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74 lines
1.8 KiB
Markdown
74 lines
1.8 KiB
Markdown
# Float32, Float64
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[Floating point numbers](https://en.wikipedia.org/wiki/IEEE_754).
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Types are equivalent to types of C:
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- `Float32` - `float`
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- `Float64` - `double`
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We recommend that you store data in integer form whenever possible. For example, convert fixed precision numbers to integer values, such as monetary amounts or page load times in milliseconds.
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## Using Floating-point Numbers
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- Computations with floating-point numbers might produce a rounding error.
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``` sql
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SELECT 1 - 0.9
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```
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```
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┌───────minus(1, 0.9)─┐
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│ 0.09999999999999998 │
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└─────────────────────┘
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```
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- The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
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- Floating-point calculations might result in numbers such as infinity (`Inf`) and "not-a-number" (`NaN`). This should be taken into account when processing the results of calculations.
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- When reading floating point numbers from rows, the result might not be the nearest machine-representable number.
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## NaN and Inf
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In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:
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- `Inf` – Infinity.
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``` sql
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SELECT 0.5 / 0
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```
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```
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┌─divide(0.5, 0)─┐
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│ inf │
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└────────────────┘
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```
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- `-Inf` – Negative infinity.
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``` sql
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SELECT -0.5 / 0
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```
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```
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┌─divide(-0.5, 0)─┐
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│ -inf │
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└─────────────────┘
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```
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- `NaN` – Not a number.
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```
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SELECT 0 / 0
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```
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```
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┌─divide(0, 0)─┐
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│ nan │
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└──────────────┘
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```
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See the rules for `NaN` sorting in the section [ORDER BY clause](../query_language/select.md).
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[Original article](https://clickhouse.yandex/docs/en/data_types/float/) <!--hide-->
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