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Documentation Grafana Loki Manage Storage Table manager

Table manager

Grafana Loki supports storing indexes and chunks in table-based data storages. When such a storage type is used, multiple tables are created over the time: each table - also called periodic table - contains the data for a specific time range.

This design brings two main benefits:

1. Schema config changes: each table is bounded to a schema config and version, so that changes can be introduced over the time and multiple schema configs can coexist
2. Retention: the retention is implemented deleting an entire table, which allows to have fast delete operations

The Table Manager is a Loki component which takes care of creating a periodic table before its time period begins, and deleting it once its data time range exceeds the retention period.

The Table Manager supports the following backends:

• Index store
  • Single Store (boltdb-shipper)
  • Amazon DynamoDB
  • Google Bigtable
  • Apache Cassandra
  • BoltDB (primarily used for local environments)
• Chunk store
  • Filesystem (primarily used for local environments)

Loki does support the following backends for both index and chunk storage, but they are deprecated and will be removed in a future release:

• Amazon DynamoDB
• Google Bigtable
• Apache Cassandra

The object storages - like Amazon S3 and Google Cloud Storage - supported by Loki to store chunks, are not managed by the Table Manager, and a custom bucket policy should be set to delete old data.

For detailed information on configuring the Table Manager, refer to the table_manager section in the Loki configuration document.

Tables and schema config

A periodic table stores the index or chunk data relative to a specific period of time. The duration of the time range of the data stored in a single table and its storage type is configured in the schema_config configuration block.

The schema_config can contain one or more configs. Each config, defines the storage used between the day set in from (in the format yyyy-mm-dd) and the next config, or “now” in the case of the last schema config entry.

This allows to have multiple non-overlapping schema configs over the time, in order to perform schema version upgrades or change storage settings (including changing the storage type).

The write path hits the table where the log entry timestamp falls into (usually the last table, except short periods close to the end of a table and the beginning of the next one), while the read path hits the tables containing data for the query time range.

Schema config example

For example, the following schema_config defines two configurations: the first one using the schema v10 and the current one using the v11.

The first config stores data between 2019-01-01 and 2019-04-14 (included), then a new config has been added - to upgrade the schema version to v11 - storing data using the v11 schema from 2019-04-15 on.

For each config, multiple tables are created, each one storing data for period time (168 hours = 7 days).

yaml Copy

schema_config:
  configs:
    - from:   2019-01-01
      store:  dynamo
      schema: v10
      index:
        prefix: loki_
        period: 168h
    - from:   2019-04-15
      store:  dynamo
      schema: v11
      index:
        prefix: loki_
        period: 168h

Table creation

The Table Manager creates new tables slightly ahead of their start period, in order to make sure that the new table is ready once the current table end period is reached.

The creation_grace_period property - in the table_manager configuration block - defines how long before a table should be created.

Retention

The retention - managed by the Table Manager - is disabled by default, due to its destructive nature. You can enable the data retention explicitly enabling it in the configuration and setting a retention_period greater than zero:

yaml Copy

table_manager:
  retention_deletes_enabled: true
  retention_period: 336h

The Table Manager implements the retention deleting the entire tables whose data exceeded the retention_period. This design allows to have fast delete operations, at the cost of having a retention granularity controlled by the table’s period.

Given each table contains data for period of time and that the entire table is deleted, the Table Manager keeps the last tables alive using this formula:

Copy

number_of_tables_to_keep = floor(retention_period / table_period) + 1

For detailed information on configuring the retention, refer to the Loki Storage Retention documentation.

Active / inactive tables

A table can be active or inactive.

A table is considered active if the current time is within the range:

• Table start period - creation_grace_period
• Table end period + max chunk age (hardcoded to 12h)

Currently, the difference between an active and inactive table only applies to the DynamoDB storage settings: capacity mode (on-demand or provisioned), read/write capacity units and autoscaling.

DynamoDB Provisioning

When configuring DynamoDB with the Table Manager, the default on-demand provisioning capacity units for reads are set to 300 and writes are set to 3000. The defaults can be overwritten:

yaml Copy

table_manager:
  index_tables_provisioning:
    provisioned_write_throughput: 10
    provisioned_read_throughput: 10
  chunk_tables_provisioning:
    provisioned_write_throughput: 10
    provisioned_read_throughput: 10

If Table Manager is not automatically managing DynamoDB, old data cannot easily be erased and the index will grow indefinitely. Manual configurations should ensure that the primary index key is set to h (string) and the sort key is set to r (binary). The “period” attribute in the configuration YAML should be set to 0.

Table Manager deployment mode

The Table Manager can be executed in two ways:

1. Implicitly executed when Loki runs in monolithic mode (single process)
2. Explicitly executed when Loki runs in microservices mode

Monolithic mode

When Loki runs in monolithic mode, the Table Manager is also started as component of the entire stack.

Microservices mode

When Loki runs in microservices mode, the Table Manager should be started as separate service named table-manager.

You can check out a production grade deployment example at table-manager.libsonnet.