Events by tag

The events by tag query is implemented by writing the events to another table in Cassandra partitioned by tag as well as a timebucket to prevent partitions becoming too big.

Due to it being a separate table and events are written from multiple nodes the events by tag is eventually consistent.


You should not use too many tags for each event. Each tag will result in a copy of the event in a separate table and that can impact write performance.

First time bucket

Tagged events are written into time buckets. The events by tag query walks the buckets in time order. When there is no offset provided to a query then it is not known which bucket to start with. It is important to override the first time bucket used to a recent time as the default will result in a delay for queries without an offset as it is a long time ago: = "20151120T00:00"


Event by tag queries are eventually consistent, i.e. if a persist for an event has completed is does not guarantee that it will be visible immediately but it will eventually.

Order of the events is maintained per persistence id per tag by maintaining a sequence number for tagged events per persistence id. This is in addition to the normal sequence nr kept per persistence id. Events for the same persistence id will never be delivered out of order.

Events from different persistent IDs are ordered by a type 2 UUID a.k.a TimeUUID, exposed in the EventEnvelope as a TimeBasedUUID offset.

As events come from multiple nodes to deliver events in TimeUUID order there is an eventual-consistency-delay to give events time to arrive in Cassandra and for the query to deliver them in their final order. The eventual-consistency-delay keeps the query a configurable amount of time in the past.

The events by tag stream updates the Cassandra query it uses to start from the latest offset to avoid reading events it has already delivered. If events are delayed and the query progresses past an event’s offset it will be detected either when the next event for that persistence id is received or if no more events are received for the persistence id then it will be detected via back tracking.

In both cases the query will go back and search for the delayed events. The search for missing events is an expensive operation so it is advised not to set the eventual-consistency-delay too low as it increased the liklihood of missed events.


Back tracking only goes back to the offset the query is started from. Meaning that if a query is restarted from an offset and there have been events delayed greater than the eventual-consistency-delay those events will not be delivered.

What can delay events?

Events can be delayed for many reasons:

  • GC pause between serializing the event in the Akka node and writing it to the database
  • Event waiting to be written as part of a batch to the tag_views table
  • Out of sync clocks
  • Overloaded database

Overloaded database

Typical write timeouts for Cassandra are between 2 and 10 seconds. If these timeouts are hit when writing to the tag_views table it means that by the time the write is retried the event will have been delayed by 2-10 seconds while the original write timed out.

Normally these delayed events will be picked up by back tracking but if the events by tag query is restarted from a later offset then back tracking won’t see these delayed events.

Back tracking

If another event for the same persistence id is received the sequence number is used to detect that an event has been missed. To detect missed events without receiving another event for the same persistence id a periodic backtrack is done to find events. Queries are run in the background to detect delayed events. A high frequency short back track is done for finding events delayed a small amount and a low frequency backtrack that scans further back.

These are configured with

sourceback-track {
  # Interval at which events by tag stages trigger a query to look for delayed events from before the
  # current offset. Delayed events are also searched for when a subsequent event for the same persistence id
  # is received. The purpose of this back track is to find delayed events for persistence ids that are no
  # longer receiving events. Depending on the size of the period of the back track this can be expensive.
  interval = 1s

  # How far back to go in time for the scan. This is the maxinum amount of time an event can be delayed
  # and be found without receiving a new event for the same persistence id. Set to a duration or "max" to search back
  # to the start of the previous bucket which is the furthest a search can go back.
  period = 5s

  # at a less frequent interval for a longer back track is done to find any events that were delayed significantly
  long-interval = 120s

  # long-period can be max, off, or a duration where max is to the start of the previous bucket or cleanup-old-persistence-ids,
  # which ever is shorter. Back tracks can not be longer than the cleanup-old-persistence-ids otherwise old events
  # will be redelivered due to the metadat having been dropped
  long-period = "max"

Tuning for lower latency

The default eventual-consistency-delay is set to 5s which is high as it is the safest. This can be set much lower for latency sensitive applications and missed events will be detected via the above mechanisms. The downside is that the search for missing events is an expensive operation and with a high eventual consistency delay it won’t happen without exceptional delays. With a low value it will happen frequently.

Like with any tuning testing should be done to see what works for your environment. The values below may be too low depending on the load and performance of your Cassandra cluster.

It is possible to flush the tag writes right away. By default they are batched and written as an unlogged batch which increases throughput. To write each individually without delay use: = 0s

Alternatively set a very small value e.g. 25ms so some batching is done. If your application has a large number of tagged events per second it is highly advised to set this to a value above 0.

Enable pub sub notifications so events by tag queries can execute a query right away rather than waiting for the next refresh-interval. Lower the refresh-interval for cases where the pub sub messages take a long time to arrive at the query. = on
akka.persistence.cassandra.query.refresh-interval = 2s

Reduce eventual-consistency-delay. You must test this has positive results for your use case. Setting this too low can decrease throughput and latency as more events will be missed initially and expensive searches carried out. = 50ms

Missing searches and gap detection

If a gap is detected then the event is held back and the stream goes into a searching mode for the missing events. The search only looks in the current bucket and the previous one. If the event is older then it won’t be found in this search.

If the missing event is not found then the stream is failed unless it is the first time the persistence id has been encountered by the query.

Gap detection when encountering a persistence id for the first time

When receiving the first event for a given persistence id in an offset query it is not known if that is actually the first event. If the offset query starts in a time bucket in the past then it is assumed to be the first.

If the query is started with an offset in the current time bucket then the query starts with a search to look for the lowest sequence number per persistenceId before delivering any events.

The above scanning only looks in the current time bucket. For persistence ids that aren’t found in this initial scan because they don’t have any events in the current time bucket then the expected tag pid sequence number is not known. If events are found out of order due to this the stage will fail. It is possible to enable configuration new-persistence-id-scan-timeout, which will use an additional query to search for more events before assuming this is the first event for that persistence id. This adds a delay each time a new persistence id is found by an offset query when the first event doesn’t have a sequenceNr of 1.

Events by tag reconciliation

In the event that the tag_views table gets corrupted there is a ReconciliationReconciliation tool that can help fix it. It can only be run while the application is offline but per persistence id operations can be used if it is known that the persistence id is not running.

It supports:

  • Deleting all events in the tag view for a given persistence id
  • Re-building the tag view for a persistence id for a tag
  • Query for all tags (inefficient query)
  • Truncate the tag view and all metadata so it can be re-built

After deleting the tag views they will be automatically re-built next time the persistence id starts or with an explicit rebuild.

For example, to rebuild the data for a persistence id:

sourceimport akka.persistence.cassandra.reconciler.Reconciliation
import scala.concurrent.Future
import akka.Done

// System should have the same Cassandra plugin configuration as your application
// but be careful to remove seed nodes so this doesn't join the cluster
val system = ActorSystem()
import system.dispatcher

val rec = new Reconciliation(system)

// Drop and re-create data for a persistence id
val pid = "pid1"
for {
  // do this before dropping the data
  tags <- rec.tagsForPersistenceId(pid)
  // drop the tag view for every tag for this persistence id
  dropData <- Future.traverse(tags)(tag => rec.deleteTagViewForPersistenceIds(Set(pid), tag))
  // optional: re-build, if this is ommited then it will be re-build next time the pid is started
  _ <- rec.rebuildTagViewForPersistenceIds(pid)
} yield Done

Other tuning

Setting a bucket size

Events by tag partitions are grouped into buckets to avoid a single partition for a tag getting too large.

  • 1000s of events per day per tag – Day
  • 100,000s of events per day per tag – Hour
  • 1,000,000s of events per day per tag – Minute

The default is Hour and can be overridden by setting This setting can not be changed after data has been written.

More billion per day per tag? You probably need a specialised schema rather than a general library like this.

If a Cassandra partition gets too large it will perform badly (and has a hard limit of 2 billion cells, a cell being similar to a column). Given there is ~10 columns in the table there was a hard limit previously of 200million but performance would degrade before reaching this.

The support for Minute bucket size means that there are 1440 partitions per day. Given a large C* cluster this should work until you have in the order of 10s of millions per minute when the write rate to that partition would likely become the bottleneck rather than partition size.

The increased scalability comes at the cost of having to query more partitions on the read side so don’t use Minute unless necessary.

Setting a write batch size

The size of the of the batch is controlled via max-message-batch-size. Cassandra imposes a limit on the serialized size of a batch, currently 50kb by default. The larger this value the more efficient the tag writes will be but care must be taken not to have batches that will be rejected by Cassandra. Two other cases cause the batch to be written before the batch size is reached:

  • Periodically: By default 250ms. To prevent eventsByTag queries being too out of date.
  • When a starting a new timebucket, which translates to a new partition in Cassandra, the events for the old timebucket are written.

Cleanup of tag_views table

By default the tag_views table keeps tagged events indefinitely, even when the original events have been removed. Depending on the volume of events this may not be suitable for production.

Before going live decide a time to live (TTL) and, if small enough, consider using the Time Window Compaction Strategy. See events-by-tag.time-to-live in reference.conf for how to set this.


The TTL must be greater than any expected delay in using the tagged events to build a read side view otherwise they’ll be deleted before being read.

The CleanupCleanup tool can also be used for deleting from the tag_views table. See Database Cleanup for more details.

How it works

The following section describes more about how the events by tag query work, it is not required knowledge for using it.

A separate table is maintained for events by tag queries to that is written to in batches.

The table is partitioned via tag and a time bucket. This allows events for the same tag can be queried and the time bucket prevents all events for the same tag being in a single partition.

The timebucket causes a hot spot for writes but this is kept to a minimum by making the bucket small enough and by batching the writes into unlogged batches.

Each events by tag query starts at the provided offset and queries the events. The stream will initially do a query per time bucket to get up to the current (many of these could be empty so very little over head, some may bring back many events). For example if you set the offset to be 30 days ago and a day time bucket, it will require 30 * nr queries to get each stream up to date. If a particular partition contains many events then it is paged back from Cassandra based on demand.

When no offset is provided EventsByTag queries start at the configured events-by-tag.first-time-bucket. For new applications increase this to the day your application is initially deployed to speed up NoOffset queries.


A TagWriter actor is created, on demand, per tag that batches (unlogged batch grouped by the partition key) up writes to a separate table partitioned by tag name and a time bucket.

When a message is persisted, if it has tags, it is sent to each of the TagWriters asynchronously.

This allows us to be more efficient and thus support more tags than the old version (limited to 3). No limit is imposed with new implementation but it is advised to keep it small i.e. less than 10.

Crash recovery

TagWriter maintains a separate table with how far it has gotten up to for each persistenceId.

CREATE TABLE akka.tag_views_progress (                              
    peristence_id text,           
    tag text,                     
    sequence_nr bigint,           
    PRIMARY KEY (peristence_id, tag)                                

Part of recovery checks that all events have been written by the tag writer.

On the happy path this table will be written to after the tag_view write is complete so it could be lost in a crash.

Recovery/Restart scenarios

On recovery the tag write progress for a given pid / tag is sent to the tag writer so that it bases its tag pid sequence nrs off the recovery. Following scenarios:

No event buffered in the tag writer. Tag write progress has been written.

  • Recovery will see that no events needs to be recovered

No events buffered in the tag writer. Tag write progress is lost.

  • Events will be in the tag_views table but not the tag_write_progress.
  • Tag write progress will be out of date
  • Events will be recovered and sent to the tag writer, should receive the same tag pid sequence nr and be upserted.

Events buffered in the tag writer.

  • Buffered events for the persistenceId should be dropped as if they are buffered the tag write progress won’t have been saved as it happens after the write of the events to tag_views.
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