ArangoDB Server WAL Options (MMFiles)
WAL is an acronym for write-ahead log.
The write-ahead log is a sequence of logfiles that are written in an append-only fashion. Full logfiles will eventually be garbage-collected, and the relevant data might be transferred into collection journals and datafiles. Unneeded and already garbage-collected logfiles will either be deleted or kept for the purpose of keeping a replication backlog.
Since ArangoDB 2.2, the MMFiles storage engine will write all data-modification operations into its write-ahead log.
With ArangoDB 3.2 another storage engine option becomes available: RocksDB. In case of using RocksDB the subsequent options don’t have a useful meaning.
The WAL logfiles directory:
Specifies the directory in which the write-ahead logfiles should be stored. If this option is not specified, it defaults to the subdirectory journals in the server’s global database directory. If the directory is not present, it will be created.
the size of each WAL logfile
Specifies the filesize (in bytes) for each write-ahead logfile. The logfile size should be chosen so that each logfile can store a considerable amount of documents. The bigger the logfile size is chosen, the longer it will take to fill up a single logfile, which also influences the delay until the data in a logfile will be garbage-collected and written to collection journals and datafiles. It also affects how long logfile recovery will take at server start.
Allow oversize entries
whether or not oversize entries are allowed
Whether or not it is allowed to store individual documents that are bigger than would fit into a single logfile. Setting the option to false will make such operations fail with an error. Setting the option to true will make such operations succeed, but with a high potential performance impact. The reason is that for each oversize operation, an individual oversize logfile needs to be created which may also block other operations. The option should be set to false if it is certain that documents will always have a size smaller than a single logfile.
Number of reserve logfiles
maximum number of reserve logfiles
The maximum number of reserve logfiles that ArangoDB will create in a background process. Reserve logfiles are useful in the situation when an operation needs to be written to a logfile but the reserve space in the logfile is too low for storing the operation. In this case, a new logfile needs to be created to store the operation. Creating new logfiles is normally slow, so ArangoDB will try to pre-create logfiles in a background process so there are always reserve logfiles when the active logfile gets full. The number of reserve logfiles that ArangoDB keeps in the background is configurable with this option.
Number of historic logfiles
maximum number of historic logfiles
The maximum number of historic logfiles that ArangoDB will keep after they have been garbage-collected. If no replication is used, there is no need to keep historic logfiles except for having a local changelog.
In a replication setup, the number of historic logfiles affects the amount of data a slave can fetch from the master’s logs. The more historic logfiles, the more historic data is available for a slave, which is useful if the connection between master and slave is unstable or slow. Not having enough historic logfiles available might lead to logfile data being deleted on the master already before a slave has fetched it.
interval for automatic, non-requested disk syncs
The interval (in milliseconds) that ArangoDB will use to automatically synchronize data in its write-ahead logs to disk. Automatic syncs will only be performed for not-yet synchronized data, and only for operations that have been executed without the waitForSync attribute.
WAL flush timeout`--wal.flush-timeout
The timeout (in milliseconds) that ArangoDB will at most wait when flushing a full WAL logfile to disk. When the timeout is reached and the flush is not completed, the operation that requested the flush will fail with a lock timeout error.
Throttle writes to WAL when at least such many operations are
waiting for garbage collection:
The maximum value for the number of write-ahead log garbage-collection queue elements. If set to 0, the queue size is unbounded, and no write-throttling will occur. If set to a non-zero value, write-throttling will automatically kick in when the garbage-collection queue contains at least as many elements as specified by this option. While write-throttling is active, data-modification operations will intentionally be delayed by a configurable amount of time. This is to ensure the write-ahead log garbage collector can catch up with the operations executed. Write-throttling will stay active until the garbage-collection queue size goes down below the specified value. Write-throttling is turned off by default.
This option determines the maximum wait time (in milliseconds) for
operations that are write-throttled. If write-throttling is active and a
new write operation is to be executed, it will wait for at most the
specified amount of time for the write-ahead log garbage-collection queue
size to fall below the throttling threshold. If the queue size decreases
before the maximum wait time is over, the operation will be executed
normally. If the queue size does not decrease before the wait time is
over, the operation will be aborted with an error.
This option only has an effect if
--wal.throttle-when-pending has a
non-zero value, which is not the default.
Number of slots
Maximum number of slots to be used in parallel:
Configures the amount of write slots the write-ahead log can give to write operations in parallel. Any write operation will lease a slot and return it to the write-ahead log when it is finished writing the data. A slot will remain blocked until the data in it was synchronized to disk. After that, a slot becomes reusable by following operations. The required number of slots is thus determined by the parallelism of write operations and the disk synchronization speed. Slow disks probably need higher values, and fast disks may only require a value lower than the default.
Ignore logfile errors
Ignore logfile errors when opening logfiles:
Ignores any recovery errors caused by corrupted logfiles on startup. When set to false, the recovery procedure on startup will fail with an error whenever it encounters a corrupted (that includes only half-written) logfile. This is a security precaution to prevent data loss in case of disk errors etc. When the recovery procedure aborts because of corruption, any corrupted files can be inspected and fixed (or removed) manually and the server can be restarted afterwards.
Setting the option to true will make the server continue with the recovery procedure even in case it detects corrupt logfile entries. In this case it will stop at the first corrupted logfile entry and ignore all others, which might cause data loss.
Ignore recovery errors
Ignore recovery errors:
Ignores any recovery errors not caused by corrupted logfiles but by logical errors. Logical errors can occur if logfiles or any other server datafiles have been manually edited or the server is somehow misconfigured.
Ignore (non-WAL) datafile errors
Ignore datafile errors when loading collections:
If set to
false, CRC mismatch and other errors in collection datafiles
will lead to a collection not being loaded at all. The collection in this
case becomes unavailable. If such collection needs to be loaded during WAL
recovery, the WAL recovery will also abort (if not forced with option
Setting this flag to
false protects users from unintentionally using a
collection with corrupted datafiles, from which only a subset of the
original data can be recovered. Working with such collection could lead
to data loss and follow up errors.
In order to access such collection, it is required to inspect and repair
the collection datafile with the datafile debugger (arango-dfdb).
If set to
true, CRC mismatch and other errors during the loading of a
collection will lead to the datafile being partially loaded, up to the
position of the first error. All data up to until the invalid position
will be loaded. This will enable users to continue with collection
even if they are corrupted, but this will result in only a partial load
of the original data and potential follow up errors. The WAL recovery
will still abort when encountering a collection with a corrupted datafile,
at least if
--wal.ignore-recovery-errors is not set to
Setting the option to
true will also automatically repair potentially
corrupted VERSION files of databases on startup, so that the startup can
The default value is false, so collections with corrupted datafiles will not be loaded at all, preventing partial loads and follow up errors. However, if such collection is required at server startup, during WAL recovery, the server will abort the recovery and refuse to start.