Unlike other logging systems, Grafana Loki is built around the idea of only indexing metadata about your logs: labels (just like Prometheus labels). Log data itself is then compressed and stored in chunks in object stores such as S3 or GCS, or even locally on the filesystem. A small index and highly compressed chunks simplifies the operation and significantly lowers the cost of Loki.
Until Loki 2.0, index data was stored in a separate index.
Loki 2.0 brings an index mechanism named ‘boltdb-shipper’ and is what we now call Single Store Loki. This index type only requires one store, the object store, for both the index and chunks. More detailed information can be found on the operations page.
Some more storage details can also be found in the operations section.
Implementations - Chunks
Cassandra is a popular database and one of Loki’s possible chunk stores and is production safe.
GCS is a hosted object store offered by Google. It is a good candidate for a managed object store, especially when you’re already running on GCP, and is production safe.
The file system is the simplest backend for chunks, although it’s also susceptible to data loss as it’s unreplicated. This is common for single binary deployments though, as well as for those trying out loki or doing local development on the project. It is similar in concept to many Prometheus deployments where a single Prometheus is responsible for monitoring a fleet.
S3 is AWS’s hosted object store. It is a good candidate for a managed object store, especially when you’re already running on AWS, and is production safe.
You may use any substitutable services, such as those that implement the S3 API like MinIO.
Implementations - Index
Also known as “boltdb-shipper” during development (and is still the schema
store name). The single store configurations for Loki utilize the chunk store for both chunks and the index, requiring just one store to run Loki.
As of 2.0, this is the recommended index storage type, performance is comparable to a dedicated index type while providing a much less expensive and less complicated deployment.
Cassandra can also be utilized for the index store and aside from the boltdb-shipper, it’s the only non-cloud offering that can be used for the index that’s horizontally scalable and has configurable replication. It’s a good candidate when you already run Cassandra, are running on-prem, or do not wish to use a managed cloud offering.
Bigtable is a cloud database offered by Google. It is a good candidate for a managed index store if you’re already using it (due to it’s heavy fixed costs) or wish to run in GCP.
DynamoDB is a cloud database offered by AWS. It is a good candidate for a managed index store, especially if you’re already running in AWS.
BoltDB is an embedded database on disk. It is not replicated and thus cannot be used for high availability or clustered Loki deployments, but is commonly paired with a
filesystem chunk store for proof of concept deployments, trying out Loki, and development. The boltdb-shipper aims to support clustered deployments using
boltdb as an index.
Azure Storage Account
An Azure storage account contains all of your Azure Storage data objects: blobs, file shares, queues, tables, and disks.
Loki aims to be backwards compatible and over the course of its development has had many internal changes that facilitate better and more efficient storage/querying. Loki allows incrementally upgrading to these new storage schemas and can query across them transparently. This makes upgrading a breeze. For instance, this is what it looks like when migrating from the v10 -> v11 schemas starting 2020-07-01:
schema_config: configs: - from: 2019-07-01 store: boltdb object_store: filesystem schema: v10 index: prefix: index_ period: 168h - from: 2020-07-01 store: boltdb object_store: filesystem schema: v11 index: prefix: index_ period: 168h
For all data ingested before 2020-07-01, Loki used the v10 schema and then switched after that point to the more effective v11. This dramatically simplifies upgrading, ensuring it’s simple to take advantages of new storage optimizations. These configs should be immutable for as long as you care about retention.
One of the subcomponents in Loki is the
table-manager. It is responsible for pre-creating and expiring index tables. This helps partition the writes and reads in loki across a set of distinct indices in order to prevent unbounded growth.
table_manager: # The retention period must be a multiple of the index / chunks # table "period" (see period_config). retention_deletes_enabled: true # This is 15 weeks retention, based on the 168h (1week) period durations used in the rest of the examples. retention_period: 2520h
For more information, see the table manager documentation.
In the case of AWS DynamoDB, you’ll likely want to tune the provisioned throughput for your tables as well. This is to prevent your tables being rate limited on one hand and assuming unnecessary cost on the other. By default Loki uses a provisioned capacity strategy for DynamoDB tables like so:
table_manager: index_tables_provisioning: # Read/write throughput requirements for the current table # (the table which would handle writes/reads for data timestamped at the current time) provisioned_write_throughput: <int> | default = 3000 provisioned_read_throughput: <int> | default = 300 # Read/write throughput requirements for non-current tables inactive_write_throughput: <int> | default = 1 inactive_read_throughput: <int> | Default = 300
Note, there are a few other DynamoDB provisioning options including DynamoDB autoscaling and on-demand capacity. See the provisioning configuration documentation for more information.
When a new schema is released and you want to gain the advantages it provides, you can! Loki can transparently query & merge data from across schema boundaries so there is no disruption of service and upgrading is easy.
First, you’ll want to create a new period_config entry in your schema_config. The important thing to remember here is to set this at some point in the future and then roll out the config file changes to Loki. This allows the table manager to create the required table in advance of writes and ensures that existing data isn’t queried as if it adheres to the new schema.
As an example, let’s say it’s 2020-07-14 and we want to start using the
v11 schema on the 20th:
schema_config: configs: - from: 2019-07-14 store: boltdb object_store: filesystem schema: v10 index: prefix: index_ period: 168h - from: 2020-07-20 store: boltdb object_store: filesystem schema: v11 index: prefix: index_ period: 168h
It’s that easy; we just created a new entry starting on the 20th.
With the exception of the
filesystem chunk store, Loki will not delete old chunk stores. This is generally handled instead by configuring TTLs (time to live) in the chunk store of your choice (bucket lifecycles in S3/GCS, and TTLs in Cassandra). Neither will Loki currently delete old data when your local disk fills when using the
filesystem chunk store – deletion is only determined by retention duration.
We’re interested in adding targeted deletion in future Loki releases (think tenant or stream level granularity) and may include other strategies as well.
For more information, see the retention configuration documentation.
Single machine/local development (boltdb+filesystem)
The repo contains a working example, you may want to checkout a tag of the repo to make sure you get a compatible example.
GCP deployment (GCS Single Store)
storage_config: boltdb_shipper: active_index_directory: /loki/boltdb-shipper-active cache_location: /loki/boltdb-shipper-cache cache_ttl: 24h # Can be increased for faster performance over longer query periods, uses more disk space shared_store: gcs gcs: bucket_name: <bucket> schema_config: configs: - from: 2020-07-01 store: boltdb-shipper object_store: gcs schema: v11 index: prefix: index_ period: 24h
AWS deployment (S3 Single Store)
storage_config: boltdb_shipper: active_index_directory: /loki/boltdb-shipper-active cache_location: /loki/boltdb-shipper-cache cache_ttl: 24h # Can be increased for faster performance over longer query periods, uses more disk space shared_store: s3 aws: s3: s3://<access_key>:<uri-encoded-secret-access-key>@<region> bucketnames: <bucket1,bucket2> schema_config: configs: - from: 2020-07-01 store: boltdb-shipper object_store: aws schema: v11 index: prefix: index_ period: 24h
If you don’t wish to hard-code S3 credentials, you can also configure an EC2
instance role by changing the
storage_config: aws: s3: s3://region bucketnames: <bucket1,bucket2> dynamodb: dynamodb_url: dynamodb://region
On prem deployment (Cassandra+Cassandra)
Keeping this for posterity, but this is likely not a common config. Cassandra should work and could be faster in some situations but is likely much more expensive.
storage_config: cassandra: addresses: <comma-separated-IPs-or-hostnames> keyspace: <keyspace> auth: <true|false> username: <username> # only applicable when auth=true password: <password> # only applicable when auth=true schema_config: configs: - from: 2020-07-01 store: cassandra object_store: cassandra schema: v11 index: prefix: index_ period: 168h chunks: prefix: chunk_ period: 168h
On prem deployment (MinIO Single Store)
We configure MinIO by using the AWS config because MinIO implements the S3 API:
storage_config: aws: # Note: use a fully qualified domain name, like localhost. # full example: http://loki:supersecret@localhost.:9000 s3: http<s>://<username>:<secret>@<fqdn>:<port> s3forcepathstyle: true boltdb_shipper: active_index_directory: /loki/boltdb-shipper-active cache_location: /loki/boltdb-shipper-cache cache_ttl: 24h # Can be increased for faster performance over longer query periods, uses more disk space shared_store: s3 schema_config: configs: - from: 2020-07-01 store: boltdb-shipper object_store: aws schema: v11 index: prefix: index_ period: 24h
Azure Storage Account
schema_config: configs: - from: "2020-12-11" index: period: 24h prefix: index_ object_store: azure schema: v11 store: boltdb-shipper storage_config: azure: # For the account-key, see docs: https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal account_key: <account-key> # Your azure account name account_name: <account-name> # See https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction#containers container_name: <container-name> request_timeout: 0 use_managed_identity: <true|false> boltdb_shipper: active_index_directory: /data/loki/boltdb-shipper-active cache_location: /data/loki/boltdb-shipper-cache cache_ttl: 24h shared_store: azure filesystem: directory: /data/loki/chunks
Related Loki resources
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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
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