Overview of Loki

Grafana Loki is a set of components that can be composed into a fully featured logging stack.

Unlike other logging systems, Loki is built around the idea of only indexing labels for logs and leaving the original log message unindexed. This means that Loki is cheaper to operate and can be orders of magnitude more efficient.

For a more detailed version of this same document, please read Architecture.

Multi Tenancy

Loki supports multi-tenancy so that data between tenants is completely separated. Multi-tenancy is achieved through a tenant ID (which is represented as an alphanumeric string). When multi-tenancy mode is disabled, all requests are internally given a tenant ID of “fake”.

Modes of Operation

Loki is optimized for both running locally (or at small scale) and for scaling horizontally: Loki comes with a single process mode that runs all of the required microservices in one process. The single process mode is great for testing Loki or for running it at a small scale. For horizontal scalability, the microservices of Loki can be broken out into separate processes, allowing them to scale independently of each other.



The distributor service is responsible for handling logs written by clients. It’s essentially the “first stop” in the write path for log data. Once the distributor receives log data, it splits them into batches and sends them to multiple ingesters in parallel.

Distributors communicate with ingesters via gRPC. They are stateless and can be scaled up and down as needed.


Distributors use consistent hashing in conjunction with a configurable replication factor to determine which instances of the ingester service should receive log data.

The hash is based on a combination of the log’s labels and the tenant ID.

A hash ring stored in Consul is used to achieve consistent hashing; all ingesters register themselves into the hash ring with a set of tokens they own. Distributors then find the token that most closely matches the value of the log’s hash and will send data to that token’s owner.

Quorum consistency

Since all distributors share access to the same hash ring, write requests can be sent to any distributor.

To ensure consistent query results, Loki uses Dynamo-style quorum consistency on reads and writes. This means that the distributor will wait for a positive response of at least one half plus one of the ingesters to send the sample to before responding to the user.


The ingester service is responsible for writing log data to long-term storage backends (DynamoDB, S3, Cassandra, etc.).

The ingester validates that ingested log lines are not out of order. When an ingester receives a log line that doesn’t follow the expected order, the line is rejected and an error is returned to the user. See the section on Timestamp ordering for more information.

The ingester validates that ingested log lines are received in timestamp-ascending order (i.e., each log has a timestamp that occurs at a later time than the log before it). When the ingester receives a log that does not follow this order, the log line is rejected and an error is returned.

Logs from each unique set of labels are built up into “chunks” in memory and then flushed to the backing storage backend.

If an ingester process crashes or exits abruptly, all the data that has not yet been flushed will be lost. Loki is usually configured to replicate multiple replicas (usually 3) of each log to mitigate this risk.

Timestamp Ordering

In general, all lines pushed to Loki for a given stream (unique combination of labels) must have a newer timestamp than the line received before it. There are, however, two cases for handling logs for the same stream with identical nanosecond timestamps:

  1. If the incoming line exactly matches the previously received line (matching both the previous timestamp and log text), the incoming line will be treated as an exact duplicate and ignored.

  2. If the incoming line has the same timestamp as the previous line but different content, the log line is accepted. This means it is possible to have two different log lines for the same timestamp.


By default, when an ingester is shutting down and tries to leave the hash ring, it will wait to see if a new ingester tries to enter before flushing and will try to initiate a handoff. The handoff will transfer all of the tokens and in-memory chunks owned by the leaving ingester to the new ingester.

This process is used to avoid flushing all chunks when shutting down, which is a slow process.

Filesystem Support

While ingesters do support writing to the filesystem through BoltDB, this only works in single-process mode as queriers need access to the same back-end store and BoltDB only allows one process to have a lock on the DB at a given time.


The querier service handles the actual LogQL evaluation of logs stored in long-term storage.

It first tries to query all ingesters for in-memory data before falling back to loading data from the backend store.

Query frontend

The query-frontend service is an optional component in front of a pool of queriers. It’s responsible for fairly scheduling requests between them, paralleling them when possible, and caching.

Chunk Store

The chunk store is Loki’s long-term data store, designed to support interactive querying and sustained writing without the need for background maintenance tasks. It consists of:

Unlike the other core components of Loki, the chunk store is not a separate service, job, or process, but rather a library embedded in the two services that need to access Loki data: the ingester and querier.

The chunk store relies on a unified interface to the “ NoSQL” stores (DynamoDB, Bigtable, and Cassandra) that can be used to back the chunk store index. This interface assumes that the index is a collection of entries keyed by:

  • A hash key. This is required for all reads and writes.
  • A range key. This is required for writes and can be omitted for reads, which can be queried by prefix or range.

The interface works somewhat differently across the supported databases:

  • DynamoDB supports range and hash keys natively. Index entries are thus modelled directly as DynamoDB entries, with the hash key as the distribution key and the range as the range key.
  • For Bigtable and Cassandra, index entries are modelled as individual column values. The hash key becomes the row key and the range key becomes the column key.

A set of schemas are used to map the matchers and label sets used on reads and writes to the chunk store into appropriate operations on the index. Schemas have been added as Loki has evolved, mainly in an attempt to better load balance writes and improve query performance.