SQL differences between Impala and Hive
Impala's SQL syntax follows the SQL-92 standard, and includes extensions, such as built-in functions. Because Impala and Hive share the same Metastore database and their tables are often used interchangeably, this topic covers differences between Impala and Hive in detail.
The current release of Impala does not support the following SQL features that available in Hive SQL:
Extensibility mechanisms such as
TRANSFORM, custom file formats, or custom SerDes.
Certain aggregate functions from Hive SQL:
collect_set. Impala supports the set of aggregate functions and analytic functions.
Lateral views. In Impala 2.3 and higher, Impala supports queries on complex types (
MAP), using join notation rather than the
Impala supports high-performance UDFs written in C++, as well as reusing some Java-based Hive UDFs.
Impala supports scalar UDFs and user-defined aggregate functions (UDAFs). Impala does not currently support user-defined table generating functions (UDTFs).
Only Impala-supported column types are supported in Java-based UDFs.
current_user()function cannot be called from a Java UDF through Impala.
Impala does not currently support these Hive SQL statements:
ANALYZE TABLE(the Impala equivalent is
SHOW TABLE EXTENDED
INSERT OVERWRITE DIRECTORY; use
INSERT OVERWRITE table_nameor
CREATE TABLE AS SELECTto materialize query results into the HDFS directory associated with an Impala table.
Impala respects the
property only for TEXT tables and ignores the property for Parquet and
other formats. Hive respects the
serialization.null.format property for Parquet and
other formats and converts matching values to NULL during the scan. See
the Data files for text tables section for using the table
property in Impala.
Semantic differences between Impala and Hive SQL features
This section covers instances where Impala and Hive have similar functionality, sometimes including the same syntax, but there are differences in the runtime semantics of those features.
SQL statements and clauses:
- Impala uses different syntax and names for query hints,
- Impala does not expose MapReduce specific features of
DISTRIBUTE BY, or
- Impala does not require queries to include a
- Impala supports a limited set of implicit casts. This can help
avoid undesired results from unexpected casting behavior.
- Impala does not implicitly cast between string and numeric or
Boolean types. Always use
CAST()for these conversions.
- Impala does perform implicit casts among the numeric types,
when going from a smaller or less precise type to a larger or more
precise one. For example, Impala will implicitly convert a
FLOAT, but to convert from
TINYINTrequires a call to
CAST()in the query.
- Impala does perform implicit casts from
TIMESTAMP. Impala has a restricted set of literal formats for the
TIMESTAMPdata type and the
- Impala does not implicitly cast between string and numeric or Boolean types. Always use
Impala does not store or interpret
TIMESTAMPvalues using the local timezone, to avoid undesired results from unexpected time zone issues. Timestamps are stored and interpreted relative to UTC. This difference can produce different results for some calls to similarly named date/time functions between Impala and Hive. See TIMESTAMP data type for a discussion of how Impala handles time zones, and configuration options you can use to make Impala match the Hive behavior more closely when dealing with Parquet-encoded
TIMESTAMPdata or when converting between the local time zone and UTC.
TIMESTAMPtype can represent dates ranging from 1400-01-01 to 9999-12-31. This is different from the Hive date range, which is 0000-01-01 to 9999-12-31.
Impala does not return column overflows as
NULL, so that customers can distinguish between
NULLdata and overflow conditions similar to how they do so with traditional database systems. Impala returns the largest or smallest value in the range for the type. For example, valid values for a
tinyintrange from -128 to 127. In Impala, a
tinyintwith a value of -200 returns -128 rather than
tinyintwith a value of 200 returns 127.
- Impala does not provide virtual columns.
- Impala does not expose locking.
- Impala does not expose some configuration properties.