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1
docs/en/engines/table-engines/index.md
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@ -61,6 +61,7 @@ Engines in the family:
- [RabbitMQ](../../engines/table-engines/integrations/rabbitmq.md) - [RabbitMQ](../../engines/table-engines/integrations/rabbitmq.md)
- [PostgreSQL](../../engines/table-engines/integrations/postgresql.md) - [PostgreSQL](../../engines/table-engines/integrations/postgresql.md)
- [S3Queue](../../engines/table-engines/integrations/s3queue.md) - [S3Queue](../../engines/table-engines/integrations/s3queue.md)
- [TimeSeries](../../engines/table-engines/integrations/time-series.md)
### Special Engines {#special-engines} ### Special Engines {#special-engines}

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---
slug: /en/engines/table-engines/special/time_series
sidebar_position: 60
sidebar_label: TimeSeries
---
# TimeSeries Engine [Experimental]
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[...] = ...
```
:::info
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](../../../operations/settings/settings.md#allow-experimental-time-series-table) setting.
Input the command `set allow_experimental_time_series_table = 1`.
:::
## Syntax {#syntax}
``` sql
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 {#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):
``` sql
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):
- [prometheus remote-write](../../../interfaces/prometheus.md#remote-write)
- [prometheus remote-read](../../../interfaces/prometheus.md#remote-read)
## Target tables {#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](../../../sql-reference/statements/create/view#materialized-view) works,
with the difference that a materialized view has one target table
whereas a `TimeSeries` table has three target tables named [data]{#data-table}, [tags]{#tags-table], and [metrics]{#metrics-table}.
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:
1. The _data_ table {#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` |
2. The _tags_ table {#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]{#data-table} 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](#settings) 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](#settings) 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](#settings) 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](#settings) is `true` |
3. The _metrics_ table {#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]{#data-table}, [tags]{#tags-table}, [metrics]{#metrics-table} tables.
## Creation {#creation}
There are multiple ways to create a table with the `TimeSeries` table engine.
The simplest statement
``` sql
CREATE TABLE my_table ENGINE=TimeSeries
```
will actually create the following table (you can see that by executing `SHOW CREATE TABLE my_table`):
``` sql
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](../../../operations/settings/settings#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:
``` sql
CREATE TABLE default.`.inner_id.data.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`
(
`id` UUID,
`timestamp` DateTime64(3),
`value` Float64
)
ENGINE = MergeTree
ORDER BY (id, timestamp)
```
``` sql
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)
```
``` sql
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 {#adjusting-column-types}
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,
``` sql
CREATE TABLE my_table
(
timestamp DateTime64(6)
) ENGINE=TimeSeries
```
will make the inner [data]{#data-table} table store timestamp in microseconds instead of milliseconds:
``` sql
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 {#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:
``` sql
CREATE TABLE my_table
(
id UInt64 DEFAULT sipHash64(metric_name, all_tags)
) ENGINE=TimeSeries
```
## The `tags` and `all_tags` columns {#tags-and-all-tags}
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:
``` sql
CREATE TABLE my_table ENGINE=TimeSeries SETTINGS = {'instance': 'instance', 'job': 'job'}
```
This statement will add columns
```
`instance` String,
`job` String
```
to the definition of both `my_table` and its inner [tags]{#tags-table} 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:
``` sql
CREATE TABLE my_table (instance LowCardinality(String), job LowCardinality(Nullable(String)))
ENGINE=TimeSeries SETTINGS = {'instance': 'instance', 'job': 'job'}
```
## Table engines of inner target tables {#inner-table-engines}
By default inner target tables use the following table engines:
- the [data]{#data-table} table uses [MergeTree](../mergetree-family/mergetree);
- the [tags]{#tags-table} table uses [AggregatingMergeTree](../mergetree-family/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]{#metrics-table} table uses [ReplacingMergeTree](../mergetree-family/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:
``` sql
CREATE TABLE my_table ENGINE=TimeSeries
DATA ENGINE=ReplicatedMergeTree
TAGS ENGINE=ReplicatedAggregatingMergeTree
METRICS ENGINE=ReplicatedReplacingMergeTree
```
## External target tables {#external-target-tables}
It's possible to make a `TimeSeries` table use a manually created table:
``` sql
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 {#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]{#tags-table} 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 {#functions}
Here is a list of functions supporting a `TimeSeries` table as an argument:
- [timeSeriesData](../../../sql-reference/table-functions/timeSeriesData.md)
- [timeSeriesTags](../../../sql-reference/table-functions/timeSeriesTags.md)
- [timeSeriesMetrics](../../../sql-reference/table-functions/timeSeriesMetrics.md)

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---
slug: /en/interfaces/prometheus
sidebar_position: 19
sidebar_label: Prometheus protocols
---
# Prometheus protocols
## Exposing metrics {#expose}
:::note
ClickHouse Cloud does not currently support connecting to Prometheus. To be notified when this feature is supported, please contact support@clickhouse.com.
:::
ClickHouse can expose its own metrics for scraping from Prometheus:
```xml
<prometheus>
<port>9363</port>
<endpoint>/metrics</endpoint>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</prometheus>
Section `<prometheus.handlers>` can be used to make more extended handlers.
This section is similar to [<http_handlers>](http.md) but works for prometheus protocols:
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/metrics</url>
<handler>
<type>expose_metrics</type>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the exposing metrics protocol. |
| `endpoint` | `/metrics` | HTTP endpoint for scraping metrics by prometheus server. Starts with `/`. Should not be used with the `<handlers>` section. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](http.md) section. |
| `metrics` | true | Expose metrics from the [system.metrics](../operations/system-tables/metrics.md) table. |
| `asynchronous_metrics` | true | Expose current metrics values from the [system.asynchronous_metrics](../operations/system-tables/asynchronous_metrics.md) table. |
| `events` | true | Expose metrics from the [system.events](../operations/system-tables/events.md) table. |
| `errors` | true | Expose the number of errors by error codes occurred since the last server restart. This information could be obtained from the [system.errors](../operations/system-tables/errors.md) as well. |
Check (replace `127.0.0.1` with the IP addr or hostname of your ClickHouse server):
```bash
curl 127.0.0.1:9363/metrics
```
## Remote-write protocol {#remote-write}
ClickHouse supports the [remote-write](https://prometheus.io/docs/specs/remote_write_spec/) protocol.
Data are received by this protocol and written to a [TimeSeries](../engines/table-engines/integrations/time-series.md) table
(which should be created beforehand).
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/write</url>
<handler>
<type>remote_write</type
<database>db_name</database>
<table>time_series_table</table>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the `remote-write` protocol. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](http.md) section. |
| `table` | none | The name of a [TimeSeries](../engines/table-engines/integrations/time-series.md) table to write data received by the `remote-write` protocol. This name can optionally contain the name of a database too. |
| `database` | none | The name of a database where the table specified in the `table` setting is located if it's not specified in the `table` setting. |
## Remote-read protocol {#remote-read}
ClickHouse supports the [remote-read](https://prometheus.io/docs/prometheus/latest/querying/remote_read_api/) protocol.
Data are read from a [TimeSeries](../engines/table-engines/integrations/time-series.md) table and sent via this protocol.
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/read</url>
<handler>
<type>remote_read</type
<database>db_name</database>
<table>time_series_table</table>
</handler>
</my_rule_1>
</handlers>
</prometheus>
```
Settings:
| Name | Default | Description |
|---|---|---|---|
| `port` | none | Port for serving the `remote-read` protocol. |
| `url` / `headers` / `method` | none | Filters used to find a matching handler for a request. Similar to the fields with the same names in the [<http_handlers>](http.md) section. |
| `table` | none | The name of a [TimeSeries](../engines/table-engines/integrations/time-series.md) table to read data to send by the `remote-read` protocol. This name can optionally contain the name of a database too. |
| `database` | none | The name of a database where the table specified in the `table` setting is located if it's not specified in the `table` setting. |
## Configuration for multiple protocols {#multiple-protocols}
Multiple protocols can be specified together in one place:
```xml
<prometheus>
<port>9363</port>
<handlers>
<my_rule_1>
<url>/metrics</url>
<handler>
<type>expose_metrics</type>
<metrics>true</metrics>
<asynchronous_metrics>true</asynchronous_metrics>
<events>true</events>
<errors>true</errors>
</handler>
</my_rule_1>
<my_rule_2>
<url>/write</url>
<handler>
<type>remote_write</type
<table>db_name.time_series_table</table>
</handler>
</my_rule_2>
<my_rule_3>
<url>/read</url>
<handler>
<type>remote_read</type
<table>db_name.time_series_table</table>
</handler>
</my_rule_3>
</handlers>
</prometheus>
```

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@ -2112,48 +2112,6 @@ The trailing slash is mandatory.
<path>/var/lib/clickhouse/</path> <path>/var/lib/clickhouse/</path>
``` ```
## Prometheus {#prometheus}
:::note
ClickHouse Cloud does not currently support connecting to Prometheus. To be notified when this feature is supported, please contact support@clickhouse.com.
:::
Exposing metrics data for scraping from [Prometheus](https://prometheus.io).
Settings:
- `endpoint` – HTTP endpoint for scraping metrics by prometheus server. Start from ‘/’.
- `port` – Port for `endpoint`.
- `metrics` – Expose metrics from the [system.metrics](../../operations/system-tables/metrics.md#system_tables-metrics) table.
- `events` – Expose metrics from the [system.events](../../operations/system-tables/events.md#system_tables-events) table.
- `asynchronous_metrics` – Expose current metrics values from the [system.asynchronous_metrics](../../operations/system-tables/asynchronous_metrics.md#system_tables-asynchronous_metrics) table.
- `errors` - Expose the number of errors by error codes occurred since the last server restart. This information could be obtained from the [system.errors](../../operations/system-tables/asynchronous_metrics.md#system_tables-errors) as well.
**Example**
``` xml
<clickhouse>
<listen_host>0.0.0.0</listen_host>
<http_port>8123</http_port>
<tcp_port>9000</tcp_port>
<!-- highlight-start -->
<prometheus>
<endpoint>/metrics</endpoint>
<port>9363</port>
<metrics>true</metrics>
<events>true</events>
<asynchronous_metrics>true</asynchronous_metrics>
<errors>true</errors>
</prometheus>
<!-- highlight-end -->
</clickhouse>
```
Check (replace `127.0.0.1` with the IP addr or hostname of your ClickHouse server):
```bash
curl 127.0.0.1:9363/metrics
```
## query_log {#query-log} ## query_log {#query-log}
Setting for logging queries received with the [log_queries=1](../../operations/settings/settings.md) setting. Setting for logging queries received with the [log_queries=1](../../operations/settings/settings.md) setting.

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Disable all insert and mutations (alter table update / alter table delete / alter table drop partition). Set to true, can make this node focus on reading queries. Disable all insert and mutations (alter table update / alter table delete / alter table drop partition). Set to true, can make this node focus on reading queries.
Default value: `false`. Default value: `false`.
## allow_experimental_time_series_table {#allow-experimental-time-series-table}
Allows creation of tables with the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine.
Possible values:
- 0 — the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine is disabled.
- 1 — the [TimeSeries](../../engines/table-engines/integrations/time-series.md) table engine is enabled.
Default value: `0`.

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---
slug: /en/sql-reference/table-functions/timeSeriesData
sidebar_position: 145
sidebar_label: timeSeriesData
---
# timeSeriesData
`timeSeriesData(db_name.time_series_table)` - Returns the [data](../../engines/table-engines/integrations/time-series.md#data-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries DATA data_table
```
The function also works if the _data_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries DATA INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesData(db_name.time_series_table);
SELECT * FROM timeSeriesData('db_name.time_series_table');
SELECT * FROM timeSeriesData('db_name', 'time_series_table');
```

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---
slug: /en/sql-reference/table-functions/timeSeriesMetrics
sidebar_position: 145
sidebar_label: timeSeriesMetrics
---
# timeSeriesMetrics
`timeSeriesMetrics(db_name.time_series_table)` - Returns the [metrics](../../engines/table-engines/integrations/time-series.md#metrics-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries METRICS metrics_table
```
The function also works if the _metrics_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries METRICS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesMetrics(db_name.time_series_table);
SELECT * FROM timeSeriesMetrics('db_name.time_series_table');
SELECT * FROM timeSeriesMetrics('db_name', 'time_series_table');
```

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---
slug: /en/sql-reference/table-functions/timeSeriesTags
sidebar_position: 145
sidebar_label: timeSeriesTags
---
# timeSeriesTags
`timeSeriesTags(db_name.time_series_table)` - Returns the [tags](../../engines/table-engines/integrations/time-series.md#tags-table) table
used by table `db_name.time_series_table` which table engine is [TimeSeries](../../engines/table-engines/integrations/time-series.md):
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries TAGS tags_table
```
The function also works if the _tags_ table is inner:
``` sql
CREATE TABLE db_name.time_series_table ENGINE=TimeSeries TAGS INNER UUID '01234567-89ab-cdef-0123-456789abcdef'
```
The following queries are equivalent:
``` sql
SELECT * FROM timeSeriesTags(db_name.time_series_table);
SELECT * FROM timeSeriesTags('db_name.time_series_table');
SELECT * FROM timeSeriesTags('db_name', 'time_series_table');
```

6
utils/check-style/aspell-ignore/en/aspell-dict.txt
View File

@ -1672,6 +1672,7 @@ fuzzQuery
fuzzer fuzzer
fuzzers fuzzers
gRPC gRPC
gaugehistogram
gccMurmurHash gccMurmurHash
gcem gcem
generateRandom generateRandom
@ -2555,6 +2556,7 @@ startsWithUTF
startswith startswith
statbox statbox
stateful stateful
stateset
stddev stddev
stddevPop stddevPop
stddevPopStable stddevPopStable
@ -2686,6 +2688,10 @@ themself
threadpool threadpool
throwIf throwIf
timeDiff timeDiff
TimeSeries
timeSeriesData
timeSeriesMetrics
timeSeriesTags
timeSlot timeSlot
timeSlots timeSlots
timeZone timeZone