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clickhouse-local

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When to use clickhouse-local vs. ClickHouse​

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 ClickHouse SQL dialect) 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 clickhouse-client. 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. 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 querying local file or reading a parquet file in S3.

Download clickhouse-local​

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:

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 Quick Start.

Query data in a file using SQL​

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:

./clickhouse local -q "SELECT * FROM 'reviews.tsv'"

This command is a shortcut of:

./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 many ClickHouse input formats:

./clickhouse local -q "SELECT * FROM file('reviews.tsv', 'TabSeparated')"

The file table function creates a table, and you can use DESCRIBE to see the inferred schema:

./clickhouse local -q "DESCRIBE file('reviews.tsv')"
Tip

You are allowed to use globs in file name (See glob substitutions).

Examples:

./clickhouse local -q "SELECT * FROM 'reviews*.jsonl'"
./clickhouse local -q "SELECT * FROM 'review_?.csv'"
./clickhouse local -q "SELECT * FROM 'review_{1..3}.csv'"
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:

./clickhouse local -q "SELECT
    argMax(product_title,star_rating),
    max(star_rating)
FROM file('reviews.tsv')"
Monopoly Junior Board Game  5

Query data in a Parquet file in AWS S3​

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:

./clickhouse local -q "
SELECT count()
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')"

The file has 2.7M rows:

2772030

It's always useful to see what the inferred schema that ClickHouse determines from the file:

./clickhouse local -q "DESCRIBE s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')"
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:

./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/house_parquet/house_0.parquet')
GROUP BY
    town,
    district
HAVING c >= 100
ORDER BY price DESC
LIMIT 10"
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 Quick Start for more details.

Format Conversions​

You can use clickhouse-local for converting data between different formats. Example:

$ clickhouse-local --input-format JSONLines --output-format CSV --query "SELECT * FROM table" < data.json > data.csv

Formats are auto-detected from file extensions: 

$ clickhouse-local --query "SELECT * FROM table" < data.json > data.csv

As a shortcut, you can write it using the --copy argument:

$ clickhouse-local --copy < data.json > data.csv

Usage​

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):

$ clickhouse-local --structure "table_structure" --input-format "format_of_incoming_data" --query "query"

Basic usage (Mac):

$ ./clickhouse local --structure "table_structure" --input-format "format_of_incoming_data" --query "query"
Note

clickhouse-local is also supported on Windows through WSL2.

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.

Examples​

$ 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:

$ echo -e "1,2\n3,4" | clickhouse-local -n --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 file table function:

$ 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 let’s output memory user for each Unix user:

Query:

$ 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:

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 │
├──────────┼──────────┤
...

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