The event journal is a distributed data structure that stores the history
of mutation actions on map or cache. Each action on the map or cache which
modifies its contents (such as
remove or scheduled tasks which are
not triggered by using the public API) creates an event which is stored in
the event journal. The event stores the event type as well as the key, old
value and updated value for the entry (when applicable). As a user, you can
only append to the journal indirectly by using the map and cache methods or
configuring the expiration and eviction. By reading from the event journal
you can recreate the state of the map or cache at any point in time.
|The event journal can be used only in data pipelines.|
The event journal has a fixed capacity and an expiration time. Internally it is structured as a ringbuffer (partitioned by ringbuffer item) and shares many similarities with it.
Configuring IMap with eviction and expiration can cause the event journal to contain different events on the different replicas of the same partition. You can run into issues if you are reading from the event journal and the partition owner is terminated. A backup replica is then promoted into the partition owner but the event journal will contain different events. The event count should stay the same but the entries which you previously thought were evicted and expired could now be "alive" and vice versa.
This is because eviction and expiration randomly choose entries to be evicted/expired. The entry is not coordinated between partition replicas. In these cases, the event journal diverges and will not converge at any future point, but will remain inconsistent just as well as the contents of the internal record stores are inconsistent between replicas. You may say that the event journal on a specific replica is in-sync with the record store on that replica but the event journals and record stores between replicas are out-of-sync.
By default, an event journal is configured with a
capacity of 10000 items.
This creates a single array per partition, roughly the size of the capacity
divided by the number of partitions. Thus, if the configured capacity is
10000 and number of partitions is 271, we create 271 arrays of size 36
time-to-live is configured, then an array of longs is also created
that stores the expiration time for every item.
A single array of the event journal keeps events that are only related
to the map entries in that partition. In a lot of cases you may want to
capacity number to something that better fits your needs.
As the capacity is shared between partitions, keep in mind not to set it
to a value which is too low for you. Setting the capacity to a number
lower than the partition count results in an error when initializing the
Below is a declarative configuration example of an event journal with a
capacity of 5000 items for a map and 10000 items for a cache:
<hazelcast> ... <map name="default"> <event-journal enabled="true"> <capacity>5000</capacity> <time-to-live-seconds>20</time-to-live-seconds> </event-journal> </map> ... <cache name="default"> <event-journal enabled="true"> <capacity>10000</capacity> <time-to-live-seconds>0</time-to-live-seconds> </event-journal> </cache> ... </hazelcast>
hazelcast: map: default: event-journal: enabled: true capacity: 5000 time-to-live-seconds: 20 cache: default: event-journal: enabled: true capacity: 10000 time-to-live-seconds: 0
You can also configure an event journal programmatically. The following is a programmatic version of the above declarative configuration:
EventJournalConfig eventJournalMapConfig = new EventJournalConfig() .setEnabled(true) .setCapacity(5000) .setTimeToLiveSeconds(20); EventJournalConfig eventJournalCacheConfig = new EventJournalConfig() .setEnabled(true) .setCapacity(10000) .setTimeToLiveSeconds(0); Config config = new Config(); config.getMapConfig("myMap").setEventJournalConfig(eventJournalMapConfig); config.getCacheConfig("myCache").setEventJournalConfig(eventJournalCacheConfig);
cacheName attributes define the map or cache to which
this event journal configuration applies. You can use pattern-matching and
default keyword when doing so. For instance, by using a
journaled*, the journal configuration applies to all maps whose names start
with "journaled" and don’t have other journal configurations that match (e.g.,
if you would have a more specific journal configuration with an exact name match).
If you specify the
default, the journal configuration
applies to all maps and caches that don’t have any other journal configuration.
This means that potentially all maps and/or caches have one single
event journal configuration.
The event journal is a partitioned data structure. The partitioning is done by the event key. Because of this, the map and cache entry with a specific key is co-located with the events for that key and will be migrated accordingly. Also, the backup count for the event journal is equal to the backup count of the map or cache for which it contains events. The events on the backup replicas will be created with the map or cache backup operations and no additional network traffic is introduced when appending events to the event journal.
You can configure Hazelcast event journal with a
time-to-live in seconds.
Using this setting, you can control how long the items remain in
the event journal before they are expired. By default, the
is set to 0, meaning that unless the item is overwritten,
it remains in the journal indefinitely. The expiration time of the existing
journal events is checked whenever a new event is appended to the event
journal or when the event journal is being read. If the journal is not
being read from or written to, the journal may keep expired items indefinitely.
In the example below, an event journal is configured with a
time-to-live of 180 seconds: