A table engine storing time series, i.e. a set of values associated with timestamps and tags (or labels):

metric_name1[tag1=value1, tag2=value2, ...] = {timestamp1: value1, timestamp2: value2, ...}
metric_name2[...] = ...

This is an experimental feature that may change in backwards-incompatible ways in the future releases. Enable usage of the TimeSeries table engine with allow_experimental_time_series_table setting. Input the command set allow_experimental_time_series_table = 1.

Syntax

CREATE TABLE name [(columns)] ENGINE=TimeSeries
[SETTINGS var1=value1, ...]
[DATA db.data_table_name | DATA ENGINE data_table_engine(arguments)]
[TAGS db.tags_table_name | TAGS ENGINE tags_table_engine(arguments)]
[METRICS db.metrics_table_name | METRICS ENGINE metrics_table_engine(arguments)]

Usage

It’s easier to start with everything set by default (it’s allowed to create a TimeSeries table without specifying a list of columns):

CREATE TABLE my_table ENGINE=TimeSeries

Then this table can be used with the following protocols (a port must be assigned in the server configuration):

Target tables

A TimeSeries table doesn’t have its own data, everything is stored in its target tables. This is similar to how a materialized view works, with the difference that a materialized view has one target table whereas a TimeSeries table has three target tables named data, tags, and metrics. The target tables can be either specified explicitly in the CREATE TABLE query or the TimeSeries table engine can generate inner target tables automatically. The target tables are the following:

Data table

The data table contains time series associated with some identifier. The data table must have columns:

Name	Mandatory?	Default type	Possible types	Description
`id`	[x]	`UUID`	any	Identifies a combination of a metric names and tags
`timestamp`	[x]	`DateTime64(3)`	`DateTime64(X)`	A time point
`value`	[x]	`Float64`	`Float32` or `Float64`	A value associated with the `timestamp`

Tags table

The tags table contains identifiers calculated for each combination of a metric name and tags. The tags table must have columns:

Name	Mandatory?	Default type	Possible types	Description
`id`	[x]	`UUID`	any (must match the type of `id` in the data table)	An `id` identifies a combination of a metric name and tags. The DEFAULT expression specifies how to calculate such an identifier
`metric_name`	[x]	`LowCardinality(String)`	`String` or `LowCardinality(String)`	The name of a metric
`<tag_value_column>`	[ ]	`String`	`String` or `LowCardinality(String)` or `LowCardinality(Nullable(String))`	The value of a specific tag, the tag’s name and the name of a corresponding column are specified in the tags_to_columns setting
`tags`	[x]	`Map(LowCardinality(String), String)`	`Map(String, String)` or `Map(LowCardinality(String), String)` or `Map(LowCardinality(String), LowCardinality(String))`	Map of tags excluding the tag `__name__` containing the name of a metric and excluding tags with names enumerated in the tags_to_columns setting
`all_tags`	[ ]	`Map(String, String)`	`Map(String, String)` or `Map(LowCardinality(String), String)` or `Map(LowCardinality(String), LowCardinality(String))`	Ephemeral column, each row is a map of all the tags excluding only the tag `__name__` containing the name of a metric. The only purpose of that column is to be used while calculating `id`
`min_time`	[ ]	`Nullable(DateTime64(3))`	`DateTime64(X)` or `Nullable(DateTime64(X))`	Minimum timestamp of time series with that `id`. The column is created if store_min_time_and_max_time is `true`
`max_time`	[ ]	`Nullable(DateTime64(3))`	`DateTime64(X)` or `Nullable(DateTime64(X))`	Maximum timestamp of time series with that `id`. The column is created if store_min_time_and_max_time is `true`

Metrics table

The metrics table contains some information about metrics been collected, the types of those metrics and their descriptions. The metrics table must have columns:

Name	Mandatory?	Default type	Possible types	Description
`metric_family_name`	[x]	`String`	`String` or `LowCardinality(String)`	The name of a metric family
`type`	[x]	`String`	`String` or `LowCardinality(String)`	The type of a metric family, one of “counter”, “gauge”, “summary”, “stateset”, “histogram”, “gaugehistogram”
`unit`	[x]	`String`	`String` or `LowCardinality(String)`	The unit used in a metric
`help`	[x]	`String`	`String` or `LowCardinality(String)`	The description of a metric

Any row inserted into a TimeSeries table will be in fact stored in those three target tables. A TimeSeries table contains all those columns from the data, tags, metrics tables.

Creation

There are multiple ways to create a table with the TimeSeries table engine. The simplest statement

CREATE TABLE my_table ENGINE=TimeSeries

will actually create the following table (you can see that by executing SHOW CREATE TABLE my_table):

CREATE TABLE my_table
(
    `id` UUID DEFAULT reinterpretAsUUID(sipHash128(metric_name, all_tags)),
    `timestamp` DateTime64(3),
    `value` Float64,
    `metric_name` LowCardinality(String),
    `tags` Map(LowCardinality(String), String),
    `all_tags` Map(String, String),
    `min_time` Nullable(DateTime64(3)),
    `max_time` Nullable(DateTime64(3)),
    `metric_family_name` String,
    `type` String,
    `unit` String,
    `help` String
)
ENGINE = TimeSeries
DATA ENGINE = MergeTree ORDER BY (id, timestamp)
DATA INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
TAGS ENGINE = AggregatingMergeTree PRIMARY KEY metric_name ORDER BY (metric_name, id)
TAGS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
METRICS ENGINE = ReplacingMergeTree ORDER BY metric_family_name
METRICS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'

So the columns were generated automatically and also there are three inner UUIDs in this statement - one per each inner target table that was created. (Inner UUIDs are not shown normally until setting show_table_uuid_in_table_create_query_if_not_nil is set.) Inner target tables have names like .inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx, .inner_id.tags.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx, .inner_id.metrics.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx and each target table has columns which is a subset of the columns of the main TimeSeries table:

CREATE TABLE default.`.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
    `id` UUID,
    `timestamp` DateTime64(3),
    `value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp)

CREATE TABLE default.`.inner_id.tags.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
    `id` UUID DEFAULT reinterpretAsUUID(sipHash128(metric_name, all_tags)),
    `metric_name` LowCardinality(String),
    `tags` Map(LowCardinality(String), String),
    `all_tags` Map(String, String) EPHEMERAL,
    `min_time` SimpleAggregateFunction(min, Nullable(DateTime64(3))),
    `max_time` SimpleAggregateFunction(max, Nullable(DateTime64(3)))
)
ENGINE = AggregatingMergeTree
PRIMARY KEY metric_name
ORDER BY (metric_name, id)

CREATE TABLE default.`.inner_id.metrics.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
    `metric_family_name` String,
    `type` String,
    `unit` String,
    `help` String
)
ENGINE = ReplacingMergeTree
ORDER BY metric_family_name

Adjusting types of columns

You can adjust the types of almost any column of the inner target tables by specifying them explicitly while defining the main table. For example,

CREATE TABLE my_table
(
    timestamp DateTime64(6)
) ENGINE=TimeSeries

will make the inner data table store timestamp in microseconds instead of milliseconds:

CREATE TABLE default.`.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
    `id` UUID,
    `timestamp` DateTime64(6),
    `value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp)

The `id` column

The id column contains identifiers, every identifier is calculated for a combination of a metric name and tags. The DEFAULT expression for the id column is an expression which will be used to calculate such identifiers. Both the type of the id column and that expression can be adjusted by specifying them explicitly:

CREATE TABLE my_table
(
  id UInt64 DEFAULT sipHash64(metric_name, all_tags)
)
ENGINE=TimeSeries

The `tags` and `all_tags` columns

There are two columns containing maps of tags - tags and all_tags. In this example they mean the same, however they can be different if setting tags_to_columns is used. This setting allows to specify that a specific tag should be stored in a separate column instead of storing in a map inside the tags column:

CREATE TABLE my_table
ENGINE = TimeSeries 
SETTINGS tags_to_columns = {'instance': 'instance', 'job': 'job'}

This statement will add columns:

`instance` String,
`job` String

to the definition of both my_table and its inner tags target table. In this case the tags column will not contain tags instance and job, but the all_tags column will contain them. The all_tags column is ephemeral and its only purpose to be used in the DEFAULT expression for the id column. The types of columns can be adjusted by specifying them explicitly:

CREATE TABLE my_table (
  instance LowCardinality(String),
  job LowCardinality(Nullable(String))
)
ENGINE=TimeSeries
SETTINGS tags_to_columns = {'instance': 'instance', 'job': 'job'}

Table engines of inner target tables

By default inner target tables use the following table engines:

the data table uses MergeTree;
the tags table uses AggregatingMergeTree because the same data is often inserted multiple times to this table so we need a way to remove duplicates, and also because it’s required to do aggregation for columns min_time and max_time;
the metrics table uses ReplacingMergeTree because the same data is often inserted multiple times to this table so we need a way to remove duplicates.

Other table engines also can be used for inner target tables if it’s specified so:

CREATE TABLE my_table ENGINE=TimeSeries
DATA ENGINE=ReplicatedMergeTree
TAGS ENGINE=ReplicatedAggregatingMergeTree
METRICS ENGINE=ReplicatedReplacingMergeTree

External target tables

It’s possible to make a TimeSeries table use a manually created table:

CREATE TABLE data_for_my_table
(
    `id` UUID,
    `timestamp` DateTime64(3),
    `value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp);

CREATE TABLE tags_for_my_table ...

CREATE TABLE metrics_for_my_table ...

CREATE TABLE my_table ENGINE=TimeSeries DATA data_for_my_table TAGS tags_for_my_table METRICS metrics_for_my_table;

Settings

Here is a list of settings which can be specified while defining a TimeSeries table:

Name	Type	Default	Description
`tags_to_columns`	Map	Map specifying which tags should be put to separate columns in the tags table. Syntax: `{'tag1': 'column1', 'tag2' : column2, ...}`
`use_all_tags_column_to_generate_id`	Bool	true	When generating an expression to calculate an identifier of a time series, this flag enables using the `all_tags` column in that calculation
`store_min_time_and_max_time`	Bool	true	If set to true then the table will store `min_time` and `max_time` for each time series
`aggregate_min_time_and_max_time`	Bool	true	When creating an inner target `tags` table, this flag enables using `SimpleAggregateFunction(min, Nullable(DateTime64(3)))` instead of just `Nullable(DateTime64(3))` as the type of the `min_time` column, and the same for the `max_time` column
`filter_by_min_time_and_max_time`	Bool	true	If set to true then the table will use the `min_time` and `max_time` columns for filtering time series

Functions

Here is a list of functions supporting a TimeSeries table as an argument:

​Syntax

​Usage

​Target tables

​Data table

​Tags table

​Metrics table

​Creation

​Adjusting types of columns

​The id column

​The tags and all_tags columns

​Table engines of inner target tables

​External target tables

​Settings

​Functions

Syntax

Usage

Target tables

Data table

Tags table

Metrics table

Creation

Adjusting types of columns

The `id` column

The `tags` and `all_tags` columns

Table engines of inner target tables

External target tables

Settings

Functions