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Data Management 2018 • By Yves Mulkers

Free, Fast MongoDB Hot Backup with Percona Server for MongoDB

Free, Fast MongoDB Hot Backup with Percona Server for MongoDB
4 min read
API, Computer architecture, Configuration file
Curated from percona.com →

Percona Server for MongoDB is Percona’s open-source fork of MongoDB, aimed at having 100% feature compatibility (much like our MySQL fork). We have added a few extra features to our fork for free that are only available with MongoDB Enterprise binaries for an additional fee.

The feature pertinent to this article is our free, open-source Hot Backup feature for WiredTiger and RocksDB, only available in Percona Server for MongoDB.

Essentially, this Hot Backup feature adds a MongoDB server command that creates a full binary backup of your data set to a new directory with no locking or impact to the database, aside from some increased resource usage due to copying the data.

It’s important to note these backups are binary-level backups, not logical backups (such as mongodump would produce).

Before the concept of a MongoDB Hot Backup, the only way to backup a MongoDB instance, cluster or replica set was using the logical backup tool ‘mongodump’, or using block-device (binary) snapshots.

A “binary-level” backup means backup data contains the data files(WiredTiger or RocksDB) that MongoDB stores on disk. This is different from the BSON representation of the data that ‘mongodump’ (a “logical” backup tool) outputs.

Binary-level backups are generally faster than logical because a logical backup (mongodump) requires the server to read all data and return it to the MongoDB Client API. Once received, ‘mongodump’ serializes the payload into .bson files on disk. Important areas like indices are not backed up in full, merely the metadata describing the index is backed up. On restore, the entire process is reversed: ‘mongorestore’must deserialize the data created by‘mongodump’, send it over the MongoDB Client API to the server, then the server’s storage engine must translate this into data files on disk. Due to only metadata of indices being backed up in this approach, all indices must be recreated at restore time. This is a serial operation for each collection! I have personally restored several databases where the majority of the restore time was the index rebuilds and NOT the actual restore of the raw data!

In contrast, binary-level backups are much simpler: the storage-engine representation of the data is what is backed up. This style of backup includes the real index data, meaning a restore is as simple as copying the backup directory to be in the location of the MongoDB dbPath and restarting MongoDB. No recreation of indices is necessary! For very large datasets, this can be a massive win. Hours or even days of restore time can be saved due to this efficiency.

Of course, there are always some tradeoffs. Binary-level backups can take a bit more space on disk and care must be taken to ensure the files are restored to the right version of MongoDB on matching CPU architecture. Generally, backing up the MongoDB Configuration File and version number with your backups addresses this concern.

The Hot Backup feature is triggered by a simple admin command via the ‘mongo’ shell named ‘createBackup’. This command requires only one input: the path to output the backup to, named ‘backupDir’. This backup directory must not exist or an error is returned.

If you have MongoDB Authorization enabled (I hope you do!), this command requires the built-in role: ‘backup’ or a role that inherits the “backup” role.

An example in the  ‘mongo’ shell:

When this command returns an “ok”, a full backup is available to be read at the location specified in ‘backupDir’. This end-result is similar to using block-device snapshots such as LVM snapshots, however with less overhead vs. the 10-30% write degradation many users report at LVM snapshot time.

This backup directory can be deleted with a regular UNIX/Linux “rm -rf …” command once it is no longer required.

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Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.

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