Relational Data - Data Types - Float

From FojiSoft Docs

If you need accurate calculations, in particular if you work with financial or business data requiring a high precision, you should consider using Decimal instead.

Floating Point Numbers might lead to inaccurate results as illustrated below: 

CREATE TABLE IF NOT EXISTS float_vs_decimal
(
   my_float Float64,
   my_decimal Decimal64(3)
)
Engine=MergeTree
ORDER BY tuple();

# Generate 1 000 000 random numbers with 2 decimal places and store them as a float and as a decimal
INSERT INTO float_vs_decimal SELECT round(randCanonical(), 3) AS res, res FROM system.numbers LIMIT 1000000;
SELECT sum(my_float), sum(my_decimal) FROM float_vs_decimal;

┌──────sum(my_float)─┬─sum(my_decimal)─┐
│ 499693.60500000004 │      499693.605 │
└────────────────────┴─────────────────┘

SELECT sumKahan(my_float), sumKahan(my_decimal) FROM float_vs_decimal;

┌─sumKahan(my_float)─┬─sumKahan(my_decimal)─┐
│         499693.605 │           499693.605 │
└────────────────────┴──────────────────────┘

The equivalent types in ClickHouse and in C are given below:

  • Float32 — float.
  • Float64 — double.

Float types in ClickHouse have the following aliases:

  • Float32 — FLOAT, REAL, SINGLE.
  • Float64 — DOUBLE, DOUBLE PRECISION.

When creating tables, numeric parameters for floating point numbers can be set (e.g. FLOAT(12), FLOAT(15, 22), DOUBLE(12), DOUBLE(4, 18)), but ClickHouse ignores them.

Using Floating-point Numbers

  • Computations with floating-point numbers might produce a rounding error.
SELECT 1 - 0.9
┌───────minus(1, 0.9)─┐
│ 0.09999999999999998 │
└─────────────────────┘
  • The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
  • 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.
  • When parsing floating-point numbers from text, the result might not be the nearest machine-representable number.

NaN and Inf

In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:

  • Inf – Infinity.
SELECT 0.5 / 0
┌─divide(0.5, 0)─┐
│            inf │
└────────────────┘
  • -Inf — Negative infinity.
SELECT -0.5 / 0
┌─divide(-0.5, 0)─┐
│            -inf │
└─────────────────┘
  • NaN — Not a number.
SELECT 0 / 0
┌─divide(0, 0)─┐
│          nan │
└──────────────┘

See the rules for NaN sorting in the section ORDER BY clause.

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