ClickHouse/docs/en/sql_reference/statements/select.md

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33	SELECT

SELECT Queries Syntax

SELECT performs data retrieval.

[WITH expr_list|(subquery)]
SELECT [DISTINCT] expr_list
[FROM [db.]table | (subquery) | table_function] [FINAL]
[SAMPLE sample_coeff]
[ARRAY JOIN ...]
[GLOBAL] [ANY|ALL] [INNER|LEFT|RIGHT|FULL|CROSS] [OUTER] JOIN (subquery)|table USING columns_list
[PREWHERE expr]
[WHERE expr]
[GROUP BY expr_list] [WITH TOTALS]
[HAVING expr]
[ORDER BY expr_list]
[LIMIT [offset_value, ]n BY columns]
[LIMIT [n, ]m]
[UNION ALL ...]
[INTO OUTFILE filename]
[FORMAT format]

All the clauses are optional, except for the required list of expressions immediately after SELECT. The clauses below are described in almost the same order as in the query execution conveyor.

If the query omits the DISTINCT, GROUP BY and ORDER BY clauses and the IN and JOIN subqueries, the query will be completely stream processed, using O(1) amount of RAM. Otherwise, the query might consume a lot of RAM if the appropriate restrictions are not specified: max_memory_usage, max_rows_to_group_by, max_rows_to_sort, max_rows_in_distinct, max_bytes_in_distinct, max_rows_in_set, max_bytes_in_set, max_rows_in_join, max_bytes_in_join, max_bytes_before_external_sort, max_bytes_before_external_group_by. For more information, see the section “Settings”. It is possible to use external sorting (saving temporary tables to a disk) and external aggregation. The system does not have "merge join".

WITH Clause

This section provides support for Common Table Expressions (CTE), with some limitations:

1. Recursive queries are not supported
2. When subquery is used inside WITH section, it’s result should be scalar with exactly one row
3. Expression’s results are not available in subqueries Results of WITH clause expressions can be used inside SELECT clause.

Example 1: Using constant expression as “variable”

WITH '2019-08-01 15:23:00' as ts_upper_bound
SELECT *
FROM hits
WHERE
    EventDate = toDate(ts_upper_bound) AND
    EventTime <= ts_upper_bound

Example 2: Evicting sum(bytes) expression result from SELECT clause column list

WITH sum(bytes) as s
SELECT
    formatReadableSize(s),
    table
FROM system.parts
GROUP BY table
ORDER BY s

Example 3: Using results of scalar subquery

/* this example would return TOP 10 of most huge tables */
WITH
    (
        SELECT sum(bytes)
        FROM system.parts
        WHERE active
    ) AS total_disk_usage
SELECT
    (sum(bytes) / total_disk_usage) * 100 AS table_disk_usage,
    table
FROM system.parts
GROUP BY table
ORDER BY table_disk_usage DESC
LIMIT 10

Example 4: Re-using expression in subquery As a workaround for current limitation for expression usage in subqueries, you may duplicate it.

WITH ['hello'] AS hello
SELECT
    hello,
    *
FROM
(
    WITH ['hello'] AS hello
    SELECT hello
)

┌─hello─────┬─hello─────┐
│ ['hello'] │ ['hello'] │
└───────────┴───────────┘

FROM Clause

If the FROM clause is omitted, data will be read from the system.one table. The system.one table contains exactly one row (this table fulfills the same purpose as the DUAL table found in other DBMSs).

The FROM clause specifies the source to read data from:

• Table
• Subquery
• Table function

ARRAY JOIN and the regular JOIN may also be included (see below).

Instead of a table, the SELECT subquery may be specified in parenthesis. In contrast to standard SQL, a synonym does not need to be specified after a subquery.

To execute a query, all the columns listed in the query are extracted from the appropriate table. Any columns not needed for the external query are thrown out of the subqueries. If a query does not list any columns (for example, SELECT count() FROM t), some column is extracted from the table anyway (the smallest one is preferred), in order to calculate the number of rows.

FINAL Modifier

Applicable when selecting data from tables from the MergeTree-engine family other than GraphiteMergeTree. When FINAL is specified, ClickHouse fully merges the data before returning the result and thus performs all data transformations that happen during merges for the given table engine.

Also supported for:

• Replicated versions of MergeTree engines.
• View, Buffer, Distributed, and MaterializedView engines that operate over other engines, provided they were created over MergeTree-engine tables.

Queries that use FINAL are executed not as fast as similar queries that don’t, because:

• Query is executed in a single thread and data is merged during query execution.
• Queries with FINAL read primary key columns in addition to the columns specified in the query.

In most cases, avoid using FINAL.

SAMPLE Clause

The SAMPLE clause allows for approximated query processing.

When data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1/10 fraction of all the visits and then multiply the result by 10.

Approximated query processing can be useful in the following cases:

• When you have strict timing requirements (like <100ms) but you can’t justify the cost of additional hardware resources to meet them.
• When your raw data is not accurate, so approximation doesn’t noticeably degrade the quality.
• Business requirements target approximate results (for cost-effectiveness, or in order to market exact results to premium users).

!!! note "Note" You can only use sampling with the tables in the MergeTree family, and only if the sampling expression was specified during table creation (see MergeTree engine).

The features of data sampling are listed below:

• Data sampling is a deterministic mechanism. The result of the same SELECT .. SAMPLE query is always the same.
• Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the IN clause. Also, you can join samples using the JOIN clause.
• Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see Creating a MergeTree Table.

For the SAMPLE clause the following syntax is supported:

SAMPLE Clause Syntax	Description
SAMPLE k	Here k is the number from 0 to 1. The query is executed on k fraction of data. For example, SAMPLE 0.1 runs the query on 10% of data. Read more
SAMPLE n	Here n is a sufficiently large integer. The query is executed on a sample of at least n rows (but not significantly more than this). For example, SAMPLE 10000000 runs the query on a minimum of 10,000,000 rows. Read more
SAMPLE k OFFSET m	Here k and m are the numbers from 0 to 1. The query is executed on a sample of k fraction of the data. The data used for the sample is offset by m fraction. Read more

SAMPLE K

Here k is the number from 0 to 1 (both fractional and decimal notations are supported). For example, SAMPLE 1/2 or SAMPLE 0.5.

In a SAMPLE k clause, the sample is taken from the k fraction of data. The example is shown below:

SELECT
    Title,
    count() * 10 AS PageViews
FROM hits_distributed
SAMPLE 0.1
WHERE
    CounterID = 34
GROUP BY Title
ORDER BY PageViews DESC LIMIT 1000

In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value count() is manually multiplied by 10.

SAMPLE N

Here n is a sufficiently large integer. For example, SAMPLE 10000000.

In this case, the query is executed on a sample of at least n rows (but not significantly more than this). For example, SAMPLE 10000000 runs the query on a minimum of 10,000,000 rows.

Since the minimum unit for data reading is one granule (its size is set by the index_granularity setting), it makes sense to set a sample that is much larger than the size of the granule.

When using the SAMPLE n clause, you don’t know which relative percent of data was processed. So you don’t know the coefficient the aggregate functions should be multiplied by. Use the _sample_factor virtual column to get the approximate result.

The _sample_factor column contains relative coefficients that are calculated dynamically. This column is created automatically when you create a table with the specified sampling key. The usage examples of the _sample_factor column are shown below.

Let’s consider the table visits, which contains the statistics about site visits. The first example shows how to calculate the number of page views:

SELECT sum(PageViews * _sample_factor)
FROM visits
SAMPLE 10000000

The next example shows how to calculate the total number of visits:

SELECT sum(_sample_factor)
FROM visits
SAMPLE 10000000

The example below shows how to calculate the average session duration. Note that you don’t need to use the relative coefficient to calculate the average values.

SELECT avg(Duration)
FROM visits
SAMPLE 10000000

SAMPLE K OFFSET M

Here k and m are numbers from 0 to 1. Examples are shown below.

Example 1

SAMPLE 1/10

In this example, the sample is 1/10th of all data:

[++------------]

Example 2

SAMPLE 1/10 OFFSET 1/2

Here, a sample of 10% is taken from the second half of the data.

[------++------]

ARRAY JOIN Clause

Allows executing JOIN with an array or nested data structure. The intent is similar to the arrayJoin function, but its functionality is broader.

SELECT <expr_list>
FROM <left_subquery>
[LEFT] ARRAY JOIN <array>
[WHERE|PREWHERE <expr>]
...

You can specify only a single ARRAY JOIN clause in a query.

The query execution order is optimized when running ARRAY JOIN. Although ARRAY JOIN must always be specified before the WHERE/PREWHERE clause, it can be performed either before WHERE/PREWHERE (if the result is needed in this clause), or after completing it (to reduce the volume of calculations). The processing order is controlled by the query optimizer.

Supported types of ARRAY JOIN are listed below:

• ARRAY JOIN - In this case, empty arrays are not included in the result of JOIN.
• LEFT ARRAY JOIN - The result of JOIN contains rows with empty arrays. The value for an empty array is set to the default value for the array element type (usually 0, empty string or NULL).

The examples below demonstrate the usage of the ARRAY JOIN and LEFT ARRAY JOIN clauses. Let’s create a table with an Array type column and insert values into it:

CREATE TABLE arrays_test
(
    s String,
    arr Array(UInt8)
) ENGINE = Memory;

INSERT INTO arrays_test
VALUES ('Hello', [1,2]), ('World', [3,4,5]), ('Goodbye', []);

┌─s───────────┬─arr─────┐
│ Hello       │ [1,2]   │
│ World       │ [3,4,5] │
│ Goodbye     │ []      │
└─────────────┴─────────┘

The example below uses the ARRAY JOIN clause:

SELECT s, arr
FROM arrays_test
ARRAY JOIN arr;

┌─s─────┬─arr─┐
│ Hello │   1 │
│ Hello │   2 │
│ World │   3 │
│ World │   4 │
│ World │   5 │
└───────┴─────┘

The next example uses the LEFT ARRAY JOIN clause:

SELECT s, arr
FROM arrays_test
LEFT ARRAY JOIN arr;

┌─s───────────┬─arr─┐
│ Hello       │   1 │
│ Hello       │   2 │
│ World       │   3 │
│ World       │   4 │
│ World       │   5 │
│ Goodbye     │   0 │
└─────────────┴─────┘

Using Aliases

An alias can be specified for an array in the ARRAY JOIN clause. In this case, an array item can be accessed by this alias, but the array itself is accessed by the original name. Example:

SELECT s, arr, a
FROM arrays_test
ARRAY JOIN arr AS a;

┌─s─────┬─arr─────┬─a─┐
│ Hello │ [1,2]   │ 1 │
│ Hello │ [1,2]   │ 2 │
│ World │ [3,4,5] │ 3 │
│ World │ [3,4,5] │ 4 │
│ World │ [3,4,5] │ 5 │
└───────┴─────────┴───┘

Using aliases, you can perform ARRAY JOIN with an external array. For example:

SELECT s, arr_external
FROM arrays_test
ARRAY JOIN [1, 2, 3] AS arr_external;

┌─s───────────┬─arr_external─┐
│ Hello       │            1 │
│ Hello       │            2 │
│ Hello       │            3 │
│ World       │            1 │
│ World       │            2 │
│ World       │            3 │
│ Goodbye     │            1 │
│ Goodbye     │            2 │
│ Goodbye     │            3 │
└─────────────┴──────────────┘

Multiple arrays can be comma-separated in the ARRAY JOIN clause. In this case, JOIN is performed with them simultaneously (the direct sum, not the cartesian product). Note that all the arrays must have the same size. Example:

SELECT s, arr, a, num, mapped
FROM arrays_test
ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num, arrayMap(x -> x + 1, arr) AS mapped;

┌─s─────┬─arr─────┬─a─┬─num─┬─mapped─┐
│ Hello │ [1,2]   │ 1 │   1 │      2 │
│ Hello │ [1,2]   │ 2 │   2 │      3 │
│ World │ [3,4,5] │ 3 │   1 │      4 │
│ World │ [3,4,5] │ 4 │   2 │      5 │
│ World │ [3,4,5] │ 5 │   3 │      6 │
└───────┴─────────┴───┴─────┴────────┘

The example below uses the arrayEnumerate function:

SELECT s, arr, a, num, arrayEnumerate(arr)
FROM arrays_test
ARRAY JOIN arr AS a, arrayEnumerate(arr) AS num;

┌─s─────┬─arr─────┬─a─┬─num─┬─arrayEnumerate(arr)─┐
│ Hello │ [1,2]   │ 1 │   1 │ [1,2]               │
│ Hello │ [1,2]   │ 2 │   2 │ [1,2]               │
│ World │ [3,4,5] │ 3 │   1 │ [1,2,3]             │
│ World │ [3,4,5] │ 4 │   2 │ [1,2,3]             │
│ World │ [3,4,5] │ 5 │   3 │ [1,2,3]             │
└───────┴─────────┴───┴─────┴─────────────────────┘

ARRAY JOIN With Nested Data Structure

ARRAYJOIN`` also works with nested data structures. Example:

CREATE TABLE nested_test
(
    s String,
    nest Nested(
    x UInt8,
    y UInt32)
) ENGINE = Memory;

INSERT INTO nested_test
VALUES ('Hello', [1,2], [10,20]), ('World', [3,4,5], [30,40,50]), ('Goodbye', [], []);

┌─s───────┬─nest.x──┬─nest.y─────┐
│ Hello   │ [1,2]   │ [10,20]    │
│ World   │ [3,4,5] │ [30,40,50] │
│ Goodbye │ []      │ []         │
└─────────┴─────────┴────────────┘

SELECT s, `nest.x`, `nest.y`
FROM nested_test
ARRAY JOIN nest;

┌─s─────┬─nest.x─┬─nest.y─┐
│ Hello │      1 │     10 │
│ Hello │      2 │     20 │
│ World │      3 │     30 │
│ World │      4 │     40 │
│ World │      5 │     50 │
└───────┴────────┴────────┘

When specifying names of nested data structures in ARRAY JOIN, the meaning is the same as ARRAY JOIN with all the array elements that it consists of. Examples are listed below:

SELECT s, `nest.x`, `nest.y`
FROM nested_test
ARRAY JOIN `nest.x`, `nest.y`;

┌─s─────┬─nest.x─┬─nest.y─┐
│ Hello │      1 │     10 │
│ Hello │      2 │     20 │
│ World │      3 │     30 │
│ World │      4 │     40 │
│ World │      5 │     50 │
└───────┴────────┴────────┘

This variation also makes sense:

SELECT s, `nest.x`, `nest.y`
FROM nested_test
ARRAY JOIN `nest.x`;

┌─s─────┬─nest.x─┬─nest.y─────┐
│ Hello │      1 │ [10,20]    │
│ Hello │      2 │ [10,20]    │
│ World │      3 │ [30,40,50] │
│ World │      4 │ [30,40,50] │
│ World │      5 │ [30,40,50] │
└───────┴────────┴────────────┘

An alias may be used for a nested data structure, in order to select either the JOIN result or the source array. Example:

SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`
FROM nested_test
ARRAY JOIN nest AS n;

┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┐
│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │
│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │
│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │
│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │
│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │
└───────┴─────┴─────┴─────────┴────────────┘

Example of using the arrayEnumerate function:

SELECT s, `n.x`, `n.y`, `nest.x`, `nest.y`, num
FROM nested_test
ARRAY JOIN nest AS n, arrayEnumerate(`nest.x`) AS num;

┌─s─────┬─n.x─┬─n.y─┬─nest.x──┬─nest.y─────┬─num─┐
│ Hello │   1 │  10 │ [1,2]   │ [10,20]    │   1 │
│ Hello │   2 │  20 │ [1,2]   │ [10,20]    │   2 │
│ World │   3 │  30 │ [3,4,5] │ [30,40,50] │   1 │
│ World │   4 │  40 │ [3,4,5] │ [30,40,50] │   2 │
│ World │   5 │  50 │ [3,4,5] │ [30,40,50] │   3 │
└───────┴─────┴─────┴─────────┴────────────┴─────┘

JOIN Clause

Joins the data in the normal SQL JOIN sense.

!!! info "Note" Not related to ARRAY JOIN.

SELECT <expr_list>
FROM <left_subquery>
[GLOBAL] [ANY|ALL] [INNER|LEFT|RIGHT|FULL|CROSS] [OUTER] JOIN <right_subquery>
(ON <expr_list>)|(USING <column_list>) ...

The table names can be specified instead of <left_subquery> and <right_subquery>. This is equivalent to the SELECT * FROM table subquery, except in a special case when the table has the Join engine – an array prepared for joining.

Supported Types Of JOIN

• INNER JOIN (or JOIN)
• LEFT JOIN (or LEFT OUTER JOIN)
• RIGHT JOIN (or RIGHT OUTER JOIN)
• FULL JOIN (or FULL OUTER JOIN)
• CROSS JOIN (or , )

See the standard SQL JOIN description.

Multiple JOIN

Performing queries, ClickHouse rewrites multi-table joins into the sequence of two-table joins. For example, if there are four tables for join ClickHouse joins the first and the second, then joins the result with the third table, and at the last step, it joins the fourth one.

If a query contains the WHERE clause, ClickHouse tries to pushdown filters from this clause through the intermediate join. If it cannot apply the filter to each intermediate join, ClickHouse applies the filters after all joins are completed.

We recommend the JOIN ON or JOIN USING syntax for creating queries. For example:

SELECT * FROM t1 JOIN t2 ON t1.a = t2.a JOIN t3 ON t1.a = t3.a

You can use comma-separated lists of tables in the FROM clause. For example:

SELECT * FROM t1, t2, t3 WHERE t1.a = t2.a AND t1.a = t3.a

Don’t mix these syntaxes.

ClickHouse doesn’t directly support syntax with commas, so we don’t recommend using them. The algorithm tries to rewrite the query in terms of CROSS JOIN and INNER JOIN clauses and then proceeds to query processing. When rewriting the query, ClickHouse tries to optimize performance and memory consumption. By default, ClickHouse treats commas as an INNER JOIN clause and converts INNER JOIN to CROSS JOIN when the algorithm cannot guarantee that INNER JOIN returns the required data.

Strictness

• ALL — If the right table has several matching rows, ClickHouse creates a Cartesian product from matching rows. This is the standard JOIN behavior in SQL.
• ANY — If the right table has several matching rows, only the first one found is joined. If the right table has only one matching row, the results of queries with ANY and ALL keywords are the same.
• ASOF — For joining sequences with a non-exact match. ASOF JOIN usage is described below.

ASOF JOIN Usage

ASOF JOIN is useful when you need to join records that have no exact match.

Tables for ASOF JOIN must have an ordered sequence column. This column cannot be alone in a table, and should be one of the data types: UInt32, UInt64, Float32, Float64, Date, and DateTime.

Syntax ASOF JOIN ... ON:

SELECT expressions_list
FROM table_1
ASOF LEFT JOIN table_2
ON equi_cond AND closest_match_cond

You can use any number of equality conditions and exactly one closest match condition. For example, SELECT count() FROM table_1 ASOF LEFT JOIN table_2 ON table_1.a == table_2.b AND table_2.t <= table_1.t.

Conditions supported for the closest match: >, >=, <, <=.

Syntax ASOF JOIN ... USING:

SELECT expressions_list
FROM table_1
ASOF JOIN table_2
USING (equi_column1, ... equi_columnN, asof_column)

ASOF JOIN uses equi_columnX for joining on equality and asof_column for joining on the closest match with the table_1.asof_column >= table_2.asof_column condition. The asof_column column always the last one in the USING clause.

For example, consider the following tables:

``` text table_1 table_2

event | ev_time | user_id event | ev_time | user_id