ArangoDB v3.7 reached End of Life (EOL) and is no longer supported.

This documentation is outdated. Please see the most recent version here: Latest Docs

Storage Engines

The MMFiles storage engine was removed. To change your MMFiles storage engine deployment to RocksDB, see: Switch storage engine

At the very bottom of the ArangoDB database system lies the storage engine. The storage engine is responsible for persisting the documents on disk, holding copies in memory, providing indexes and caches to speed up queries.

Up to version 3.1 ArangoDB only supported memory-mapped files (MMFiles) as sole storage engine. In version 3.2, ArangoDB gained support for pluggable storage engines and a second engine based on Facebook’s RocksDB was added. MMFiles remained the default engine for 3.3, but in 3.4 RocksDB became the new default. MMFiles was deprecated in version 3.6.0 and removed in 3.7.0.

For practical information on how to switch storage engine please refer to the Switching the storage engine page.

MMFiles RocksDB
removed default
dataset needs to fit into memory work with as much data as fits on disk
indexes in memory hot set in memory, data and indexes on disk
slow restart due to index rebuilding fast startup (no rebuilding of indexes)
volatile collections (only in memory, optional) collection data always persisted
collection level locking (writes block reads) concurrent reads and writes

Blog article: Comparing new RocksDB and MMFiles storage engines

MMFiles

The MMFiles (Memory-Mapped Files) engine was optimized for the use-case where the data fit into the main memory. It allowed for very fast concurrent reads. However, writes blocked reads and locking was on collection level.

Indexes were always in memory and rebuilt on startup. This gave better performance but imposed a longer startup time.

RocksDB

RocksDB is an embeddable persistent key-value store. It is a log structure database and is optimized for fast storage.

The RocksDB engine is optimized for large data-sets and allows for a steady insert performance even if the data-set is much larger than the main memory. Indexes are always stored on disk but caches are used to speed up performance. RocksDB uses document-level locks allowing for concurrent writes. Writes do not block reads. Reads do not block writes.

Advantages

RocksDB is a very flexible engine that can be configured for various use cases.

The main advantages of RocksDB are:

  • document-level locks
  • support for large data-sets
  • persistent indexes

Caveats

RocksDB allows concurrent writes. However, when touching the same document a write conflict is raised. This cannot happen with the MMFiles engine, therefore applications that switch to RocksDB need to be prepared that such exception can arise. It is possible to exclusively lock collections when executing AQL. This will avoid write conflicts but also inhibits concurrent writes.

Currently, another restriction is due to the transaction handling in RocksDB. Transactions are limited in total size. If you have a statement modifying a lot of documents it is necessary to commit data in-between. This will be done automatically for AQL by default. Transactions that get too big (in terms of number of operations involved or the total size of data modified by the transaction) will be committed automatically. Effectively this means that big user transactions are split into multiple smaller RocksDB transactions that are committed individually. The entire user transaction will not necessarily have ACID properties in this case.

The threshold values for transaction sizes can be configured globally using the startup options

It is also possible to override these thresholds per transaction.

Performance

RocksDB is based on a log-structured merge tree. A good introduction can be found in:

The basic idea is that data is organized in levels were each level is a factor larger than the previous. New data will reside in smaller levels while old data is moved down to the larger levels. This allows to support high rate of inserts over an extended period. In principle it is possible that the different levels reside on different storage media. The smaller ones on fast SSD, the larger ones on bigger spinning disks.

RocksDB itself provides a lot of different knobs to fine tune the storage engine according to your use-case. ArangoDB supports the most common ones using the options below.

Performance reports for the storage engine can be found here:

ArangoDB options

ArangoDB has a cache for the persistent indexes in RocksDB. The total size of this cache is controlled by the option

--cache.size

RocksDB also has a cache for the blocks stored on disk. The size of this cache is controlled by the option

--rocksdb.block-cache-size

ArangoDB distributes the available memory equally between the two caches by default.

ArangoDB chooses a size for the various levels in RocksDB that is suitable for general purpose applications.

RocksDB log strutured data levels have increasing size

MEM: --
L0:  --
L1:  -- --
L2:  -- -- -- --
...

New or updated Documents are first stored in memory. If this memtable reaches the limit given by

--rocksdb.write-buffer-size

it will converted to an SST file and inserted at level 0.

The following option controls the size of each level and the depth.

--rocksdb.num-levels N

Limits the number of levels to N. By default it is 7 and there is seldom a reason to change this. A new level is only opened if there is too much data in the previous one.

--rocksdb.max-bytes-for-level-base B

L0 will hold at most B bytes.

--rocksdb.max-bytes-for-level-multiplier M

Each level is at most M times as much bytes as the previous one. Therefore the maximum number of bytes-for-level L can be calculated as

max-bytes-for-level-base * (max-bytes-for-level-multiplier ^ (L-1))

Future

RocksDB imposes a limit on the transaction size. It is optimized to handle small transactions very efficiently, but is effectively limiting the total size of transactions.

ArangoDB currently uses RocksDB’s transactions to implement the ArangoDB transaction handling. Therefore the same restrictions apply for ArangoDB transactions when using the RocksDB engine.

We will improve this by introducing distributed transactions in a future version of ArangoDB. This will allow handling large transactions as a series of small RocksDB transactions and hence removing the size restriction.