数値データ型¶

This topic describes the numeric data types supported in Snowflake, along with the supported formats for numeric constants and literals.

固定小数点数のデータ型¶

Snowflakeは、固定小数点数について次のデータ型をサポートしています。

NUMBER¶

オプションの精度とスケールを使用した、最大38桁の数字です。

精度:: 許可される合計桁数です。
スケール:: 小数点の右側に許可される桁数です。

By default, precision is 38, and scale is 0; that is, NUMBER(38, 0). Precision limits the range of values that can be inserted into or cast to columns of a given type. For example, the value 999 fits into NUMBER(38,0) but not into NUMBER(2,0).

Because precision is the total number of digits allowed, you can't load a value into a NUMBER column if the number of digits to the left of the decimal point exceeds the precision of the column minus its scale. For example, NUMBER(20, 2) allows 18 digits on the left side of the decimal point and two digits on the right side of the decimal point, for a total of 20 digits.

The maximum scale, which is the number of digits to the right of the decimal point, is 37. Numbers that have fewer than 38 significant digits, but whose least significant digit is past the 37th decimal place --- for example, 0.0000000000000000000000000000000000000012 (1.2e-39) --- can't be represented without losing some digits of precision.

注釈

If data is converted to another data type with lower precision, and then converted back to the higher-precision data type, the data can lose precision. For example, precision is lost if you convert a NUMBER(38,37) value to a DOUBLE value --- which has a precision of approximately 15 decimal digits --- and then back to NUMBER.

Snowflakeは FLOAT データ型もサポートしているため、精度は低くなりますが、より広い範囲の値を使用できます。

DECIMAL , DEC , NUMERIC¶

NUMBERと同義語です。

INT , INTEGER , BIGINT , SMALLINT , TINYINT , BYTEINT¶

NUMBER と同義ですが、精度とスケールは指定できません（つまり、デフォルトは常に NUMBER(38, 0)）。したがって、すべての INTEGER データ型について、値の範囲は-99999999999999999999999999999999999999から+99999999999999999999999999999999999999（両端を含む）までの整数値すべてです。

The various names --- for example, TINYINT, BYTEINT, and so on ---are to simplify porting from other systems and to suggest the expected range of values for a column of the specified type.

ストレージサイズに対する精度とスケールの影響¶

Precision --- the total number of digits --- doesn't affect storage. The storage requirements for the same number in columns with different precisions, such as NUMBER(2,0) and NUMBER(38,0), are the same. For each micro-partition, Snowflake determines the minimum and maximum values for a given column and uses that information to determine the storage size for all values for that column in the partition. For example:

If a column contains only values between -128 and +127, each of the values consumes 1 byte (uncompressed).
If the largest value in the column is 10000000, each of the values consumes 4 bytes (uncompressed).

However, scale --- the number of digits following the decimal point --- affects storage. For example, the same value stored in a column of type NUMBER(10,5) consumes more space than NUMBER(5,0). Also, processing values with a larger scale might be slightly slower and consume more memory.

スペースを節約するために、Snowflakeは値をストレージに書き込む前に圧縮します。圧縮の量は、データ値やその他の要因によって異なります。

テーブル内の固定小数点データ型の例¶

次のステートメントは、さまざまな固定小数点データ型の列を持つテーブルを作成します。

CREATE OR REPLACE TABLE test_fixed(
  num0 NUMBER,
  num10 NUMBER(10,1),
  dec20 DECIMAL(20,2),
  numeric30 NUMERIC(30,3),
  int1 INT,
  int2 INTEGER);

DESC TABLE test_fixed;

Copy

+-----------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+
| name      | type         | kind   | null? | default | primary key | unique key | check | expression | comment | policy name | privacy domain |
|-----------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------|
| NUM0      | NUMBER(38,0) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| NUM10     | NUMBER(10,1) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| DEC20     | NUMBER(20,2) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| NUMERIC30 | NUMBER(30,3) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| INT1      | NUMBER(38,0) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| INT2      | NUMBER(38,0) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
+-----------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+

浮動小数点数のデータ型¶

Snowflakeは、浮動小数点数に対して次のデータ型をサポートしています。

FLOAT , FLOAT4 , FLOAT8¶

FLOAT、 FLOAT4、 FLOAT8 という名前は、他のシステムとの互換性を確保するためです。Snowflakeはこの3つをすべて64ビット浮動小数点数として扱います。

精度¶

Snowflakeは、倍精度（64ビット） IEEE 754浮動小数点数を使用します。

Precision is approximately 15 digits. For example, for integers, the range is from -9007199254740991 to +9007199254740991 (-2⁵³ + 1 to +2⁵³ - 1). Floating-point values can range from approximately 10^-308 to 10⁺³⁰⁸. Snowflake can represent more extreme values between approximately 10^-324 and 10^-308 with less precision. For more details, see the Wikipedia article on double-precision numbers.

Snowflakeは固定小数点データ型 NUMBER をサポートしているため、指数の範囲は狭くなりますが、精度が向上します。

特別な価値¶

Snowflakeは、 FLOAT に対して次の特別な値をサポートしています。

'NaN' （非数）
'inf' （無限大）
'-inf' （負の無限大）

記号 'NaN'、 'inf'、および '-inf' は一重引用符で囲む必要があり、大文字と小文字は区別されません。

'NaN' の比較セマンティクスは、次の点で IEEE 754標準と異なります。

条件	Snowflake	IEEE 754	コメント
`'NaN' = 'NaN'`	`TRUE`	`FALSE`	Snowflakeでは、 `'NaN'` の値はすべて等しくなります。
`'NaN' > X` . where `X` is any FLOAT value, including . infinity, other than `NaN` itself.	`TRUE`	`FALSE`	Snowflakeでは、 `'NaN'` は . 無限大を含め、 . 他のどの FLOAT 値よりも大きくなります。

丸め誤差¶

浮動小数点操作には、最下位桁に小さな丸めエラーが生じることがあります。丸めエラーは、三角関数、統計関数、地理空間関数を含む、あらゆるタイプの浮動小数点処理で発生する可能性があります。

The following list shows considerations for rounding errors:

エラーは、クエリが実行されるたびに変わる可能性があります。
オペランドの精度やスケールが異なると、エラーが大きくなる可能性があります。
Errors can accumulate, especially when aggregate functions ---for example, SUM or AVG --- process large numbers of rows. Casting to a fixed-point data type before aggregating can reduce or eliminate these errors.
Rounding errors can occur not only when working with SQL, but also when working with other code --- for example, Java, JavaScript, or Python --- that runs inside Snowflake --- for example, in UDFs and stored procedures.
2つの浮動小数点数を比較する場合、Snowflakeは、正確な等式ではなく、近似的な等式を比較することをお勧めします。

It might be possible to avoid these types of approximation errors by using the exact DECFLOAT data type.

DOUBLE , DOUBLE PRECISION , REAL¶

FLOAT と同義語です。

テーブル内の浮動小数点データ型の例¶

次のステートメントは、さまざまな浮動小数点データ型の列を持つテーブルを作成します。

CREATE OR REPLACE TABLE test_float(
  double1 DOUBLE,
  float1 FLOAT,
  dp1 DOUBLE PRECISION,
  real1 REAL);

DESC TABLE test_float;

Copy

+---------+-------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+
| name    | type  | kind   | null? | default | primary key | unique key | check | expression | comment | policy name | privacy domain |
|---------+-------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------|
| DOUBLE1 | FLOAT | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| FLOAT1  | FLOAT | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| DP1     | FLOAT | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| REAL1   | FLOAT | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
+---------+-------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+

注釈

The DESC TABLE command's type column displays the data type FLOAT not only for FLOAT, but also for synonyms of FLOAT; for example, DOUBLE, DOUBLE PRECISION, and REAL.

DECFLOAT¶

The decimal float (DECFLOAT) data type stores numbers exactly, with up to 38 significant digits of precision, and uses a dynamic base-10 exponent to represent very large or small values. The exponent range is from -16383 to 16384, allowing values approximately between -10^(16384) and 10^(16384). The DECFLOAT data type supports a variable scale so that the scale varies depending on the specific value being stored. In contrast to the FLOAT data type, which represents values as approximations, the DECFLOAT data type represents exact values in the specified precision.

The DECFLOAT data type doesn't support the following special values that are supported by the FLOAT data type: 'NaN' (not a number), 'inf' (infinity), and '-inf' (negative infinity).

Use cases for the DECFLOAT data type¶

Use the DECFLOAT data type when you need exact decimal results and a wide, variable scale in the same column.

The DECFLOAT data type is appropriate for the following general use cases:

You are ingesting data, and the scale of incoming numeric values is unknown or highly variable.
You require exact numeric values; for example, ledgers, taxes, or compliance.
You are migrating from systems that rely on the IEEE 754-decimal representation or 128-bit decimals. These migrations might be blocked by the precision or range limitations of other Snowflake data types.
You want to avoid Number out of representable range errors when you sum, multiply, or divide high-precision numeric values.

For example, you can use the DECFLOAT data type for the following specific use cases:

You are ingesting heterogeneously scaled data from Oracle DECIMAL or DB2 DECFLOAT columns.
You are performing financial modeling that involves computations with scales of results that are hard to predict.
You are running scientific measurements that swing from nano units to astronomical units.

You can continue to use the NUMBER data type for fixed-scale numeric columns or the FLOAT data type for high-throughput analytics where imprecise results are acceptable.

Usage notes for the DECFLOAT data type¶

If an operation produces a result with more than 38 digits, the DECFLOAT value is rounded to 38-digit precision, with the least-significant digits rounded off according to the current rounding mode. Snowflake uses the half up rounding mode for DECFLOAT values.
When you specify a DECFLOAT value or you cast to a DECFLOAT value, avoid using numeric literals in SQL. If you use numeric literals in SQL, the values are interpreted as NUMBER or FLOAT values before being cast to a DECFLOAT value, which can result in range errors or loss of exactness. Instead, use either string literals --- such as SELECT '<value>'::DECFLOAT --- or the DECFLOAT literal --- such as SELECT DECFLOAT '<value>'.
When operations mix DECFLOAT values and values of other numeric types, coercion prefers the DECFLOAT values. For example, when you add a value of NUMBER type and DECFLOAT type, the result is a DECFLOAT value.
Use of the DECFLOAT type might cause storage consumption to increase.

Drivers and driver versions that support the DECFLOAT data type¶

The following Snowflake drivers and driver versions support the DECFLOAT data type. You might need to update your drivers to the versions that support DECFLOAT:

Driver	Minimum supported version	Notes
Snowflake Connector for Python	3.14.1	pandas DataFrames don't support the DECFLOAT type.
ODBC	3.12.0	None.
JDBC	3.27.0	None.
Go Snowflake Driver	1.17.0	None.
SQL API	2.0.0	None.

Unsupported drivers treat DECFLOAT values as TEXT values. For some drivers, a driver parameter must be set to map the DECFLOAT type to a language-native type. For more information, see ドライバー.

Limitations for the DECFLOAT data type¶

The following limitations apply to the DECFLOAT type:

DECFLOAT values can't be stored in VARIANT, OBJECT, or ARRAY values. To cast a DECFLOAT value to a VARIANT value, you can first cast it to a VARCHAR value, and then cast it to a VARIANT value.
DECFLOAT values aren't supported in the following types of tables:
- Tables in external formats, such as Iceberg
- Hybrid tables
The DECFLOAT data type isn't supported in Snowflake Scripting stored procedures. However, it is supported in Snowflake Scripting user-defined functions (UDFs).
The DECFLOAT data type isn't supported in stored procedures or UDFs written in a language other than SQL, such as Python or Java.
The DECFLOAT data type isn't supported in Snowpark.
Snowsight has limited support for the DECFLOAT data type.
The following features don't support the DECFLOAT data type:
The NUMBER and FLOAT types might provide better performance than the DECFLOAT type.

Examples for the DECFLOAT data type¶

The following examples use the DECFLOAT data type:

Show the differences between DECFLOAT and FLOAT
Use DECFLOAT values with aggregate functions

Show the differences between DECFLOAT and FLOAT¶

The following example shows the differences between the DECFLOAT and FLOAT data types:

Create a table with a DECFLOAT column and a FLOAT column, and then insert the same values for both types into the table:

CREATE OR REPLACE TABLE decfloat_sample (
  id INT,
  decfloat_val DECFLOAT,
  float_val FLOAT);

INSERT INTO decfloat_sample VALUES
  (
    1,
    DECFLOAT '123e7000',
    FLOAT '123e7000'
  ),
  (
    2,
    12345678901234567890123456789::DECFLOAT,
    12345678901234567890123456789::FLOAT
  ),
  (
    3,
    '-4.2e-5432'::DECFLOAT,
    '-4.2e-5432'::FLOAT
  ),
  (
    4,
    '1.00000000000000000000000000000000000014'::DECFLOAT,
    '1.00000000000000000000000000000000000014'::FLOAT
  ),
  (
    5,
    '1.00000000000000000000000000000000000015'::DECFLOAT,
    '1.00000000000000000000000000000000000015'::FLOAT
  );

Copy

The statement inserts DECFLOAT values in the following ways:

The first value is inserted by using the DECFLOAT literal.
The second value is inserted by casting an INTEGER value to a DECFLOAT value.
The third, fourth, and fifth values are inserted by casting a VARCHAR value to a DECFLOAT value.

To show the types, describe the table by using the DESC TABLE command.

The precision wasn't specified in the table definition for either column, but the output shows that the DECFLOAT data type supports up to 38 significant digits of precision:

DESC TABLE decfloat_sample;

Copy

+--------------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+
| name         | type         | kind   | null? | default | primary key | unique key | check | expression | comment | policy name | privacy domain |
|--------------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------|
| ID           | NUMBER(38,0) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| DECFLOAT_VAL | DECFLOAT(38) | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
| FLOAT_VAL    | FLOAT        | COLUMN | Y     | NULL    | N           | N          | NULL  | NULL       | NULL    | NULL        | NULL           |
+--------------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+

To show the differences in the values, query the table by using the SELECT statement:
```
SELECT * FROM decfloat_sample;
```
Copy
```
+----+-----------------------------------------+------------------------+
| ID | DECFLOAT_VAL                            |              FLOAT_VAL |
|----+-----------------------------------------+------------------------|
|  1 | 1.23e7002                               | inf                    |
|  2 | 12345678901234567890123456789           |   1.23456789012346e+28 |
|  3 | -4.2e-5432                              |  -0                    |
|  4 | 1.0000000000000000000000000000000000001 |   1                    |
|  5 | 1.0000000000000000000000000000000000002 |   1                    |
+----+-----------------------------------------+------------------------+
```
The output shows the following differences:
- The first row shows that the DECFLOAT type supports a wider range of values than the FLOAT type. The DECFLOAT value is very large (1.23e7002). The FLOAT value is inf, which means that the value is larger than any value that the FLOAT type can represent.
- The second row shows that the DECFLOAT type retains the specified value exactly. The FLOAT value is an approximation that is stored in scientific notation.
- The third row shows that the DECFLOAT type supports very small values (-4.2e-5432). The FLOAT value is approximated to -0.
- The fourth and fifth rows show that the DECFLOAT type supports up to 38 digits of precision and uses rounding rules for values beyond the limit. The FLOAT value is approximated to 1 in both rows.

Use DECFLOAT values with aggregate functions¶

The following example uses DECFLOAT values with aggregate functions:

Create a table, and then insert DECFLOAT values into the table:

CREATE OR REPLACE TABLE decfloat_agg_sample (decfloat_val DECFLOAT);

INSERT INTO decfloat_agg_sample VALUES
  (DECFLOAT '1e1000'),
  (DECFLOAT '-2.47e999'),
  (DECFLOAT '22e-75');

Copy

Query the table by using some aggregate functions:

SELECT SUM(decfloat_val),
       AVG(decfloat_val),
       MAX(decfloat_val),
       MIN(decfloat_val)
  FROM decfloat_agg_sample;

Copy

+-------------------+-------------------+-------------------+-------------------+
| SUM(DECFLOAT_VAL) | AVG(DECFLOAT_VAL) | MAX(DECFLOAT_VAL) | MIN(DECFLOAT_VAL) |
|-------------------+-------------------+-------------------+-------------------|
| 7.53e999          | 2.51e999          | 1e1000            | -2.47e999         |
+-------------------+-------------------+-------------------+-------------------+

数値定数¶

The term constants --- also known as literals --- refers to fixed data values. The following formats are supported for numeric constants:

[+-][digits][.digits][e[+-]digits]

条件：

+ または - は正または負の値を示します。デフォルトは正です。
digits は、0から9までの1桁以上です。
e （または E）は、科学表記法の指数を示します。指数マーカーが存在する場合は、少なくとも1桁が指数マーカーの後に続く必要があります。

次の数値は、すべてサポートされている数値定数の例です。

15
+1.34
0.2
15e-03
1.234E2
1.234E+2
-1