Health Checks

LogScale exposes information about its own health. This information is available through the Health Check API. This feature is in development and as such will continue to change. Please check the documentation and release notes for updates.

The overall health of a LogScale node can have 3 states:

  • OK — All health checks are within normal operational parameters.

  • WARN — At least one health check is in a WARN state and an operator should look into why that is.

  • DOWN — At least one health check is in a DOWN state. The node is down and not working. With dynamic load balancers, nodes that are down should be removed from the active set of nodes.

Part of the overall health state is the uptime value. When a node has just started, there is a grace period where the overall state will always be WARN even if some checks are DOWN. This is to give the system time to become stable, but also to indicate that things might fluctuate a bit. The grace period can be set with the configuration parameter HEALTH_CHECK_GRACE_PERIOD_SEC. The default value is 30.

Determining if a situation is affecting the health of a node can be specific to each installation. Therefore, most checks will have configuration parameters for controlling thresholds. See the reference below.

Health Check Reference

Health Check Name Description
backup-disk-usage Percent used of the backup disk (only present when backup disk is used). Will be WARN if used % >= HEALTH_CHECK__BACKUP_DISK_USAGE__WARN_THRESHOLD_SEC seconds (defaults to 90) LogScale (and Kafka) will crash if data directories run out of free disk space and doing so will demand manual intervention and quite possible loose data in transit.
event-latency-p99

Ingest latency (99th percentile). This latency is measured from the time an event in received by LogScale and until the digest phase is done processing that event (running live searches and persisting to disk). This check will return a WARN status if the latency is higher than the configuration parameter HEALTH_CHECK__EVENT_LATENCY_P99__WARN_THRESHOLD_SEC (defaults to 30). LogScale is build for low ingest latency, often sub-second, and a high latency is usually a sign that something is not working as expected There are a number of situations that will give higher latency for a shorter time period because LogScale will need to catch up with ingest flow.

  • When a node has just started.

  • When a node takes over ingest from another (failed) node.

  • When changing the digest partition schema. In a number of situations high latency is a symptom of something stopping the system from running as expected. LogScale will take corrective measures in a number of different ways, but when ingest latency starts to raise the following is a list of causes that we have seen historically can cause high latency. (We have an ongoing effort to make LogScale resilient to overload situations and are improving both the corrective measure LogScale takes by itself and making it more transparent what is happening in the cluster.

If you experience overload situations we will be very interested in working with you to fix the situation and improve the resiliency of LogScale going forward.)

  • It might be that the amount of ingest is bigger than what the cluster can cope with. LogScale can handle a high amount of ingest, but for a given cluster there is always a breaking point. Overloading a LogScale cluster will result in ingest latency raising.

  • Heavy historical queries. LogScale has an efficient query engine, but some searches is inherently heavy and running those over large data sets will can use up so much CPU that ingest falls behind. Especially if the system is nearing it maximum capacity.

The Query Monitor in the administrative page can be used to find and disable problematic queries if this happens.

  • Heavy live searched using too much CPU. Live searches sits on the critical path for ingest and add latency. If these are heavy they might make the ingest fall behind. Live searches can, similarly to historically searches, be seen in the Query Monitor.

  • Kafka too slow. Kafka is usually not the limiting factor in a LogScale cluster, but it should be noted that Kafka sits in the critical path for ingest and if Kafka in not dimensioned to the ingest load of a given cluster, ingest will fall behind. Making LogScale behave resilient in overload situations in an ongoing effort.

The following is some of the corrective measures LogScale will take. LogScale will strive towards having low ingest latency, at the expense of searches if necessary.

  • Auto-sharding — If a given data source is falling behind it will be split artificially in auto-shards until each shard is small enough to cope with the ingest load. This can happen if the ingest suddenly increases or a heavy live search is started.

  • Quotas — Users can be assigned limited search quotas, so they cannot take all resources for an extended period.

  • Cancelling heavy searches — In situation when LogScale detects that ingest latency is raising it will start to cancel the heavy searches, starting with dashboard searches that has not been polled for a long time.

failing-http-status-checks Number of failed http status checks within the last 60 seconds. This check will return a WARN status if the count is 1 or more. Note, this check might count the same host multiple times within the same time interval. Nodes in LogScale use HTTPS when running searches and can only work if all (digest and storage) nodes can reach each other. Note that when a node dies, both this health check and missing-nodes-via-kafka will eventually fails, but this might not happen at the same time as the checks run asynchronously.
missing-nodes-via-kafka Number of nodes not heard from via kafka within the last 90 seconds. This check will return a WARN status if the node count is 1 or more. Kafka is used to ship ingest data around the cluster and for sharing global meta data. A node not present in Kafka means that it is unavailable. As long as the number of nodes missing is fewer that the replication factor this should not effect LogScale, but an operator should immediately take corrective measures to get all nodes back up and running.
primary-disk-usage Percent used of the primary disk. Will be WARN if used % >= HEALTH_CHECK__PRIMARY_DISK_USAGE__WARN_THRESHOLD_SEC seconds (defaults to 90) LogScale (and Kafka) will crash if data directories run out of free disk space and doing so will demand manual intervention and quite possible loose data in transit.
secondary-disk-usage Percent used of the secondary disk (only present when secondary disk is used). Will be WARN if used % >= HEALTH_CHECK__SECONDARY_DISK_USAGE__WARN_THRESHOLD_SEC seconds (defaults to 90). LogScale (and Kafka) will crash if data directories run out of free disk space and doing so will demand manual intervention and quite possible loose data in transit.