FLOAT Data Type
A single precision floating-point data type used in CREATE TABLE and ALTER TABLE statements.
In the column definition of a CREATE TABLE statement:
Range: 1.40129846432481707e-45 .. 3.40282346638528860e+38, positive or negative
Precision: 6 to 9 significant digits, depending on usage. The number of significant digits does not depend on the position of the decimal point.
Representation: The values are stored in 4 bytes, using IEEE 754 Single Precision Binary Floating Point format.
Conversions: Impala automatically converts FLOAT to more precise DOUBLE values, but not the other way around. You can use CAST() to convert FLOAT values to TINYINT, SMALLINT, INT, BIGINT, STRING, TIMESTAMP, or BOOLEAN. You can use exponential notation in FLOAT literals or when casting from STRING, for example 1.0e6 to represent one million. Casting an integer or floating-point value N to TIMESTAMP produces a value that is N seconds past the start of the epoch date (January 1, 1970).
CREATE TABLE t1 (x FLOAT); SELECT CAST(1000.5 AS FLOAT);
Partitioning: Because fractional values of this type are not always represented precisely, when this type is used for a partition key column, the underlying HDFS directories might not be named exactly as you expect. Prefer to partition on a DECIMAL column instead.
HBase considerations: This data type is fully compatible with HBase tables.
Parquet considerations: This type is fully compatible with Parquet tables.
Text table considerations: Values of this type are potentially larger in text tables than in tables using Parquet or other binary formats.
Internal details: Represented in memory as a 4-byte value.
Column statistics considerations: Because this type has a fixed size, the maximum and average size fields are always filled in for column statistics, even before you run the COMPUTE STATS statement.
Due to the way arithmetic on FLOAT and DOUBLE columns uses high-performance hardware instructions, and distributed queries can perform these operations in different order for each query, results can vary slightly for aggregate function calls such as SUM() and AVG() for FLOAT and DOUBLE columns, particularly on large data sets where millions or billions of values are summed or averaged. For perfect consistency and repeatability, use the DECIMAL data type for such operations instead of FLOAT or DOUBLE.
The inability to exactly represent certain floating-point values means that DECIMAL is sometimes a better choice than DOUBLE or FLOAT when precision is critical, particularly when transferring data from other database systems that use different representations or file formats.