WAL-G is an archival restoration tool for Postgres.

WAL-G is the successor of WAL-E with a number of key differences. WAL-G uses LZ4 compression, multiple processors and non-exclusive base backups for Postgres. More information on the design and implementation of WAL-G can be found on the Citus Data blog post "Introducing WAL-G by Citus: Faster Disaster Recovery for Postgres".

Table of Contents

• Installation
• Configuration
• Usage
• Development
  • Installing
  • Testing
• Authors
• License
• Acknowledgements

A precompiled binary for Linux AMD 64 of the latest version of WAL-G can be obtained under the Releases tab.

To decompress the binary, use:

tar -zxvf wal-g.linux-amd64.tar.gz

For other incompatible systems, please consult the Development section for more information.

Required

To connect to Amazon S3, WAL-G requires that these variables be set:

• WALE_S3_PREFIX (eg. s3://bucket/path/to/folder)

WAL-G determines AWS credentials like other AWS tools. You can set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (optionally with AWS_SECURITY_TOKEN), or ~/.aws/credentials (optionally with AWS_PROFILE), or you can set nothing to automatically fetch credentials from the EC2 metadata service.

WAL-G uses the usual PostgreSQL environment variables to configure its connection, especially including PGHOST, PGPORT, PGUSER, and PGPASSWORD/PGPASSFILE/~/.pgpass.

PGHOST can connect over a UNIX socket. This mode is preferred for localhost connections, set PGHOST=/var/run/postgresql to use it. WAL-G will connect over TCP if PGHOST is an IP address.

Optional

WAL-G can automatically determine the S3 bucket's region using s3:GetBucketLocation, but if you wish to avoid this API call or forbid it from the applicable IAM policy, specify:

• AWS_REGION(eg. us-west-2)

Concurrency values can be configured using:

• WALG_DOWNLOAD_CONCURRENCY

To configure how many goroutines to use during backup-fetch and wal-push, use WALG_DOWNLOAD_CONCURRENCY. By default, WAL-G uses the minimum of the number of files to extract and 10.

• WALG_UPLOAD_CONCURRENCY

To configure how many concurrency streams to use during backup uploading, use WALG_UPLOAD_CONCURRENCY. By default, WAL-G uses 10 streams.

• AWS_ENDPOINT

Overrides the default hostname to connect to an S3-compatible service. i.e, http://s3-like-service:9000

• AWS_S3_FORCE_PATH_STYLE

To enable path-style addressing(i.e., http://s3.amazonaws.com/BUCKET/KEY) when connecting to an S3-compatible service that lack of support for sub-domain style bucket URLs (i.e., http://BUCKET.s3.amazonaws.com/KEY). Defaults to false.

Example: Using Minio.io S3-compatible storage

AWS_ACCESS_KEY_ID: "<minio-key>"
AWS_SECRET_ACCESS_KEY: "<minio-secret>"
WALE_S3_PREFIX: "s3://my-minio-bucket/sub-dir"
AWS_ENDPOINT: "http://minio:9000"
AWS_S3_FORCE_PATH_STYLE: "true"
AWS_REGION: us-east-1

• WALG_S3_STORAGE_CLASS

To configure the S3 storage class used for backup files, use WALG_S3_STORAGE_CLASS. By default, WAL-G uses the "STANDARD" storage class. Other supported values include "STANDARD_IA" for Infrequent Access and "REDUCED_REDUNDANCY" for Reduced Redundancy.

• WALE_GPG_KEY_ID

To configure GPG key for encryption and decryption. By default, no encryption is used. Public keyring is cached in the file "/.walg_key_cache".

• WALG_DELTA_MAX_STEPS

Delta-backup is difference between previously taken backup and present state. WALG_DELTA_MAX_STEPS determines how many delta backups can be between full backups. Defaults to 0. Restoration process will automatically fetch all necessary deltas and base backup and compose valid restored backup (you still need WALs after start of last backup to restore consistent cluster). Delta computation is based on ModTime of file system and LSN number of pages in datafiles.

• WALG_DELTA_ORIGIN

To configure base for next delta backup (only if WALG_DELTA_MAX_STEPS is not exceeded). WALG_DELTA_ORIGIN can be LATEST (chaining increments), LATEST_FULL (for bases where volatile part is compact and chaining has no meaning - deltas overwrite each other). Defaults to LATEST.

WAL-G currently supports these commands:

• backup-fetch

When fetching base backups, the user should pass in the name of the backup and a path to a directory to extract to. If this directory does not exist, WAL-G will create it and any dependent subdirectories.

wal-g backup-fetch ~/extract/to/here example-backup

WAL-G can also fetch the latest backup using:

wal-g backup-fetch ~/extract/to/here LATEST

• backup-push

When uploading backups to S3, the user should pass in the path containing the backup started by Postgres as in:

wal-g backup-push /backup/directory/path

If backup is pushed from replication slave, WAL-G will control timeline of the server. In case of promotion to master or timeline switch, backup will be uploaded but not finalized, WAL-G will exit with an error. In this case logs will contain information necessary to finalize the backup. You can use backuped data if you clearly understand entangled risks.

• wal-fetch

When fetching WAL archives from S3, the user should pass in the archive name and the name of the file to download to. This file should not exist as WAL-G will create it for you.

WAL-G will also prefetch WAL files ahead of asked WAL file. These files will be cached in ./.wal-g/prefetch directory. Cache files older than recently asked WAL file will be deleted from the cache, to prevent cache bloat. If the file is requested with wal-fetch this will also remove it from cache, but trigger fulfilment of cache with new file.

wal-g wal-fetch example-archive new-file-name

• wal-push

When uploading WAL archives to S3, the user should pass in the absolute path to where the archive is located.

wal-g wal-push /path/to/archive

• backup-list

Lists names and creation time of available backups.

• delete

Is used to delete backups and WALs before them. By default delete will perform dry run. If you want to execute deletion you have to add --confirm flag at the end of the command.

delete can operate in two modes: retain and before.

retain [FULL|FIND_FULL] %number%

if FULL is specified keep 5 full backups and everything in the middle

before [FIND_FULL] %name%

if FIND_FULL is specified WAL-G will calculate minimum backup needed to keep all deltas alive. If FIND_FULL is not specified and call can produce orphaned deltas - call will fail with the list.

retain 5 will fail if 5th is delta

retain FULL 5 will keep 5 full backups and all deltas of them

retail FIND_FULL will find necessary full for 5th

before base_000010000123123123 will fail if base_000010000123123123 is delta

before FIND_FULL base_000010000123123123 will keep everything after base of base_000010000123123123

To compile and build the binary:

go get github.com/wal-g/wal-g
make all

Users can also install WAL-G by using make install. Specifying the GOBIN environment variable before installing allows the user to specify the installation location. On default, make install puts the compiled binary in go/bin.

export GOBIN=/usr/local/bin
make install

WAL-G relies heavily on unit tests. These tests do not require S3 configuration as the upload/download parts are tested using mocked objects. For more information on testing, please consult test_tools.

WAL-G will perform a round-trip compression/decompression test that generates a directory for data (eg. data...), compressed files (eg. compressed), and extracted files (eg. extracted). These directories will only get cleaned up if the files in the original data directory match the files in the extracted one.

Test coverage can be obtained using:

go test -v -coverprofile=coverage.out
go tool cover -html=coverage.out

• Katie Li
• Daniel Farina

See also the list of contributors who participated in this project.

This project is licensed under the Apache License, Version 2.0, but the lzo support is licensed under GPL 3.0+. Please refer to the LICENSE.md file for more details.

WAL-G would not have happened without the support of Citus Data

WAL-G came into existence as a result of the collaboration between a summer engineering intern at Citus, Katie Li, and Daniel Farina, the original author of WAL-E who currently serves as a principal engineer on the Citus Cloud team. Citus Data also has an open source extension to Postgres that distributes database queries horizontally to deliver scale and performance.