Introducing the S3A Committers
Amazon's S3 Object Store is not a filesystem: some expected behaviors of a filesystem are missing.
Some of the following aspects of S3 that are different from a filesystem are as follows:
- Directory listings are only eventually consistent.
- File overwrites and deletes are only eventually consistent: readers may get old data.
- There is no rename operation; it is mimicked in the S3A client by listing a directory and copying each file underneath, one-by-one.
Because directory rename is mimicked by listing and then copying files the eventual consistency of both listing and reading fails may result in incorrect data. And, because of the copying: it is slow.
S3Guard addresses the listing inconsistency problem. However, it does not address the update consistency or performance.
The normal means by which Hadoop MapReduce and Apache Spark commit work from multiple tasks is through renaming the output. Each task attempt writes to a private task attempt directory; when the task is given permission to commit by the MapReduce Application Master or Spark Driver, this task attempt directory is renamed into the job attempt directory. When the job is ready to commit, the output of all the tasks is merged into the final output directory, again by renaming files and directories.
This is fast and safe on HDFS and similar filesystems, and on object stores with rename operations, such as Azure WASB. On S3, it is unreliable without S3Guard, and even with S3Guard, the the time to commit work to S3 is proportional to the amount of data written. An operation which may work well during development can turn out to be unusable in production.
To address this the S3A committers were developed. They allow the output of MapReduce and Spark jobs to be written directly to S3, with a time to commit the job independent of the amount of data created.
What Are the S3A Committers?
The S3A committers are three different committers which can be used to commit work directly to Map-reduce and Spark. They differ in how they deal with conflict and how they upload data to the destination bucket —but underneath they all share much of the same code.
They rely on a specific S3 feature: multipart upload of large files.
When writing a large object to S3, S3A and other S3 clients use a mechanism called “Multipart Upload”.
The caller initiates a “multipart upload request”, listing the destination path and receiving an upload ID to use in the upload operations.
The caller then uploads the data in a series of HTTP POST requests, closing with a final POST listing the blocks uploaded.
The uploaded data is not visible until that final POST request is made, at which point it is published in a single atomic operation.
This mechanism is always used by S3A whenever it writes large files; the size of each
part is set to the value of
The S3A Committers use the same multipart upload process, but convert it into a mechanism for committing the work of tasks because of a special feature of the mechanism: The final POST request does not need to be issued by the same process or host which uploaded the data.
The output of each worker task can be uploaded to S3 as a multipart upload, but without issuing the final POST request to complete the upload. Instead, all the information needed to issue that request is saved to a cluster-wide filesystem (HDFS or potentially S3 itself)
When a job is committed, this information is loaded, and the upload completed. If a a task is aborted of fails: the upload is not completed, so the output does not become visible. If the entire job fails, none of its output becomes visible.
For further reading, see:
HADOOP-13786: Add S3A committers for zero-rename commits to S3 endpoints.
A Zero-Rename Committer: Object-storage as a Destination for Apache Hadoop and Spark.
The Three Committers
- Directory Committer: Buffers working data to the local disk, uses HDFS to propagate commit information from tasks to job committer, and manages conflict across the entire destination directory tree.
- Partitioned Committer: Identical to the Directory committer except that conflict is managed on a partition-by-partition basis. This allows it to be used for in-place updates of existing datasets. It is only suitable for use with Spark.
- Magic Committer: Data is written directly to S3, but “magically” re-targeted at the final destination. Conflict is managed across the directory tree. It requires a consistent S3 object store, which means S3Guard is a mandatory pre-requisite.
We currently recommend use of the “Directory” committer: it is the simplest of the set, and by using HDFS to propagate data from workers to the job committer, does not directly require S3Guard – this makes it easier to set up.
The rest of the documentation only covers the Directory Committer: to explore the other options, consult the Apache documentation.