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A S3 based WAL-shipping disaster recovery and standby toolkit

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README.rst

WAL-E Disaster Recovery

WAL-E is a program designed perform continuous archiving of PostgreSQL WAL files and manage the use of pg_start_backup and pg_stop_backup. It has four critical operators:

  • backup-fetch
  • backup-push
  • wal-fetch
  • wal-push

Of these, the "push" operators send things to S3, and "fetch" operators get things from S3. "wal" operators send/get write ahead log, and "backup" send/get a hot backup of the base database that WAL segments can be applied to.

All of these operators work in a context of three important environment-variable based settings:

  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • WALE_S3_PREFIX

With the exception of AWS_SECRET_ACCESS_KEY, all of these can be specified as arguments as well. The AWS_* variables are the standard access-control keying system provided by Amazon.

The WALE_S3_PREFIX can be thought of a context whereby this program operates on a single database cluster at a time. Generally, for any one database the WALE_S3_PREFIX will be the same between all four operators. This context-driven approach attempts to help users avoid errors such as one database overwriting the WAL segments of another, as long as the WALE_S3_PREFIX is set uniquely for each database.

Important

Ensure that all servers have different WALE_S3_PREFIXes set. Reuse of a value between two servers will likely cause unrecoverable backups.

Dependencies

  • s3cmd
  • lzop
  • psql
  • mbuffer
  • python-argparse or Python 2.7

Examples

Pushing a base backup to S3:

$ AWS_SECRET_ACCESS_KEY=... python wal_e.py           \
  -k AWS_ACCESS_KEY_ID                                \
  --s3-prefix=s3://some-bucket/directory/or/whatever  \
  backup-push /var/lib/my/database

Sending a WAL segment to S3:

$ AWS_SECRET_ACCESS_KEY=... python wal_e.py           \
  -k AWS_ACCESS_KEY_ID                                \
  --s3-prefix=s3://some-bucket/directory/or/whatever  \
  wal-push /var/lib/my/database/pg_xlog/WAL_SEGMENT_LONG_HEX

It is generally recommended that one use some sort of environment variable management with WAL-E: working with it this way less verbose, less prone to error, and less likely to expose secret information in logs.

At this time, AWS_SECRET_KEY is the only secret value, and recording it frequently in logs is not recommended. The tool has never and should never accept secret information in argv to avoid process table security problems. However, the user running PostgreSQL (typically 'postgres') must be able to run a program that can access this secret information, as part of its archive_command.

envdir, part of the daemontools package is one recommended approach to setting environment variables. One can prepare an envdir-compatible directory like so:

# Assumption: the group is trusted to read secret information
$ umask u=rwx,g=rx,o=
$ mkdir -p /etc/wal-e.d/env
$ echo "secret-key-content" > /etc/wal-e.d/env/AWS_SECRET_ACCESS_KEY
$ echo "access-key" > /etc/wal-e.d/env/AWS_ACCESS_KEY_ID
$ echo 's3://some-bucket/directory/or/whatever' > \
  /etc/wal-e.d/env/WALE_S3_PREFIX
$ chown -R root:postgres /etc/wal-e.d

After having done this preparation, it is possible to run WAL-E commands much more simply, with less risk of accidentally using incorrect values:

$ envdir /etc/wal-e.d/env python wal_e.py backup-push ...
$ envdir /etc/wal-e.d/env python wal_e.py wal-push ...

envdir is conveniently combined with the archive_command functionality used by PostgreSQL to enable continuous archiving. To enable continuous archiving, one needs to edit postgresql.conf and restart the server. The important settings to enable continuous archiving are related here:

wal_level = archive # hot_standby in 9.0 is also acceptable
archive_mode = on
archive_command = 'envdir /etc/wal-e.d/env python /path/wal_e.py wal-push %p'
archive_timeout = 60

Every segment archived will be noted in the PostgreSQL log.

Warning

PostgreSQL users can check the pg_settings table and see the archive_command employed. Do not put secret information into postgresql.conf for that reason, and use envdir instead.

A base backup (via backup-push) can be uploaded at any time, but this must be done at least once in order to perform a restoration. It must be done again if any WAL segment was not correctly uploaded: point in time recovery will not be able to continue if there are any gaps in the WAL segments.

Pulling a base backup from S3:

$ sudo -u postgres bash -c                          \
"envdir /etc/wal-e.d/pull-env wal-e                 \
--s3-prefix=s3://some-bucket/directory/or/whatever  \
backup-fetch /var/lib/my/database LATEST"

This command makes use of the "LATEST" pseudo-name for a backup, which defaults to querying S3 to find the latest complete backup. Otherwise, a real name can be used:

$ sudo -u postgres bash -c                          \
"envdir /etc/wal-e.d/pull-env wal-e                 \
--s3-prefix=s3://some-bucket/directory/or/whatever  \
backup-fetch                                        \
/var/lib/my/database base_LONGWALNUMBER_POSITION_NUMBER"

One can find the name of available backups via the experimental backup-list operator, or using one's S3 browsing program of choice, by looking at the S3PREFIX/basebackups_NNN/... directory.

it is also likely one will need to provide a recovery.conf file, as documented in the PostgreSQL manual, to recover the base backup, as WAL files will need to be downloaded to make the hot-backup taken with backup-push. The WAL-E's wal-fetch subcommand is designed to be useful for this very purpose, as it may be used in a recovery.conf file like this:

restore_command = 'envdir /etc/wal-e.d/env wal-e wal-fetch "%f" "%p"'

Compression and Temporary Files

All assets pushed to S3 are run through the program "lzop" which compresses the object using the very fast lzo compression algorithm. It takes roughly 2 CPU seconds to compress a gigabyte, which when sending things to S3 at about 25MB/s occupies about 5% CPU time. Compression ratios are expected to make file sizes 10%-30% of the original file size, making backups and restorations considerably faster.

Because S3 requires the Content-Length header of a stored object to be set up-front, it is necessary to completely finish compressing an entire input file and storing the compressed output in a temporary file. Thus, the temporary file directory needs to be big enough and fast enough to support this, although this tool is designed to avoid calling fsync(), so some memory can be leveraged.

Base backups first have their files consolidated into disjoint tar files of limited length to avoid the relatively large per-file S3 overhead. This has the effect of making base backups and restores much faster when many small relations and ancillary files are involved.

To reduce the read load on base backups, they are sent through the tool "mbuffer" first.

TODO

  • Investigate pg_lesslog. This tool strips the WAL file of full-page binary images, making it much smaller, but this also makes the recovery process more expensive (has to do more seeking to do recovery). The question is: is the increased speed of fetching a WAL segment dominated by recovery time, or vice-versa?
  • Ask pgsql-hackers about a pg_cancel_backup() function
  • Sane error messages, such as on Ctrl-C or during errors.
  • Pipeline-WAL-Segment Management: S3 ACK is long enough that a totally non-pipelined, non-parallel archive_command can fall behind.
  • Eliminate some copy-pasta from interrupt-processing with multiprocessing pools
  • Eliminate copy-pasta in formatting URLs for getting/putting things
  • do_lzop_s3_get do_lzop_s3_push, do_partition_put, do_partition_get should probably share more code, since they take common arguments.
  • Write a new class to handle addressing paths of a WAL-E context: its base backups and WAL segments.
  • Verify Tar paths instead of using tarfile.extractall()
  • Handle shrinking files gracefully (growing files are already handled gracefully). This is because the tarfile module's copyfileobj procedure raises an exception if the file has been truncated. Unfortunately the best solution I can see is to cook up a custom tarfile.addfile() equivalent.
  • Handle unlinked-file race conditions gracefully
  • Consider replacing s3cmd with boto, as metadata checking requirements become more elaborate.
  • For small databases, the --cluster-rate-limit feature will over-restrict the amount of disk bandwidth used: the number provided by the user is divided by the number of processes that can theoretically send data, but for small databases only one process will ever be scheduled, so the result is the actual limit may be only (limit / pool-size) -- much smaller than indicated. Fix this by increasing the rate limit when there are few processes that are scheduled to run.
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