remote, remoteSecure 

Allows to access remote servers without creating a Distributed table. remoteSecure - same as remote but with a secured connection.

Both functions can be used in SELECT and INSERT queries.


remote('addresses_expr', db, table[, 'user'[, 'password'], sharding_key])
remote('addresses_expr', db.table[, 'user'[, 'password'], sharding_key])
remoteSecure('addresses_expr', db, table[, 'user'[, 'password'], sharding_key])
remoteSecure('addresses_expr', db.table[, 'user'[, 'password'], sharding_key])


  • addresses_expr — An expression that generates addresses of remote servers. This may be just one server address. The server address is host:port, or just host.

    The host can be specified as the server name, or as the IPv4 or IPv6 address. An IPv6 address is specified in square brackets.

    The port is the TCP port on the remote server. If the port is omitted, it uses tcp_port from the server’s config file in remote (by default, 9000) and tcp_port_secure in remoteSecure (by default, 9440).

    The port is required for an IPv6 address.

    Type: String.

  • db — Database name. Type: String.

  • table — Table name. Type: String.
  • user — User name. If the user is not specified, default is used. Type: String.
  • password — User password. If the password is not specified, an empty password is used. Type: String.
  • sharding_key — Sharding key to support distributing data across nodes. For example: insert into remote(',', db, table, 'default', rand()). Type: UInt32.

Returned value

The dataset from remote servers.


Using the remote table function is less optimal than creating a Distributed table because in this case the server connection is re-established for every request. Also, if hostnames are set, the names are resolved, and errors are not counted when working with various replicas. When processing a large number of queries, always create the Distributed table ahead of time, and don’t use the remote table function.

The remote table function can be useful in the following cases:

  • Accessing a specific server for data comparison, debugging, and testing.
  • Queries between various ClickHouse clusters for research purposes.
  • Infrequent distributed requests that are made manually.
  • Distributed requests where the set of servers is re-defined each time.



Multiple addresses can be comma-separated. In this case, ClickHouse will use distributed processing, so it will send the query to all specified addresses (like shards with different data). Example:


Part of the expression can be specified in curly brackets. The previous example can be written as follows:


Curly brackets can contain a range of numbers separated by two dots (non-negative integers). In this case, the range is expanded to a set of values that generate shard addresses. If the first number starts with zero, the values are formed with the same zero alignment. The previous example can be written as follows:


If you have multiple pairs of curly brackets, it generates the direct product of the corresponding sets.

Addresses and parts of addresses in curly brackets can be separated by the pipe symbol (|). In this case, the corresponding sets of addresses are interpreted as replicas, and the query will be sent to the first healthy replica. However, the replicas are iterated in the order currently set in the load_balancing setting. This example specifies two shards that each have two replicas:


The number of addresses generated is limited by a constant. Right now this is 1000 addresses.


Selecting data from a remote server:

SELECT * FROM remote('', db.remote_engine_table) LIMIT 3;

Inserting data from a remote server into a table:

CREATE TABLE remote_table (name String, value UInt32) ENGINE=Memory;
INSERT INTO FUNCTION remote('', currentDatabase(), 'remote_table') VALUES ('test', 42);
SELECT * FROM remote_table;

Original article

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