# CREATE Queries {#create-queries}

## CREATE DATABASE {#query-language-create-database}

Creates database.

``` sql
CREATE DATABASE [IF NOT EXISTS] db_name [ON CLUSTER cluster] [ENGINE = engine(...)]
```

### Clauses {#clauses}

-   `IF NOT EXISTS`

        If the `db_name` database already exists, then ClickHouse doesn't create a new database and:

        - Doesn't throw an exception if clause is specified.
        - Throws an exception if clause isn't specified.

-   `ON CLUSTER`

        ClickHouse creates the `db_name` database on all the servers of a specified cluster.

-   `ENGINE`

        - [MySQL](../database_engines/mysql.md)

            Allows you to retrieve data from the remote MySQL server.

        By default, ClickHouse uses its own [database engine](../database_engines/index.md).

## CREATE TABLE {#create-table-query}

The `CREATE TABLE` query can have several forms.

``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1] [compression_codec] [TTL expr1],
    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2] [compression_codec] [TTL expr2],
    ...
) ENGINE = engine
```

Creates a table named ‘name’ in the ‘db’ database or the current database if ‘db’ is not set, with the structure specified in brackets and the ‘engine’ engine.
The structure of the table is a list of column descriptions. If indexes are supported by the engine, they are indicated as parameters for the table engine.

A column description is `name type` in the simplest case. Example: `RegionID UInt32`.
Expressions can also be defined for default values (see below).

``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name AS [db2.]name2 [ENGINE = engine]
```

Creates a table with the same structure as another table. You can specify a different engine for the table. If the engine is not specified, the same engine will be used as for the `db2.name2` table.

``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name AS table_function()
```

Creates a table with the structure and data returned by a [table function](table_functions/index.md).

``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name ENGINE = engine AS SELECT ...
```

Creates a table with a structure like the result of the `SELECT` query, with the ‘engine’ engine, and fills it with data from SELECT.

In all cases, if `IF NOT EXISTS` is specified, the query won’t return an error if the table already exists. In this case, the query won’t do anything.

There can be other clauses after the `ENGINE` clause in the query. See detailed documentation on how to create tables in the descriptions of [table engines](../operations/table_engines/index.md#table_engines).

### Default Values {#create-default-values}

The column description can specify an expression for a default value, in one of the following ways:`DEFAULT expr`, `MATERIALIZED expr`, `ALIAS expr`.
Example: `URLDomain String DEFAULT domain(URL)`.

If an expression for the default value is not defined, the default values will be set to zeros for numbers, empty strings for strings, empty arrays for arrays, and `0000-00-00` for dates or `0000-00-00 00:00:00` for dates with time. NULLs are not supported.

If the default expression is defined, the column type is optional. If there isn’t an explicitly defined type, the default expression type is used. Example: `EventDate DEFAULT toDate(EventTime)` – the ‘Date’ type will be used for the ‘EventDate’ column.

If the data type and default expression are defined explicitly, this expression will be cast to the specified type using type casting functions. Example: `Hits UInt32 DEFAULT 0` means the same thing as `Hits UInt32 DEFAULT toUInt32(0)`.

Default expressions may be defined as an arbitrary expression from table constants and columns. When creating and changing the table structure, it checks that expressions don’t contain loops. For INSERT, it checks that expressions are resolvable – that all columns they can be calculated from have been passed.

`DEFAULT expr`

Normal default value. If the INSERT query doesn’t specify the corresponding column, it will be filled in by computing the corresponding expression.

`MATERIALIZED expr`

Materialized expression. Such a column can’t be specified for INSERT, because it is always calculated.
For an INSERT without a list of columns, these columns are not considered.
In addition, this column is not substituted when using an asterisk in a SELECT query. This is to preserve the invariant that the dump obtained using `SELECT *` can be inserted back into the table using INSERT without specifying the list of columns.

`ALIAS expr`

Synonym. Such a column isn’t stored in the table at all.
Its values can’t be inserted in a table, and it is not substituted when using an asterisk in a SELECT query.
It can be used in SELECTs if the alias is expanded during query parsing.

When using the ALTER query to add new columns, old data for these columns is not written. Instead, when reading old data that does not have values for the new columns, expressions are computed on the fly by default. However, if running the expressions requires different columns that are not indicated in the query, these columns will additionally be read, but only for the blocks of data that need it.

If you add a new column to a table but later change its default expression, the values used for old data will change (for data where values were not stored on the disk). Note that when running background merges, data for columns that are missing in one of the merging parts is written to the merged part.

It is not possible to set default values for elements in nested data structures.

### Constraints {#constraints}

Along with columns descriptions constraints could be defined:

``` sql
CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1] [compression_codec] [TTL expr1],
    ...
    CONSTRAINT constraint_name_1 CHECK boolean_expr_1,
    ...
) ENGINE = engine
```

`boolean_expr_1` could by any boolean expression. If constraints are defined for the table, each of them will be checked for every row in `INSERT` query. If any constraint is not satisfied — server will raise an exception with constraint name and checking expression.

Adding large amount of constraints can negatively affect performance of big `INSERT` queries.

### TTL Expression {#ttl-expression}

Defines storage time for values. Can be specified only for MergeTree-family tables. For the detailed description, see [TTL for columns and tables](../operations/table_engines/mergetree.md#table_engine-mergetree-ttl).

### Column Compression Codecs {#codecs}

By default, ClickHouse applies the `lz4` compression method. For `MergeTree`-engine family you can change the default compression method in the [compression](../operations/server_settings/settings.md#server-settings-compression) section of a server configuration. You can also define the compression method for each individual column in the `CREATE TABLE` query.

``` sql
CREATE TABLE codec_example
(
    dt Date CODEC(ZSTD),
    ts DateTime CODEC(LZ4HC),
    float_value Float32 CODEC(NONE),
    double_value Float64 CODEC(LZ4HC(9))
    value Float32 CODEC(Delta, ZSTD)
)
ENGINE = <Engine>
...
```

If a codec is specified, the default codec doesn’t apply. Codecs can be combined in a pipeline, for example, `CODEC(Delta, ZSTD)`. To select the best codec combination for you project, pass benchmarks similar to described in the Altinity [New Encodings to Improve ClickHouse Efficiency](https://www.altinity.com/blog/2019/7/new-encodings-to-improve-clickhouse) article.

!!! warning "Warning"
    You can’t decompress ClickHouse database files with external utilities like `lz4`. Instead, use the special [clickhouse-compressor](https://github.com/ClickHouse/ClickHouse/tree/master/programs/compressor) utility.

Compression is supported for the following table engines:

-   [MergeTree](../operations/table_engines/mergetree.md) family. Supports column compression codecs and selecting the default compression method by [compression](../operations/server_settings/settings.md#server-settings-compression) settings.
-   [Log](../operations/table_engines/log_family.md) family. Uses the `lz4` compression method by default and supports column compression codecs.
-   [Set](../operations/table_engines/set.md). Only supported the default compression.
-   [Join](../operations/table_engines/join.md). Only supported the default compression.

ClickHouse supports common purpose codecs and specialized codecs.

#### Specialized Codecs {#create-query-specialized-codecs}

These codecs are designed to make compression more effective by using specific features of data. Some of these codecs don’t compress data themself. Instead, they prepare the data for a common purpose codec, which compresses it better than without this preparation.

Specialized codecs:

-   `Delta(delta_bytes)` — Compression approach in which raw values are replaced by the difference of two neighboring values, except for the first value that stays unchanged. Up to `delta_bytes` are used for storing delta values, so `delta_bytes` is the maximum size of raw values. Possible `delta_bytes` values: 1, 2, 4, 8. The default value for `delta_bytes` is `sizeof(type)` if equal to 1, 2, 4, or 8. In all other cases, it’s 1.
-   `DoubleDelta` — Calculates delta of deltas and writes it in compact binary form. Optimal compression rates are achieved for monotonic sequences with a constant stride, such as time series data. Can be used with any fixed-width type. Implements the algorithm used in Gorilla TSDB, extending it to support 64-bit types. Uses 1 extra bit for 32-byte deltas: 5-bit prefixes instead of 4-bit prefixes. For additional information, see Compressing Time Stamps in [Gorilla: A Fast, Scalable, In-Memory Time Series Database](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf).
-   `Gorilla` — Calculates XOR between current and previous value and writes it in compact binary form. Efficient when storing a series of floating point values that change slowly, because the best compression rate is achieved when neighboring values are binary equal. Implements the algorithm used in Gorilla TSDB, extending it to support 64-bit types. For additional information, see Compressing Values in [Gorilla: A Fast, Scalable, In-Memory Time Series Database](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf).
-   `T64` — Compression approach that crops unused high bits of values in integer data types (including `Enum`, `Date` and `DateTime`). At each step of its algorithm, codec takes a block of 64 values, puts them into 64x64 bit matrix, transposes it, crops the unused bits of values and returns the rest as a sequence. Unused bits are the bits, that don’t differ between maximum and minimum values in the whole data part for which the compression is used.

`DoubleDelta` and `Gorilla` codecs are used in Gorilla TSDB as the components of its compressing algorithm. Gorilla approach is effective in scenarios when there is a sequence of slowly changing values with their timestamps. Timestamps are effectively compressed by the `DoubleDelta` codec, and values are effectively compressed by the `Gorilla` codec. For example, to get an effectively stored table, you can create it in the following configuration:

``` sql
CREATE TABLE codec_example
(
    timestamp DateTime CODEC(DoubleDelta),
    slow_values Float32 CODEC(Gorilla)
)
ENGINE = MergeTree()
```

#### Common purpose codecs {#create-query-common-purpose-codecs}

Codecs:

-   `NONE` — No compression.
-   `LZ4` — Lossless [data compression algorithm](https://github.com/lz4/lz4) used by default. Applies LZ4 fast compression.
-   `LZ4HC[(level)]` — LZ4 HC (high compression) algorithm with configurable level. Default level: 9. Setting `level <= 0` applies the default level. Possible levels: \[1, 12\]. Recommended level range: \[4, 9\].
-   `ZSTD[(level)]` — [ZSTD compression algorithm](https://en.wikipedia.org/wiki/Zstandard) with configurable `level`. Possible levels: \[1, 22\]. Default value: 1.

High compression levels are useful for asymmetric scenarios, like compress once, decompress repeatedly. Higher levels mean better compression and higher CPU usage.

## Temporary Tables {#temporary-tables}

ClickHouse supports temporary tables which have the following characteristics:

-   Temporary tables disappear when the session ends, including if the connection is lost.
-   A temporary table uses the Memory engine only.
-   The DB can’t be specified for a temporary table. It is created outside of databases.
-   Impossible to create a temporary table with distributed DDL query on all cluster servers (by using `ON CLUSTER`): this table exists only in the current session.
-   If a temporary table has the same name as another one and a query specifies the table name without specifying the DB, the temporary table will be used.
-   For distributed query processing, temporary tables used in a query are passed to remote servers.

To create a temporary table, use the following syntax:

``` sql
CREATE TEMPORARY TABLE [IF NOT EXISTS] table_name
(
    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
    ...
)
```

In most cases, temporary tables are not created manually, but when using external data for a query, or for distributed `(GLOBAL) IN`. For more information, see the appropriate sections

It’s possible to use tables with [ENGINE = Memory](../operations/table_engines/memory.md) instead of temporary tables.

## Distributed DDL queries (ON CLUSTER clause) {#distributed-ddl-queries-on-cluster-clause}

The `CREATE`, `DROP`, `ALTER`, and `RENAME` queries support distributed execution on a cluster.
For example, the following query creates the `all_hits` `Distributed` table on each host in `cluster`:

``` sql
CREATE TABLE IF NOT EXISTS all_hits ON CLUSTER cluster (p Date, i Int32) ENGINE = Distributed(cluster, default, hits)
```

In order to run these queries correctly, each host must have the same cluster definition (to simplify syncing configs, you can use substitutions from ZooKeeper). They must also connect to the ZooKeeper servers.
The local version of the query will eventually be implemented on each host in the cluster, even if some hosts are currently not available. The order for executing queries within a single host is guaranteed.

## CREATE VIEW {#create-view}

``` sql
CREATE [MATERIALIZED] VIEW [IF NOT EXISTS] [db.]table_name [TO[db.]name] [ENGINE = engine] [POPULATE] AS SELECT ...
```

Creates a view. There are two types of views: normal and MATERIALIZED.

Normal views don’t store any data, but just perform a read from another table. In other words, a normal view is nothing more than a saved query. When reading from a view, this saved query is used as a subquery in the FROM clause.

As an example, assume you’ve created a view:

``` sql
CREATE VIEW view AS SELECT ...
```

and written a query:

``` sql
SELECT a, b, c FROM view
```

This query is fully equivalent to using the subquery:

``` sql
SELECT a, b, c FROM (SELECT ...)
```

Materialized views store data transformed by the corresponding SELECT query.

When creating a materialized view without `TO [db].[table]`, you must specify ENGINE – the table engine for storing data.

When creating a materialized view with `TO [db].[table]`, you must not use `POPULATE`.

A materialized view is arranged as follows: when inserting data to the table specified in SELECT, part of the inserted data is converted by this SELECT query, and the result is inserted in the view.

If you specify POPULATE, the existing table data is inserted in the view when creating it, as if making a `CREATE TABLE ... AS SELECT ...` . Otherwise, the query contains only the data inserted in the table after creating the view. We don’t recommend using POPULATE, since data inserted in the table during the view creation will not be inserted in it.

A `SELECT` query can contain `DISTINCT`, `GROUP BY`, `ORDER BY`, `LIMIT`… Note that the corresponding conversions are performed independently on each block of inserted data. For example, if `GROUP BY` is set, data is aggregated during insertion, but only within a single packet of inserted data. The data won’t be further aggregated. The exception is when using an ENGINE that independently performs data aggregation, such as `SummingMergeTree`.

The execution of `ALTER` queries on materialized views has not been fully developed, so they might be inconvenient. If the materialized view uses the construction `TO [db.]name`, you can `DETACH` the view, run `ALTER` for the target table, and then `ATTACH` the previously detached (`DETACH`) view.

Views look the same as normal tables. For example, they are listed in the result of the `SHOW TABLES` query.

There isn’t a separate query for deleting views. To delete a view, use `DROP TABLE`.

## CREATE DICTIONARY {#create-dictionary-query}

``` sql
CREATE DICTIONARY [IF NOT EXISTS] [db.]dictionary_name [ON CLUSTER cluster]
(
    key1 type1  [DEFAULT|EXPRESSION expr1] [HIERARCHICAL|INJECTIVE|IS_OBJECT_ID],
    key2 type2  [DEFAULT|EXPRESSION expr2] [HIERARCHICAL|INJECTIVE|IS_OBJECT_ID],
    attr1 type2 [DEFAULT|EXPRESSION expr3],
    attr2 type2 [DEFAULT|EXPRESSION expr4]
)
PRIMARY KEY key1, key2
SOURCE(SOURCE_NAME([param1 value1 ... paramN valueN]))
LAYOUT(LAYOUT_NAME([param_name param_value]))
LIFETIME([MIN val1] MAX val2)
```

Creates [external dictionary](dicts/external_dicts.md) with given [structure](dicts/external_dicts_dict_structure.md), [source](dicts/external_dicts_dict_sources.md), [layout](dicts/external_dicts_dict_layout.md) and [lifetime](dicts/external_dicts_dict_lifetime.md).

External dictionary structure consists of attributes. Dictionary attributes are specified similarly to table columns. The only required attribute property is its type, all other properties may have default values.

Depending on dictionary [layout](dicts/external_dicts_dict_layout.md) one or more attributes can be specified as dictionary keys.

For more information, see [External Dictionaries](dicts/external_dicts.md) section.

[Original article](https://clickhouse.tech/docs/en/query_language/create/) <!--hide-->