SQL Differences Between Impala and Hive
Impala's SQL syntax follows the SQL-92 standard, and includes many industry extensions in areas such as built-in functions. See Porting SQL from Other Database Systems to Impala for a general discussion of adapting SQL code from a variety of database systems to Impala.
Because Impala and Hive share the same metastore database and their tables are often used interchangeably, the following section covers differences between Impala and Hive in detail.
HiveQL Features not Available in Impala
The current release of Impala does not support the following SQL features that you might be familiar with from HiveQL:
Extensibility mechanisms such as
TRANSFORM, custom file formats, or custom SerDes.
- XML and JSON functions.
Certain aggregate functions from HiveQL:
collect_set; Impala supports the set of aggregate functions listed in Impala Aggregate Functions and analytic functions listed in Impala Analytic Functions.
Lateral views. In Impala 2.3 and higher, Impala supports queries on complex types
MAP), using join notation rather than the
EXPLODE()keyword. See Complex Types (Impala 2.3 or higher only) for details about Impala support for complex types.
DISTINCTclauses per query, although Impala includes some workarounds for this limitation.Note:
By default, Impala only allows a single
COUNT(DISTINCT columns)expression in each query.
If you do not need precise accuracy, you can produce an estimate of the distinct values for a column by specifying
NDV(column); a query can contain multiple instances of
NDV(column). To make Impala automatically rewrite
NDV(), enable the
To produce the same result as multiple
COUNT(DISTINCT)expressions, you can use the following technique for queries involving a single table:
select v1.c1 result1, v2.c1 result2 from (select count(distinct col1) as c1 from t1) v1 cross join (select count(distinct col2) as c1 from t1) v2;
CROSS JOINis an expensive operation, prefer to use the
NDV()technique wherever practical.
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 HiveQL 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
property for Parquet and other formats and converts matching values
to NULL during the scan. See Using Text Data Files with Impala Tables for
using the table property in Impala.
Semantic Differences Between Impala and HiveQL 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.
Impala utilizes the Apache Sentry authorization framework, which provides fine-grained role-based access control to protect data against unauthorized access or tampering.
The Hive component now includes Sentry-enabled
CREATE/DROP ROLE statements. Earlier Hive releases had a
privilege system with
REVOKE statements that were primarily
intended to prevent accidental deletion of data, rather than a security mechanism to protect against
Impala can make use of privileges set up through Hive
Impala has its own
REVOKE statements in Impala 2.0 and higher.
See Enabling Sentry Authorization for Impala for the details of authorization in Impala, including
how to switch from the original policy file-based privilege model to the Sentry service using privileges
stored in the metastore database.
SQL statements and clauses:
The semantics of Impala SQL statements varies from HiveQL in some cases where they use similar SQL statement and clause names:
Impala uses different syntax and names for query hints,
StreamJoin. See Joins in Impala SELECT Statements for the Impala details.
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
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 string to timestamp. Impala has a restricted set of literal
formats for the
TIMESTAMPdata type and the
from_unixtime()format string; see TIMESTAMP Data Type for details.
- Impala does not implicitly cast between string and numeric or Boolean types. Always use
Impala does not store or interpret timestamps 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 Impala Date and Time Functions for details about the Impala
functions. 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.