Documentation Grafana Enterprise Logs Manage Storage Write Ahead Log

Write Ahead Log

Ingesters temporarily store data in memory. In the event of a crash, there could be data loss. The Write Ahead Log (WAL) helps fill this gap in reliability.

The WAL in Grafana Loki records incoming data and stores it on the local file system in order to guarantee persistence of acknowledged data in the event of a process crash. Upon restart, Loki will “replay” all of the data in the log before registering itself as ready for subsequent writes. This allows Loki to maintain the performance and cost benefits of buffering data in memory and durability benefits (it won’t lose data once a write has been acknowledged).

This section will use Kubernetes as a reference deployment paradigm in the examples.

Disclaimer and WAL nuances

The Write Ahead Log in Loki takes a few particular tradeoffs compared to other WALs you may be familiar with. The WAL aims to add additional durability guarantees, but not at the expense of availability. Particularly, there are two scenarios where the WAL sacrifices these guarantees.

1. Corruption/Deletion of the WAL prior to replaying it

In the event the WAL is corrupted/partially deleted, Loki will not be able to recover all of its data. In this case, Loki will attempt to recover any data it can, but will not prevent Loki from starting.

You can use the Prometheus metric loki_ingester_wal_corruptions_total to track and alert when this happens.

1. No space left on disk

In the event the underlying WAL disk is full, Loki will not fail incoming writes, but neither will it log them to the WAL. In this case, the persistence guarantees across process restarts will not hold.

You can use the Prometheus metric loki_ingester_wal_disk_full_failures_total to track and alert when this happens.

Backpressure

The WAL also includes a backpressure mechanism to allow a large WAL to be replayed within a smaller memory bound. This is helpful after bad scenarios (i.e. an outage) when a WAL has grown past the point it may be recovered in memory. In this case, the ingester will track the amount of data being replayed and once it’s passed the ingester.wal-replay-memory-ceiling threshold, will flush to storage. When this happens, it’s likely that the Loki attempt to deduplicate chunks via content addressable storage will suffer. We deemed this efficiency loss an acceptable tradeoff considering how it simplifies operation and that it should not occur during regular operation (rollouts, rescheduling) where the WAL can be replayed without triggering this threshold.

Metrics

Changes to deployment

1. Since ingesters need to have the same persistent volume across restarts/rollout, all the ingesters should be run on StatefulSet with fixed volumes.

2. Following flags needs to be set

   • --ingester.wal-enabled to true which enables writing to WAL during ingestion.
   • --ingester.wal-dir to the directory where the WAL data should be stored and/or recovered from. Note that this should be on the mounted volume.
   • --ingester.checkpoint-duration to the interval at which checkpoints should be created.
   • --ingester.wal-replay-memory-ceiling (default 4GB) may be set higher/lower depending on your resource settings. It handles memory pressure during WAL replays, allowing a WAL many times larger than available memory to be replayed. This is provided to minimize reconciliation time after very bad situations, i.e. an outage, and will likely not impact regular operations/rollouts at all. We suggest setting this to a high percentage (~75%) of available memory.

Changes in lifecycle when WAL is enabled

Flushing of data to chunk store during rollouts or scale down is disabled. This is because during a rollout of statefulset there are no ingesters that are simultaneously leaving and joining, rather the same ingester is shut down and brought back again with updated config. Hence flushing is skipped and the data is recovered from the WAL. If you need to ensure that data is always flushed to the chunk store when your pod shuts down, you can set the --ingester.flush-on-shutdown flag to true.

Disk space requirements

Based on tests in real world:

• Numbers from an ingester with 5000 series ingesting ~5mb/s.
• Checkpoint period was 5mins.
• disk utilization on a WAL-only disk was steady at ~10-15GB.

You should not target 100% disk utilisation.

Migrating from stateless deployments

The ingester Deployment without WAL and StatefulSet with WAL should be scaled down and up respectively in sync without transfer of data between them to ensure that any ingestion after migration is reliable immediately.

Let’s take an example of 4 ingesters. The migration would look something like this:

1. Bring up one stateful ingester ingester-0 and wait until it’s ready (accepting read and write requests).
2. Scale down the old ingester deployment to 3 and wait until the leaving ingester flushes all the data to chunk store.
3. Once that ingester has disappeared from kc get pods ..., add another stateful ingester and wait until it’s ready. Now you have ingester-0 and ingester-1.
4. Repeat step 2 to reduce remove another ingester from old deployment.
5. Repeat step 3 to add another stateful ingester. Now you have ingester-0 ingester-1 ingester-2.
6. Repeat step 4 and 5, and now you will finally have ingester-0 ingester-1 ingester-2 ingester-3.

How to scale up/down

Scale up

Scaling up is same as what you would do without WAL or statefulsets. Nothing to change here.

Scale down

When scaling down, we must ensure existing data on the leaving ingesters are flushed to storage instead of just the WAL. This is because we won’t be replaying the WAL on an ingester that will no longer exist and we need to make sure the data is not orphaned.

Consider you have 4 ingesters ingester-0 ingester-1 ingester-2 ingester-3 and you want to scale down to 2 ingesters, the ingesters which will be shut down according to StatefulSet rules are ingester-3 and then ingester-2.

Hence before actually scaling down in Kubernetes, port forward those ingesters and hit the /ingester/shutdown?flush=true endpoint. This will flush the chunks and remove itself from the ring, after which it will register as unready and may be deleted.

After hitting the endpoint for ingester-2 ingester-3, scale down the ingesters to 2.

Also you can set the --ingester.flush-on-shutdown flag to true. This enables chunks to be flushed to long-term storage when the ingester is shut down.

Additional notes

Kubernetes hacking

Statefulsets are significantly more cumbersome to work with, upgrade, and so on. Much of this stems from immutable fields on the specification. For example, if one wants to start using the WAL with single store Loki and wants separate volume mounts for the WAL and the boltdb-shipper, you may see immutability errors when attempting updates the Kubernetes statefulsets.

In this case, try kubectl -n <namespace> delete sts ingester --cascade=false. This will leave the Pods alive but delete the StatefulSet. Then you may recreate the (updated) StatefulSet and one-by-one start deleting the ingester-0 through ingester-n Pods in that order, allowing the StatefulSet to spin up new pods to replace them.

Scaling Down Using /flush_shutdown Endpoint and Lifecycle Hook

1. StatefulSets for Ordered Scaling Down: The Loki ingesters should be scaled down one by one, which is efficiently handled by Kubernetes StatefulSets. This ensures an ordered and reliable scaling process, as described in the Deployment and Scaling Guarantees documentation.

2. Using PreStop Lifecycle Hook: During the Pod scaling down process, the PreStop lifecycle hook triggers the /flush_shutdown endpoint on the ingester. This action flushes the chunks and removes the ingester from the ring, allowing it to register as unready and become eligible for deletion.

3. Using terminationGracePeriodSeconds: Provides time for the ingester to flush its data before being deleted, if flushing data takes more than 30 minutes, you may need to increase it.

4. Cleaning Persistent Volumes: Persistent volumes are automatically cleaned up by leveraging the enableStatefulSetAutoDeletePVC feature in Kubernetes.

By following the above steps, you can ensure a smooth scaling down process for the Loki ingesters while maintaining data integrity and minimizing potential disruptions.

Non-Kubernetes or baremetal deployments

• When the ingester restarts for any reason (upgrade, crash, etc), it should be able to attach to the same volume in order to recover back the WAL and tokens.
• 2 ingesters should not be working with the same volume/directory for the WAL.
• A Rollout should bring down an ingester completely and then start the new ingester, not the other way around.