> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-fix-nav-issues.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Documentation for the Dynamic data type in ClickHouse, which can store values of different types in a single column

# Dynamic

This type allows to store values of any type inside it without knowing all of them in advance.

To declare a column of `Dynamic` type, use the following syntax:

```sql theme={null}
<column_name> Dynamic(max_types=N)
```

Where `N` is an optional parameter between `0` and `254` indicating how many different data types can be stored as separate subcolumns inside a column with type `Dynamic` across single block of data that is stored separately (for example across single data part for MergeTree table). If this limit is exceeded, all values with new types will be stored together in a special shared data structure in binary form. Default value of `max_types` is `32`.

<h2 id="creating-dynamic">
  Creating Dynamic
</h2>

Using `Dynamic` type in table column definition:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('Hello, World!'), ([1, 2, 3]);
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ          │ None           │
│ 42            │ Int64          │
│ Hello, World! │ String         │
│ [1,2,3]       │ Array(Int64)   │
└───────────────┴────────────────┘
```

Using CAST from ordinary column:

```sql theme={null}
SELECT 'Hello, World!'::Dynamic AS d, dynamicType(d);
```

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┐
│ Hello, World! │ String         │
└───────────────┴────────────────┘
```

Using CAST from `Variant` column:

```sql theme={null}
SET use_variant_as_common_type = 1;
SELECT multiIf((number % 3) = 0, number, (number % 3) = 1, range(number + 1), NULL)::Dynamic AS d, dynamicType(d) FROM numbers(3)
```

```text theme={null}
┌─d─────┬─dynamicType(d)─┐
│ 0     │ UInt64         │
│ [0,1] │ Array(UInt64)  │
│ ᴺᵁᴸᴸ  │ None           │
└───────┴────────────────┘
```

<h2 id="reading-dynamic-nested-types-as-subcolumns">
  Reading Dynamic nested types as subcolumns
</h2>

`Dynamic` type supports reading a single nested type from a `Dynamic` column using the type name as a subcolumn.
So, if you have column `d Dynamic` you can read a subcolumn of any valid type `T` using syntax `d.T`,
this subcolumn will have type `Nullable(T)` if `T` can be inside `Nullable` and `T` otherwise. This subcolumn will
be the same size as original `Dynamic` column and will contain `NULL` values (or empty values if `T` cannot be inside `Nullable`)
in all rows in which original `Dynamic` column doesn't have type `T`.

`Dynamic` subcolumns can be also read using function `dynamicElement(dynamic_column, type_name)`.

Examples:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('Hello, World!'), ([1, 2, 3]);
SELECT d, dynamicType(d), d.String, d.Int64, d.`Array(Int64)`, d.Date, d.`Array(String)` FROM test;
```

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┬─d.String──────┬─d.Int64─┬─d.Array(Int64)─┬─d.Date─┬─d.Array(String)─┐
│ ᴺᵁᴸᴸ          │ None           │ ᴺᵁᴸᴸ          │    ᴺᵁᴸᴸ │ []             │   ᴺᵁᴸᴸ │ []              │
│ 42            │ Int64          │ ᴺᵁᴸᴸ          │      42 │ []             │   ᴺᵁᴸᴸ │ []              │
│ Hello, World! │ String         │ Hello, World! │    ᴺᵁᴸᴸ │ []             │   ᴺᵁᴸᴸ │ []              │
│ [1,2,3]       │ Array(Int64)   │ ᴺᵁᴸᴸ          │    ᴺᵁᴸᴸ │ [1,2,3]        │   ᴺᵁᴸᴸ │ []              │
└───────────────┴────────────────┴───────────────┴─────────┴────────────────┴────────┴─────────────────┘
```

```sql theme={null}
SELECT toTypeName(d.String), toTypeName(d.Int64), toTypeName(d.`Array(Int64)`), toTypeName(d.Date), toTypeName(d.`Array(String)`)  FROM test LIMIT 1;
```

```text theme={null}
┌─toTypeName(d.String)─┬─toTypeName(d.Int64)─┬─toTypeName(d.Array(Int64))─┬─toTypeName(d.Date)─┬─toTypeName(d.Array(String))─┐
│ Nullable(String)     │ Nullable(Int64)     │ Array(Int64)               │ Nullable(Date)     │ Array(String)               │
└──────────────────────┴─────────────────────┴────────────────────────────┴────────────────────┴─────────────────────────────┘
```

````sql theme={null}
SELECT d, dynamicType(d), dynamicElement(d, 'String'), dynamicElement(d, 'Int64'), dynamicElement(d, 'Array(Int64)'), dynamicElement(d, 'Date'), dynamicElement(d, 'Array(String)') FROM test;```
````

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┬─dynamicElement(d, 'String')─┬─dynamicElement(d, 'Int64')─┬─dynamicElement(d, 'Array(Int64)')─┬─dynamicElement(d, 'Date')─┬─dynamicElement(d, 'Array(String)')─┐
│ ᴺᵁᴸᴸ          │ None           │ ᴺᵁᴸᴸ                        │                       ᴺᵁᴸᴸ │ []                                │                      ᴺᵁᴸᴸ │ []                                 │
│ 42            │ Int64          │ ᴺᵁᴸᴸ                        │                         42 │ []                                │                      ᴺᵁᴸᴸ │ []                                 │
│ Hello, World! │ String         │ Hello, World!               │                       ᴺᵁᴸᴸ │ []                                │                      ᴺᵁᴸᴸ │ []                                 │
│ [1,2,3]       │ Array(Int64)   │ ᴺᵁᴸᴸ                        │                       ᴺᵁᴸᴸ │ [1,2,3]                           │                      ᴺᵁᴸᴸ │ []                                 │
└───────────────┴────────────────┴─────────────────────────────┴────────────────────────────┴───────────────────────────────────┴───────────────────────────┴────────────────────────────────────┘
```

To know what variant is stored in each row function `dynamicType(dynamic_column)` can be used. It returns `String` with value type name for each row (or `'None'` if row is `NULL`).

Example:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('Hello, World!'), ([1, 2, 3]);
SELECT dynamicType(d) FROM test;
```

```text theme={null}
┌─dynamicType(d)─┐
│ None           │
│ Int64          │
│ String         │
│ Array(Int64)   │
└────────────────┘
```

<h2 id="conversion-between-dynamic-column-and-other-columns">
  Conversion between Dynamic column and other columns
</h2>

There are 4 possible conversions that can be performed with `Dynamic` column.

<h3 id="converting-an-ordinary-column-to-a-dynamic-column">
  Converting an ordinary column to a Dynamic column
</h3>

```sql theme={null}
SELECT 'Hello, World!'::Dynamic AS d, dynamicType(d);
```

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┐
│ Hello, World! │ String         │
└───────────────┴────────────────┘
```

<h3 id="converting-a-string-column-to-a-dynamic-column-through-parsing">
  Converting a String column to a Dynamic column through parsing
</h3>

To parse `Dynamic` type values from a `String` column you can enable setting `cast_string_to_dynamic_use_inference`:

```sql theme={null}
SET cast_string_to_dynamic_use_inference = 1;
SELECT CAST(materialize(map('key1', '42', 'key2', 'true', 'key3', '2020-01-01')), 'Map(String, Dynamic)') as map_of_dynamic, mapApply((k, v) -> (k, dynamicType(v)), map_of_dynamic) as map_of_dynamic_types;
```

```text theme={null}
┌─map_of_dynamic──────────────────────────────┬─map_of_dynamic_types─────────────────────────┐
│ {'key1':42,'key2':true,'key3':'2020-01-01'} │ {'key1':'Int64','key2':'Bool','key3':'Date'} │
└─────────────────────────────────────────────┴──────────────────────────────────────────────┘
```

<h3 id="converting-a-dynamic-column-to-an-ordinary-column">
  Converting a Dynamic column to an ordinary column
</h3>

It is possible to convert a `Dynamic` column to an ordinary column. In this case all nested types will be converted to a destination type:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('42.42'), (true), ('e10');
SELECT d::Nullable(Float64) FROM test;
```

```text theme={null}
┌─CAST(d, 'Nullable(Float64)')─┐
│                         ᴺᵁᴸᴸ │
│                           42 │
│                        42.42 │
│                            1 │
│                            0 │
└──────────────────────────────┘
```

<h3 id="converting-a-variant-column-to-dynamic-column">
  Converting a Variant column to Dynamic column
</h3>

```sql theme={null}
CREATE TABLE test (v Variant(UInt64, String, Array(UInt64))) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), ('String'), ([1, 2, 3]);
SELECT v::Dynamic AS d, dynamicType(d) FROM test; 
```

```text theme={null}
┌─d───────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ    │ None           │
│ 42      │ UInt64         │
│ String  │ String         │
│ [1,2,3] │ Array(UInt64)  │
└─────────┴────────────────┘
```

<h3 id="converting-a-dynamicmax_typesn-column-to-another-dynamicmax_typesk">
  Converting a Dynamic(max\_types=N) column to another Dynamic(max\_types=K)
</h3>

If `K >= N` than during conversion the data doesn't change:

```sql theme={null}
CREATE TABLE test (d Dynamic(max_types=3)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true);
SELECT d::Dynamic(max_types=5) as d2, dynamicType(d2) FROM test;
```

```text theme={null}
┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ 42    │ Int64          │
│ 43    │ Int64          │
│ 42.42 │ String         │
│ true  │ Bool           │
└───────┴────────────────┘
```

If `K < N`, then the values with the rarest types will be inserted into a single special subcolumn, but still will be accessible:

```text theme={null}
CREATE TABLE test (d Dynamic(max_types=4)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true), ([1, 2, 3]);
SELECT d, dynamicType(d), d::Dynamic(max_types=2) as d2, dynamicType(d2), isDynamicElementInSharedData(d2) FROM test;
```

```text theme={null}
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┬─isDynamicElementInSharedData(d2)─┐
│ ᴺᵁᴸᴸ    │ None           │ ᴺᵁᴸᴸ    │ None            │ false                            │
│ 42      │ Int64          │ 42      │ Int64           │ false                            │
│ 43      │ Int64          │ 43      │ Int64           │ false                            │
│ 42.42   │ String         │ 42.42   │ String          │ false                            │
│ true    │ Bool           │ true    │ Bool            │ true                             │
│ [1,2,3] │ Array(Int64)   │ [1,2,3] │ Array(Int64)    │ true                             │
└─────────┴────────────────┴─────────┴─────────────────┴──────────────────────────────────┘
```

Functions `isDynamicElementInSharedData` returns `true` for rows that are stored in a special shared data structure inside `Dynamic` and as we can see, resulting column contains only 2 types that are not stored in shared data structure.

If `K=0`, all types will be inserted into single special subcolumn:

```text theme={null}
CREATE TABLE test (d Dynamic(max_types=4)) ENGINE = Memory;
INSERT INTO test VALUES (NULL), (42), (43), ('42.42'), (true), ([1, 2, 3]);
SELECT d, dynamicType(d), d::Dynamic(max_types=0) as d2, dynamicType(d2), isDynamicElementInSharedData(d2) FROM test;
```

```text theme={null}
┌─d───────┬─dynamicType(d)─┬─d2──────┬─dynamicType(d2)─┬─isDynamicElementInSharedData(d2)─┐
│ ᴺᵁᴸᴸ    │ None           │ ᴺᵁᴸᴸ    │ None            │ false                            │
│ 42      │ Int64          │ 42      │ Int64           │ true                             │
│ 43      │ Int64          │ 43      │ Int64           │ true                             │
│ 42.42   │ String         │ 42.42   │ String          │ true                             │
│ true    │ Bool           │ true    │ Bool            │ true                             │
│ [1,2,3] │ Array(Int64)   │ [1,2,3] │ Array(Int64)    │ true                             │
└─────────┴────────────────┴─────────┴─────────────────┴──────────────────────────────────┘
```

<h2 id="reading-dynamic-type-from-the-data">
  Reading Dynamic type from the data
</h2>

All text formats (TSV, CSV, CustomSeparated, Values, JSONEachRow, etc) supports reading `Dynamic` type. During data parsing ClickHouse tries to infer the type of each value and use it during insertion to `Dynamic` column.

Example:

```sql theme={null}
SELECT
    d,
    dynamicType(d),
    dynamicElement(d, 'String') AS str,
    dynamicElement(d, 'Int64') AS num,
    dynamicElement(d, 'Float64') AS float,
    dynamicElement(d, 'Date') AS date,
    dynamicElement(d, 'Array(Int64)') AS arr
FROM format(JSONEachRow, 'd Dynamic', $$
{"d" : "Hello, World!"},
{"d" : 42},
{"d" : 42.42},
{"d" : "2020-01-01"},
{"d" : [1, 2, 3]}
$$)
```

```text theme={null}
┌─d─────────────┬─dynamicType(d)─┬─str───────────┬──num─┬─float─┬───────date─┬─arr─────┐
│ Hello, World! │ String         │ Hello, World! │ ᴺᵁᴸᴸ │  ᴺᵁᴸᴸ │       ᴺᵁᴸᴸ │ []      │
│ 42            │ Int64          │ ᴺᵁᴸᴸ          │   42 │  ᴺᵁᴸᴸ │       ᴺᵁᴸᴸ │ []      │
│ 42.42         │ Float64        │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ │ 42.42 │       ᴺᵁᴸᴸ │ []      │
│ 2020-01-01    │ Date           │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ │  ᴺᵁᴸᴸ │ 2020-01-01 │ []      │
│ [1,2,3]       │ Array(Int64)   │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ │  ᴺᵁᴸᴸ │       ᴺᵁᴸᴸ │ [1,2,3] │
└───────────────┴────────────────┴───────────────┴──────┴───────┴────────────┴─────────┘
```

<h2 id="using-dynamic-type-in-functions">
  Using Dynamic type in functions
</h2>

Most of the functions support arguments with type `Dynamic`. In this case the function is executed separately on each internal data type stored inside `Dynamic` column.
When the result type of the function depends on the arguments types, the result of such function executed with `Dynamic` arguments will be `Dynamic`. When the result type of the function doesn't depend on the arguments types - the result will be `Nullable(T)` where `T` the usual result type of this function.

Examples:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE=Memory;
INSERT INTO test VALUES (NULL), (1::Int8), (2::Int16), (3::Int32), (4::Int64);
```

```sql theme={null}
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ │ None           │
│ 1    │ Int8           │
│ 2    │ Int16          │
│ 3    │ Int32          │
│ 4    │ Int64          │
└──────┴────────────────┘
```

```sql theme={null}
SELECT d, d + 1 AS res, toTypeName(res), dynamicType(res) FROM test;
```

```text theme={null}
┌─d────┬─res──┬─toTypeName(res)─┬─dynamicType(res)─┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Dynamic         │ None             │
│ 1    │ 2    │ Dynamic         │ Int16            │
│ 2    │ 3    │ Dynamic         │ Int32            │
│ 3    │ 4    │ Dynamic         │ Int64            │
│ 4    │ 5    │ Dynamic         │ Int64            │
└──────┴──────┴─────────────────┴──────────────────┘
```

```sql theme={null}
SELECT d, d + d AS res, toTypeName(res), dynamicType(res) FROM test;
```

```text theme={null}
┌─d────┬─res──┬─toTypeName(res)─┬─dynamicType(res)─┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Dynamic         │ None             │
│ 1    │ 2    │ Dynamic         │ Int16            │
│ 2    │ 4    │ Dynamic         │ Int32            │
│ 3    │ 6    │ Dynamic         │ Int64            │
│ 4    │ 8    │ Dynamic         │ Int64            │
└──────┴──────┴─────────────────┴──────────────────┘
```

```sql theme={null}
SELECT d, d < 3 AS res, toTypeName(res) FROM test;
```

```text theme={null}
┌─d────┬──res─┬─toTypeName(res)─┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Nullable(UInt8) │
│ 1    │    1 │ Nullable(UInt8) │
│ 2    │    1 │ Nullable(UInt8) │
│ 3    │    0 │ Nullable(UInt8) │
│ 4    │    0 │ Nullable(UInt8) │
└──────┴──────┴─────────────────┘
```

```sql theme={null}
SELECT d, exp2(d) AS res, toTypeName(res) FROM test;
```

```sql theme={null}
┌─d────┬──res─┬─toTypeName(res)───┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Nullable(Float64) │
│ 1    │    2 │ Nullable(Float64) │
│ 2    │    4 │ Nullable(Float64) │
│ 3    │    8 │ Nullable(Float64) │
│ 4    │   16 │ Nullable(Float64) │
└──────┴──────┴───────────────────┘
```

```sql theme={null}
TRUNCATE TABLE test;
INSERT INTO test VALUES (NULL), ('str_1'), ('str_2');
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ str_1 │ String         │
│ str_2 │ String         │
└───────┴────────────────┘
```

```sql theme={null}
SELECT d, upper(d) AS res, toTypeName(res) FROM test;
```

```text theme={null}
┌─d─────┬─res───┬─toTypeName(res)──┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ  │ Nullable(String) │
│ str_1 │ STR_1 │ Nullable(String) │
│ str_2 │ STR_2 │ Nullable(String) │
└───────┴───────┴──────────────────┘
```

```sql theme={null}
SELECT d, extract(d, '([0-3])') AS res, toTypeName(res) FROM test;
```

```text theme={null}
┌─d─────┬─res──┬─toTypeName(res)──┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ │ Nullable(String) │
│ str_1 │ 1    │ Nullable(String) │
│ str_2 │ 2    │ Nullable(String) │
└───────┴──────┴──────────────────┘
```

```sql theme={null}
TRUNCATE TABLE test;
INSERT INTO test VALUES (NULL), ([1, 2]), ([3, 4]);
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ [1,2] │ Array(Int64)   │
│ [3,4] │ Array(Int64)   │
└───────┴────────────────┘
```

```sql theme={null}
SELECT d, d[1] AS res, toTypeName(res), dynamicType(res) FROM test;
```

```text theme={null}
┌─d─────┬─res──┬─toTypeName(res)─┬─dynamicType(res)─┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ │ Dynamic         │ None             │
│ [1,2] │ 1    │ Dynamic         │ Int64            │
│ [3,4] │ 3    │ Dynamic         │ Int64            │
└───────┴──────┴─────────────────┴──────────────────┘
```

If function cannot be executed on some type inside `Dynamic` column, the exception will be thrown:

```sql theme={null}
INSERT INTO test VALUES (42), (43), ('str_1');
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d─────┬─dynamicType(d)─┐
│ 42    │ Int64          │
│ 43    │ Int64          │
│ str_1 │ String         │
└───────┴────────────────┘
┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ [1,2] │ Array(Int64)   │
│ [3,4] │ Array(Int64)   │
└───────┴────────────────┘
```

```sql theme={null}
SELECT d, d + 1 AS res, toTypeName(res), dynamicType(d) FROM test;
```

```text theme={null}
Received exception:
Code: 43. DB::Exception: Illegal types Array(Int64) and UInt8 of arguments of function plus: while executing 'FUNCTION plus(__table1.d : 3, 1_UInt8 :: 1) -> plus(__table1.d, 1_UInt8) Dynamic : 0'. (ILLEGAL_TYPE_OF_ARGUMENT)
```

We can filter out unneeded types:

```sql theme={null}
SELECT d, d + 1 AS res, toTypeName(res), dynamicType(res) FROM test WHERE dynamicType(d) NOT IN ('String', 'Array(Int64)', 'None')
```

```text theme={null}
┌─d──┬─res─┬─toTypeName(res)─┬─dynamicType(res)─┐
│ 42 │ 43  │ Dynamic         │ Int64            │
│ 43 │ 44  │ Dynamic         │ Int64            │
└────┴─────┴─────────────────┴──────────────────┘
```

Or extract required type as subcolumn:

```sql theme={null}
SELECT d, d.Int64 + 1 AS res, toTypeName(res) FROM test;
```

```text theme={null}
┌─d─────┬──res─┬─toTypeName(res)─┐
│ 42    │   43 │ Nullable(Int64) │
│ 43    │   44 │ Nullable(Int64) │
│ str_1 │ ᴺᵁᴸᴸ │ Nullable(Int64) │
└───────┴──────┴─────────────────┘
┌─d─────┬──res─┬─toTypeName(res)─┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ │ Nullable(Int64) │
│ [1,2] │ ᴺᵁᴸᴸ │ Nullable(Int64) │
│ [3,4] │ ᴺᵁᴸᴸ │ Nullable(Int64) │
└───────┴──────┴─────────────────┘
```

<h3 id="dynamic-type-mismatch-behavior">
  Type mismatch behavior
</h3>

The setting `dynamic_throw_on_type_mismatch` controls what happens when a function is applied to a `Dynamic` column and the actual stored type of a row is incompatible with the function:

* `true` (default) — throw an exception (`ILLEGAL_TYPE_OF_ARGUMENT`) on the first incompatible row.
* `false` — return `NULL` for incompatible rows and keep the result for compatible rows.

**Example:**

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE = Memory;
INSERT INTO test VALUES ('world'), (123), (456);

-- Default (throw on mismatch): length() does not accept integers, so the query throws.
SELECT length(d) FROM test;  -- throws ILLEGAL_TYPE_OF_ARGUMENT

-- With throw disabled: incompatible rows return NULL.
SET dynamic_throw_on_type_mismatch = false;
SELECT d, length(d) FROM test ORDER BY d::String NULLS LAST;
```

```text theme={null}
┌─d─────┬─length(d)─┐
│ world │         5 │
│ 123   │      ᴺᵁᴸᴸ │
│ 456   │      ᴺᵁᴸᴸ │
└───────┴───────────┘
```

<h2 id="using-dynamic-type-in-order-by-and-group-by">
  Using Dynamic type in ORDER BY and GROUP BY
</h2>

During `ORDER BY` and `GROUP BY` values of `Dynamic` types are compared similar to values of `Variant` type:
The result of operator `<` for values `d1` with underlying type `T1` and `d2` with underlying type `T2`  of a type `Dynamic` is defined as follows:

* If `T1 = T2 = T`, the result will be `d1.T < d2.T` (underlying values will be compared).
* If `T1 != T2`, the result will be `T1 < T2` (type names will be compared).

By default `Dynamic` type is not allowed in `GROUP BY`/`ORDER BY` keys, if you want to use it consider its special comparison rule and enable `allow_suspicious_types_in_group_by`/`allow_suspicious_types_in_order_by` settings.

Examples:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE=Memory;
INSERT INTO test VALUES (42), (43), ('abc'), ('abd'), ([1, 2, 3]), ([]), (NULL);
```

```sql theme={null}
SELECT d, dynamicType(d) FROM test;
```

```text theme={null}
┌─d───────┬─dynamicType(d)─┐
│ 42      │ Int64          │
│ 43      │ Int64          │
│ abc     │ String         │
│ abd     │ String         │
│ [1,2,3] │ Array(Int64)   │
│ []      │ Array(Int64)   │
│ ᴺᵁᴸᴸ    │ None           │
└─────────┴────────────────┘
```

```sql theme={null}
SELECT d, dynamicType(d) FROM test ORDER BY d SETTINGS allow_suspicious_types_in_order_by=1;
```

```sql theme={null}
┌─d───────┬─dynamicType(d)─┐
│ []      │ Array(Int64)   │
│ [1,2,3] │ Array(Int64)   │
│ 42      │ Int64          │
│ 43      │ Int64          │
│ abc     │ String         │
│ abd     │ String         │
│ ᴺᵁᴸᴸ    │ None           │
└─────────┴────────────────┘
```

**Note:** values of dynamic types with different numeric types are considered as different values and not compared between each other, their type names are compared instead.

Example:

```sql theme={null}
CREATE TABLE test (d Dynamic) ENGINE=Memory;
INSERT INTO test VALUES (1::UInt32), (1::Int64), (100::UInt32), (100::Int64);
SELECT d, dynamicType(d) FROM test ORDER BY d SETTINGS allow_suspicious_types_in_order_by=1;
```

```text theme={null}
┌─v───┬─dynamicType(v)─┐
│ 1   │ Int64          │
│ 100 │ Int64          │
│ 1   │ UInt32         │
│ 100 │ UInt32         │
└─────┴────────────────┘
```

```sql theme={null}
SELECT d, dynamicType(d) FROM test GROUP BY d SETTINGS allow_suspicious_types_in_group_by=1;
```

```text theme={null}
┌─d───┬─dynamicType(d)─┐
│ 1   │ Int64          │
│ 100 │ UInt32         │
│ 1   │ UInt32         │
│ 100 │ Int64          │
└─────┴────────────────┘
```

**Note:** the described comparison rule is not applied during execution of comparison functions like `<`/`>`/`=` and others because of [special work](#using-dynamic-type-in-functions) of functions with `Dynamic` type

<h2 id="reaching-the-limit-in-number-of-different-data-types-stored-inside-dynamic">
  Reaching the limit in number of different data types stored inside Dynamic
</h2>

`Dynamic` data type can store only limited number of different data types as separate subcolumns. By default, this limit is 32, but you can change it in type declaration using syntax `Dynamic(max_types=N)` where N is between 0 and 254 (due to implementation details, it's impossible to have more than 254 different data types that can be stored as separate subcolumns inside Dynamic).
When the limit is reached, all new data types inserted to `Dynamic` column will be inserted into a single shared data structure that stores values with different data types in binary form.

Let's see what happens when the limit is reached in different scenarios.

<h3 id="reaching-the-limit-during-data-parsing">
  Reaching the limit during data parsing
</h3>

During parsing of `Dynamic` values from the data, when the limit is reached for current block of data, all new values will be inserted into shared data structure:

```sql theme={null}
SELECT d, dynamicType(d), isDynamicElementInSharedData(d) FROM format(JSONEachRow, 'd Dynamic(max_types=3)', '
{"d" : 42}
{"d" : [1, 2, 3]}
{"d" : "Hello, World!"}
{"d" : "2020-01-01"}
{"d" : ["str1", "str2", "str3"]}
{"d" : {"a" : 1, "b" : [1, 2, 3]}}
')
```

```text theme={null}
┌─d──────────────────────┬─dynamicType(d)─────────────────┬─isDynamicElementInSharedData(d)─┐
│ 42                     │ Int64                          │ false                           │
│ [1,2,3]                │ Array(Int64)                   │ false                           │
│ Hello, World!          │ String                         │ false                           │
│ 2020-01-01             │ Date                           │ true                            │
│ ['str1','str2','str3'] │ Array(String)                  │ true                            │
│ (1,[1,2,3])            │ Tuple(a Int64, b Array(Int64)) │ true                            │
└────────────────────────┴────────────────────────────────┴─────────────────────────────────┘
```

As we can see, after inserting 3 different data types `Int64`, `Array(Int64)` and `String` all new types were inserted into special shared data structure.

<h3 id="during-merges-of-data-parts-in-mergetree-table-engines">
  During merges of data parts in MergeTree table engines
</h3>

During merge of several data parts in MergeTree table the `Dynamic` column in the resulting data part can reach the limit of different data types that can be stored in separate subcolumns inside and won't be able to store all types as subcolumns from source parts.
In this case ClickHouse chooses what types will remain as separate subcolumns after merge and what types will be inserted into shared data structure. In most cases ClickHouse tries to keep the most frequent types and store the rarest types in shared data structure, but it depends on the implementation.

Let's see an example of such merge. First, let's create a table with `Dynamic` column, set the limit of different data types to `3` and insert values with `5` different types:

```sql theme={null}
CREATE TABLE test (id UInt64, d Dynamic(max_types=3)) ENGINE=MergeTree ORDER BY id;
SYSTEM STOP MERGES test;
INSERT INTO test SELECT number, number FROM numbers(5);
INSERT INTO test SELECT number, range(number) FROM numbers(4);
INSERT INTO test SELECT number, toDate(number) FROM numbers(3);
INSERT INTO test SELECT number, map(number, number) FROM numbers(2);
INSERT INTO test SELECT number, 'str_' || toString(number) FROM numbers(1);
```

Each insert will create a separate data pert with `Dynamic` column containing single type:

```sql theme={null}
SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count();
```

```text theme={null}
┌─count()─┬─dynamicType(d)──────┬─isDynamicElementInSharedData(d)─┬─_part─────┐
│       5 │ UInt64              │ false                           │ all_1_1_0 │
│       4 │ Array(UInt64)       │ false                           │ all_2_2_0 │
│       3 │ Date                │ false                           │ all_3_3_0 │
│       2 │ Map(UInt64, UInt64) │ false                           │ all_4_4_0 │
│       1 │ String              │ false                           │ all_5_5_0 │
└─────────┴─────────────────────┴─────────────────────────────────┴───────────┘
```

Now, let's merge all parts into one and see what will happen:

```sql theme={null}
SYSTEM START MERGES test;
OPTIMIZE TABLE test FINAL;
SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count() desc;
```

```text theme={null}
┌─count()─┬─dynamicType(d)──────┬─isDynamicElementInSharedData(d)─┬─_part─────┐
│       5 │ UInt64              │ false                           │ all_1_5_2 │
│       4 │ Array(UInt64)       │ false                           │ all_1_5_2 │
│       3 │ Date                │ false                           │ all_1_5_2 │
│       2 │ Map(UInt64, UInt64) │ true                            │ all_1_5_2 │
│       1 │ String              │ true                            │ all_1_5_2 │
└─────────┴─────────────────────┴─────────────────────────────────┴───────────┘
```

As we can see, ClickHouse kept the most frequent types `UInt64` and `Array(UInt64)` as subcolumns and inserted all other types into shared data.

<h2 id="jsonextract-functions-with-dynamic">
  JSONExtract functions with Dynamic
</h2>

All `JSONExtract*` functions support `Dynamic` type:

```sql theme={null}
SELECT JSONExtract('{"a" : [1, 2, 3]}', 'a', 'Dynamic') AS dynamic, dynamicType(dynamic) AS dynamic_type;
```

```text theme={null}
┌─dynamic─┬─dynamic_type───────────┐
│ [1,2,3] │ Array(Nullable(Int64)) │
└─────────┴────────────────────────┘
```

```sql theme={null}
SELECT JSONExtract('{"obj" : {"a" : 42, "b" : "Hello", "c" : [1,2,3]}}', 'obj', 'Map(String, Dynamic)') AS map_of_dynamics, mapApply((k, v) -> (k, dynamicType(v)), map_of_dynamics) AS map_of_dynamic_types
```

```text theme={null}
┌─map_of_dynamics──────────────────┬─map_of_dynamic_types────────────────────────────────────┐
│ {'a':42,'b':'Hello','c':[1,2,3]} │ {'a':'Int64','b':'String','c':'Array(Nullable(Int64))'} │
└──────────────────────────────────┴─────────────────────────────────────────────────────────┘
```

````sql theme={null}
SELECT JSONExtractKeysAndValues('{"a" : 42, "b" : "Hello", "c" : [1,2,3]}', 'Dynamic') AS dynamics, arrayMap(x -> (x.1, dynamicType(x.2)), dynamics) AS dynamic_types```
````

```text theme={null}
┌─dynamics───────────────────────────────┬─dynamic_types─────────────────────────────────────────────────┐
│ [('a',42),('b','Hello'),('c',[1,2,3])] │ [('a','Int64'),('b','String'),('c','Array(Nullable(Int64))')] │
└────────────────────────────────────────┴───────────────────────────────────────────────────────────────┘
```

<h3 id="binary-output-format">
  Binary output format
</h3>

In RowBinary format values of `Dynamic` type are serialized in the following format:

```text theme={null}
<binary_encoded_data_type><value_in_binary_format_according_to_the_data_type>
```