DROP TABLE Statement
Removes an Impala table. Also removes the underlying HDFS data files for internal tables, although not for external tables.
Syntax:
DROP TABLE [IF EXISTS] [db_name.]table_name [PURGE]
IF EXISTS clause:
The optional IF EXISTS
clause makes the statement succeed whether or not the table exists.
If the table does exist, it is dropped; if it does not exist, the statement has no effect. This capability is
useful in standardized setup scripts that remove existing schema objects and create new ones. By using some
combination of IF EXISTS
for the DROP
statements and IF NOT
EXISTS
clauses for the CREATE
statements, the script can run successfully the first
time you run it (when the objects do not exist yet) and subsequent times (when some or all of the objects do
already exist).
PURGE clause:
The optional PURGE
keyword, available in
Impala 2.3 and higher, causes Impala to remove the associated
HDFS data files immediately, rather than going through the HDFS trashcan
mechanism. Use this keyword when dropping a table if it is crucial to
remove the data as quickly as possible to free up space, or if there is a
problem with the trashcan, such as the trash cannot being configured or
being in a different HDFS encryption zone than the data files.
Statement type: DDL
Usage notes:
By default, Impala removes the associated HDFS directory and data files for the table. If you issue a
DROP TABLE
and the data files are not deleted, it might be for the following reasons:
-
If the table was created with the
EXTERNAL
clause, Impala leaves all files and directories untouched. Use external tables when the data is under the control of other Hadoop components, and Impala is only used to query the data files from their original locations. -
Impala might leave the data files behind unintentionally, if there is no HDFS location available to hold
the HDFS trashcan for the
impala
user. See User Account Requirements for the procedure to set up the required HDFS home directory.
Make sure that you are in the correct database before dropping a table, either by issuing a
USE
statement first or by using a fully qualified name
db_name.table_name
.
If you intend to issue a DROP DATABASE
statement, first issue DROP TABLE
statements to remove all the tables in that database.
Examples:
create database temporary;
use temporary;
create table unimportant (x int);
create table trivial (s string);
-- Drop a table in the current database.
drop table unimportant;
-- Switch to a different database.
use default;
-- To drop a table in a different database...
drop table trivial;
ERROR: AnalysisException: Table does not exist: default.trivial
-- ...use a fully qualified name.
drop table temporary.trivial;
For other tips about managing and reclaiming Impala disk space, see Managing Disk Space for Impala Data.
Amazon S3 considerations:
The DROP TABLE
statement can remove data files from S3
if the associated S3 table is an internal table.
In Impala 2.6 and higher, as part of improved support for writing
to S3, Impala also removes the associated folder when dropping an internal table
that resides on S3.
See Using Impala with Amazon S3 Object Store for details about working with S3 tables.
- Use native Hadoop techniques to create data files in S3 for querying through Impala.
- Use the
PURGE
clause ofDROP TABLE
when dropping internal (managed) tables.
DROP TABLE table_name
PURGE
rather than the default DROP TABLE
statement. The PURGE
clause makes Impala delete the
data files immediately, skipping the HDFS trashcan. For the
PURGE
clause to work effectively, you must originally
create the data files on S3 using one of the tools from the Hadoop
ecosystem, such as hadoop fs -cp
, or
INSERT
in Impala or Hive.
In Impala 2.6 and higher, Impala DDL statements such as
CREATE DATABASE
, CREATE TABLE
, DROP DATABASE
CASCADE
, DROP TABLE
, and ALTER TABLE [ADD|DROP]
PARTITION
can create or remove folders as needed in the Amazon S3 system. Prior
to Impala 2.6, you had to create folders yourself and point
Impala database, tables, or partitions at them, and manually remove folders when no
longer needed. See Using Impala with Amazon S3 Object Store for details about reading
and writing S3 data with Impala.
Cancellation: Cannot be cancelled.
HDFS permissions:
For an internal table, the user ID that the impalad daemon runs under,
typically the impala
user, must have write
permission for all the files and directories that make up the table.
For an external table, dropping the table only involves changes to metadata in the metastore database. Because Impala does not remove any HDFS files or directories when external tables are dropped, no particular permissions are needed for the associated HDFS files or directories.
Kudu considerations:
Kudu tables can be managed or external, the same as with HDFS-based
tables. For a managed table, the underlying Kudu table and its data
are removed by DROP TABLE
. For an external table,
the underlying Kudu table and its data remain after a
DROP TABLE
.
Related information:
Overview of Impala Tables, ALTER TABLE Statement, CREATE TABLE Statement, Partitioning for Impala Tables, Internal Tables, External Tables