ADS replicates the log for each topic’s partitions across a configurable number of servers (you can set this replication factor on a topic-by-topic basis). This allows automatic failover to these replicas when a server in the cluster fails so messages remain available in the presence of failures.

Other messaging systems provide some replication-related features, but, in our (totally biased) opinion, this appears to be a tacked-on thing, not heavily used, and with large downsides: slaves are inactive, throughput is heavily impacted, it requires fiddly manual configuration, etc. ADS is meant to be used with replication by default – in fact we implement un-replicated topics as replicated topics where the replication factor is one.

The unit of replication is the topic partition. Under non-failure conditions, each partition in ADS has a single leader and zero or more followers. The total number of replicas including the leader constitute the replication factor. All reads and writes go to the leader of the partition. Typically, there are many more partitions than brokers and the leaders are evenly distributed among brokers. The logs on the followers are identical to the leader’s log – all have the same offsets and messages in the same order (though, of course, at any given time the leader may have a few as-yet unreplicated messages at the end of its log).

Followers consume messages from the leader just as a normal ADS consumer would and apply them to their own log. Having the followers pull from the leader has the nice property of allowing the follower to naturally batch together log entries they are applying to their log.

As with most distributed systems automatically handling failures requires having a precise definition of what it means for a node to be “alive”. For ADS node liveness has two conditions:

  1. A node must be able to maintain its session with ZooKeeper (via ZooKeeper’s heartbeat mechanism).
  2. If it is a slave it must replicate the writes happening on the leader and not fall “too far” behind.

We refer to nodes satisfying these two conditions as being “in sync” to avoid the vagueness of “alive” or “failed”. The leader keeps track of the set of “in sync” nodes. If a follower dies, gets stuck, or falls behind, the leader will remove it from the list of in sync replicas. The determination of stuck and lagging replicas is controlled by the configuration.

In distributed systems terminology we only attempt to handle a “fail/recover” model of failures where nodes suddenly cease working and then later recover (perhaps without knowing that they have died). ADS does not handle so-called “Byzantine” failures in which nodes produce arbitrary or malicious responses (perhaps due to bugs or foul play).

Now can more precisely define that a message is considered committed when all in sync replicas for that partition have applied it to their log. Only committed messages are ever given out to the consumer. This means that the consumer need not worry about potentially seeing a message that could be lost if the leader fails. Producers, on the other hand, have the option of either waiting for the message to be committed or not, depending on their preference for tradeoff between latency and durability. This preference is controlled by the acks setting that the producer uses. Note that topics have a setting for the “minimum number” of in-sync replicas that is checked when the producer requests acknowledgment that a message has been written to the full set of in-sync replicas. If a less stringent acknowledgement is requested by the producer, then the message can be committed, and consumed, even if the number of in-sync replicas is lower than the minimum (e.g. it can be as low as just the leader).


The guarantee that ADS offers is that a committed message will not be lost, as long as there is at least one in sync replica alive, at all times

ADS will remain available in the presence of node failures after a short fail-over period, but may not remain available in the presence of network partitions.