Storage Engine

The storage engine is responsible for persisting data on disk, holding copies in memory, providing indexes and caches to speed up queries

ArangoDB’s storage engine is based on Facebook’s RocksDB and the only storage engine available in ArangoDB 3.7 and above. It is the bottom layer of the database system.


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.


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


RocksDB allows concurrent writes. However, when touching the same document at the same time, a write conflict is raised. It is possible to exclusively lock collections when executing AQL. This avoids write conflicts, but also inhibits concurrent writes.

ArangoDB uses RocksDB transactions to implement the transaction handling for standalone AQL queries (outside of JavaScript Transactions and Stream Transactions).

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. If you have an AQL query that modifies a lot of documents, it is necessary to commit data in-between. Transactions that get too big (in terms of number of operations involved or the total size of data modified by the transaction) are committed automatically. Effectively, this means that big user transactions are split into multiple smaller RocksDB transactions that are committed individually. The entire user transaction does not necessarily have ACID properties in this case.

The threshold values for transaction sizes can be configured globally as well as overridden per transaction. See Known limitations for AQL queries.

Write-ahead log

Write-ahead logging is used for data recovery after a server crash and for replication.

ArangoDB’s RocksDB storage engine stores all data-modification operation in a write-ahead log (WAL). The WAL is sequence of append-only files containing all the write operations that were executed on the server. It is used to run data recovery after a server crash, and can also be used in a replication setup when Followers need to replay the same sequence of operations as on the Leader.

The individual RocksDB WAL files are per default about 64 MiB big. The size is always proportionally sized to the value specified via --rocksdb.write-buffer-size. The value specifies the amount of data to build up in memory (backed by the unsorted WAL on disk) before converting it to a sorted on-disk file.

Larger values can increase performance, especially during bulk loads. Up to --rocksdb.max-write-buffer-number write buffers may be held in memory at the same time, so you may wish to adjust this parameter to control memory usage. A larger write buffer results in a longer recovery time the next time the database is opened.

The RocksDB WAL only contains committed transactions. This means you never see partial transactions in the replication log, but it also means transactions are tracked completely in-memory. In practice this causes RocksDB transaction sizes to be limited, for more information see the RocksDB Configuration


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 resides 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:


RocksDB can compress the content it stores to save disk space and improve the I/O performance at the cost of an increased CPU utilization. It is controlled by the --rocksdb.compression-type startup option of the ArangoDB server.

ArangoDB has compression enabled by default using the LZ4 algorithm. It is optimized for compressing and decompressing with a low CPU overhead instead of a high compression ratio but can still achieve a 1:6 ratio for lexical content. The Snappy compression algorithm has similar characteristics but is slower at decompression.

No compression is used for the lowest levels of the RocksDB LSM tree as this data gets rewritten into the higher levels quickly. By default, only level 2 and higher use compression. This is configurable via the --rocksdb.num-uncompressed-levels startup option.

ArangoDB options

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


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


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 structured 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


it is 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

It 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 holds 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 follows:

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

Also see Reducing the Memory Footprint of ArangoDB servers and ArangoDB Server Options for descriptions of RocksDB options.