Tipos de dados numéricos¶

Precisão¶

O Snowflake utiliza números de ponto flutuante de precisão dupla (64 bits) IEEE 754.

A precisão é de aproximadamente 15 dígitos. Por exemplo, para números inteiros, o intervalo é de -9007199254740991 a +9007199254740991 (-2⁵³ + 1 a +2⁵³ - 1). Os valores de ponto flutuante podem variar de aproximadamente 10^-308 a 10⁺³⁰⁸. (O Snowflake pode representar valores mais extremos entre aproximadamente 10^-324 e 10^-308 com menos precisão) Para obter mais detalhes, consulte o artigo da Wikipédia sobre números de precisão dupla.

O Snowflake oferece suporte ao tipo de dados de ponto fixo NUMBER, que permite maior precisão, embora uma gama menor de expoentes.

Valores especiais¶

O Snowflake oferece suporte aos seguintes valores especiais para FLOAT:

• 'NaN' (não é um número)

• 'inf' (infinito)

• '-inf' (infinito negativo)

Os símbolos 'NaN', 'inf' e '-inf' devem estar entre aspas simples e fazem a distinção entre maiúsculas e minúsculas.

A semântica comparativa para 'NaN' difere do padrão IEEE 754 das seguintes maneiras:

Erros de arredondamento¶

As operações de ponto flutuante podem apresentar pequenos erros de arredondamento nos dígitos menos significativos. Erros de arredondamento podem ocorrer em qualquer tipo de processamento de ponto flutuante, incluindo funções trigonométricas, estatísticas e geoespaciais.

A seguir, considerações sobre erros de arredondamento:

• Os erros podem variar a cada vez que a consulta é executada.

• Os erros podem ser maiores quando os operandos têm precisão ou escala diferente.

• Os erros podem se acumular, especialmente quando as funções de agregação (por exemplo, SUM ou AVG) processam um grande número de linhas. Converter em um tipo de dados de ponto fixo antes de agregar pode reduzir ou eliminar esses erros.

• Os erros de arredondamento podem ocorrer não apenas ao trabalhar com SQL, mas também ao trabalhar com outro código (por exemplo, Java, JavaScript ou Python) executado dentro do Snowflake (por exemplo, em UDFs e procedimentos armazenados).

• Ao comparar dois números de ponto flutuante, o Snowflake recomenda a comparação para uma igualdade aproximada, em vez de uma igualdade exata.

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

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 involving 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 specifying a DECFLOAT value or casting to a DECFLOAT value, avoid using numeric literals in SQL. If you do this, 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. 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 adding 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:

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 Drivers.

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 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:

  • Clustering keys

  • Differential privacy

  • Sensitive data classification

  • Serviço de otimização de pesquisa

• 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

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
  );

DESC TABLE decfloat_sample;

+--------------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+
| 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           |
+--------------+--------------+--------+-------+---------+-------------+------------+-------+------------+---------+-------------+----------------+

SELECT * FROM decfloat_sample;

+----+-----------------------------------------+------------------------+
| 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                    |
+----+-----------------------------------------+------------------------+

CREATE OR REPLACE TABLE decfloat_agg_sample (decfloat_val DECFLOAT);

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

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

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