Project: ClickHouse (App Definition and Development / Database)
File: clickhouse-local.md

slug: /en/operations/utilities/clickhouse-local sidebar_position: 60 sidebar_label: clickhouse-local Blog: `clickhouse-local` is an easy-to-use version of ClickHouse that is ideal for developers who need to perform fast processing on local and remote files using SQL without having to install a full database server. With `clickhouse-local`, developers can use SQL commands (using the ) directly from the command line, providing a simple and efficient way to access ClickHouse features without the need for a full ClickHouse installation. One of the main benefits of `clickhouse-local` is that it is already included when installing . This means that developers can get started with `clickhouse-local` quickly, without the need for a complex installation process. While `clickhouse-local` is a great tool for development and testing purposes, and for processing files, it is not suitable for serving end users or applications. In these scenarios, it is recommended to use the open-source . ClickHouse is a powerful OLAP database that is designed to handle large-scale analytical workloads. It provides fast and efficient processing of complex queries on large datasets, making it ideal for use in production environments where high-performance is critical. Additionally, ClickHouse offers a wide range of features such as replication, sharding, and high availability, which are essential for scaling up to handle large datasets and serving applications. If you need to handle larger datasets or serve end users or applications, we recommend using open-source ClickHouse instead of `clickhouse-local`. Please read the docs below that show example use cases for `clickhouse-local`, such as or . `clickhouse-local` is executed using the same `clickhouse` binary that runs the ClickHouse server and `clickhouse-client`. The easiest way to download the latest version is with the following command: ```bash curl https://clickhouse.com/ | sh ``` :::note The binary you just downloaded can run all sorts of ClickHouse tools and utilities. If you want to run ClickHouse as a database server, check out the . ::: A common use of `clickhouse-local` is to run ad-hoc queries on files: where you don't have to insert the data into a table. `clickhouse-local` can stream the data from a file into a temporary table and execute your SQL. If the file is sitting on the same machine as `clickhouse-local`, you can simply specify the file to load. The following `reviews.tsv` file contains a sampling of Amazon product reviews: ```bash ./clickhouse local -q "SELECT * FROM 'reviews.tsv'" ``` This command is a shortcut of: ```bash ./clickhouse local -q "SELECT * FROM file('reviews.tsv')" ``` ClickHouse knows the file uses a tab-separated format from filename extension. If you need to explicitly specify the format, simply add one of the : ```bash ./clickhouse local -q "SELECT * FROM

'TabSeparated')" ``` The `file` table function creates a table, and you can use `DESCRIBE` to see the inferred schema: ```bash ./clickhouse local -q "DESCRIBE file('reviews.tsv')" ``` :::tip You are allowed to use globs in file name (See ). Examples: ```bash ./clickhouse local -q "SELECT FROM 'reviews.jsonl'" ./clickhouse local -q "SELECT * FROM 'review_?.csv'" ./clickhouse local -q "SELECT * FROM 'review_{1..3}.csv'" ``` ::: ```response marketplace Nullable(String) customer_id Nullable(Int64) review_id Nullable(String) product_id Nullable(String) product_parent Nullable(Int64) product_title Nullable(String) product_category Nullable(String) star_rating Nullable(Int64) helpful_votes Nullable(Int64) total_votes Nullable(Int64) vine Nullable(String) verified_purchase Nullable(String) review_headline Nullable(String) review_body Nullable(String) review_date Nullable(Date) ``` Let's find a product with the highest rating: ```bash ./clickhouse local -q "SELECT argMax(producttitle,starrating), max(star_rating) FROM file('reviews.tsv')" ``` ```response Monopoly Junior Board Game 5 ``` If you have a file in S3, use `clickhouse-local` and the `s3` table function to query the file in place (without inserting the data into a ClickHouse table). We have a file named `house_0.parquet` in a public bucket that contains home prices of property sold in the United Kingdom. Let's see how many rows it has: ```bash ./clickhouse local -q " SELECT count() FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/houseparquet/house0.parquet')" ``` The file has 2.7M rows: ```response 2772030 ``` It's always useful to see what the inferred schema that ClickHouse determines from the file: ```bash ./clickhouse local -q "DESCRIBE s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/houseparquet/house0.parquet')" ``` ```response price Nullable(Int64) date Nullable(UInt16) postcode1 Nullable(String) postcode2 Nullable(String) type Nullable(String) is_new Nullable(UInt8) duration Nullable(String) addr1 Nullable(String) addr2 Nullable(String) street Nullable(String) locality Nullable(String) town Nullable(String) district Nullable(String) county Nullable(String) ``` Let's see what the most expensive neighborhoods are: ```bash ./clickhouse local -q " SELECT town, district, count() AS c, round(avg(price)) AS price, bar(price, 0, 5000000, 100) FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/houseparquet/house0.parquet') GROUP BY town, district HAVING c >= 100 ORDER BY price DESC LIMIT 10" ``` ```response LONDON CITY OF LONDON 886 2271305 LEATHERHEAD ELMBRIDGE 206 1176680 LONDON CITY OF WESTMINSTER 12577 1108221 LONDON KENSINGTON AND CHELSEA 8728 1094496 HYTHE FOLKESTONE AND HYTHE 130 1023980 CHALFONT ST GILES CHILTERN 113 835754 AMERSHAM BUCKINGHAMSHIRE 113 799596 VIRGINIA WATER RUNNYMEDE 356 789301 BARNET ENFIELD 282 740514 NORTHWOOD THREE RIVERS 184 731609 ``` :::tip When you are ready to insert your files into ClickHouse, startup a ClickHouse server and insert the results of your `file` and `s3` table functions into a `MergeTree` table. View the for more details. ::: By default `clickhouse-local` has access to data of a ClickHouse server on the same host, and it does not depend on the server's configuration. It also supports loading server configuration using `--config-file` argument. For temporary data, a unique temporary data directory is created by default. Basic usage (Linux): ``` bash $ clickhouse-local --structure "tablestructure" --input-format "formatofincomingdata" --query "query" ``` Basic usage (Mac): ``` bash $ ./clickhouse local --structure "tablestructure" --input-format "formatofincomingdata" --query "query" ``` :::note `clickhouse-local` is also supported on Windows through

::: Arguments: `-S`, `--structure` table structure for input data. `--input-format` input format, `TSV` by default. `-F`, `--file` path to data, `stdin` by default. `-q`, `--query` queries to execute with `;` as delimiter. `--query` can be specified multiple times, e.g. `--query "SELECT 1" --query "SELECT 2"`. Cannot be used simultaneously with `--queries-file`. `--queries-file` - file path with queries to execute. `--queries-file` can be specified multiple times, e.g. `--query queries1.sql --query queries2.sql`. Cannot be used simultaneously with `--query`. `--multiquery, -n` If specified, multiple queries separated by semicolons can be listed after the `--query` option. For convenience, it is also possible to omit `--query` and pass the queries directly after `--multiquery`. `-N`, `--table` table name where to put output data, `table` by default. `-f`, `--format`, `--output-format` output format, `TSV` by default. `-d`, `--database` default database, `_local` by default. `--stacktrace` whether to dump debug output in case of exception. `--echo` print query before execution. `--verbose` more details on query execution. `--logger.console` Log to console. `--logger.log` Log file name. `--logger.level` Log level. `--ignore-error` do not stop processing if a query failed. `-c`, `--config-file` path to configuration file in same format as for ClickHouse server, by default the configuration empty. `--no-system-tables` do not attach system tables. `--help` arguments references for `clickhouse-local`. `-V`, `--version` print version information and exit. Also, there are arguments for each ClickHouse configuration variable which are more commonly used instead of `--config-file`. ``` bash $ echo -e "1,2\n3,4" | clickhouse-local --structure "a Int64, b Int64" \ --input-format "CSV" --query "SELECT * FROM table" Read 2 rows, 32.00 B in 0.000 sec., 5182 rows/sec., 80.97 KiB/sec. 1 2 3 4 ``` Previous example is the same as: ``` bash $ echo -e "1,2\n3,4" | clickhouse-local --query " CREATE TABLE table (a Int64, b Int64) ENGINE = File(CSV, stdin); SELECT a, b FROM table; DROP TABLE table" Read 2 rows, 32.00 B in 0.000 sec., 4987 rows/sec., 77.93 KiB/sec. 1 2 3 4 ``` You don't have to use `stdin` or `--file` argument, and can open any number of files using the : ``` bash $ echo 1 | tee 1.tsv 1 $ echo 2 | tee 2.tsv 2 $ clickhouse-local --query " select * from file('1.tsv', TSV, 'a int') t1 cross join file('2.tsv', TSV, 'b int') t2" 1 2 ``` Now lets output memory user for each Unix user: Query: ``` bash $ ps aux | tail -n +2 | awk '{ printf("%s\t%s\n", $1, $4) }' \ | clickhouse-local --structure "user String, mem Float64" \ --query "SELECT user, round(sum(mem), 2) as memTotal FROM table GROUP BY user ORDER BY memTotal DESC FORMAT Pretty" ``` Result: ``` text Read 186 rows, 4.15 KiB in 0.035 sec., 5302 rows/sec., 118.34 KiB/sec. user memTotal bayonet 113.5 root 8.8 ... ```