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Content was updated:Apr 3, 2025
This is an archived version of the documentation downloaded from LogScale Documentation Archives. Up to date documentation, and documentation for later versions, is available on LogScale Documentation

## Health Checks

exposes information about its own health. This information is available through the Health Check API.

The overall health of a 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 investigate the cause.

- 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 have configuration parameters to control thresholds. See the reference below.

### Health Check Reference

Health Check Name | Description
--- | ---
`cluster-time-skew` | Measures time skew between cluster nodes. This check returns WARN if time skew is greater than or equal to the value set in `HEALTH_CHECK__CLUSTER_TIME_SKEW__WARN_THRESHOLD_MS` (defaults to 15000). will exhibit strange behavior if the cluster nodes are out of sync. Always run NTP on a cluster.
`cluster-versions` | Checks if all nodes are running the same version. This check returns a WARN if there are two or more reported versions in the cluster. Only updated once per minute. clusters should always run the same version on each node.
`event-latency-p99` | Ingest latency (99th percentile). This latency is measured from the time an event in received by and until the digest phase is done processing that event (running live searches and persisting to disk). This check returns a WARN status if the latency is higher than the configuration parameter `HEALTH_CHECK__EVENT_LATENCY_P99__WARN_THRESHOLD_SEC` (defaults to 30). 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 will need to catch up with ingest flow, such as: 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. will take corrective measures in a number of different ways, but when ingest latency starts to rise the following is a list of causes that have historically caused high latency. If you experience overload situations would be very interested in working with you to fix the situation and improve the resiliency of going forward. It might be that the amount of ingest is bigger than what the cluster can handle. can handle a high amount of ingest, but for a given cluster there is always a breaking point. Overloading a cluster will result in ingest latency raising. Heavy historical queries. 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 searches using too much CPU. Live searches sit 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 cluster. But it should be noted that Kafka sits in the critical path for ingest, and if Kafka is not dimensioned to the ingest load of a given cluster, ingest will fall behind. Improving 's resiliency in overload situations is an ongoing effort. The following are some of the corrective measures will take to attempt to lower latency. strives 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. Canceling heavy searches — In situations when detects that ingest latency is increasing it will start to cancel the heavy searches, starting with dashboard searches that have not been polled for a long time.
`failing-http-status-checks` | Number of failed http status checks within the last 60 seconds. This check returns 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 use HTTPS when running searches and can only work if all (digest and storage) nodes can reach each other. When a node dies, both this health check and `missing-nodes-via-kafka` will eventually fail. This might not happen at the same time as the checks run asynchronously.
`global-topic-latency-median` | This monitors the latency on the global-events topic that is the shared channel within the cluster for internal communication. This check returns a WARN status if the latency is higher than the configuration parameter `HEALTH_CHECK__GLOBAL_TOPIC_LATENCY_P50__WARN_THRESHOLD_MSEC` (defaults to 50). requires fairly low round trip latency on this specific topic partition. The target median latency is maximum 5ms. See the `global-publish-wait-for-value` metric for the underlying metric values.
`missing-nodes-via-kafka` | Number of nodes not heard from via Kafka within the last 90 seconds. This check returns 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 metadata. A node not present in Kafka means that it is unavailable for processing data. As long as the number of nodes missing is fewer that the replication factor this should not effect , but an operator should take corrective measures immediately to get all nodes back up and running. The `CLUSTER_PING_TIMEOUT_SECONDS` sets the time interval when waiting for a response.
`primary-disk-usage` | Percent used of the primary disk. This check returns WARN if `% >=` the value set in `HEALTH_CHECK__PRIMARY_DISK_USAGE__WARN_THRESHOLD_SEC` percent (defaults to 90). (and Kafka) will crash if data directories run out of free disk space; a crash demands manual intervention and will quite possibly result in loss of data in transit.
`secondary-disk-usage` | Percent used of the secondary disk (only present when secondary disk is used). This check returns WARN if `% >=` the value set in `HEALTH_CHECK__SECONDARY_DISK_USAGE__WARN_THRESHOLD_SEC` percent (defaults to 90). (and Kafka) will crash if data directories run out of free disk space; a crash demands manual intervention and will quite possibly result in loss of data in transit.

#### Enter search term