Recording rules are evaluated by the
ruler component. Each
ruler acts as its own
querier, in the sense that it
executes queries against the store without using the
querier components. It will respect all query
limits put in place for the
Loki’s implementation of recording rules largely reuses Prometheus’ code.
Samples generated by recording rules are sent to Prometheus using Prometheus’ remote-write feature.
Write-Ahead Log (WAL)
All samples generated by recording rules are written to a WAL. The WAL’s main benefit is that it persists the samples
generated by recording rules to disk, which means that if your
ruler crashes, you won’t lose any data.
We are trading off extra memory usage and slower start-up times for this functionality.
A WAL is created per tenant; this is done to prevent cross-tenant interactions. If all samples were to be written to a single WAL, this would increase the chances that one tenant could cause data-loss for others. A typical scenario here is that Prometheus will, for example, reject a remote-write request with 100 samples if just 1 of those samples is invalid in some way.
ruler starts up, it will load the WALs for the tenants who have recording rules. These WAL files are stored
on disk and are loaded into memory.
Note: WALs are loaded one at a time upon start-up. This is a current limitation of the Loki ruler. For this reason, it is adviseable that the number of rule groups serviced by a ruler be kept to a reasonable size, since no rule evaluation occurs while WAL replay is in progress (this includes alerting rules).
WAL files are regularly truncated to reduce their size on disk. This guide from one of the Prometheus maintainers (Ganesh Vernekar) gives an excellent overview of the truncation, checkpointing, and replaying of the WAL.
WAL Cleaner is an experimental feature.
The WAL Cleaner watches for abandoned WALs (tenants who no longer have recording rules associated) and deletes them. Enable this feature only if you are running into storage concerns with WALs that are too large. WALs should not grow excessively large due to truncation.
See Mimir’s guide for configuring Grafana Mimir hash rings for scaling the ruler using a ring.
ruler shards by rule group, not by individual rules. This is an artifact of the fact that Prometheus
recording rules need to run in order since one recording rule can reuse another - but this is not possible in Loki.
ruler needs to persist its WAL files to disk, and it incurs a bit of a start-up cost by reading these WALs into memory.
As such, it is recommended that you try to minimize churn of individual
ruler instances since rule evaluation is blocked
while the WALs are being read from disk.
It is recommended that you run the
ruler will write its WAL files to persistent storage,
Persistent Volume should be utilised.
Remote-write can be configured at a global level in the base configuration, and certain parameters tuned specifically on a per-tenant basis. Most of the configuration options defined here have override options (which can be also applied at runtime!).
Remote-write can be tuned if the default configuration is insufficient (see Failure Modes below).
There is a guide on the Prometheus website, all of which applies to Loki, too.
Since Loki reuses the Prometheus code for recording rules and WALs, it also gains all of Prometheus’ observability.
Prometheus exposes a number of metrics for its WAL implementation, and these have all been prefixed with
Additional metrics are exposed, also with the prefix
loki_ruler_wal_. All per-tenant metrics contain a
label, so be aware that cardinality could begin to be a concern if the number of tenants grows sufficiently large.
Some key metrics to note are:
loki_ruler_wal_appender_ready: whether a WAL appender is ready to accept samples (1) or not (0)
loki_ruler_wal_prometheus_remote_storage_samples_total: number of samples sent per tenant to remote storage
loki_ruler_wal_prometheus_remote_storage_samples_pending_total: samples buffered in memory, waiting to be sent to remote storage
loki_ruler_wal_prometheus_remote_storage_samples_failed_total: samples that failed when sent to remote storage
loki_ruler_wal_prometheus_remote_storage_samples_dropped_total: samples dropped by relabel configurations
loki_ruler_wal_prometheus_remote_storage_samples_retried_total: samples re-resent to remote storage
loki_ruler_wal_prometheus_remote_storage_highest_timestamp_in_seconds: highest timestamp of sample appended to WAL
loki_ruler_wal_prometheus_remote_storage_queue_highest_sent_timestamp_seconds: highest timestamp of sample sent to remote storage.
We’ve created a basic dashboard in our loki-mixin which you can use to administer recording rules.
Remote-write can lag behind for many reasons:
- Remote-write storage (Prometheus) is temporarily unavailable
- A tenant is producing samples too quickly from a recording rule
- Remote-write is tuned too low, creating backpressure
It can be determined by subtracting
In case 1, the
ruler will continue to retry sending these samples until the remote storage becomes available again. Be
aware that if the remote storage is down for longer than
ruler.wal.max-age, data loss may occur after truncation occurs.
In cases 2 & 3, you should consider tuning remote-write appropriately.
Further reading: see this blog post by Prometheus maintainer Callum Styan.
Appender Not Ready
Each tenant’s WAL has an “appender” internally; this appender is used to append samples to the WAL. The appender is marked
as not ready until the WAL replay is complete upon startup. If the WAL is corrupted for some reason, or is taking a long
time to replay, you can determine this by alerting on
loki_ruler_wal_appender_ready < 1.
If a disk fails or the
ruler does not terminate correctly, there’s a chance one or more tenant WALs can become corrupted.
A mechanism exists for automatically repairing the WAL, but this cannot handle every conceivable scenario. In this case,
loki_ruler_wal_corruptions_repair_failed_total metric will be incremented.
Found another failure mode?
Please open an issue and tell us about it!
Related Loki resources
Getting started with logging and Grafana Loki (Part 3 of 4)
Join this webinar to learn why correlating metrics and logs is critical across the development lifecycle, and how Loki helps reduce logging costs and operations overhead.
Logging with Loki: Essential configuration settings
This webinar focuses on Grafana Loki configuration including agents Promtail and Docker; the Loki server; and Loki storage for popular backends.