Partitioning for Impala Tables
By default, all the data files for a table are located in a single directory. Partitioning is a technique for physically dividing the
data during loading, based on values from one or more columns, to speed up queries that test those columns. For example, with a
school_records table partitioned on a
year column, there is a separate data directory for each
different year value, and all the data for that year is stored in a data file in that directory. A query that includes a
WHERE condition such as
YEAR IN (1989,1999), or
1984 AND 1989 can examine only the data files from the appropriate directory or directories, greatly reducing the amount of
data to read and test.
See Attaching an External Partitioned Table to an HDFS Directory Structure for an example that illustrates the syntax for creating partitioned tables, the underlying directory structure in HDFS, and how to attach a partitioned Impala external table to data files stored elsewhere in HDFS.
Parquet is a popular format for partitioned Impala tables because it is well suited to handle huge data volumes. See Query Performance for Impala Parquet Tables for performance considerations for partitioned Parquet tables.
See NULL for details about how
NULL values are represented in partitioned tables.
See Using Impala with the Amazon S3 Filesystem for details about setting up tables where some or all partitions reside on the Amazon Simple Storage Service (S3).
When to Use Partitioned Tables
Partitioning is typically appropriate for:
- Tables that are very large, where reading the entire data set takes an impractical amount of time.
Tables that are always or almost always queried with conditions on the partitioning columns. In our example of a table partitioned
SELECT COUNT(*) FROM school_records WHERE year = 1985is efficient, only examining a small fraction of the data; but
SELECT COUNT(*) FROM school_recordshas to process a separate data file for each year, resulting in more overall work than in an unpartitioned table. You would probably not partition this way if you frequently queried the table based on last name, student ID, and so on without testing the year.
Columns that have reasonable cardinality (number of different values). If a column only has a small number of values, for example
Female, you do not gain much efficiency by eliminating only about 50% of the data to read for each query. If a column has only a few rows matching each value, the number of directories to process can become a limiting factor, and the data file in each directory could be too small to take advantage of the Hadoop mechanism for transmitting data in multi-megabyte blocks. For example, you might partition census data by year, store sales data by year and month, and web traffic data by year, month, and day. (Some users with high volumes of incoming data might even partition down to the individual hour and minute.)
- Data that already passes through an extract, transform, and load (ETL) pipeline. The values of the partitioning columns are stripped from the original data files and represented by directory names, so loading data into a partitioned table involves some sort of transformation or preprocessing.
SQL Statements for Partitioned Tables
In terms of Impala SQL syntax, partitioning affects these statements:
CREATE TABLE: you specify a
PARTITIONED BYclause when creating the table to identify names and data types of the partitioning columns. These columns are not included in the main list of columns for the table.
In Impala 2.5 and higher, you can also use the
PARTITIONED BYclause in a
CREATE TABLE AS SELECTstatement. This syntax lets you use a single statement to create a partitioned table, copy data into it, and create new partitions based on the values in the inserted data.
ALTER TABLE: you can add or drop partitions, to work with different portions of a huge data set. You can designate the HDFS directory that holds the data files for a specific partition. With data partitioned by date values, you might "age out" data that is no longer relevant.Note: If you are creating a partition for the first time and specifying its location, for maximum efficiency, use a single
ALTER TABLEstatement including both the
LOCATIONclauses, rather than separate statements with
INSERT: When you insert data into a partitioned table, you identify the partitioning columns. One or more values from each inserted row are not stored in data files, but instead determine the directory where that row value is stored. You can also specify which partition to load a set of data into, with
INSERT OVERWRITEstatements; you can replace the contents of a specific partition but you cannot append data to a specific partition.
By default, if an
INSERTstatement creates any new subdirectories underneath a partitioned table, those subdirectories are assigned default HDFS permissions for the
impalauser. To make each subdirectory have the same permissions as its parent directory in HDFS, specify the
--insert_inherit_permissionsstartup option for the impalad daemon.
Although the syntax of the
SELECTstatement is the same whether or not the table is partitioned, the way queries interact with partitioned tables can have a dramatic impact on performance and scalability. The mechanism that lets queries skip certain partitions during a query is known as partition pruning; see Partition Pruning for Queries for details.
In Impala 1.4 and later, there is a
SHOW PARTITIONSstatement that displays information about each partition in a table. See SHOW Statement for details.
Static and Dynamic Partitioning Clauses
Specifying all the partition columns in a SQL statement is called static partitioning, because the statement affects a
single predictable partition. For example, you use static partitioning with an
ALTER TABLE statement that affects
only one partition, or with an
INSERT statement that inserts all values into the same partition:
insert into t1 partition(x=10, y='a') select c1 from some_other_table;
When you specify some partition key columns in an
INSERT statement, but leave out the values, Impala determines
which partition to insert. This technique is called dynamic partitioning:
insert into t1 partition(x, y='b') select c1, c2 from some_other_table; -- Create new partition if necessary based on variable year, month, and day; insert a single value. insert into weather partition (year, month, day) select 'cloudy',2014,4,21; -- Create new partition if necessary for specified year and month but variable day; insert a single value. insert into weather partition (year=2014, month=04, day) select 'sunny',22;
The more key columns you specify in the
PARTITION clause, the fewer columns you need in the
list. The trailing columns in the
SELECT list are substituted in order for the partition key columns with no
Refreshing a Single Partition
REFRESH statement is typically used with partitioned tables when new data files are loaded into a partition by
some non-Impala mechanism, such as a Hive or Spark job. The
REFRESH statement makes Impala aware of the new data
files so that they can be used in Impala queries. Because partitioned tables typically contain a high volume of data, the
REFRESH operation for a full partitioned table can take significant time.
In Impala 2.7 and higher, you can include a
PARTITION (partition_spec) clause in the
REFRESH statement so that only a single partition is refreshed. For example,
REFRESH big_table PARTITION
(year=2017, month=9, day=30). The partition spec must include all the partition key columns. See
REFRESH Statement for more details and examples of
REFRESH syntax and usage.
Permissions for Partition Subdirectories
By default, if an
INSERT statement creates any new subdirectories underneath a partitioned
table, those subdirectories are assigned default HDFS permissions for the
impala user. To
make each subdirectory have the same permissions as its parent directory in HDFS, specify the
--insert_inherit_permissions startup option for the impalad daemon.
Partition Pruning for Queries
Partition pruning refers to the mechanism where a query can skip reading the data files corresponding to one or more partitions. If you can arrange for queries to prune large numbers of unnecessary partitions from the query execution plan, the queries use fewer resources and are thus proportionally faster and more scalable.
For example, if a table is partitioned by columns
WHERE clauses such as
WHERE year = 2013,
WHERE year < 2010, or
year BETWEEN 1995 AND 1998 allow Impala to skip the data files in all partitions outside the specified range. Likewise,
WHERE year = 2013 AND month BETWEEN 1 AND 3 could prune even more partitions, reading the data files for only a
portion of one year.
Checking if Partition Pruning Happens for a Query
To check the effectiveness of partition pruning for a query, check the
EXPLAIN output for the query before
running it. For example, this example shows a table with 3 partitions, where the query only reads 1 of them. The notation
#partitions=1/3 in the
EXPLAIN plan confirms that Impala can do the appropriate partition
[localhost:21000] > insert into census partition (year=2010) values ('Smith'),('Jones'); [localhost:21000] > insert into census partition (year=2011) values ('Smith'),('Jones'),('Doe'); [localhost:21000] > insert into census partition (year=2012) values ('Smith'),('Doe'); [localhost:21000] > select name from census where year=2010; +-------+ | name | +-------+ | Smith | | Jones | +-------+ [localhost:21000] > explain select name from census where year=2010; +------------------------------------------------------------------+ | Explain String | +------------------------------------------------------------------+ | PLAN FRAGMENT 0 | | PARTITION: UNPARTITIONED | | | | 1:EXCHANGE | | | | PLAN FRAGMENT 1 | | PARTITION: RANDOM | | | | STREAM DATA SINK | | EXCHANGE ID: 1 | | UNPARTITIONED | | | | 0:SCAN HDFS | | table=predicate_propagation.census #partitions=1/3 size=12B | +------------------------------------------------------------------+
For a report of the volume of data that was actually read and processed at each stage of the query, check the output of the
SUMMARY command immediately after running the query. For a more detailed analysis, look at the output of the
PROFILE command; it includes this same summary report near the start of the profile output.
What SQL Constructs Work with Partition Pruning
Impala can even do partition pruning in cases where the partition key column is not directly compared to a constant, by applying
the transitive property to other parts of the
WHERE clause. This technique is known as predicate propagation, and
is available in Impala 1.2.2 and later. In this example, the census table includes another column indicating when the data was
collected, which happens in 10-year intervals. Even though the query does not compare the partition key column
YEAR) to a constant value, Impala can deduce that only the partition
YEAR=2010 is required, and
again only reads 1 out of 3 partitions.
[localhost:21000] > drop table census; [localhost:21000] > create table census (name string, census_year int) partitioned by (year int); [localhost:21000] > insert into census partition (year=2010) values ('Smith',2010),('Jones',2010); [localhost:21000] > insert into census partition (year=2011) values ('Smith',2020),('Jones',2020),('Doe',2020); [localhost:21000] > insert into census partition (year=2012) values ('Smith',2020),('Doe',2020); [localhost:21000] > select name from census where year = census_year and census_year=2010; +-------+ | name | +-------+ | Smith | | Jones | +-------+ [localhost:21000] > explain select name from census where year = census_year and census_year=2010; +------------------------------------------------------------------+ | Explain String | +------------------------------------------------------------------+ | PLAN FRAGMENT 0 | | PARTITION: UNPARTITIONED | | | | 1:EXCHANGE | | | | PLAN FRAGMENT 1 | | PARTITION: RANDOM | | | | STREAM DATA SINK | | EXCHANGE ID: 1 | | UNPARTITIONED | | | | 0:SCAN HDFS | | table=predicate_propagation.census #partitions=1/3 size=22B | | predicates: census_year = 2010, year = census_year | +------------------------------------------------------------------+
If a view applies to a partitioned table, any partition pruning considers the clauses on both
the original query and any additional
WHERE predicates in the query that refers to the view.
Prior to Impala 1.4, only the
WHERE clauses on the original query from the
CREATE VIEW statement were used for partition pruning.
In queries involving both analytic functions and partitioned tables, partition pruning only occurs for columns named in the
clause of the analytic function call. For example, if an analytic function query has a clause such as
the way to make the query prune all other
YEAR partitions is to include
PARTITION BY yearin the analytic function call;
OVER (PARTITION BY year,other_columns other_analytic_clauses).
Dynamic Partition Pruning
The original mechanism uses to prune partitions is static partition pruning, in which the conditions in the
WHERE clause are analyzed to determine in advance which partitions can be safely skipped. In Impala 2.5
and higher, Impala can perform dynamic partition pruning, where information about the partitions is collected during
the query, and Impala prunes unnecessary partitions in ways that were impractical to predict in advance.
For example, if partition key columns are compared to literal values in a
WHERE clause, Impala can perform static
partition pruning during the planning phase to only read the relevant partitions:
-- The query only needs to read 3 partitions whose key values are known ahead of time. -- That's static partition pruning. SELECT COUNT(*) FROM sales_table WHERE year IN (2005, 2010, 2015);
Dynamic partition pruning involves using information only available at run time, such as the result of a subquery:
create table yy (s string) partitioned by (year int) stored as parquet; insert into yy partition (year) values ('1999', 1999), ('2000', 2000), ('2001', 2001), ('2010',2010); compute stats yy; create table yy2 (s string) partitioned by (year int) stored as parquet; insert into yy2 partition (year) values ('1999', 1999), ('2000', 2000), ('2001', 2001); compute stats yy2; -- The query reads an unknown number of partitions, whose key values are only -- known at run time. The 'runtime filters' lines show how the information about -- the partitions is calculated in query fragment 02, and then used in query -- fragment 00 to decide which partitions to skip. explain select s from yy2 where year in (select year from yy where year between 2000 and 2005); +----------------------------------------------------------+ | Explain String | +----------------------------------------------------------+ | Estimated Per-Host Requirements: Memory=16.00MB VCores=2 | | | | 04:EXCHANGE [UNPARTITIONED] | | | | | 02:HASH JOIN [LEFT SEMI JOIN, BROADCAST] | | | hash predicates: year = year | | | runtime filters: RF000 <- year | | | | | |--03:EXCHANGE [BROADCAST] | | | | | | | 01:SCAN HDFS [dpp.yy] | | | partitions=2/4 files=2 size=468B | | | | | 00:SCAN HDFS [dpp.yy2] | | partitions=2/3 files=2 size=468B | | runtime filters: RF000 -> year | +----------------------------------------------------------+
In this case, Impala evaluates the subquery, sends the subquery results to all Impala nodes participating in the query, and then each impalad daemon uses the dynamic partition pruning optimization to read only the partitions with the relevant key values.
Dynamic partition pruning is especially effective for queries involving joins of several large partitioned tables. Evaluating the
ON clauses of the join predicates might normally require reading data from all partitions of certain tables. If
WHERE clauses of the query refer to the partition key columns, Impala can now often skip reading many of the
partitions while evaluating the
ON clauses. The dynamic partition pruning optimization reduces the amount of I/O
and the amount of intermediate data stored and transmitted across the network during the query.
When the spill-to-disk feature is activated for a join node within a query, Impala does not produce any runtime filters for that join operation on that host. Other join nodes within the query are not affected.
Dynamic partition pruning is part of the runtime filtering feature, which applies to other kinds of queries in addition to queries against partitioned tables. See Runtime Filtering for Impala Queries (Impala 2.5 or higher only) for full details about this feature.
Partition Key Columns
The columns you choose as the partition keys should be ones that are frequently used to filter query results in important, large-scale queries. Popular examples are some combination of year, month, and day when the data has associated time values, and geographic region when the data is associated with some place.
For time-based data, split out the separate parts into their own columns, because Impala cannot partition based on a
The data type of the partition columns does not have a significant effect on the storage required, because the values from those columns are not stored in the data files, rather they are represented as strings inside HDFS directory names.
In Impala 2.5 and higher, you can enable the
OPTIMIZE_PARTITION_KEY_SCANSquery option to speed up queries that only refer to partition key columns, such as
SELECT MAX(year). This setting is not enabled by default because the query behavior is slightly different if the table contains partition directories without actual data inside. See OPTIMIZE_PARTITION_KEY_SCANS Query Option (Impala 2.5 or higher only) for details.
Partitioned tables can contain complex type columns. All the partition key columns must be scalar types.
Remember that when Impala queries data stored in HDFS, it is most efficient to use multi-megabyte files to take advantage of the HDFS block size. For Parquet tables, the block size (and ideal size of the data files) is 256 MB in Impala 2.0 and later. Therefore, avoid specifying too many partition key columns, which could result in individual partitions containing only small amounts of data. For example, if you receive 1 GB of data per day, you might partition by year, month, and day; while if you receive 5 GB of data per minute, you might partition by year, month, day, hour, and minute. If you have data with a geographic component, you might partition based on postal code if you have many megabytes of data for each postal code, but if not, you might partition by some larger region such as city, state, or country. state
If you frequently run aggregate functions such as
COUNT(DISTINCT) on partition key columns, consider enabling the
query option, which optimizes such queries. This feature is available in Impala 2.5 and higher.
See OPTIMIZE_PARTITION_KEY_SCANS Query Option (Impala 2.5 or higher only)
for the kinds of queries that this option applies to, and slight differences in how partitions are
evaluated when this query option is enabled.
Setting Different File Formats for Partitions
Partitioned tables have the flexibility to use different file formats for different partitions. (For background information about the different file formats Impala supports, see How Impala Works with Hadoop File Formats.) For example, if you originally received data in text format, then received new data in RCFile format, and eventually began receiving data in Parquet format, all that data could reside in the same table for queries. You just need to ensure that the table is structured so that the data files that use different file formats reside in separate partitions.
For example, here is how you might switch from text to Parquet data as you receive data for different years:
[localhost:21000] > create table census (name string) partitioned by (year smallint); [localhost:21000] > alter table census add partition (year=2012); -- Text format; [localhost:21000] > alter table census add partition (year=2013); -- Text format switches to Parquet before data loaded; [localhost:21000] > alter table census partition (year=2013) set fileformat parquet; [localhost:21000] > insert into census partition (year=2012) values ('Smith'),('Jones'),('Lee'),('Singh'); [localhost:21000] > insert into census partition (year=2013) values ('Flores'),('Bogomolov'),('Cooper'),('Appiah');
At this point, the HDFS directory for
year=2012 contains a text-format data file, while the HDFS directory for
year=2013 contains a Parquet data file. As always, when loading non-trivial data, you would use
LOAD DATA to import data in large batches, rather than
INSERT ... VALUES which
produces small files that are inefficient for real-world queries.
For other file types that Impala cannot create natively, you can switch into Hive and issue the
ALTER TABLE ... SET
FILEFORMAT statements and
LOAD DATA statements there. After switching back to
Impala, issue a
REFRESH table_name statement so that Impala recognizes any partitions or new
data added through Hive.
You can add, drop, set the expected file format, or set the HDFS location of the data files for individual partitions within an Impala table. See ALTER TABLE Statement for syntax details, and Setting Different File Formats for Partitions for tips on managing tables containing partitions with different file formats.
ALTER TABLEstatement including both the
LOCATIONclauses, rather than separate statements with
What happens to the data files when a partition is dropped depends on whether the partitioned table is designated as internal or external. For an internal (managed) table, the data files are deleted. For example, if data in the partitioned table is a copy of raw data files stored elsewhere, you might save disk space by dropping older partitions that are no longer required for reporting, knowing that the original data is still available if needed later. For an external table, the data files are left alone. For example, dropping a partition without deleting the associated files lets Impala consider a smaller set of partitions, improving query efficiency and reducing overhead for DDL operations on the table; if the data is needed again later, you can add the partition again. See Overview of Impala Tables for details and examples.
Using Partitioning with Kudu Tables
Kudu tables use a more fine-grained partitioning scheme than tables containing HDFS data files. You specify a
BY clause with the
CREATE TABLE statement to identify how to divide the values from the partition key
See Partitioning for Kudu Tables for details and examples of the partitioning techniques for Kudu tables.