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Inserts data into a table.


INSERT INTO [TABLE] [db.]table [(c1, c2, c3)] [SETTINGS ...] VALUES (v11, v12, v13), (v21, v22, v23), ...

You can specify a list of columns to insert using the (c1, c2, c3). You can also use an expression with column matcher such as * and/or modifiers such as APPLY, EXCEPT, REPLACE.

For example, consider the table:

SHOW CREATE insert_select_testtable;
CREATE TABLE insert_select_testtable
`a` Int8,
`b` String,
`c` Int8
ENGINE = MergeTree()
INSERT INTO insert_select_testtable (*) VALUES (1, 'a', 1) ;

If you want to insert data in all the columns, except 'b', you need to pass so many values how many columns you chose in parenthesis then:

INSERT INTO insert_select_testtable (* EXCEPT(b)) Values (2, 2);
SELECT * FROM insert_select_testtable;
│ 2 │ │ 2 │
│ 1 │ a │ 1 │

In this example, we see that the second inserted row has a and c columns filled by the passed values, and b filled with value by default. It is also possible to use DEFAULT keyword to insert default values:

INSERT INTO insert_select_testtable VALUES (1, DEFAULT, 1) ;

If a list of columns does not include all existing columns, the rest of the columns are filled with:

  • The values calculated from the DEFAULT expressions specified in the table definition.
  • Zeros and empty strings, if DEFAULT expressions are not defined.

Data can be passed to the INSERT in any format supported by ClickHouse. The format must be specified explicitly in the query:

INSERT INTO [db.]table [(c1, c2, c3)] FORMAT format_name data_set

For example, the following query format is identical to the basic version of INSERT ... VALUES:

INSERT INTO [db.]table [(c1, c2, c3)] FORMAT Values (v11, v12, v13), (v21, v22, v23), ...

ClickHouse removes all spaces and one line feed (if there is one) before the data. When forming a query, we recommend putting the data on a new line after the query operators (this is important if the data begins with spaces).


11 Hello, world!
22 Qwerty

You can insert data separately from the query by using the command-line client or the HTTP interface.


If you want to specify SETTINGS for INSERT query then you have to do it before FORMAT clause since everything after FORMAT format_name is treated as data. For example:

INSERT INTO table SETTINGS ... FORMAT format_name data_set


If table has constraints, their expressions will be checked for each row of inserted data. If any of those constraints is not satisfied — server will raise an exception containing constraint name and expression, the query will be stopped.

Inserting the Results of SELECT


INSERT INTO [TABLE] [db.]table [(c1, c2, c3)] SELECT ...

Columns are mapped according to their position in the SELECT clause. However, their names in the SELECT expression and the table for INSERT may differ. If necessary, type casting is performed.

None of the data formats except Values allow setting values to expressions such as now(), 1 + 2, and so on. The Values format allows limited use of expressions, but this is not recommended, because in this case inefficient code is used for their execution.

Other queries for modifying data parts are not supported: UPDATE, DELETE, REPLACE, MERGE, UPSERT, INSERT UPDATE. However, you can delete old data using ALTER TABLE ... DROP PARTITION.

FORMAT clause must be specified in the end of query if SELECT clause contains table function input().

To insert a default value instead of NULL into a column with not nullable data type, enable insert_null_as_default setting.

Inserting Data from a File


INSERT INTO [TABLE] [db.]table [(c1, c2, c3)] FROM INFILE file_name [COMPRESSION type] [SETTINGS ...] [FORMAT format_name]

Use the syntax above to insert data from a file, or files, stored on the client side. file_name and type are string literals. Input file format must be set in the FORMAT clause.

Compressed files are supported. The compression type is detected by the extension of the file name. Or it can be explicitly specified in a COMPRESSION clause. Supported types are: 'none', 'gzip', 'deflate', 'br', 'xz', 'zstd', 'lz4', 'bz2'.

This functionality is available in the command-line client and clickhouse-local.


Single file with FROM INFILE

Execute the following queries using command-line client:

echo 1,A > input.csv ; echo 2,B >> input.csv
clickhouse-client --query="CREATE TABLE table_from_file (id UInt32, text String) ENGINE=MergeTree() ORDER BY id;"
clickhouse-client --query="INSERT INTO table_from_file FROM INFILE 'input.csv' FORMAT CSV;"
clickhouse-client --query="SELECT * FROM table_from_file FORMAT PrettyCompact;"


│ 1 │ A │
│ 2 │ B │

Multiple files with FROM INFILE using globs

This example is very similar to the previous one but inserts from multiple files using FROM INFILE 'input_*.csv.

echo 1,A > input_1.csv ; echo 2,B > input_2.csv
clickhouse-client --query="CREATE TABLE infile_globs (id UInt32, text String) ENGINE=MergeTree() ORDER BY id;"
clickhouse-client --query="INSERT INTO infile_globs FROM INFILE 'input_*.csv' FORMAT CSV;"
clickhouse-client --query="SELECT * FROM infile_globs FORMAT PrettyCompact;"

In addition to selecting multiple files with *, you can use ranges ({1,2} or {1..9}) and other glob substitutions. These three all would work with the above example:

INSERT INTO infile_globs FROM INFILE 'input_*.csv' FORMAT CSV;
INSERT INTO infile_globs FROM INFILE 'input_{1,2}.csv' FORMAT CSV;
INSERT INTO infile_globs FROM INFILE 'input_?.csv' FORMAT CSV;

Inserting using a Table Function

Data can be inserted into tables referenced by table functions.




remote table function is used in the following queries:

CREATE TABLE simple_table (id UInt32, text String) ENGINE=MergeTree() ORDER BY id;
INSERT INTO TABLE FUNCTION remote('localhost', default.simple_table)
VALUES (100, 'inserted via remote()');
SELECT * FROM simple_table;


│ 100 │ inserted via remote() │

Inserting into ClickHouse Cloud

By default, services on ClickHouse Cloud provide multiple replicas for high availability. When you connect to a service, a connection is established to one of these replicas.

After an INSERT succeeds, data is written to the underlying storage. However, it may take some time for replicas to receive these updates. Therefore, if you use a different connection that executes a SELECT query on one of these other replicas, the updated data may not yet be reflected.

It is possible to use the select_sequential_consistency to force the replica to receive the latest updates. Here is an example of a SELECT query using this setting:

SELECT .... SETTINGS select_sequential_consistency = 1;

Note that using select_sequential_consistency will increase the load on ClickHouse Keeper (used by ClickHouse Cloud internally) and may result in slower performance depending on the load on the service. We recommend against enabling this setting unless necessary. The recommended approach is to execute read/writes in the same session or to use a client driver that uses the native protocol (and thus supports sticky connections).

Inserting into a replicated setup

In a replicated setup, data will be visible on other replicas after it has been replicated. Data begins being replicated (downloaded on other replicas) immediately after an INSERT. This differs from ClickHouse Cloud, where data is immediately written to shared storage and replicas subscribe to metadata changes.

Note that for replicated setups, INSERTs can sometimes take a considerable amount of time (in the order of one second) as it requires committing to ClickHouse Keeper for distributed consensus. Using S3 for storage also adds additional latency.

Performance Considerations

INSERT sorts the input data by primary key and splits them into partitions by a partition key. If you insert data into several partitions at once, it can significantly reduce the performance of the INSERT query. To avoid this:

  • Add data in fairly large batches, such as 100,000 rows at a time.
  • Group data by a partition key before uploading it to ClickHouse.

Performance will not decrease if:

  • Data is added in real time.
  • You upload data that is usually sorted by time.

Asynchronous inserts

It is possible to asynchronously insert data in small but frequent inserts. The data from such insertions is combined into batches and then safely inserted into a table. To use asynchronous inserts, enable the async_insert setting.

Using async_insert or the Buffer table engine results in additional buffering.

Large or long-running inserts

When you are inserting large amounts of data, ClickHouse will optimize write performance through a process called "squashing". Small blocks of inserted data in memory are merged and squashed into larger blocks before being written to disk. Squashing reduces the overhead associated with each write operation. In this process, inserted data will be available to query after ClickHouse completes writing each max_insert_block_size rows.

See Also