LogScale High-Availability and Disaster Recovery (HA/DR) Implementation

LogScale uses a combination of replicated files and data in Kafka topics to make sure to not lose any data. LogScale relies on Kafka as the commit-log for recent and in-progress work, and local file systems (or Bucket Storage) as the trusted persistent long term storage.

When events or requests arrive on the HTTP API the 200 OK message is issued only after Kafka has acknowledged all writes that stem from them. This delegates the responsibility of not losing incoming changes to Kafka which is a proven solution for this task.

When the digest nodes construct segment files from incoming events they include the offsets from the Kafka partition the events went into as part of the meta data in Global. Segment files then get replicated to other nodes in the cluster. The cluster calculates the offset for each partition where all events are in files that are properly replicated and then tells Kafka that it is okay for Kafka to delete events older than that.

LogScale can be configured to be resilient against data loss in more than one way depending on the hosting environment.

In all cases the hosting environment must ensure that Kafka has sufficient replicas and fail-over nodes to support the desired resilience.

Without Bucket Storage

LogScale is able to retain all ingested events in the case of a single node loss if the digest and storage replication factors are set to 2 or higher. Events may be deleted from Kafka once the resulting segment files have been replicated to reach the configured factors.

Using Bucket Storage

With bucket storage enabled on top of ephemeral disks LogScale is able to retain all ingested events even when only 1 single node is assigned to every partition: LogScale deletes ingested events from Kafka only once the resulting segment files have been uploaded to the bucket and the remote checksum of the uploaded file has been verified.


Ingest can run as long as any LogScale node is reachable and provided that node is able to publish the incoming events to Kafka.