limit</name>
  <value>1000</value>
</property>

<property>
  <name>mapreduce.manifest.committer.cleanup.parallel.delete.base.first</name>
  <value>true</value>
</property>

And optional settings for debugging/performance analysis

<property>
  <name>mapreduce.manifest.committer.summary.report.directory</name>
  <value>Path within same store/separate store</value>
  <description>Optional: path to where job summaries are saved</description>
</property>

Full set of ABFS options for spark

spark.hadoop.mapreduce.outputcommitter.factory.scheme.abfs org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory
spark.hadoop.fs.azure.io.rate.limit 1000
spark.hadoop.mapreduce.manifest.committer.cleanup.parallel.delete.base.first true
spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter
spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol

spark.hadoop.mapreduce.manifest.committer.summary.report.directory  (optional: URI of a directory for job summaries)

ABFS Rename Rate Limiting fs.azure.io.rate.limit

To avoid triggering store throttling and backoff delays, as well as other throttling-related failure conditions file renames during job commit are throttled through a “rate limiter” which limits the number of rename operations per second a single instance of the ABFS FileSystem client may issue.

Set the option to 0 remove all rate limiting.

The default value of this is set to 1000.

<property>
  <name>fs.azure.io.rate.limit</name>
  <value>1000</value>
  <description>maximum number of renames attempted per second</description>
</property>

This capacity is set at the level of the filesystem client, and so not shared across all processes within a single application, let alone other applications sharing the same storage account.

It will be shared with all jobs being committed by the same Spark driver, as these do share that filesystem connector.

If rate limiting is imposed, the statistic store_io_rate_limited will report the time to acquire permits for committing files.

If server-side throttling took place, signs of this can be seen in * The store service’s logs and their throttling status codes (usually 503 or 500). * The job statistic commit_file_rename_recovered. This statistic indicates that ADLS throttling manifested as failures in renames, failures which were recovered from in the committer.

If these are seen -or other applications running at the same time experience throttling/throttling-triggered problems, consider reducing the value of fs.azure.io.rate.limit, and/or requesting a higher IO capacity from Microsoft.

Important if you do get extra capacity from Microsoft and you want to use it to speed up job commits, increase the value of fs.azure.io.rate.limit either across the cluster, or specifically for those jobs which you wish to allocate extra priority to.

This is still a work in progress; it may be expanded to support all IO operations performed by a single filesystem instance.

Working with Google Cloud Storage

The manifest committer is compatible with and tested against Google cloud storage through the gcs-connector library from google, which provides a Hadoop filesystem client for the schema gs.

Google cloud storage has the semantics needed for the commit protocol to work safely.

The Spark settings to switch to this committer are

spark.hadoop.mapreduce.outputcommitter.factory.scheme.gs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory
spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter
spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol
spark.hadoop.mapreduce.manifest.committer.cleanup.parallel.delete.base.first false
spark.hadoop.mapreduce.manifest.committer.summary.report.directory  (optional: URI of a directory for job summaries)

The store’s directory delete operations are O(files) so the value of mapreduce.manifest.committer.cleanup.parallel.delete SHOULD be left at the default of true, but mapreduce.manifest.committer.cleanup.parallel.delete.base.first changed to false

For mapreduce, declare the binding in core-site.xmlor mapred-site.xml

<property>
  <name>mapreduce.outputcommitter.factory.scheme.gcs</name>
  <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory</value>
</property>

Working with HDFS

This committer does work with HDFS, it has just been targeted at object stores with reduced performance on some operations, especially listing and renaming, and semantics too reduced for the classic FileOutputCommitter to rely on (specifically GCS).

To use on HDFS, set the ManifestCommitterFactory as the committer factory for hdfs:// URLs.

Because HDFS does fast directory deletion, there is no need to parallelize deletion of task attempt directories during cleanup, so set mapreduce.manifest.committer.cleanup.parallel.delete to false

The final spark bindings becomes

spark.hadoop.mapreduce.outputcommitter.factory.scheme.hdfs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory
spark.hadoop.mapreduce.manifest.committer.cleanup.parallel.delete false
spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter
spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol

spark.hadoop.mapreduce.manifest.committer.summary.report.directory  (optional: URI of a directory for job summaries)

Advanced Configuration options

There are some advanced options which are intended for development and testing, rather than production use.

<property>
  <name>mapreduce.manifest.committer.store.operations.classname</name>
  <value>org.apache.hadoop.fs.azurebfs.commit.AbfsManifestStoreOperations</value>
</property>

<property>
  <name>mapreduce.manifest.committer.store.operations.classname</name>
  <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.impl.ManifestStoreOperationsThroughFileSystem</value>
</property>

<property>
  <name>mapreduce.manifest.committer.writer.queue.capacity</name>
  <value>32</value>
</property>

mapreduce.manifest.committer.writer.queue.capacity + mapreduce.manifest.committer.io.threads

Support for concurrent jobs to the same directory

It may be possible to run multiple jobs targeting the same directory tree.

For this to work, a number of conditions must be met:

• When using spark, unique job IDs must be set. This meangs the Spark distribution MUST contain the patches for SPARK-33402 and SPARK-33230.
• Cleanup of the _temporary directory must be disabled by setting mapreduce.fileoutputcommitter.cleanup.skipped to true.
• All jobs/tasks must create files with unique filenames.
• All jobs must create output with the same directory partition structure.
• The job/queries MUST NOT be using Spark Dynamic Partitioning “INSERT OVERWRITE TABLE”; data may be lost. This holds for all committers, not just the manifest committer.
• Remember to delete the _temporary directory later!

This has NOT BEEN TESTED