ALTER TABLE statement
The ALTER TABLE
statement changes the structure or properties of an
existing Impala table.
In Impala, this is primarily a logical operation that updates the table metadata in the
metastore database that Impala shares with Hive. Most ALTER TABLE
operations do not actually rewrite, move, and so on the actual data files. (The
RENAME TO
clause is the one exception; it can cause HDFS files to be
moved to different paths.) When you do an ALTER TABLE
operation, you
typically need to perform corresponding physical filesystem operations, such as rewriting
the data files to include extra fields, or converting them to a different file format.
Syntax:
ALTER TABLE [old_db_name.]old_table_name RENAME TO [new_db_name.]new_table_name
ALTER TABLE name ADD [IF NOT EXISTS] COLUMNS (col_spec[, col_spec ...])
ALTER TABLE name REPLACE COLUMNS (col_spec[, col_spec ...])
ALTER TABLE name ADD COLUMN [IF NOT EXISTS] col_spec
ALTER TABLE name DROP [COLUMN] column_name
ALTER TABLE name CHANGE column_name col_spec
ALTER TABLE name SET OWNER USER user_name
ALTER TABLE name SET OWNER ROLE role_name
-- Kudu tables only.
ALTER TABLE name ALTER [COLUMN] column_name
{ SET kudu_storage_attr attr_value
| DROP DEFAULT }
kudu_storage_attr ::= { DEFAULT | BLOCK_SIZE | ENCODING | COMPRESSION }
-- Non-Kudu tables only.
ALTER TABLE name ALTER [COLUMN] column_name
SET COMMENT 'comment_text'
ALTER TABLE name ADD [IF NOT EXISTS] PARTITION (partition_spec)
[location_spec]
[cache_spec]
ALTER TABLE name ADD [IF NOT EXISTS] RANGE PARTITION kudu_partition_spec
ALTER TABLE name DROP [IF EXISTS] PARTITION (partition_spec)
[PURGE]
ALTER TABLE name DROP [IF EXISTS] RANGE PARTITION kudu_partition_spec
ALTER TABLE name RECOVER PARTITIONS
ALTER TABLE name [PARTITION (partition_spec)]
SET { FILEFORMAT file_format
| ROW FORMAT row_format
| LOCATION 'hdfs_path_of_directory'
| TBLPROPERTIES (table_properties)
| SERDEPROPERTIES (serde_properties) }
ALTER TABLE name colname
('statsKey'='val, ...)
statsKey ::= numDVs | numNulls | avgSize | maxSize
ALTER TABLE name [PARTITION (partition_spec)] SET { CACHED IN 'pool_name' [WITH REPLICATION = integer] | UNCACHED }
new_name ::= [new_database.]new_table_name
col_spec ::= col_name type_name COMMENT 'column-comment' [kudu_attributes]
kudu_attributes ::= { [NOT] NULL | ENCODING codec | COMPRESSION algorithm |
DEFAULT constant | BLOCK_SIZE number }
partition_spec ::= simple_partition_spec | complex_partition_spec
simple_partition_spec ::= partition_col=constant_value
complex_partition_spec ::= comparison_expression_on_partition_col
kudu_partition_spec ::= constant range_operator VALUES range_operator constant | VALUE = constant
cache_spec ::= CACHED IN 'pool_name' [WITH REPLICATION = integer] | UNCACHED
location_spec ::= LOCATION 'hdfs_path_of_directory'
table_properties ::= 'name'='value'[, 'name'='value' ...]
serde_properties ::= 'name'='value'[, 'name'='value' ...]
file_format ::= { PARQUET | TEXTFILE | RCFILE | SEQUENCEFILE | AVRO }
row_format ::= DELIMITED
[FIELDS TERMINATED BY 'char' [ESCAPED BY 'char']]
[LINES TERMINATED BY 'char']
Statement type: DDL
Complex type considerations:
In Impala 2.3 and higher, the ALTER TABLE
statement can change the
metadata for tables containing complex types (ARRAY
,
STRUCT
, and MAP
). For example, you can use an
ADD COLUMNS
, DROP COLUMN
, or CHANGE
clause to modify the table layout for complex type columns. Although Impala queries only
work for complex type columns in Parquet tables, the complex type support in the
ALTER TABLE
statement applies to all file formats. For example, you can
use Impala to update metadata for a staging table in a non-Parquet file format where the
data is populated by Hive. Or you can use ALTER TABLE SET FILEFORMAT
to
change the format of an existing table to Parquet so that Impala can query it. Remember that
changing the file format for a table does not convert the data files within the table; you
must prepare any Parquet data files containing complex types outside Impala, and bring them
into the table using LOAD DATA
or updating the table's
LOCATION
property.
Usage notes:
Whenever you specify partitions in an ALTER TABLE
statement, through the
PARTITION (partition_spec)
clause, you must include
all the partitioning columns in the specification.
Most of the ALTER TABLE
operations work the same for internal tables
(managed by Impala) as for external tables (with data files located in arbitrary
locations). The exception is renaming a table; for an external table, the underlying data
directory is not renamed or moved.
To drop or alter multiple partitions:
In Impala 2.8 and higher, the expression for the partition clause with a
DROP
or SET
operation can include comparison operators
such as <
, IN
, or BETWEEN
, and Boolean
operators such as AND
and OR
.
For example, you might drop a group of partitions corresponding to a particular date range
after the data ages out
:
alter table historical_data drop partition (year < 1995);
alter table historical_data drop partition (year = 1996 and month between 1 and 6);
For tables with multiple partition keys columns, you can specify multiple conditions
separated by commas, and the operation only applies to the partitions that match all the
conditions (similar to using an AND
clause):
alter table historical_data drop partition (year < 1995, last_name like 'A%');
This technique can also be used to change the file format of groups of partitions, as part of an ETL pipeline that periodically consolidates and rewrites the underlying data files in a different file format:
alter table fast_growing_data partition (year = 2016, month in (10,11,12)) set fileformat parquet;
Amazon S3 considerations:
You can specify an s3a://
prefix on the
LOCATION
attribute of a table or partition to make Impala query data from
the Amazon S3 filesystem. In Impala 2.6 and higher, Impala automatically handles creating or
removing the associated folders when you issue ALTER TABLE
statements with
the ADD PARTITION
or DROP PARTITION
clauses.
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 Related Information for details about reading and writing S3 data with Impala.
HDFS caching (CACHED IN clause):
If you specify the CACHED IN
clause, any existing or future data
files in the table directory or the partition subdirectories are designated to be loaded
into memory with the HDFS caching mechanism.
In Impala 2.2 and higher, the optional
WITH REPLICATION
clause for CREATE TABLE
and
ALTER TABLE
lets you specify a replication factor, the
number of hosts on which to cache the same data blocks. When Impala processes a cached data
block, where the cache replication factor is greater than 1, Impala randomly selects a host
that has a cached copy of that data block. This optimization avoids excessive CPU usage on a
single host when the same cached data block is processed multiple times. Cloudera recommends specifying a value greater than or equal to the HDFS block
replication factor.
If you connect to different Impala nodes within an
impala-shell session for load-balancing purposes, you can enable the
SYNC_DDL
query option to make each DDL statement wait before returning,
until the new or changed metadata has been received by all the Impala nodes.
The following sections show examples of the use cases for various ALTER
TABLE
clauses.
To rename a table (RENAME TO clause):
The RENAME TO
clause lets you change the name of an existing table, and
optionally which database it is located in.
For internal tables, this operation physically renames the directory within HDFS that contains the data files; the original directory name no longer exists. By qualifying the table names with database names, you can use this technique to move an internal table (and its associated data directory) from one database to another. For example:
create database d1;
create database d2;
create database d3;
use d1;
create table mobile (x int) TBLPROPERTIES ('transactional'='false');
use d2;
-- Move table from another database to the current one.
alter table d1.mobile rename to mobile;
use d1;
-- Move table from one database to another.
alter table d2.mobile rename to d3.mobile;
To change the owner of a table:
ALTER TABLE name SET OWNER USER user_name;
ALTER TABLE name SET OWNER ROLE role_name;
The table owner is originally set to the user who creates the table. When
object ownership is enabled in Ranger, an owner of a table can have the
ALL
with GRANT
or
ALL
without GRANT
privilege. The
term OWNER
is used to differentiate between the
ALL
privilege that is explicitly granted via the
GRANT
statement and a privilege that is implicitly
granted by the CREATE TABLE
statement.
Use the ALTER TABLE SET OWNER
to transfer the ownership from the current
owner to another user or a role.
To change the physical location where Impala looks for data files associated with a table or partition:
ALTER TABLE table_name [PARTITION (partition_spec)] SET LOCATION 'hdfs_path_of_directory';
The path you specify is the full HDFS path where the data files reside, or will be created. Impala does not create any additional subdirectory named after the table. Impala does not move any data files to this new location or change any data files that might already exist in that directory.
To set the location for a single partition, include the PARTITION
clause.
Specify all the same partitioning columns for the table, with a constant value for each,
to precisely identify the single partition affected by the statement:
create table p1 (s string) partitioned by (month int, day int);
-- Each ADD PARTITION clause creates a subdirectory in HDFS.
alter table p1 add partition (month=1, day=1);
alter table p1 add partition (month=1, day=2);
alter table p1 add partition (month=2, day=1);
alter table p1 add partition (month=2, day=2);
-- Redirect queries, INSERT, and LOAD DATA for one partition
-- to a specific different directory.
alter table p1 partition (month=1, day=1) set location '/usr/external_data/new_years_day';
To automatically detect new partition directories added through Hive or HDFS operations:
In Impala 2.3 and higher, the RECOVER
PARTITIONS
clause scans a partitioned table to detect if any new partition
directories were added outside of Impala, such as by Hive ALTER TABLE
statements or by hdfs dfs or hadoop fs commands. The
RECOVER PARTITIONS
clause automatically recognizes any data files present
in these new directories, the same as the REFRESH
statement does.
For example, here is a sequence of examples showing how you might create a partitioned table in Impala, create new partitions through Hive, copy data files into the new partitions with the hdfs command, and have Impala recognize the new partitions and new data:
In Impala, create the table, and a single partition for demonstration purposes:
create database recover_partitions;
use recover_partitions;
create table t1 (s string) partitioned by (yy int, mm int);
insert into t1 partition (yy = 2016, mm = 1) values ('Partition exists');
show files in t1;
+---------------------------------------------------------------------+------+--------------+
| Path | Size | Partition |
+---------------------------------------------------------------------+------+--------------+
| /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=1/data.txt | 17B | yy=2016/mm=1 |
+---------------------------------------------------------------------+------+--------------+
quit;
In Hive, create some new partitions. In a real use case, you might create the partitions and populate them with data as the final stages of an ETL pipeline.
hive> use recover_partitions;
OK
hive> alter table t1 add partition (yy = 2016, mm = 2);
OK
hive> alter table t1 add partition (yy = 2016, mm = 3);
OK
hive> quit;
For demonstration purposes, manually copy data (a single row) into these new partitions, using manual HDFS operations:
$ hdfs dfs -ls /user/hive/warehouse/recover_partitions.db/t1/yy=2016/
Found 3 items
drwxr-xr-x - impala hive 0 2016-05-09 16:06 /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=1
drwxr-xr-x - jrussell hive 0 2016-05-09 16:14 /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=2
drwxr-xr-x - jrussell hive 0 2016-05-09 16:13 /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=3
$ hdfs dfs -cp /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=1/data.txt \
/user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=2/data.txt
$ hdfs dfs -cp /user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=1/data.txt \
/user/hive/warehouse/recover_partitions.db/t1/yy=2016/mm=3/data.txt
hive> select * from t1;
OK
Partition exists 2016 1
Partition exists 2016 2
Partition exists 2016 3
hive> quit;
In Impala, initially the partitions and data are not visible. Running ALTER
TABLE
with the RECOVER PARTITIONS
clause scans the table data
directory to find any new partition directories, and the data files inside them:
select * from t1;
+------------------+------+----+
| s | yy | mm |
+------------------+------+----+
| Partition exists | 2016 | 1 |
+------------------+------+----+
alter table t1 recover partitions;
select * from t1;
+------------------+------+----+
| s | yy | mm |
+------------------+------+----+
| Partition exists | 2016 | 1 |
| Partition exists | 2016 | 3 |
| Partition exists | 2016 | 2 |
+------------------+------+----+
To change the key-value pairs of the TBLPROPERTIES and SERDEPROPERTIES fields:
ALTER TABLE table_name SET TBLPROPERTIES ('key1'='value1', 'key2'='value2'[, ...]);
ALTER TABLE table_name SET SERDEPROPERTIES ('key1'='value1', 'key2'='value2'[, ...]);
The TBLPROPERTIES
clause is primarily a way to associate arbitrary
user-specified data items with a particular table.
The SERDEPROPERTIES
clause sets up metadata defining how tables are read
or written, needed in some cases by Hive but not used extensively by Impala. You would use
this clause primarily to change the delimiter in an existing text table or partition, by
setting the 'serialization.format'
and 'field.delim'
property values to the new delimiter character. The SERDEPROPERTIES
clause does not change the existing data in the table. The change only affects the future
inserts into the table.
Use the DESCRIBE FORMATTED
statement to see the current values of these
properties for an existing table.
To manually set or update table or column statistics:
Although for most tables the COMPUTE STATS
or COMPUTE INCREMENTAL
STATS
statement is all you need to keep table and column statistics up to date
for a table, sometimes for a very large table or one that is updated frequently, the
length of time to recompute all the statistics might make it impractical to run those
statements as often as needed. As a workaround, you can use the ALTER
TABLE
statement to set table statistics at the level of the entire table or a
single partition, or column statistics at the level of the entire table.
numrows
value for table statistics by changing the
TBLPROPERTIES
setting for a table or partition. For example: create table analysis_data stored as parquet as select * from raw_data;
Inserted 1000000000 rows in 181.98s
compute stats analysis_data;
insert into analysis_data select * from smaller_table_we_forgot_before;
Inserted 1000000 rows in 15.32s
-- Now there are 1001000000 rows. We can update this single data point in the stats.
alter table analysis_data set tblproperties('numRows'='1001000000', 'STATS_GENERATED_VIA_STATS_TASK'='true');
-- If the table originally contained 1 million rows, and we add another partition with 30 thousand rows,
-- change the numRows property for the partition and the overall table.
alter table partitioned_data partition(year=2009, month=4) set tblproperties ('numRows'='30000', 'STATS_GENERATED_VIA_STATS_TASK'='true');
alter table partitioned_data set tblproperties ('numRows'='1030000', 'STATS_GENERATED_VIA_STATS_TASK'='true');
In Impala 2.6 and higher, you can use the SET COLUMN
STATS
clause to set a specific stats value for a particular column.
numDVs
, numNulls
, avgSize
, maxSize
.
The key names and values are both quoted. This operation applies to an entire table,
not a specific partition. For example:
create table t1 (x int, s string);
insert into t1 values (1, 'one'), (2, 'two'), (2, 'deux');
show column stats t1;
+--------+--------+------------------+--------+----------+----------+
| Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size |
+--------+--------+------------------+--------+----------+----------+
| x | INT | -1 | -1 | 4 | 4 |
| s | STRING | -1 | -1 | -1 | -1 |
+--------+--------+------------------+--------+----------+----------+
alter table t1 set column stats x ('numDVs'='2','numNulls'='0');
alter table t1 set column stats s ('numdvs'='3','maxsize'='4');
show column stats t1;
+--------+--------+------------------+--------+----------+----------+
| Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size |
+--------+--------+------------------+--------+----------+----------+
| x | INT | 2 | 0 | 4 | 4 |
| s | STRING | 3 | -1 | 4 | -1 |
+--------+--------+------------------+--------+----------+----------+
To reorganize columns for a table:
You can add multiple columns at a time using the ALTER
TABLE
statement. If you specify the IF NOT
EXISTS
clause, Impala silently ignores the ADD
request and does not return an error if a column with the same name exists
in the table.
When you replace columns, all the original column definitions are discarded.
You might use these statements if you receive a new set of data files
with different data types or columns in a different order. The data files
are retained, so if the new columns are incompatible with the old ones,
use INSERT OVERWRITE
or LOAD DATA
OVERWRITE
to replace all the data before issuing any further
queries.
For example, here is how you might add columns to an existing table. The first
ALTER TABLE
adds two new columns, and the second ALTER
TABLE
adds one new column. A single Impala query reads both the old and new data
files, containing different numbers of columns. For any columns not present in a
particular data file, all the column values are considered to be NULL
.
create table t1 (x int);
insert into t1 values (1), (2);
alter table t1 add columns (s string, t timestamp);
insert into t1 values (3, 'three', now());
alter table t1 add columns (b boolean);
insert into t1 values (4, 'four', now(), true);
select * from t1 order by x;
+---+-------+-------------------------------+------+
| x | s | t | b |
+---+-------+-------------------------------+------+
| 1 | NULL | NULL | NULL |
| 2 | NULL | NULL | NULL |
| 3 | three | 2016-05-11 11:19:45.054457000 | NULL |
| 4 | four | 2016-05-11 11:20:20.260733000 | true |
+---+-------+-------------------------------+------+
You might use the CHANGE
clause to rename a single column, or to treat an
existing column as a different type than before, such as to switch between treating a
column as STRING
and TIMESTAMP
, or between
INT
and BIGINT
. You can only drop a single column at a
time; to drop multiple columns, issue multiple ALTER TABLE
statements, or
define the new set of columns with a single ALTER TABLE ... REPLACE
COLUMNS
statement.
The following examples show some safe operations to drop or change columns. Dropping the
final column in a table lets Impala ignore the data causing any disruption to existing
data files. Changing the type of a column works if existing data values can be safely
converted to the new type. The type conversion rules depend on the file format of the
underlying table. For example, in a text table, the same value can be interpreted as a
STRING
or a numeric value, while in a binary format such as Parquet, the
rules are stricter and type conversions only work between certain sizes of integers.
create table optional_columns (x int, y int, z int, a1 int, a2 int);
insert into optional_columns values (1,2,3,0,0), (2,3,4,100,100);
-- When the last column in the table is dropped, Impala ignores the
-- values that are no longer needed. (Dropping A1 but leaving A2
-- would cause problems, as we will see in a subsequent example.)
alter table optional_columns drop column a2;
alter table optional_columns drop column a1;
select * from optional_columns;
+---+---+---+
| x | y | z |
+---+---+---+
| 1 | 2 | 3 |
| 2 | 3 | 4 |
+---+---+---+
create table int_to_string (s string, x int);
insert into int_to_string values ('one', 1), ('two', 2);
-- What was an INT column will now be interpreted as STRING.
-- This technique works for text tables but not other file formats.
-- The second X represents the new name of the column, which we keep the same.
alter table int_to_string change x x string;
-- Once the type is changed, we can insert non-integer values into the X column
-- and treat that column as a string, for example by uppercasing or concatenating.
insert into int_to_string values ('three', 'trois');
select s, upper(x) from int_to_string;
+-------+----------+
| s | upper(x) |
+-------+----------+
| one | 1 |
| two | 2 |
| three | TROIS |
+-------+----------+
Remember that Impala does not actually do any conversion for the underlying data files as
a result of ALTER TABLE
statements. If you use ALTER
TABLE
to create a table layout that does not agree with the contents of the
underlying files, you must replace the files yourself, such as using LOAD
DATA
to load a new set of data files, or INSERT OVERWRITE
to
copy from another table and replace the original data.
The following example shows what happens if you delete the middle column from a Parquet
table containing three columns. The underlying data files still contain three columns of
data. Because the columns are interpreted based on their positions in the data file
instead of the specific column names, a SELECT *
query now reads the
first and second columns from the data file, potentially leading to unexpected results or
conversion errors. For this reason, if you expect to someday drop a column, declare it as
the last column in the table, where its data can be ignored by queries after the column is
dropped. Or, re-run your ETL process and create new data files if you drop or change the
type of a column in a way that causes problems with existing data files.
-- Parquet table showing how dropping a column can produce unexpected results.
create table p1 (s1 string, s2 string, s3 string);
insert into p1 values ('one', 'un', 'uno'), ('two', 'deux', 'dos'),
('three', 'trois', 'tres');
select * from p1;
+-------+-------+------+
| s1 | s2 | s3 |
+-------+-------+------+
| one | un | uno |
| two | deux | dos |
| three | trois | tres |
+-------+-------+------+
alter table p1 drop column s2;
-- The S3 column contains unexpected results.
-- Because S2 and S3 have compatible types, the query reads
-- values from the dropped S2, because the existing data files
-- still contain those values as the second column.
select * from p1;
+-------+-------+
| s1 | s3 |
+-------+-------+
| one | un |
| two | deux |
| three | trois |
+-------+-------+
-- Parquet table showing how dropping a column can produce conversion errors.
create table p2 (s1 string, x int, s3 string);
insert into p2 values ('one', 1, 'uno'), ('two', 2, 'dos'), ('three', 3, 'tres');
select * from p2;
+-------+---+------+
| s1 | x | s3 |
+-------+---+------+
| one | 1 | uno |
| two | 2 | dos |
| three | 3 | tres |
+-------+---+------+
alter table p2 drop column x;
select * from p2;
WARNINGS:
File 'hdfs_filename' has an incompatible Parquet schema for column 'add_columns.p2.s3'.
Column type: STRING, Parquet schema:
optional int32 x [i:1 d:1 r:0]
File 'hdfs_filename' has an incompatible Parquet schema for column 'add_columns.p2.s3'.
Column type: STRING, Parquet schema:
optional int32 x [i:1 d:1 r:0]
In Impala 2.6 and higher, if an Avro table is created without column
definitions in the CREATE TABLE
statement, and columns are later added
through ALTER TABLE
, the resulting table is now queryable. Missing values
from the newly added columns now default to NULL
.
To change the file format that Impala expects data to be in, for a table or partition:
Use an ALTER TABLE ... SET FILEFORMAT
clause. You can include an optional
PARTITION (col1=val1,
col2=val2, ...
clause so that the file
format is changed for a specific partition rather than the entire table.
Because this operation only changes the table metadata, you must do any conversion of
existing data using regular Hadoop techniques outside of Impala. Any new data created by
the Impala INSERT
statement will be in the new format. You cannot specify
the delimiter for Text files; the data files must be comma-delimited.
To set the file format for a single partition, include the PARTITION
clause. Specify all the same partitioning columns for the table, with a constant value for
each, to precisely identify the single partition affected by the statement:
create table p1 (s string) partitioned by (month int, day int);
-- Each ADD PARTITION clause creates a subdirectory in HDFS.
alter table p1 add partition (month=1, day=1);
alter table p1 add partition (month=1, day=2);
alter table p1 add partition (month=2, day=1);
alter table p1 add partition (month=2, day=2);
-- Queries and INSERT statements will read and write files
-- in this format for this specific partition.
alter table p1 partition (month=2, day=2) set fileformat parquet;
To change the row format with different delimiter characters:
SET ROW FORMAT DELIMITED
clause to ingest data files that use a
different delimiter character or a different line end character. When specifying delimiter
and line end characters with the FIELDS TERMINATED BY
, ESCAPED
BY
, and LINES TERMINATED BY
clauses, you can use the following:
- A regular ASCII character surrounded by single or double quotation marks.
-
An octal sequence, such as
'\054'
representing a comma or'\0'
for ASCII null (hex 00). -
Special characters, such as:
-
'\t'
for tab -
'\n'
for newline or linefeed -
'\r'
for carriage return
-
-
An integer in the range '-127'..'128' (with quotation marks but no backslash)
Negative values are subtracted from 256. For example,
FIELDS TERMINATED BY '-2'
sets the field delimiter to ASCII code 254.
For the ESCAPED BY
clause, choose an escape character that is not used
anywhere else in the file. The character following the escape character is taken literally
as part of a field value.
Surrounding field values with quotation marks does not help Impala to parse fields with embedded delimiter characters as the quotation marks are considered to be part of the column value.
If you want to use \
as the escape character, specify the clause in
impala-shell as ESCAPED BY '\\'
.
To add or drop partitions for a table, the table must already be partitioned (that
is, created with a PARTITIONED BY
clause). The partition is a physical
directory in HDFS, with a name that encodes a particular column value (the partition
key). The Impala INSERT
statement already creates the partition if
necessary, so the ALTER TABLE ... ADD PARTITION
is primarily useful for
importing data by moving or copying existing data files into the HDFS directory
corresponding to a partition. (You can use the LOAD DATA
statement to
move files into the partition directory, or ALTER TABLE ... PARTITION (...) SET
LOCATION
to point a partition at a directory that already contains data files.
The DROP PARTITION
clause is used to remove the HDFS directory and
associated data files for a particular set of partition key values; for example, if you
always analyze the last 3 months worth of data, at the beginning of each month you might
drop the oldest partition that is no longer needed. Removing partitions reduces the amount
of metadata associated with the table and the complexity of calculating the optimal query
plan, which can simplify and speed up queries on partitioned tables, particularly join
queries. Here is an example showing the ADD PARTITION
and DROP
PARTITION
clauses.
To avoid errors while adding or dropping partitions whose existence is not certain, add
the optional IF [NOT] EXISTS
clause between the ADD
or
DROP
keyword and the PARTITION
keyword. That is, the
entire clause becomes ADD IF NOT EXISTS PARTITION
or DROP IF
EXISTS PARTITION
. The following example shows how partitions can be created
automatically through INSERT
statements, or manually through
ALTER TABLE
statements. The IF [NOT] EXISTS
clauses let
the ALTER TABLE
statements succeed even if a new requested partition
already exists, or a partition to be dropped does not exist.
Inserting 2 year values creates 2 partitions:
create table partition_t (s string) partitioned by (y int);
insert into partition_t (s,y) values ('two thousand',2000), ('nineteen ninety',1990);
show partitions partition_t;
+-------+-------+--------+------+--------------+-------------------+--------+-------------------+
| y | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format | Incremental stats |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
| 1990 | -1 | 1 | 16B | NOT CACHED | NOT CACHED | TEXT | false |
| 2000 | -1 | 1 | 13B | NOT CACHED | NOT CACHED | TEXT | false |
| Total | -1 | 2 | 29B | 0B | | | |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
Without the IF NOT EXISTS
clause, an attempt to add a new partition might
fail:
alter table partition_t add partition (y=2000);
ERROR: AnalysisException: Partition spec already exists: (y=2000).
The IF NOT EXISTS
clause makes the statement succeed whether or not there
was already a partition with the specified key value:
alter table partition_t add if not exists partition (y=2000);
alter table partition_t add if not exists partition (y=2010);
show partitions partition_t;
+-------+-------+--------+------+--------------+-------------------+--------+-------------------+
| y | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format | Incremental stats |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
| 1990 | -1 | 1 | 16B | NOT CACHED | NOT CACHED | TEXT | false |
| 2000 | -1 | 1 | 13B | NOT CACHED | NOT CACHED | TEXT | false |
| 2010 | -1 | 0 | 0B | NOT CACHED | NOT CACHED | TEXT | false |
| Total | -1 | 2 | 29B | 0B | | | |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
Likewise, the IF EXISTS
clause lets DROP PARTITION
succeed whether or not the partition is already in the table:
alter table partition_t drop if exists partition (y=2000);
alter table partition_t drop if exists partition (y=1950);
show partitions partition_t;
+-------+-------+--------+------+--------------+-------------------+--------+-------------------+
| y | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format | Incremental stats |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
| 1990 | -1 | 1 | 16B | NOT CACHED | NOT CACHED | TEXT | false |
| 2010 | -1 | 0 | 0B | NOT CACHED | NOT CACHED | TEXT | false |
| Total | -1 | 1 | 16B | 0B | | | |
+-------+-------+--------+------+--------------+-------------------+--------+-------+
The optional PURGE
keyword, available in Impala 2.3 and higher, is
used with the DROP PARTITION
clause to remove associated HDFS data files
immediately rather than going through the HDFS trashcan mechanism. Use this keyword when
dropping a partition 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.
-- Create an empty table and define the partitioning scheme.
create table part_t (x int) partitioned by (month int);
-- Create an empty partition into which you could copy data files from some other source.
alter table part_t add partition (month=1);
-- After changing the underlying data, issue a REFRESH statement to make the data visible in Impala.
refresh part_t;
-- Later, do the same for the next month.
alter table part_t add partition (month=2);
-- Now you no longer need the older data.
alter table part_t drop partition (month=1);
-- If the table was partitioned by month and year, you would issue a statement like:
-- alter table part_t drop partition (year=2003,month=1);
-- which would require 12 ALTER TABLE statements to remove a year's worth of data.
-- If the data files for subsequent months were in a different file format,
-- you could set a different file format for the new partition as you create it.
alter table part_t add partition (month=3) set fileformat=parquet;
The value specified for a partition key can be an arbitrary constant expression, without any references to columns. For example:
alter table time_data add partition (month=concat('Decem','ber'));
alter table sales_data add partition (zipcode = cast(9021 * 10 as string));
To switch a table between internal and external:
ALTER TABLE
statement:
-- Switch a table from internal to external.
ALTER TABLE table_name SET TBLPROPERTIES('EXTERNAL'='TRUE');
-- Switch a table from external to internal.
ALTER TABLE table_name SET TBLPROPERTIES('EXTERNAL'='FALSE');
If the Kudu service is integrated with the Hive Metastore, the above
operations are not supported.
Cancellation: Cannot be cancelled.
HDFS permissions:
Most ALTER TABLE
clauses do not actually read or write any HDFS files,
and so do not depend on specific HDFS permissions. For example, the SET
FILEFORMAT
clause does not actually check the file format existing data files or
convert them to the new format, and the SET LOCATION
clause does not
require any special permissions on the new location. (Any permission-related failures
would come later, when you actually query or insert into the table.)
In general, ALTER TABLE
clauses that do touch HDFS files and directories
require the same HDFS permissions as corresponding CREATE
,
INSERT
, or SELECT
statements. The permissions allow the
user ID that the impalad daemon runs under, typically the
impala
user, to read or write files or directories, or (in the case of
the execute bit) descend into a directory. The RENAME TO
clause requires
read, write, and execute permission in the source and destination database directories and
in the table data directory, and read and write permission for the data files within the
table. The ADD PARTITION
and DROP PARTITION
clauses
require write and execute permissions for the associated partition directory.
Kudu considerations:
NOT
NULL
and DEFAULT
attributes for columns, ALTER
TABLE
has specific requirements related to Kudu tables:
-
In an
ADD COLUMNS
operation, you can specify theNULL
,NOT NULL
, andDEFAULT default_value
column attributes. -
In Impala 2.9 and higher, you can also specify the
ENCODING
,COMPRESSION
, andBLOCK_SIZE
attributes when adding a column. -
If you add a column with a
NOT NULL
attribute, it must also have aDEFAULT
attribute, so the default value can be assigned to that column for all existing rows. -
The
DROP COLUMN
clause works the same for a Kudu table as for other kinds of tables. -
Although you can change the name of a column with the
CHANGE
clause, you cannot change the type of a column in a Kudu table. -
You cannot change the nullability of existing columns in a Kudu table.
-
In Impala 2.10, you can change the default value, encoding, compression, or block size of existing columns in a Kudu table by using the
SET
clause. -
You cannot use the
REPLACE COLUMNS
clause with a Kudu table. -
The
RENAME TO
clause for a Kudu table only affects the name stored in the metastore database that Impala uses to refer to the table. To change which underlying Kudu table is associated with an Impala table name, you must change theTBLPROPERTIES
property of the table:SET TBLPROPERTIES('kudu.table_name'='kudu_tbl_name)
. You can only change underlying Kudu tables for the external tables.
The following are some examples of using the ADD COLUMNS
clause for a
Kudu table:
CREATE TABLE t1 ( x INT, PRIMARY KEY (x) )
PARTITION BY HASH (x) PARTITIONS 16
STORED AS KUDU
ALTER TABLE t1 ADD COLUMNS (y STRING ENCODING prefix_encoding);
ALTER TABLE t1 ADD COLUMNS (z INT DEFAULT 10);
ALTER TABLE t1 ADD COLUMNS (a STRING NOT NULL DEFAULT '', t TIMESTAMP COMPRESSION default_compression);
The following are some examples of modifying column defaults and storage attributes for a Kudu table:
create table kt (x bigint primary key, s string default 'yes', t timestamp)
stored as kudu;
-- You can change the default value for a column, which affects any rows
-- inserted after this change is made.
alter table kt alter column s set default 'no';
-- You can remove the default value for a column, which affects any rows
-- inserted after this change is made. If the column is nullable, any
-- future inserts default to NULL for this column. If the column is marked
-- NOT NULL, any future inserts must specify a value for the column.
alter table kt alter column s drop default;
insert into kt values (1, 'foo', now());
-- Because of the DROP DEFAULT above, omitting S from the insert
-- gives it a value of NULL.
insert into kt (x, t) values (2, now());
select * from kt;
+---+------+-------------------------------+
| x | s | t |
+---+------+-------------------------------+
| 2 | NULL | 2017-10-02 00:03:40.652156000 |
| 1 | foo | 2017-10-02 00:03:04.346185000 |
+---+------+-------------------------------+
-- Other storage-related attributes can also be changed for columns.
-- These changes take effect for any newly inserted rows, or rows
-- rearranged due to compaction after deletes or updates.
alter table kt alter column s set encoding prefix_encoding;
-- The COLUMN keyword is optional in the syntax.
alter table kt alter x set block_size 2048;
alter table kt alter column t set compression zlib;
desc kt;
+------+-----------+---------+-------------+----------+---------------+-----------------+---------------------+------------+
| name | type | comment | primary_key | nullable | default_value | encoding | compression | block_size |
+------+-----------+---------+-------------+----------+---------------+-----------------+---------------------+------------+
| x | bigint | | true | false | | AUTO_ENCODING | DEFAULT_COMPRESSION | 2048 |
| s | string | | false | true | | PREFIX_ENCODING | DEFAULT_COMPRESSION | 0 |
| t | timestamp | | false | true | | AUTO_ENCODING | ZLIB | 0 |
+------+-----------+---------+-------------+----------+---------------+-----------------+---------------------+------------+
Kudu tables all use an underlying partitioning mechanism. The partition syntax is
different than for non-Kudu tables. You can use the ALTER TABLE
statement
to add and drop range partitions from a Kudu table. Any new range must not
overlap with any existing ranges. Dropping a range removes all the associated rows from the
table.