Dynamic

この型は、あらかじめすべての型を把握していなくても、任意の型の値を格納できます。

Dynamic 型のカラムを宣言するには、次の構文を使用します。

<column_name> Dynamic(max_types=N)

ここで N はオプションのパラメータで、0 から 254 の範囲の値を取り、個別に保存される 1 つのデータブロック（たとえば MergeTree テーブルの 1 つのデータパート）内で、型が Dynamic のカラムに個別のサブカラムとして保存できる異なるデータ型の数を指定します。この上限を超えると、新しい型を持つすべての値は、バイナリ形式で特別な共有データ構造の中にまとめて保存されます。max_types のデフォルト値は 32 です。

Dynamic の作成

テーブルの列定義で Dynamic 型を使用します:

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

┌─d─────────────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ          │ None           │
│ 42            │ Int64          │
│ Hello, World! │ String         │
│ [1,2,3]       │ Array(Int64)   │
└───────────────┴────────────────┘

通常の列に対する CAST の使用:

SELECT 'Hello, World!'::Dynamic AS d, dynamicType(d);

┌─d─────────────┬─dynamicType(d)─┐
│ Hello, World! │ String         │
└───────────────┴────────────────┘

Variant 列の値に対して CAST を使用する:

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)

┌─d─────┬─dynamicType(d)─┐
│ 0     │ UInt64         │
│ [0,1] │ Array(UInt64)  │
│ ᴺᵁᴸᴸ  │ None           │
└───────┴────────────────┘

Dynamic のネストされた型をサブカラムとして読み取る

Dynamic 型では、型名をサブカラム名として指定することで、Dynamic カラムから単一のネストされた型を読み取ることができます。そのため、d Dynamic というカラムがある場合、有効な任意の型 T のサブカラムを構文 d.T を使って読み取ることができます。このサブカラムは、T が Nullable の中に入ることができる場合は型 Nullable(T) になり、それ以外の場合は T になります。このサブカラムの行数は元の Dynamic カラムと同じであり、元の Dynamic カラムに型 T が存在しないすべての行では、 NULL 値（あるいは、T が Nullable の中に入ることができない場合は空の値）を含みます。

Dynamic のサブカラムは、関数 dynamicElement(dynamic_column, type_name) を使って読み取ることもできます。

例:

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;

┌─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 │ []              │
└───────────────┴────────────────┴───────────────┴─────────┴────────────────┴────────┴─────────────────┘

SELECT toTypeName(d.String), toTypeName(d.Int64), toTypeName(d.`Array(Int64)`), toTypeName(d.Date), toTypeName(d.`Array(String)`)  FROM test LIMIT 1;

┌─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)               │
└──────────────────────┴─────────────────────┴────────────────────────────┴────────────────────┴─────────────────────────────┘

SELECT d, dynamicType(d), dynamicElement(d, 'String'), dynamicElement(d, 'Int64'), dynamicElement(d, 'Array(Int64)'), dynamicElement(d, 'Date'), dynamicElement(d, 'Array(String)') FROM test;```

┌─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:

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

┌─dynamicType(d)─┐
│ None           │
│ Int64          │
│ String         │
│ Array(Int64)   │
└────────────────┘

Conversion between Dynamic column and other columns

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

Converting an ordinary column to a Dynamic column

SELECT 'Hello, World!'::Dynamic AS d, dynamicType(d);

┌─d─────────────┬─dynamicType(d)─┐
│ Hello, World! │ String         │
└───────────────┴────────────────┘

Converting a String column to a Dynamic column through parsing

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

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;

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

Converting a Dynamic column to an ordinary column

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:

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

┌─CAST(d, 'Nullable(Float64)')─┐
│                         ᴺᵁᴸᴸ │
│                           42 │
│                        42.42 │
│                            1 │
│                            0 │
└──────────────────────────────┘

Converting a Variant column to Dynamic column

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;

┌─d───────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ    │ None           │
│ 42      │ UInt64         │
│ String  │ String         │
│ [1,2,3] │ Array(UInt64)  │
└─────────┴────────────────┘

Converting a Dynamic(max_types=N) column to another Dynamic(max_types=K)

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

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;

┌─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:

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;

┌─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:

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;

┌─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                             │
└─────────┴────────────────┴─────────┴─────────────────┴──────────────────────────────────┘

Reading Dynamic type from the data

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:

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', $$
{"d" : "Hello, World!"},
{"d" : 42},
{"d" : 42.42},
{"d" : "2020-01-01"},
{"d" : [1, 2, 3]}
$$)

┌─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] │
└───────────────┴────────────────┴───────────────┴──────┴───────┴────────────┴─────────┘

Using Dynamic type in functions

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:

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

SELECT d, dynamicType(d) FROM test;

┌─d────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ │ None           │
│ 1    │ Int8           │
│ 2    │ Int16          │
│ 3    │ Int32          │
│ 4    │ Int64          │
└──────┴────────────────┘

SELECT d, d + 1 AS res, toTypeName(res), dynamicType(res) FROM test;

┌─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            │
└──────┴──────┴─────────────────┴──────────────────┘

SELECT d, d + d AS res, toTypeName(res), dynamicType(res) FROM test;

┌─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            │
└──────┴──────┴─────────────────┴──────────────────┘

SELECT d, d < 3 AS res, toTypeName(res) FROM test;

┌─d────┬──res─┬─toTypeName(res)─┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Nullable(UInt8) │
│ 1    │    1 │ Nullable(UInt8) │
│ 2    │    1 │ Nullable(UInt8) │
│ 3    │    0 │ Nullable(UInt8) │
│ 4    │    0 │ Nullable(UInt8) │
└──────┴──────┴─────────────────┘

SELECT d, exp2(d) AS res, toTypeName(res) FROM test;

┌─d────┬──res─┬─toTypeName(res)───┐
│ ᴺᵁᴸᴸ │ ᴺᵁᴸᴸ │ Nullable(Float64) │
│ 1    │    2 │ Nullable(Float64) │
│ 2    │    4 │ Nullable(Float64) │
│ 3    │    8 │ Nullable(Float64) │
│ 4    │   16 │ Nullable(Float64) │
└──────┴──────┴───────────────────┘

TRUNCATE TABLE test;
INSERT INTO test VALUES (NULL), ('str_1'), ('str_2');
SELECT d, dynamicType(d) FROM test;

┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ str_1 │ String         │
│ str_2 │ String         │
└───────┴────────────────┘

SELECT d, upper(d) AS res, toTypeName(res) FROM test;

┌─d─────┬─res───┬─toTypeName(res)──┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ  │ Nullable(String) │
│ str_1 │ STR_1 │ Nullable(String) │
│ str_2 │ STR_2 │ Nullable(String) │
└───────┴───────┴──────────────────┘

SELECT d, extract(d, '([0-3])') AS res, toTypeName(res) FROM test;

┌─d─────┬─res──┬─toTypeName(res)──┐
│ ᴺᵁᴸᴸ  │ ᴺᵁᴸᴸ │ Nullable(String) │
│ str_1 │ 1    │ Nullable(String) │
│ str_2 │ 2    │ Nullable(String) │
└───────┴──────┴──────────────────┘

TRUNCATE TABLE test;
INSERT INTO test VALUES (NULL), ([1, 2]), ([3, 4]);
SELECT d, dynamicType(d) FROM test;

┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ [1,2] │ Array(Int64)   │
│ [3,4] │ Array(Int64)   │
└───────┴────────────────┘

SELECT d, d[1] AS res, toTypeName(res), dynamicType(res) FROM test;

┌─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:

INSERT INTO test VALUES (42), (43), ('str_1');
SELECT d, dynamicType(d) FROM test;

┌─d─────┬─dynamicType(d)─┐
│ 42    │ Int64          │
│ 43    │ Int64          │
│ str_1 │ String         │
└───────┴────────────────┘
┌─d─────┬─dynamicType(d)─┐
│ ᴺᵁᴸᴸ  │ None           │
│ [1,2] │ Array(Int64)   │
│ [3,4] │ Array(Int64)   │
└───────┴────────────────┘

SELECT d, d + 1 AS res, toTypeName(res), dynamicType(d) FROM test;

例外が発生しました:
Code: 43. DB::Exception: 関数plusの引数の型が不正です Array(Int64) と UInt8: 'FUNCTION plus(__table1.d : 3, 1_UInt8 :: 1) -> plus(__table1.d, 1_UInt8) Dynamic : 0' の実行中。(ILLEGAL_TYPE_OF_ARGUMENT)

We can filter out unneeded types:

SELECT d, d + 1 AS res, toTypeName(res), dynamicType(res) FROM test WHERE dynamicType(d) NOT IN ('String', 'Array(Int64)', 'None')

┌─d──┬─res─┬─toTypeName(res)─┬─dynamicType(res)─┐
│ 42 │ 43  │ Dynamic         │ Int64            │
│ 43 │ 44  │ Dynamic         │ Int64            │
└────┴─────┴─────────────────┴──────────────────┘

Or extract required type as subcolumn:

SELECT d, d.Int64 + 1 AS res, toTypeName(res) FROM test;

┌─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) │
└───────┴──────┴─────────────────┘

Using Dynamic type in ORDER BY and GROUP BY

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:

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

SELECT d, dynamicType(d) FROM test;

┌─d───────┬─dynamicType(d)─┐
│ 42      │ Int64          │
│ 43      │ Int64          │
│ abc     │ String         │
│ abd     │ String         │
│ [1,2,3] │ Array(Int64)   │
│ []      │ Array(Int64)   │
│ ᴺᵁᴸᴸ    │ None           │
└─────────┴────────────────┘

SELECT d, dynamicType(d) FROM test ORDER BY d SETTINGS allow_suspicious_types_in_order_by=1;

┌─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:

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;

┌─v───┬─dynamicType(v)─┐
│ 1   │ Int64          │
│ 100 │ Int64          │
│ 1   │ UInt32         │
│ 100 │ UInt32         │
└─────┴────────────────┘

SELECT d, dynamicType(d) FROM test GROUP BY d SETTINGS allow_suspicious_types_in_group_by=1;

┌─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 of functions with Dynamic type

Reaching the limit in number of different data types stored inside Dynamic

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.

Reaching the limit during data parsing

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:

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]}}
')

┌─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.

During merges of data parts in MergeTree table engines

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:

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:

SELECT count(), dynamicType(d), isDynamicElementInSharedData(d), _part FROM test GROUP BY _part, dynamicType(d), isDynamicElementInSharedData(d) ORDER BY _part, count();

┌─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:

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;

┌─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.

JSONExtract functions with Dynamic

All JSONExtract* functions support Dynamic type:

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

┌─dynamic─┬─dynamic_type───────────┐
│ [1,2,3] │ Array(Nullable(Int64)) │
└─────────┴────────────────────────┘

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

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

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

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

バイナリ出力形式

RowBinary 形式では、Dynamic 型の値は次のフォーマットでシリアライズされます。

<binary_encoded_data_type><value_in_binary_format_according_to_the_data_type>

Dynamic の作成​

Dynamic のネストされた型をサブカラムとして読み取る​

Conversion between Dynamic column and other columns​

Converting an ordinary column to a Dynamic column​

Converting a String column to a Dynamic column through parsing​

Converting a Dynamic column to an ordinary column​

Converting a Variant column to Dynamic column​

Converting a Dynamic(max_types=N) column to another Dynamic(max_types=K)​

Reading Dynamic type from the data​

Using Dynamic type in functions​

Using Dynamic type in ORDER BY and GROUP BY​

Reaching the limit in number of different data types stored inside Dynamic​

Reaching the limit during data parsing​

During merges of data parts in MergeTree table engines​

JSONExtract functions with Dynamic​

バイナリ出力形式​

Dynamic の作成

Dynamic のネストされた型をサブカラムとして読み取る

Conversion between Dynamic column and other columns

Converting an ordinary column to a Dynamic column

Converting a String column to a Dynamic column through parsing

Converting a Dynamic column to an ordinary column

Converting a Variant column to Dynamic column

Converting a Dynamic(max_types=N) column to another Dynamic(max_types=K)

Reading Dynamic type from the data

Using Dynamic type in functions

Using Dynamic type in ORDER BY and GROUP BY

Reaching the limit in number of different data types stored inside Dynamic

Reaching the limit during data parsing

During merges of data parts in MergeTree table engines

JSONExtract functions with Dynamic

バイナリ出力形式