Impala supports inserting into tables and partitions that you create with the Impala CREATE TABLE statement, or pre-defined tables and partitions created through Hive.

Syntax:

[with_clause]
  INSERT [hint_clause] { INTO | OVERWRITE } [TABLE] table_name
  [(column_list)]
  [ PARTITION (partition_clause)]
{
    [hint_clause] select_statement
  | VALUES (value [, value ...]) [, (value [, value ...]) ...]
}

partition_clause ::= col_name [= constant] [, col_name [= constant] ...]

hint_clause ::=
  hint_with_dashes |
  hint_with_cstyle_delimiters |
  hint_with_brackets

hint_with_dashes ::= -- +SHUFFLE | -- +NOSHUFFLE -- +CLUSTERED

hint_with_cstyle_comments ::= /* +SHUFFLE */ | /* +NOSHUFFLE */ | /* +CLUSTERED */

hint_with_brackets ::= [SHUFFLE] | [NOSHUFFLE]
  (With this hint format, the square brackets are part of the syntax.)

  Note: The square bracket style of hint is now deprecated and might be removed in a future release. For that reason, any newly added hints are not available with the square bracket syntax.

Appending or replacing (INTO and OVERWRITE clauses):

The INSERT INTO syntax appends data to a table. The existing data files are left as-is, and the inserted data is put into one or more new data files.

The INSERT OVERWRITE syntax replaces the data in a table. Currently, the overwritten data files are deleted immediately; they do not go through the HDFS trash mechanism.

Complex type considerations:

The INSERT statement currently does not support writing data files containing complex types (ARRAY, STRUCT, and MAP). To prepare Parquet data for such tables, you generate the data files outside Impala and then use LOAD DATA or CREATE EXTERNAL TABLE to associate those data files with the table. Currently, such tables must use the Parquet file format. See Complex Types (CDH 5.5 or higher only) for details about working with complex types.

Kudu considerations:

Currently, the INSERT OVERWRITE syntax cannot be used with Kudu tables.

Kudu tables require a unique primary key for each row. If an INSERT statement attempts to insert a row with the same values for the primary key columns as an existing row, that row is discarded and the insert operation continues. When rows are discarded due to duplicate primary keys, the statement finishes with a warning, not an error. (This is a change from early releases of Kudu where the default was to return in error in such cases, and the syntax INSERT IGNORE was required to make the statement succeed. The IGNORE clause is no longer part of the INSERT syntax.)

For situations where you prefer to replace rows with duplicate primary key values, rather than discarding the new data, you can use the UPSERT statement instead of INSERT. UPSERT inserts rows that are entirely new, and for rows that match an existing primary key in the table, the non-primary-key columns are updated to reflect the values in the "upserted" data.

If you really want to store new rows, not replace existing ones, but cannot do so because of the primary key uniqueness constraint, consider recreating the table with additional columns included in the primary key.

See Using Impala to Query Kudu Tables for more details about using Impala with Kudu.

Usage notes:

Impala currently supports:

• Copy data from another table using SELECT query. In Impala 1.2.1 and higher, you can combine CREATE TABLE and INSERT operations into a single step with the CREATE TABLE AS SELECT syntax, which bypasses the actual INSERT keyword.
• An optional WITH clause before the INSERT keyword, to define a subquery referenced in the SELECT portion.
• Create one or more new rows using constant expressions through VALUES clause. (The VALUES clause was added in Impala 1.0.1.)

• By default, the first column of each newly inserted row goes into the first column of the table, the second column into the second column, and so on.

  You can also specify the columns to be inserted, an arbitrarily ordered subset of the columns in the destination table, by specifying a column list immediately after the name of the destination table. This feature lets you adjust the inserted columns to match the layout of a SELECT statement, rather than the other way around. (This feature was added in Impala 1.1.)

  The number of columns mentioned in the column list (known as the "column permutation") must match the number of columns in the SELECT list or the VALUES tuples. The order of columns in the column permutation can be different than in the underlying table, and the columns of each input row are reordered to match. If the number of columns in the column permutation is less than in the destination table, all unmentioned columns are set to NULL.

• An optional hint clause immediately either before the SELECT keyword or after the INSERT keyword, to fine-tune the behavior when doing an INSERT ... SELECT operation into partitioned Parquet tables. The hint clause cannot be specified in multiple places. The hint keywords are [SHUFFLE] and [NOSHUFFLE], including the square brackets. Inserting into partitioned Parquet tables can be a resource-intensive operation because it potentially involves many files being written to HDFS simultaneously, and separate large memory buffers being allocated to buffer the data for each partition. For usage details, see Loading Data into Parquet Tables.

  Note:

• Insert commands that partition or add files result in changes to Hive metadata. Because Impala uses Hive metadata, such changes may necessitate a metadata refresh. For more information, see the REFRESH function.
• Currently, Impala can only insert data into tables that use the text and Parquet formats. For other file formats, insert the data using Hive and use Impala to query it.
• As an alternative to the INSERT statement, if you have existing data files elsewhere in HDFS, the LOAD DATA statement can move those files into a table. This statement works with tables of any file format.

Statement type: DML (but still affected by SYNC_DDL query option)

Usage notes:

When you insert the results of an expression, particularly of a built-in function call, into a small numeric column such as INT, SMALLINT, TINYINT, or FLOAT, you might need to use a CAST() expression to coerce values into the appropriate type. Impala does not automatically convert from a larger type to a smaller one. For example, to insert cosine values into a FLOAT column, write CAST(COS(angle) AS FLOAT) in the INSERT statement to make the conversion explicit.

File format considerations:

Because Impala can read certain file formats that it cannot write, the INSERT statement does not work for all kinds of Impala tables. See How Impala Works with Hadoop File Formats for details about what file formats are supported by the INSERT statement.

Any INSERT statement for a Parquet table requires enough free space in the HDFS filesystem to write one block. Because Parquet data files use a block size of 1 GB by default, an INSERT might fail (even for a very small amount of data) if your HDFS is running low on space.

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. See SYNC_DDL Query Option for details.

  Important: After adding or replacing data in a table used in performance-critical queries, issue a COMPUTE STATS statement to make sure all statistics are up-to-date. Consider updating statistics for a table after any INSERT, LOAD DATA, or CREATE TABLE AS SELECT statement in Impala, or after loading data through Hive and doing a REFRESH table_name in Impala. This technique is especially important for tables that are very large, used in join queries, or both.

Examples:

The following example sets up new tables with the same definition as the TAB1 table from the Tutorial section, using different file formats, and demonstrates inserting data into the tables created with the STORED AS TEXTFILE and STORED AS PARQUET clauses:

CREATE DATABASE IF NOT EXISTS file_formats;
USE file_formats;

DROP TABLE IF EXISTS text_table;
CREATE TABLE text_table
( id INT, col_1 BOOLEAN, col_2 DOUBLE, col_3 TIMESTAMP )
STORED AS TEXTFILE;

DROP TABLE IF EXISTS parquet_table;
CREATE TABLE parquet_table
( id INT, col_1 BOOLEAN, col_2 DOUBLE, col_3 TIMESTAMP )
STORED AS PARQUET;

With the INSERT INTO TABLE syntax, each new set of inserted rows is appended to any existing data in the table. This is how you would record small amounts of data that arrive continuously, or ingest new batches of data alongside the existing data. For example, after running 2 INSERT INTO TABLE statements with 5 rows each, the table contains 10 rows total:

[localhost:21000] > insert into table text_table select * from default.tab1;
Inserted 5 rows in 0.41s

[localhost:21000] > insert into table text_table select * from default.tab1;
Inserted 5 rows in 0.46s

[localhost:21000] > select count(*) from text_table;
+----------+
| count(*) |
+----------+
| 10       |
+----------+
Returned 1 row(s) in 0.26s

With the INSERT OVERWRITE TABLE syntax, each new set of inserted rows replaces any existing data in the table. This is how you load data to query in a data warehousing scenario where you analyze just the data for a particular day, quarter, and so on, discarding the previous data each time. You might keep the entire set of data in one raw table, and transfer and transform certain rows into a more compact and efficient form to perform intensive analysis on that subset.

For example, here we insert 5 rows into a table using the INSERT INTO clause, then replace the data by inserting 3 rows with the INSERT OVERWRITE clause. Afterward, the table only contains the 3 rows from the final INSERT statement.

[localhost:21000] > insert into table parquet_table select * from default.tab1;
Inserted 5 rows in 0.35s

[localhost:21000] > insert overwrite table parquet_table select * from default.tab1 limit 3;
Inserted 3 rows in 0.43s
[localhost:21000] > select count(*) from parquet_table;
+----------+
| count(*) |
+----------+
| 3        |
+----------+
Returned 1 row(s) in 0.43s

The VALUES clause lets you insert one or more rows by specifying constant values for all the columns. The number, types, and order of the expressions must match the table definition.

  Note: The INSERT ... VALUES technique is not suitable for loading large quantities of data into HDFS-based tables, because the insert operations cannot be parallelized, and each one produces a separate data file. Use it for setting up small dimension tables or tiny amounts of data for experimenting with SQL syntax, or with HBase tables. Do not use it for large ETL jobs or benchmark tests for load operations. Do not run scripts with thousands of INSERT ... VALUES statements that insert a single row each time. If you do run INSERT ... VALUES operations to load data into a staging table as one stage in an ETL pipeline, include multiple row values if possible within each VALUES clause, and use a separate database to make cleanup easier if the operation does produce many tiny files.

The following example shows how to insert one row or multiple rows, with expressions of different types, using literal values, expressions, and function return values:

create table val_test_1 (c1 int, c2 float, c3 string, c4 boolean, c5 timestamp);
insert into val_test_1 values (100, 99.9/10, 'abc', true, now());
create table val_test_2 (id int, token string);
insert overwrite val_test_2 values (1, 'a'), (2, 'b'), (-1,'xyzzy');

These examples show the type of "not implemented" error that you see when attempting to insert data into a table with a file format that Impala currently does not write to:

DROP TABLE IF EXISTS sequence_table;
CREATE TABLE sequence_table
( id INT, col_1 BOOLEAN, col_2 DOUBLE, col_3 TIMESTAMP )
STORED AS SEQUENCEFILE;

DROP TABLE IF EXISTS rc_table;
CREATE TABLE rc_table
( id INT, col_1 BOOLEAN, col_2 DOUBLE, col_3 TIMESTAMP )
STORED AS RCFILE;

[localhost:21000] > insert into table rc_table select * from default.tab1;
Remote error
Backend 0:RC_FILE not implemented.

[localhost:21000] > insert into table sequence_table select * from default.tab1;
Remote error
Backend 0:SEQUENCE_FILE not implemented. 

The following examples show how you can copy the data in all the columns from one table to another, copy the data from only some columns, or specify the columns in the select list in a different order than they actually appear in the table:

-- Start with 2 identical tables.
create table t1 (c1 int, c2 int);
create table t2 like t1;

-- If there is no () part after the destination table name,
-- all columns must be specified, either as * or by name.
insert into t2 select * from t1;
insert into t2 select c1, c2 from t1;

-- With the () notation following the destination table name,
-- you can omit columns (all values for that column are NULL
-- in the destination table), and/or reorder the values
-- selected from the source table. This is the "column permutation" feature.
insert into t2 (c1) select c1 from t1;
insert into t2 (c2, c1) select c1, c2 from t1;

-- The column names can be entirely different in the source and destination tables.
-- You can copy any columns, not just the corresponding ones, from the source table.
-- But the number and type of selected columns must match the columns mentioned in the () part.
alter table t2 replace columns (x int, y int);
insert into t2 (y) select c1 from t1;

Sorting considerations: Although you can specify an ORDER BY clause in an INSERT ... SELECT statement, any ORDER BY clause is ignored and the results are not necessarily sorted. An INSERT ... SELECT operation potentially creates many different data files, prepared on different data nodes, and therefore the notion of the data being stored in sorted order is impractical.

Concurrency considerations: Each INSERT operation creates new data files with unique names, so you can run multiple INSERT INTO statements simultaneously without filename conflicts. While data is being inserted into an Impala table, the data is staged temporarily in a subdirectory inside the data directory; during this period, you cannot issue queries against that table in Hive. If an INSERT operation fails, the temporary data file and the subdirectory could be left behind in the data directory. If so, remove the relevant subdirectory and any data files it contains manually, by issuing an hdfs dfs -rm -r command, specifying the full path of the work subdirectory, whose name ends in _dir.