ORDER BY Clause
The ORDER BY clause contains a list of expressions, which can each be attributed with DESC (descending) or ASC (ascending) modifier which determine the sorting direction. If the direction is not specified, ASC is assumed, so it’s usually omitted. The sorting direction applies to a single expression, not to the entire list. Example: ORDER BY Visits DESC, SearchPhrase. Sorting is case-sensitive.
If you want to sort by column numbers instead of column names, enable the setting enable_positional_arguments.
Rows that have identical values for the list of sorting expressions are output in an arbitrary order, which can also be non-deterministic (different each time). If the ORDER BY clause is omitted, the order of the rows is also undefined, and may be non-deterministic as well.
Sorting of Special Values
There are two approaches to NaN and NULL sorting order:
- By default or with the
NULLS LASTmodifier: first the values, thenNaN, thenNULL. - With the
NULLS FIRSTmodifier: firstNULL, thenNaN, then other values.
Example
For the table
┌─x─┬────y─┐
│ 1 │ ᴺᵁᴸᴸ │
│ 2 │ 2 │
│ 1 │ nan │
│ 2 │ 2 │
│ 3 │ 4 │
│ 5 │ 6 │
│ 6 │ nan │
│ 7 │ ᴺᵁᴸᴸ │
│ 6 │ 7 │
│ 8 │ 9 │
└───┴──────┘
Run the query SELECT * FROM t_null_nan ORDER BY y NULLS FIRST to get:
┌─x─┬────y─┐
│ 1 │ ᴺᵁᴸᴸ │
│ 7 │ ᴺᵁᴸᴸ │
│ 1 │ nan │
│ 6 │ nan │
│ 2 │ 2 │
│ 2 │ 2 │
│ 3 │ 4 │
│ 5 │ 6 │
│ 6 │ 7 │
│ 8 │ 9 │
└───┴──────┘
When floating point numbers are sorted, NaNs are separate from the other values. Regardless of the sorting order, NaNs come at the end. In other words, for ascending sorting they are placed as if they are larger than all the other numbers, while for descending sorting they are placed as if they are smaller than the rest.
Collation Support
For sorting by String values, you can specify collation (comparison). Example: ORDER BY SearchPhrase COLLATE 'tr' - for sorting by keyword in ascending order, using the Turkish alphabet, case insensitive, assuming that strings are UTF-8 encoded. COLLATE can be specified or not for each expression in ORDER BY independently. If ASC or DESC is specified, COLLATE is specified after it. When using COLLATE, sorting is always case-insensitive.
Collate is supported in LowCardinality, Nullable, Array and Tuple.
We only recommend using COLLATE for final sorting of a small number of rows, since sorting with COLLATE is less efficient than normal sorting by bytes.
Collation Examples
Example only with String values:
Input table:
┌─x─┬─s────┐
│ 1 │ bca │
│ 2 │ ABC │
│ 3 │ 123a │
│ 4 │ abc │
│ 5 │ BCA │
└───┴──────┘
Query:
SELECT * FROM collate_test ORDER BY s ASC COLLATE 'en';
Result:
┌─x─┬─s────┐
│ 3 │ 123a │
│ 4 │ abc │
│ 2 │ ABC │
│ 1 │ bca │
│ 5 │ BCA │
└───┴──────┘
Example with Nullable:
Input table:
┌─x─┬─s────┐
│ 1 │ bca │
│ 2 │ ᴺᵁᴸᴸ │
│ 3 │ ABC │
│ 4 │ 123a │
│ 5 │ abc │
│ 6 │ ᴺᵁᴸᴸ │
│ 7 │ BCA │
└───┴──────┘
Query:
SELECT * FROM collate_test ORDER BY s ASC COLLATE 'en';
Result:
┌─x─┬─s────┐
│ 4 │ 123a │
│ 5 │ abc │
│ 3 │ ABC │
│ 1 │ bca │
│ 7 │ BCA │
│ 6 │ ᴺᵁᴸᴸ │
│ 2 │ ᴺᵁᴸᴸ │
└───┴──────┘
Example with Array:
Input table:
┌─x─┬─s─────────────┐
│ 1 │ ['Z'] │
│ 2 │ ['z'] │
│ 3 │ ['a'] │
│ 4 │ ['A'] │
│ 5 │ ['z','a'] │
│ 6 │ ['z','a','a'] │
│ 7 │ [''] │
└───┴───────────────┘
Query:
SELECT * FROM collate_test ORDER BY s ASC COLLATE 'en';
Result:
┌─x─┬─s─────────────┐
│ 7 │ [''] │
│ 3 │ ['a'] │
│ 4 │ ['A'] │
│ 2 │ ['z'] │
│ 5 │ ['z','a'] │
│ 6 │ ['z','a','a'] │
│ 1 │ ['Z'] │
└───┴───────────────┘
Example with LowCardinality string:
Input table:
┌─x─┬─s───┐
│ 1 │ Z │
│ 2 │ z │
│ 3 │ a │
│ 4 │ A │
│ 5 │ za │
│ 6 │ zaa │
│ 7 │ │
└───┴─────┘
Query:
SELECT * FROM collate_test ORDER BY s ASC COLLATE 'en';
Result:
┌─x─┬─s───┐
│ 7 │ │
│ 3 │ a │
│ 4 │ A │
│ 2 │ z │
│ 1 │ Z │
│ 5 │ za │
│ 6 │ zaa │
└───┴─────┘
Example with Tuple:
┌─x─┬─s───────┐
│ 1 │ (1,'Z') │
│ 2 │ (1,'z') │
│ 3 │ (1,'a') │
│ 4 │ (2,'z') │
│ 5 │ (1,'A') │
│ 6 │ (2,'Z') │
│ 7 │ (2,'A') │
└───┴─────────┘
Query:
SELECT * FROM collate_test ORDER BY s ASC COLLATE 'en';
Result:
┌─x─┬─s───────┐
│ 3 │ (1,'a') │
│ 5 │ (1,'A') │
│ 2 │ (1,'z') │
│ 1 │ (1,'Z') │
│ 7 │ (2,'A') │
│ 4 │ (2,'z') │
│ 6 │ (2,'Z') │
└───┴─────────┘
Implementation Details
Less RAM is used if a small enough LIMIT is specified in addition to ORDER BY. Otherwise, the amount of memory spent is proportional to the volume of data for sorting. For distributed query processing, if GROUP BY is omitted, sorting is partially done on remote servers, and the results are merged on the requestor server. This means that for distributed sorting, the volume of data to sort can be greater than the amount of memory on a single server.
If there is not enough RAM, it is possible to perform sorting in external memory (creating temporary files on a disk). Use the setting max_bytes_before_external_sort for this purpose. If it is set to 0 (the default), external sorting is disabled. If it is enabled, when the volume of data to sort reaches the specified number of bytes, the collected data is sorted and dumped into a temporary file. After all data is read, all the sorted files are merged and the results are output. Files are written to the /var/lib/clickhouse/tmp/ directory in the config (by default, but you can use the tmp_path parameter to change this setting).
Running a query may use more memory than max_bytes_before_external_sort. For this reason, this setting must have a value significantly smaller than max_memory_usage. As an example, if your server has 128 GB of RAM and you need to run a single query, set max_memory_usage to 100 GB, and max_bytes_before_external_sort to 80 GB.
External sorting works much less effectively than sorting in RAM.
Optimization of Data Reading
If ORDER BY expression has a prefix that coincides with the table sorting key, you can optimize the query by using the optimize_read_in_order setting.
When the optimize_read_in_order setting is enabled, the ClickHouse server uses the table index and reads the data in order of the ORDER BY key. This allows to avoid reading all data in case of specified LIMIT. So queries on big data with small limit are processed faster.
Optimization works with both ASC and DESC and does not work together with GROUP BY clause and FINAL modifier.
When the optimize_read_in_order setting is disabled, the ClickHouse server does not use the table index while processing SELECT queries.
Consider disabling optimize_read_in_order manually, when running queries that have ORDER BY clause, large LIMIT and WHERE condition that requires to read huge amount of records before queried data is found.
Optimization is supported in the following table engines:
- MergeTree
- Merge, Buffer, and MaterializedView table engines over
MergeTree-engine tables
In MaterializedView-engine tables the optimization works with views like SELECT ... FROM merge_tree_table ORDER BY pk. But it is not supported in the queries like SELECT ... FROM view ORDER BY pk if the view query does not have the ORDER BY clause.
ORDER BY Expr WITH FILL Modifier
This modifier also can be combined with LIMIT … WITH TIES modifier.
WITH FILL modifier can be set after ORDER BY expr with optional FROM expr, TO expr and STEP expr parameters.
All missed values of expr column will be filled sequentially and other columns will be filled as defaults.
To fill multiple columns, add WITH FILL modifier with optional parameters after each field name in ORDER BY section.
ORDER BY expr [WITH FILL] [FROM const_expr] [TO const_expr] [STEP const_numeric_expr], ... exprN [WITH FILL] [FROM expr] [TO expr] [STEP numeric_expr]
[INTERPOLATE [(col [AS expr], ... colN [AS exprN])]]
WITH FILL can be applied for fields with Numeric (all kinds of float, decimal, int) or Date/DateTime types. When applied for String fields, missed values are filled with empty strings.
When FROM const_expr not defined sequence of filling use minimal expr field value from ORDER BY.
When TO const_expr not defined sequence of filling use maximum expr field value from ORDER BY.
When STEP const_numeric_expr defined then const_numeric_expr interprets as is for numeric types, as days for Date type, as seconds for DateTime type. It also supports INTERVAL data type representing time and date intervals.
When STEP const_numeric_expr omitted then sequence of filling use 1.0 for numeric type, 1 day for Date type and 1 second for DateTime type.
INTERPOLATE can be applied to columns not participating in ORDER BY WITH FILL. Such columns are filled based on previous fields values by applying expr. If expr is not present will repeat previous value. Omitted list will result in including all allowed columns.
Example of a query without WITH FILL:
SELECT n, source FROM (
SELECT toFloat32(number % 10) AS n, 'original' AS source
FROM numbers(10) WHERE number % 3 = 1
) ORDER BY n;
Result:
┌─n─┬─source───┐
│ 1 │ original │
│ 4 │ original │
│ 7 │ original │
└───┴──────────┘
Same query after applying WITH FILL modifier:
SELECT n, source FROM (
SELECT toFloat32(number % 10) AS n, 'original' AS source
FROM numbers(10) WHERE number % 3 = 1
) ORDER BY n WITH FILL FROM 0 TO 5.51 STEP 0.5;
Result:
┌───n─┬─source───┐
│ 0 │ │
│ 0.5 │ │
│ 1 │ original │
│ 1.5 │ │
│ 2 │ │
│ 2.5 │ │
│ 3 │ │
│ 3.5 │ │
│ 4 │ original │
│ 4.5 │ │
│ 5 │ │
│ 5.5 │ │
│ 7 │ original │
└─────┴──────────┘
For the case with multiple fields ORDER BY field2 WITH FILL, field1 WITH FILL order of filling will follow the order of fields in the ORDER BY clause.
Example:
SELECT
toDate((number * 10) * 86400) AS d1,
toDate(number * 86400) AS d2,
'original' AS source
FROM numbers(10)
WHERE (number % 3) = 1
ORDER BY
d2 WITH FILL,
d1 WITH FILL STEP 5;
Result:
┌───d1───────┬───d2───────┬─source───┐
│ 1970-01-11 │ 1970-01-02 │ original │
│ 1970-01-01 │ 1970-01-03 │ │
│ 1970-01-01 │ 1970-01-04 │ │
│ 1970-02-10 │ 1970-01-05 │ original │
│ 1970-01-01 │ 1970-01-06 │ │
│ 1970-01-01 │ 1970-01-07 │ │
│ 1970-03-12 │ 1970-01-08 │ original │
└────────────┴────────────┴──────────┘
Field d1 does not fill in and use the default value cause we do not have repeated values for d2 value, and the sequence for d1 can’t be properly calculated.
The following query with the changed field in ORDER BY:
SELECT
toDate((number * 10) * 86400) AS d1,
toDate(number * 86400) AS d2,
'original' AS source
FROM numbers(10)
WHERE (number % 3) = 1
ORDER BY
d1 WITH FILL STEP 5,
d2 WITH FILL;
Result:
┌───d1───────┬───d2───────┬─source───┐
│ 1970-01-11 │ 1970-01-02 │ original │
│ 1970-01-16 │ 1970-01-01 │ │
│ 1970-01-21 │ 1970-01-01 │ │
│ 1970-01-26 │ 1970-01-01 │ │
│ 1970-01-31 │ 1970-01-01 │ │
│ 1970-02-05 │ 1970-01-01 │ │
│ 1970-02-10 │ 1970-01-05 │ original │
│ 1970-02-15 │ 1970-01-01 │ │
│ 1970-02-20 │ 1970-01-01 │ │
│ 1970-02-25 │ 1970-01-01 │ │
│ 1970-03-02 │ 1970-01-01 │ │
│ 1970-03-07 │ 1970-01-01 │ │
│ 1970-03-12 │ 1970-01-08 │ original │
└────────────┴────────────┴──────────┘
The following query uses the INTERVAL data type of 1 day for each data filled on column d1:
SELECT
toDate((number * 10) * 86400) AS d1,
toDate(number * 86400) AS d2,
'original' AS source
FROM numbers(10)
WHERE (number % 3) = 1
ORDER BY
d1 WITH FILL STEP INTERVAL 1 DAY,
d2 WITH FILL;
Result:
┌─────────d1─┬─────────d2─┬─source───┐
│ 1970-01-11 │ 1970-01-02 │ original │
│ 1970-01-12 │ 1970-01-01 │ │
│ 1970-01-13 │ 1970-01-01 │ │
│ 1970-01-14 │ 1970-01-01 │ │
│ 1970-01-15 │ 1970-01-01 │ │
│ 1970-01-16 │ 1970-01-01 │ │
│ 1970-01-17 │ 1970-01-01 │ │
│ 1970-01-18 │ 1970-01-01 │ │
│ 1970-01-19 │ 1970-01-01 │ │
│ 1970-01-20 │ 1970-01-01 │ │
│ 1970-01-21 │ 1970-01-01 │ │
│ 1970-01-22 │ 1970-01-01 │ │
│ 1970-01-23 │ 1970-01-01 │ │
│ 1970-01-24 │ 1970-01-01 │ │
│ 1970-01-25 │ 1970-01-01 │ │
│ 1970-01-26 │ 1970-01-01 │ │
│ 1970-01-27 │ 1970-01-01 │ │
│ 1970-01-28 │ 1970-01-01 │ │
│ 1970-01-29 │ 1970-01-01 │ │
│ 1970-01-30 │ 1970-01-01 │ │
│ 1970-01-31 │ 1970-01-01 │ │
│ 1970-02-01 │ 1970-01-01 │ │
│ 1970-02-02 │ 1970-01-01 │ │
│ 1970-02-03 │ 1970-01-01 │ │
│ 1970-02-04 │ 1970-01-01 │ │
│ 1970-02-05 │ 1970-01-01 │ │
│ 1970-02-06 │ 1970-01-01 │ │
│ 1970-02-07 │ 1970-01-01 │ │
│ 1970-02-08 │ 1970-01-01 │ │
│ 1970-02-09 │ 1970-01-01 │ │
│ 1970-02-10 │ 1970-01-05 │ original │
│ 1970-02-11 │ 1970-01-01 │ │
│ 1970-02-12 │ 1970-01-01 │ │
│ 1970-02-13 │ 1970-01-01 │ │
│ 1970-02-14 │ 1970-01-01 │ │
│ 1970-02-15 │ 1970-01-01 │ │
│ 1970-02-16 │ 1970-01-01 │ │
│ 1970-02-17 │ 1970-01-01 │ │
│ 1970-02-18 │ 1970-01-01 │ │
│ 1970-02-19 │ 1970-01-01 │ │
│ 1970-02-20 │ 1970-01-01 │ │
│ 1970-02-21 │ 1970-01-01 │ │
│ 1970-02-22 │ 1970-01-01 │ │
│ 1970-02-23 │ 1970-01-01 │ │
│ 1970-02-24 │ 1970-01-01 │ │
│ 1970-02-25 │ 1970-01-01 │ │
│ 1970-02-26 │ 1970-01-01 │ │
│ 1970-02-27 │ 1970-01-01 │ │
│ 1970-02-28 │ 1970-01-01 │ │
│ 1970-03-01 │ 1970-01-01 │ │
│ 1970-03-02 │ 1970-01-01 │ │
│ 1970-03-03 │ 1970-01-01 │ │
│ 1970-03-04 │ 1970-01-01 │ │
│ 1970-03-05 │ 1970-01-01 │ │
│ 1970-03-06 │ 1970-01-01 │ │
│ 1970-03-07 │ 1970-01-01 │ │
│ 1970-03-08 │ 1970-01-01 │ │
│ 1970-03-09 │ 1970-01-01 │ │
│ 1970-03-10 │ 1970-01-01 │ │
│ 1970-03-11 │ 1970-01-01 │ │
│ 1970-03-12 │ 1970-01-08 │ original │
└────────────┴────────────┴──────────┘
Example of a query without INTERPOLATE:
SELECT n, source, inter FROM (
SELECT toFloat32(number % 10) AS n, 'original' AS source, number as inter
FROM numbers(10) WHERE number % 3 = 1
) ORDER BY n WITH FILL FROM 0 TO 5.51 STEP 0.5;
Result:
┌───n─┬─source───┬─inter─┐
│ 0 │ │ 0 │
│ 0.5 │ │ 0 │
│ 1 │ original │ 1 │
│ 1.5 │ │ 0 │
│ 2 │ │ 0 │
│ 2.5 │ │ 0 │
│ 3 │ │ 0 │
│ 3.5 │ │ 0 │
│ 4 │ original │ 4 │
│ 4.5 │ │ 0 │
│ 5 │ │ 0 │
│ 5.5 │ │ 0 │
│ 7 │ original │ 7 │
└─────┴──────────┴───────┘
Same query after applying INTERPOLATE:
SELECT n, source, inter FROM (
SELECT toFloat32(number % 10) AS n, 'original' AS source, number as inter
FROM numbers(10) WHERE number % 3 = 1
) ORDER BY n WITH FILL FROM 0 TO 5.51 STEP 0.5 INTERPOLATE (inter AS inter + 1);
Result:
┌───n─┬─source───┬─inter─┐
│ 0 │ │ 0 │
│ 0.5 │ │ 0 │
│ 1 │ original │ 1 │
│ 1.5 │ │ 2 │
│ 2 │ │ 3 │
│ 2.5 │ │ 4 │
│ 3 │ │ 5 │
│ 3.5 │ │ 6 │
│ 4 │ original │ 4 │
│ 4.5 │ │ 5 │
│ 5 │ │ 6 │
│ 5.5 │ │ 7 │
│ 7 │ original │ 7 │
└─────┴──────────┴───────┘
Filling grouped by sorting prefix
It can be useful to fill rows which have the same values in particular columns independently, - a good example is filling missing values in time series. Assume there is the following time series table:
CREATE TABLE timeseries
(
`sensor_id` UInt64,
`timestamp` DateTime64(3, 'UTC'),
`value` Float64
)
ENGINE = Memory;
SELECT * FROM timeseries;
┌─sensor_id─┬───────────────timestamp─┬─value─┐
│ 234 │ 2021-12-01 00:00:03.000 │ 3 │
│ 432 │ 2021-12-01 00:00:01.000 │ 1 │
│ 234 │ 2021-12-01 00:00:07.000 │ 7 │
│ 432 │ 2021-12-01 00:00:05.000 │ 5 │
└───────────┴─────────────────────────┴───────┘
And we'd like to fill missing values for each sensor independently with 1 second interval.
The way to achieve it is to use sensor_id column as sorting prefix for filling column timestamp:
SELECT *
FROM timeseries
ORDER BY
sensor_id,
timestamp WITH FILL
INTERPOLATE ( value AS 9999 )
┌─sensor_id─┬───────────────timestamp─┬─value─┐
│ 234 │ 2021-12-01 00:00:03.000 │ 3 │
│ 234 │ 2021-12-01 00:00:04.000 │ 9999 │
│ 234 │ 2021-12-01 00:00:05.000 │ 9999 │
│ 234 │ 2021-12-01 00:00:06.000 │ 9999 │
│ 234 │ 2021-12-01 00:00:07.000 │ 7 │
│ 432 │ 2021-12-01 00:00:01.000 │ 1 │
│ 432 │ 2021-12-01 00:00:02.000 │ 9999 │
│ 432 │ 2021-12-01 00:00:03.000 │ 9999 │
│ 432 │ 2021-12-01 00:00:04.000 │ 9999 │
│ 432 │ 2021-12-01 00:00:05.000 │ 5 │
└───────────┴─────────────────────────┴───────┘
Here, the value column was interpolated with 9999 just to make filled rows more noticeable.
This behavior is controlled by setting use_with_fill_by_sorting_prefix (enabled by default)