GROUP BY Clause

`GROUP BY` clause switches the `SELECT` query into an aggregation mode, which works as follows:

• `GROUP BY` clause contains a list of expressions (or a single expression, which is considered to be the list of length one). This list acts as a “grouping key”, while each individual expression will be referred to as a “key expression”.
• All the expressions in the SELECT, HAVING, and ORDER BY clauses must be calculated based on key expressions or on aggregate functions over non-key expressions (including plain columns). In other words, each column selected from the table must be used either in a key expression or inside an aggregate function, but not both.
• Result of aggregating `SELECT` query will contain as many rows as there were unique values of “grouping key” in source table. Usually this signficantly reduces the row count, often by orders of magnitude, but not necessarily: row count stays the same if all “grouping key” values were distinct.

NULL Processing

For grouping, ClickHouse interprets NULL as a value, and `NULL==NULL`. It differs from `NULL` processing in most other contexts.

Here’s an example to show what this means.

Assume you have this table:

``````┌─x─┬────y─┐
│ 1 │    2 │
│ 2 │ ᴺᵁᴸᴸ │
│ 3 │    2 │
│ 3 │    3 │
│ 3 │ ᴺᵁᴸᴸ │
└───┴──────┘
``````

The query `SELECT sum(x), y FROM t_null_big GROUP BY y` results in:

``````┌─sum(x)─┬────y─┐
│      4 │    2 │
│      3 │    3 │
│      5 │ ᴺᵁᴸᴸ │
└────────┴──────┘
``````

You can see that `GROUP BY` for `y = NULL` summed up `x`, as if `NULL` is this value.

If you pass several keys to `GROUP BY`, the result will give you all the combinations of the selection, as if `NULL` were a specific value.

WITH ROLLUP Modifier

`WITH ROLLUP` modifier is used to calculate subtotals for the key expressions, based on their order in the `GROUP BY` list. The subtotals rows are added after the result table.

The subtotals are calculated in the reverse order: at first subtotals are calculated for the last key expression in the list, then for the previous one, and so on up to the first key expression.

In the subtotals rows the values of already "grouped" key expressions are set to `0` or empty line.

Example

Consider the table t:

``````┌─year─┬─month─┬─day─┐
│ 2019 │     1 │   5 │
│ 2019 │     1 │  15 │
│ 2020 │     1 │   5 │
│ 2020 │     1 │  15 │
│ 2020 │    10 │   5 │
│ 2020 │    10 │  15 │
└──────┴───────┴─────┘
``````

Query:

``````SELECT year, month, day, count(*) FROM t GROUP BY year, month, day WITH ROLLUP;
``````

As `GROUP BY` section has three key expressions, the result contains four tables with subtotals "rolled up" from right to left:

• `GROUP BY year, month, day`;
• `GROUP BY year, month` (and `day` column is filled with zeros);
• `GROUP BY year` (now `month, day` columns are both filled with zeros);
• and totals (and all three key expression columns are zeros).
``````┌─year─┬─month─┬─day─┬─count()─┐
│ 2020 │    10 │  15 │       1 │
│ 2020 │     1 │   5 │       1 │
│ 2019 │     1 │   5 │       1 │
│ 2020 │     1 │  15 │       1 │
│ 2019 │     1 │  15 │       1 │
│ 2020 │    10 │   5 │       1 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│ 2019 │     1 │   0 │       2 │
│ 2020 │     1 │   0 │       2 │
│ 2020 │    10 │   0 │       2 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│ 2019 │     0 │   0 │       2 │
│ 2020 │     0 │   0 │       4 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│    0 │     0 │   0 │       6 │
└──────┴───────┴─────┴─────────┘
``````

WITH CUBE Modifier

`WITH CUBE` modifier is used to calculate subtotals for every combination of the key expressions in the `GROUP BY` list. The subtotals rows are added after the result table.

In the subtotals rows the values of all "grouped" key expressions are set to `0` or empty line.

Example

Consider the table t:

``````┌─year─┬─month─┬─day─┐
│ 2019 │     1 │   5 │
│ 2019 │     1 │  15 │
│ 2020 │     1 │   5 │
│ 2020 │     1 │  15 │
│ 2020 │    10 │   5 │
│ 2020 │    10 │  15 │
└──────┴───────┴─────┘
``````

Query:

``````SELECT year, month, day, count(*) FROM t GROUP BY year, month, day WITH CUBE;
``````

As `GROUP BY` section has three key expressions, the result contains eight tables with subtotals for all key expression combinations:

• `GROUP BY year, month, day`
• `GROUP BY year, month`
• `GROUP BY year, day`
• `GROUP BY year`
• `GROUP BY month, day`
• `GROUP BY month`
• `GROUP BY day`
• and totals.

Columns, excluded from `GROUP BY`, are filled with zeros.

``````┌─year─┬─month─┬─day─┬─count()─┐
│ 2020 │    10 │  15 │       1 │
│ 2020 │     1 │   5 │       1 │
│ 2019 │     1 │   5 │       1 │
│ 2020 │     1 │  15 │       1 │
│ 2019 │     1 │  15 │       1 │
│ 2020 │    10 │   5 │       1 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│ 2019 │     1 │   0 │       2 │
│ 2020 │     1 │   0 │       2 │
│ 2020 │    10 │   0 │       2 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│ 2020 │     0 │   5 │       2 │
│ 2019 │     0 │   5 │       1 │
│ 2020 │     0 │  15 │       2 │
│ 2019 │     0 │  15 │       1 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│ 2019 │     0 │   0 │       2 │
│ 2020 │     0 │   0 │       4 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│    0 │     1 │   5 │       2 │
│    0 │    10 │  15 │       1 │
│    0 │    10 │   5 │       1 │
│    0 │     1 │  15 │       2 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│    0 │     1 │   0 │       4 │
│    0 │    10 │   0 │       2 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│    0 │     0 │   5 │       3 │
│    0 │     0 │  15 │       3 │
└──────┴───────┴─────┴─────────┘
┌─year─┬─month─┬─day─┬─count()─┐
│    0 │     0 │   0 │       6 │
└──────┴───────┴─────┴─────────┘
``````

WITH TOTALS Modifier

If the `WITH TOTALS` modifier is specified, another row will be calculated. This row will have key columns containing default values (zeros or empty lines), and columns of aggregate functions with the values calculated across all the rows (the “total” values).

This extra row is only produced in `JSON*`, `TabSeparated*`, and `Pretty*` formats, separately from the other rows:

• In `JSON*` formats, this row is output as a separate ‘totals’ field.
• In `TabSeparated*` formats, the row comes after the main result, preceded by an empty row (after the other data).
• In `Pretty*` formats, the row is output as a separate table after the main result.
• In the other formats it is not available.

`WITH TOTALS` can be run in different ways when HAVING is present. The behavior depends on the `totals_mode` setting.

Configuring Totals Processing

By default, `totals_mode = 'before_having'`. In this case, ‘totals’ is calculated across all rows, including the ones that do not pass through HAVING and `max_rows_to_group_by`.

The other alternatives include only the rows that pass through HAVING in ‘totals’, and behave differently with the setting `max_rows_to_group_by` and `group_by_overflow_mode = 'any'`.

`after_having_exclusive` – Don’t include rows that didn’t pass through `max_rows_to_group_by`. In other words, ‘totals’ will have less than or the same number of rows as it would if `max_rows_to_group_by` were omitted.

`after_having_inclusive` – Include all the rows that didn’t pass through ‘max_rows_to_group_by’ in ‘totals’. In other words, ‘totals’ will have more than or the same number of rows as it would if `max_rows_to_group_by` were omitted.

`after_having_auto` – Count the number of rows that passed through HAVING. If it is more than a certain amount (by default, 50%), include all the rows that didn’t pass through ‘max_rows_to_group_by’ in ‘totals’. Otherwise, do not include them.

`totals_auto_threshold` – By default, 0.5. The coefficient for `after_having_auto`.

If `max_rows_to_group_by` and `group_by_overflow_mode = 'any'` are not used, all variations of `after_having` are the same, and you can use any of them (for example, `after_having_auto`).

You can use `WITH TOTALS` in subqueries, including subqueries in the JOIN clause (in this case, the respective total values are combined).

Examples

Example:

``````SELECT
count(),
median(FetchTiming > 60 ? 60 : FetchTiming),
count() - sum(Refresh)
FROM hits
``````

As opposed to MySQL (and conforming to standard SQL), you can’t get some value of some column that is not in a key or aggregate function (except constant expressions). To work around this, you can use the ‘any’ aggregate function (get the first encountered value) or ‘min/max’.

Example:

``````SELECT
domainWithoutWWW(URL) AS domain,
count(),
any(Title) AS title -- getting the first occurred page header for each domain.
FROM hits
GROUP BY domain
``````

For every different key value encountered, `GROUP BY` calculates a set of aggregate function values.

Implementation Details

Aggregation is one of the most important features of a column-oriented DBMS, and thus it’s implementation is one of the most heavily optimized parts of ClickHouse. By default, aggregation is done in memory using a hash-table. It has 40+ specializations that are chosen automatically depending on “grouping key” data types.

GROUP BY Optimization Depending on Table Sorting Key

The aggregation can be performed more effectively, if a table is sorted by some key, and `GROUP BY` expression contains at least prefix of sorting key or injective functions. In this case when a new key is read from table, the in-between result of aggregation can be finalized and sent to client. This behaviour is switched on by the optimize_aggregation_in_order setting. Such optimization reduces memory usage during aggregation, but in some cases may slow down the query execution.

GROUP BY in External Memory

You can enable dumping temporary data to the disk to restrict memory usage during `GROUP BY`.
The max_bytes_before_external_group_by setting determines the threshold RAM consumption for dumping `GROUP BY` temporary data to the file system. If set to 0 (the default), it is disabled.

When using `max_bytes_before_external_group_by`, we recommend that you set `max_memory_usage` about twice as high. This is necessary because there are two stages to aggregation: reading the data and forming intermediate data (1) and merging the intermediate data (2). Dumping data to the file system can only occur during stage 1. If the temporary data wasn’t dumped, then stage 2 might require up to the same amount of memory as in stage 1.

For example, if max_memory_usage was set to 10000000000 and you want to use external aggregation, it makes sense to set `max_bytes_before_external_group_by` to 10000000000, and `max_memory_usage` to 20000000000. When external aggregation is triggered (if there was at least one dump of temporary data), maximum consumption of RAM is only slightly more than `max_bytes_before_external_group_by`.

With distributed query processing, external aggregation is performed on remote servers. In order for the requester server to use only a small amount of RAM, set `distributed_aggregation_memory_efficient` to 1.

When merging data flushed to the disk, as well as when merging results from remote servers when the `distributed_aggregation_memory_efficient` setting is enabled, consumes up to `1/256 * the_number_of_threads` from the total amount of RAM.

When external aggregation is enabled, if there was less than `max_bytes_before_external_group_by` of data (i.e. data was not flushed), the query runs just as fast as without external aggregation. If any temporary data was flushed, the run time will be several times longer (approximately three times).

If you have an ORDER BY with a LIMIT after `GROUP BY`, then the amount of used RAM depends on the amount of data in `LIMIT`, not in the whole table. But if the `ORDER BY` does not have `LIMIT`, do not forget to enable external sorting (`max_bytes_before_external_sort`).

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