Table and Column Statistics
Impala can do better optimization for complex or multi-table queries when it has access to statistics about the volume of data and how the values are distributed. Impala uses this information to help parallelize and distribute the work for a query. For example, optimizing join queries requires a way of determining if one table is "bigger" than another, which is a function of the number of rows and the average row size for each table. The following sections describe the categories of statistics Impala can work with, and how to produce them and keep them up to date.
Continue reading:
- Overview of Table Statistics
- Overview of Column Statistics
- How Table and Column Statistics Work for Partitioned Tables
- Generating Table and Column Statistics
- Detecting Missing Statistics
- Manually Setting Table and Column Statistics with ALTER TABLE
- Examples of Using Table and Column Statistics with Impala
Overview of Table Statistics
The Impala query planner can make use of statistics about entire tables and partitions. This information includes physical characteristics such as the number of rows, number of data files, the total size of the data files, and the file format. For partitioned tables, the numbers are calculated per partition, and as totals for the whole table. This metadata is stored in the metastore database, and can be updated by either Impala or Hive. If a number is not available, the value -1 is used as a placeholder. Some numbers, such as number and total sizes of data files, are always kept up to date because they can be calculated cheaply, as part of gathering HDFS block metadata.
The following example shows table stats for an unpartitioned Parquet table. The values for the number and sizes of files are always available. Initially, the number of rows is not known, because it requires a potentially expensive scan through the entire table, and so that value is displayed as -1. The COMPUTE STATS statement fills in any unknown table stats values.
show table stats parquet_snappy; +-------+--------+---------+--------------+-------------------+---------+-------------------+... | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format | Incremental stats |... +-------+--------+---------+--------------+-------------------+---------+-------------------+... | -1 | 96 | 23.35GB | NOT CACHED | NOT CACHED | PARQUET | false |... +-------+--------+---------+--------------+-------------------+---------+-------------------+... compute stats parquet_snappy; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 1 partition(s) and 6 column(s). | +-----------------------------------------+ show table stats parquet_snappy; +------------+--------+---------+--------------+-------------------+---------+-------------------+... | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format | Incremental stats |... +------------+--------+---------+--------------+-------------------+---------+-------------------+... | 1000000000 | 96 | 23.35GB | NOT CACHED | NOT CACHED | PARQUET | false |... +------------+--------+---------+--------------+-------------------+---------+-------------------+...
Impala performs some optimizations using this metadata on its own, and other optimizations by using a combination of table and column statistics.
To check that table statistics are available for a table, and see the details of those statistics, use the statement SHOW TABLE STATS table_name. See SHOW Statement for details.
If you use the Hive-based methods of gathering statistics, see the Hive wiki for information about the required configuration on the Hive side. Where practical, use the Impala COMPUTE STATS statement to avoid potential configuration and scalability issues with the statistics-gathering process.
If you run the Hive statement ANALYZE TABLE COMPUTE STATISTICS FOR COLUMNS, Impala can only use the resulting column statistics if the table is unpartitioned. Impala cannot use Hive-generated column statistics for a partitioned table.
Overview of Column Statistics
The Impala query planner can make use of statistics about individual columns when that metadata is available in the metastore database. This technique is most valuable for columns compared across tables in join queries, to help estimate how many rows the query will retrieve from each table. These statistics are also important for correlated subqueries using the EXISTS() or IN() operators, which are processed internally the same way as join queries.
The following example shows column stats for an unpartitioned Parquet table. The values for the maximum and average sizes of some types are always available, because those figures are constant for numeric and other fixed-size types. Initially, the number of distinct values is not known, because it requires a potentially expensive scan through the entire table, and so that value is displayed as -1. The same applies to maximum and average sizes of variable-sized types, such as STRING. The COMPUTE STATS statement fills in most unknown column stats values. (It does not record the number of NULL values, because currently Impala does not use that figure for query optimization.)
show column stats parquet_snappy; +-------------+----------+------------------+--------+----------+----------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +-------------+----------+------------------+--------+----------+----------+ | id | BIGINT | -1 | -1 | 8 | 8 | | val | INT | -1 | -1 | 4 | 4 | | zerofill | STRING | -1 | -1 | -1 | -1 | | name | STRING | -1 | -1 | -1 | -1 | | assertion | BOOLEAN | -1 | -1 | 1 | 1 | | location_id | SMALLINT | -1 | -1 | 2 | 2 | +-------------+----------+------------------+--------+----------+----------+ compute stats parquet_snappy; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 1 partition(s) and 6 column(s). | +-----------------------------------------+ show column stats parquet_snappy; +-------------+----------+------------------+--------+----------+-------------------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +-------------+----------+------------------+--------+----------+-------------------+ | id | BIGINT | 183861280 | -1 | 8 | 8 | | val | INT | 139017 | -1 | 4 | 4 | | zerofill | STRING | 101761 | -1 | 6 | 6 | | name | STRING | 145636240 | -1 | 22 | 13.00020027160645 | | assertion | BOOLEAN | 2 | -1 | 1 | 1 | | location_id | SMALLINT | 339 | -1 | 2 | 2 | +-------------+----------+------------------+--------+----------+-------------------+
To check whether column statistics are available for a particular set of columns, use the SHOW COLUMN STATS table_name statement, or check the extended EXPLAIN output for a query against that table that refers to those columns. See SHOW Statement and EXPLAIN Statement for details.
If you run the Hive statement ANALYZE TABLE COMPUTE STATISTICS FOR COLUMNS, Impala can only use the resulting column statistics if the table is unpartitioned. Impala cannot use Hive-generated column statistics for a partitioned table.
How Table and Column Statistics Work for Partitioned Tables
When you use Impala for "big data", you are highly likely to use partitioning for your biggest tables, the ones representing data that can be logically divided based on dates, geographic regions, or similar criteria. The table and column statistics are especially useful for optimizing queries on such tables. For example, a query involving one year might involve substantially more or less data than a query involving a different year, or a range of several years. Each query might be optimized differently as a result.
The following examples show how table and column stats work with a partitioned table. The table for this example is partitioned by year, month, and day. For simplicity, the sample data consists of 5 partitions, all from the same year and month. Table stats are collected independently for each partition. (In fact, the SHOW PARTITIONS statement displays exactly the same information as SHOW TABLE STATS for a partitioned table.) Column stats apply to the entire table, not to individual partitions. Because the partition key column values are represented as HDFS directories, their characteristics are typically known in advance, even when the values for non-key columns are shown as -1.
show partitions year_month_day; +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... | year | month | day | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format |... +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... | 2013 | 12 | 1 | -1 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 2 | -1 | 1 | 2.53MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 3 | -1 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 4 | -1 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 5 | -1 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | Total | | | -1 | 5 | 12.58MB | 0B | | |... +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... show table stats year_month_day; +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... | year | month | day | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format |... +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... | 2013 | 12 | 1 | -1 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 2 | -1 | 1 | 2.53MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 3 | -1 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 4 | -1 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 5 | -1 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | Total | | | -1 | 5 | 12.58MB | 0B | | |... +-------+-------+-----+-------+--------+---------+--------------+-------------------+---------+... show column stats year_month_day; +-----------+---------+------------------+--------+----------+----------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +-----------+---------+------------------+--------+----------+----------+ | id | INT | -1 | -1 | 4 | 4 | | val | INT | -1 | -1 | 4 | 4 | | zfill | STRING | -1 | -1 | -1 | -1 | | name | STRING | -1 | -1 | -1 | -1 | | assertion | BOOLEAN | -1 | -1 | 1 | 1 | | year | INT | 1 | 0 | 4 | 4 | | month | INT | 1 | 0 | 4 | 4 | | day | INT | 5 | 0 | 4 | 4 | +-----------+---------+------------------+--------+----------+----------+ compute stats year_month_day; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 5 partition(s) and 5 column(s). | +-----------------------------------------+ show table stats year_month_day; +-------+-------+-----+--------+--------+---------+--------------+-------------------+---------+... | year | month | day | #Rows | #Files | Size | Bytes Cached | Cache Replication | Format |... +-------+-------+-----+--------+--------+---------+--------------+-------------------+---------+... | 2013 | 12 | 1 | 93606 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 2 | 94158 | 1 | 2.53MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 3 | 94122 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 4 | 93559 | 1 | 2.51MB | NOT CACHED | NOT CACHED | PARQUET |... | 2013 | 12 | 5 | 93845 | 1 | 2.52MB | NOT CACHED | NOT CACHED | PARQUET |... | Total | | | 469290 | 5 | 12.58MB | 0B | | |... +-------+-------+-----+--------+--------+---------+--------------+-------------------+---------+... show column stats year_month_day; +-----------+---------+------------------+--------+----------+-------------------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +-----------+---------+------------------+--------+----------+-------------------+ | id | INT | 511129 | -1 | 4 | 4 | | val | INT | 364853 | -1 | 4 | 4 | | zfill | STRING | 311430 | -1 | 6 | 6 | | name | STRING | 471975 | -1 | 22 | 13.00160026550293 | | assertion | BOOLEAN | 2 | -1 | 1 | 1 | | year | INT | 1 | 0 | 4 | 4 | | month | INT | 1 | 0 | 4 | 4 | | day | INT | 5 | 0 | 4 | 4 | +-----------+---------+------------------+--------+----------+-------------------+
If you run the Hive statement ANALYZE TABLE COMPUTE STATISTICS FOR COLUMNS, Impala can only use the resulting column statistics if the table is unpartitioned. Impala cannot use Hive-generated column statistics for a partitioned table.
Generating Table and Column Statistics
Use the COMPUTE STATS family of commands to collect table and column statistics. The COMPUTE STATS variants offer different tradeoffs between computation cost, staleness, and maintenance workflows which are explained below.
COMPUTE STATS
The COMPUTE STATS command collects and sets the table-level and partition-level row counts as well as all column statistics for a given table. The collection process is CPU-intensive and can take a long time to complete for very large tables.
-
Limit the number of columns for which statistics are collected to increase the efficiency of COMPUTE STATS. Queries benefit from statistics for those columns involved in filters, join conditions, group by or partition by clauses. Other columns are good candidates to exclude from COMPUTE STATS. This feature is available since Impala 2.12.
-
Set the MT_DOP query option to use more threads within each participating impalad to compute the statistics faster - but not more efficiently. Note that computing stats on a large table with a high MT_DOP value can negatively affect other queries running at the same time if the COMPUTE STATS claims most CPU cycles. This feature is available since Impala 2.8.
-
Consider the experimental extrapolation and sampling features (see below) to further increase the efficiency of computing stats.
COMPUTE STATS is intended to be run periodically, e.g. weekly, or on-demand when the contents of a table have changed significantly. Due to the high resource utilization and long repsonse time of tCOMPUTE STATS, it is most practical to run it in a scheduled maintnance window where the Impala cluster is idle enough to accommodate the expensive operation. The degree of change that qualifies as "significant" depends on the query workload, but typically, if 30% of the rows have changed then it is recommended to recompute statistics.
If you reload a complete new set of data for a table, but the number of rows and number of distinct values for each column is relatively unchanged from before, you do not need to recompute stats for the table.
Experimental: Extrapolation and Sampling
-
Newly added partitions do not have row count statistics. Table scans that only access those new partitions are treated as not having stats. Similarly, table scans that access both new and old partitions estimate the scan cardinality based on those old partitions that have stats, and the new partitions without stats are treated as having 0 rows.
-
The row counts of existing partitions become stale when data is added or dropped.
-
Computing stats for tables with a 100,000 or more partitions might fail or be very slow due to the high cost of updating the partition metadata in the Hive Metastore.
-
With transient compute resources it is important to minimize the time from starting a new cluster to successfully running queries. Since the cluster might be relatively short-lived, users might prefer to quickly collect stats that are "good enough" as opposed to spending a lot of time and resouces on computing full-fidelity stats.
The sampling feature makes COMPUTE STATS more efficient by processing a fraction of the table data, and the extrapolation feature aims to reduce the frequency at which COMPUTE STATS needs to be re-run by estimating the row count of new and modified partitions.
The sampling and extrapolation features are disabled by default. They can be enabled globally or for specific tables, as follows. Set the impalad start-up configuration "--enable_stats_extrapolation" to enable the features globally. To enable them only for a specific table, set the "impala.enable.stats.extrapolation" table property to "true" for the desired table. The table-level property overrides the global setting, so it is also possible to enable sampling and extrapolation globally, but disable it for specific tables by setting the table property to "false". Example: ALTER TABLE mytable test_table SET TBLPROPERTIES("impala.enable.stats.extrapolation"="true")
Stats Extrapolation
The main idea of stats extrapolation is to estimate the row count of new and modified partitions based on the result of the last COMPUTE STATS. Enabling stats extrapolation changes the behavior of COMPUTE STATS, as well as the cardinality estimation of table scans. COMPUTE STATS no longer computes and stores per-partition row counts, and instead, only computes a table-level row count together with the total number of file bytes in the table at that time. No partition metadata is modified. The input cardinality of a table scan is estimated by converting the data volume of relevant partitions to a row count, based on the table-level row count and file bytes statistics. It is assumed that within the same table, different sets of files with the same data volume correspond to the similar number of rows on average. With extrapolation enabled, the scan cardinality estimation ignores per-partition row counts. It only relies on the table-level statistics and the scanned data volume.
The SHOW TABLE STATS and EXPLAIN commands distinguish between row counts stored in the Hive Metastore, and the row counts extrapolated based on the above process. Consult the SHOW TABLE STATS and EXPLAIN documentation for more details.
Sampling
A TABLESAMPLE clause may be added to COMPUTE STATS to limit the percentage of data to be processed. The final statistics are obtained by extrapolating the statistics from the data sample over the entire table. The extrapolated statistics are stored in the Hive Metastore, just as if no sampling was used. The following example runs COMPUTE STATS over a 10 percent data sample: COMPUTE STATS test_table TABLESAMPLE SYSTEM(10)
We have found that a 10 percent sampling rate typically offers a good tradeoff between statistics accuracy and execution cost. A sampling rate well below 10 percent has shown poor results and is not recommended.
COMPUTE INCREMENTAL STATS
In Impala 2.1.0 and higher, you can use the COMPUTE INCREMENTAL STATS and DROP INCREMENTAL STATS commands. The INCREMENTAL clauses work with incremental statistics, a specialized feature for partitioned tables.
When you compute incremental statistics for a partitioned table, by default Impala only processes those partitions that do not yet have incremental statistics. By processing only newly added partitions, you can keep statistics up to date without incurring the overhead of reprocessing the entire table each time.
You can also compute or drop statistics for a specified subset of partitions by including a PARTITION clause in the COMPUTE INCREMENTAL STATS or DROP INCREMENTAL STATS statement.
The metadata for incremental statistics is handled differently from the original style of statistics:
-
Issuing a COMPUTE INCREMENTAL STATS without a partition clause causes Impala to compute incremental stats for all partitions that do not already have incremental stats. This might be the entire table when running the command for the first time, but subsequent runs should only update new partitions. You can force updating a partition that already has incremental stats by issuing a DROP INCREMENTAL STATS before running COMPUTE INCREMENTAL STATS.
-
The SHOW TABLE STATS and SHOW PARTITIONS statements now include an additional column showing whether incremental statistics are available for each column. A partition could already be covered by the original type of statistics based on a prior COMPUTE STATS statement, as indicated by a value other than -1 under the #Rows column. Impala query planning uses either kind of statistics when available.
-
COMPUTE INCREMENTAL STATS takes more time than COMPUTE STATS for the same volume of data. Therefore it is most suitable for tables with large data volume where new partitions are added frequently, making it impractical to run a full COMPUTE STATS operation for each new partition. For unpartitioned tables, or partitioned tables that are loaded once and not updated with new partitions, use the original COMPUTE STATS syntax.
-
COMPUTE INCREMENTAL STATS uses some memory in the catalogd process, proportional to the number of partitions and number of columns in the applicable table. The memory overhead is approximately 400 bytes for each column in each partition. This memory is reserved in the catalogd daemon, the statestored daemon, and in each instance of the impalad daemon.
-
In cases where new files are added to an existing partition, issue a REFRESH statement for the table, followed by a DROP INCREMENTAL STATS and COMPUTE INCREMENTAL STATS sequence for the changed partition.
-
The DROP INCREMENTAL STATS statement operates only on a single partition at a time. To remove statistics (whether incremental or not) from all partitions of a table, issue a DROP STATS statement with no INCREMENTAL or PARTITION clauses.
The following considerations apply to incremental statistics when the structure of an existing table is changed (known as schema evolution):
-
If you use an ALTER TABLE statement to drop a column, the existing statistics remain valid and COMPUTE INCREMENTAL STATS does not rescan any partitions.
-
If you use an ALTER TABLE statement to add a column, Impala rescans all partitions and fills in the appropriate column-level values the next time you run COMPUTE INCREMENTAL STATS.
-
If you use an ALTER TABLE statement to change the data type of a column, Impala rescans all partitions and fills in the appropriate column-level values the next time you run COMPUTE INCREMENTAL STATS.
-
If you use an ALTER TABLE statement to change the file format of a table, the existing statistics remain valid and a subsequent COMPUTE INCREMENTAL STATS does not rescan any partitions.
See COMPUTE STATS Statement and DROP STATS Statement for syntax details.
Maximum Serialized Stats Size
In Impala 3.0 / CDH 5.15 and lower, when executing COMPUTE INCREMENTAL STATS on very large tables, use the configuration setting --inc_stats_size_limit_bytes to prevent Impala from running out of memory while updating table metadata. If this limit is reached, Impala will stop loading the table and return an error. The error serves as an indication that COMPUTE INCREMENTAL STATS should not be used on the particular table. Consider spitting the table and using regular COMPUTE STATS ]if possible.
The --inc_stats_size_limit_bytes limit is set as a safety check, to prevent Impala from hitting the maximum limit for the table metadata. Note that this limit is only one part of the entire table's metadata all of which together must be below 2 GB.
The default value for --inc_stats_size_limit_bytes is 209715200, 200 MB.
To change the --inc_stats_size_limit_bytes value, restart impalad and catalogd with the new value specified in bytes, for example, 1048576000 for 1 GB. See Modifying Impala Startup Options for the steps to change the option and restart Impala daemons.
In Impala 3.1 / CDH 5.16 / CDH 6.1 and higher, Impala improved how metadata is updated when executing COMPUTE INCREMENTAL STATS, significantly reducing the need for --inc_stats_size_limit_bytes.
Loading Incremental Statistics from Catalogd
Starting in Impala 3.1 / CDH 5.16 / CDH 6.1, a new configuration setting, --pull_incremental_statistics, was added and set to true by default. When you start Impala catalogd and impalad coordinators with this setting enabled:
- Newly created incremental stats will be smaller in size thus reducing memory pressure on the catalogd daemon. Your users can keep more tables and partitions in the same catalog and have lower chances of crashing catalogd due to out-of-memory issues.
- Incremental stats will not be replicated to impalad and will be accessed on demand from catalogd, resulting in a reduced memory footprint of impalad.
We do not recommend you change the default setting of --pull_incremental_statistics.
Detecting Missing Statistics
You can check whether a specific table has statistics using the SHOW TABLE STATS statement (for any table) or the SHOW PARTITIONS statement (for a partitioned table). Both statements display the same information. If a table or a partition does not have any statistics, the #Rows field contains -1. Once you compute statistics for the table or partition, the #Rows field changes to an accurate value.
The following example shows a table that initially does not have any statistics. The SHOW TABLE STATS statement displays different values for #Rows before and after the COMPUTE STATS operation.
[localhost:21000] > create table no_stats (x int); [localhost:21000] > show table stats no_stats; +-------+--------+------+--------------+--------+-------------------+ | #Rows | #Files | Size | Bytes Cached | Format | Incremental stats | +-------+--------+------+--------------+--------+-------------------+ | -1 | 0 | 0B | NOT CACHED | TEXT | false | +-------+--------+------+--------------+--------+-------------------+ [localhost:21000] > compute stats no_stats; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 1 partition(s) and 1 column(s). | +-----------------------------------------+ [localhost:21000] > show table stats no_stats; +-------+--------+------+--------------+--------+-------------------+ | #Rows | #Files | Size | Bytes Cached | Format | Incremental stats | +-------+--------+------+--------------+--------+-------------------+ | 0 | 0 | 0B | NOT CACHED | TEXT | false | +-------+--------+------+--------------+--------+-------------------+
The following example shows a similar progression with a partitioned table. Initially, #Rows is -1. After a COMPUTE STATS operation, #Rows changes to an accurate value. Any newly added partition starts with no statistics, meaning that you must collect statistics after adding a new partition.
[localhost:21000] > create table no_stats_partitioned (x int) partitioned by (year smallint); [localhost:21000] > show table stats no_stats_partitioned; +-------+-------+--------+------+--------------+--------+-------------------+ | year | #Rows | #Files | Size | Bytes Cached | Format | Incremental stats | +-------+-------+--------+------+--------------+--------+-------------------+ | Total | -1 | 0 | 0B | 0B | | | +-------+-------+--------+------+--------------+--------+-------------------+ [localhost:21000] > show partitions no_stats_partitioned; +-------+-------+--------+------+--------------+--------+-------------------+ | year | #Rows | #Files | Size | Bytes Cached | Format | Incremental stats | +-------+-------+--------+------+--------------+--------+-------------------+ | Total | -1 | 0 | 0B | 0B | | | +-------+-------+--------+------+--------------+--------+-------------------+ [localhost:21000] > alter table no_stats_partitioned add partition (year=2013); [localhost:21000] > compute stats no_stats_partitioned; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 1 partition(s) and 1 column(s). | +-----------------------------------------+ [localhost:21000] > alter table no_stats_partitioned add partition (year=2014); [localhost:21000] > show partitions no_stats_partitioned; +-------+-------+--------+------+--------------+--------+-------------------+ | year | #Rows | #Files | Size | Bytes Cached | Format | Incremental stats | +-------+-------+--------+------+--------------+--------+-------------------+ | 2013 | 0 | 0 | 0B | NOT CACHED | TEXT | false | | 2014 | -1 | 0 | 0B | NOT CACHED | TEXT | false | | Total | 0 | 0 | 0B | 0B | | | +-------+-------+--------+------+--------------+--------+-------------------+
If checking each individual table is impractical, due to a large number of tables or views that hide the underlying base tables, you can also check for missing statistics for a particular query. Use the EXPLAIN statement to preview query efficiency before actually running the query. Use the query profile output available through the PROFILE command in impala-shell or the web UI to verify query execution and timing after running the query. Both the EXPLAIN plan and the PROFILE output display a warning if any tables or partitions involved in the query do not have statistics.
[localhost:21000] > create table no_stats (x int); [localhost:21000] > explain select count(*) from no_stats; +------------------------------------------------------------------------------------+ | Explain String | +------------------------------------------------------------------------------------+ | Estimated Per-Host Requirements: Memory=10.00MB VCores=1 | | WARNING: The following tables are missing relevant table and/or column statistics. | | incremental_stats.no_stats | | | | 03:AGGREGATE [FINALIZE] | | | output: count:merge(*) | | | | | 02:EXCHANGE [UNPARTITIONED] | | | | | 01:AGGREGATE | | | output: count(*) | | | | | 00:SCAN HDFS [incremental_stats.no_stats] | | partitions=1/1 files=0 size=0B | +------------------------------------------------------------------------------------+
Because Impala uses the partition pruning technique when possible to only evaluate certain partitions, if you have a partitioned table with statistics for some partitions and not others, whether or not the EXPLAIN statement shows the warning depends on the actual partitions used by the query. For example, you might see warnings or not for different queries against the same table:
-- No warning because all the partitions for the year 2012 have stats. EXPLAIN SELECT ... FROM t1 WHERE year = 2012; -- Missing stats warning because one or more partitions in this range -- do not have stats. EXPLAIN SELECT ... FROM t1 WHERE year BETWEEN 2006 AND 2009;
To confirm if any partitions at all in the table are missing statistics, you might explain a query that scans the entire table, such as SELECT COUNT(*) FROM table_name.
Manually Setting Table and Column Statistics with ALTER TABLE
Setting Table Statistics
The most crucial piece of data in all the statistics is the number of rows in the table (for an unpartitioned or partitioned table) and for each partition (for a partitioned table). The COMPUTE STATS statement always gathers statistics about all columns, as well as overall table statistics. If it is not practical to do a full COMPUTE STATS or COMPUTE INCREMENTAL STATS operation after adding a partition or inserting data, or if you can see that Impala would produce a more efficient plan if the number of rows was different, you can manually set the number of rows through an ALTER TABLE statement:
-- Set total number of rows. Applies to both unpartitioned and partitioned tables.
alter table table_name set tblproperties('numRows'='new_value', 'STATS_GENERATED_VIA_STATS_TASK'='true');
-- Set total number of rows for a specific partition. Applies to partitioned tables only.
-- You must specify all the partition key columns in the PARTITION clause.
alter table table_name partition (keycol1=val1,keycol2=val2...) set tblproperties('numRows'='new_value', 'STATS_GENERATED_VIA_STATS_TASK'='true');
This statement avoids re-scanning any data files. (The requirement to include the STATS_GENERATED_VIA_STATS_TASK property is relatively new, as a result of the issue HIVE-8648 for the Hive metastore.)
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');
For a partitioned table, update both the per-partition number of rows and the number of rows for the whole table:
-- 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 practice, the COMPUTE STATS statement, or COMPUTE INCREMENTAL STATS for a partitioned table, should be fast and convenient enough that this technique is only useful for the very largest partitioned tables. Because the column statistics might be left in a stale state, do not use this technique as a replacement for COMPUTE STATS. Only use this technique if all other means of collecting statistics are impractical, or as a low-overhead operation that you run in between periodic COMPUTE STATS or COMPUTE INCREMENTAL STATS operations.
Setting Column Statistics
In CDH 5.8 / Impala 2.6 and higher, you can also use the SET COLUMN STATS clause of ALTER TABLE to manually set or change column statistics. Only use this technique in cases where it is impractical to run COMPUTE STATS or COMPUTE INCREMENTAL STATS frequently enough to keep up with data changes for a huge table.
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 |
+--------+--------+------------------+--------+----------+----------+
Examples of Using Table and Column Statistics with Impala
The following examples walk through a sequence of SHOW TABLE STATS, SHOW COLUMN STATS, ALTER TABLE, and SELECT and INSERT statements to illustrate various aspects of how Impala uses statistics to help optimize queries.
This example shows table and column statistics for the STORE column used in the TPC-DS benchmarks for decision support systems. It is a tiny table holding data for 12 stores. Initially, before any statistics are gathered by a COMPUTE STATS statement, most of the numeric fields show placeholder values of -1, indicating that the figures are unknown. The figures that are filled in are values that are easily countable or deducible at the physical level, such as the number of files, total data size of the files, and the maximum and average sizes for data types that have a constant size such as INT, FLOAT, and TIMESTAMP.
[localhost:21000] > show table stats store; +-------+--------+--------+--------+ | #Rows | #Files | Size | Format | +-------+--------+--------+--------+ | -1 | 1 | 3.08KB | TEXT | +-------+--------+--------+--------+ Returned 1 row(s) in 0.03s [localhost:21000] > show column stats store; +--------------------+-----------+------------------+--------+----------+----------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +--------------------+-----------+------------------+--------+----------+----------+ | s_store_sk | INT | -1 | -1 | 4 | 4 | | s_store_id | STRING | -1 | -1 | -1 | -1 | | s_rec_start_date | TIMESTAMP | -1 | -1 | 16 | 16 | | s_rec_end_date | TIMESTAMP | -1 | -1 | 16 | 16 | | s_closed_date_sk | INT | -1 | -1 | 4 | 4 | | s_store_name | STRING | -1 | -1 | -1 | -1 | | s_number_employees | INT | -1 | -1 | 4 | 4 | | s_floor_space | INT | -1 | -1 | 4 | 4 | | s_hours | STRING | -1 | -1 | -1 | -1 | | s_manager | STRING | -1 | -1 | -1 | -1 | | s_market_id | INT | -1 | -1 | 4 | 4 | | s_geography_class | STRING | -1 | -1 | -1 | -1 | | s_market_desc | STRING | -1 | -1 | -1 | -1 | | s_market_manager | STRING | -1 | -1 | -1 | -1 | | s_division_id | INT | -1 | -1 | 4 | 4 | | s_division_name | STRING | -1 | -1 | -1 | -1 | | s_company_id | INT | -1 | -1 | 4 | 4 | | s_company_name | STRING | -1 | -1 | -1 | -1 | | s_street_number | STRING | -1 | -1 | -1 | -1 | | s_street_name | STRING | -1 | -1 | -1 | -1 | | s_street_type | STRING | -1 | -1 | -1 | -1 | | s_suite_number | STRING | -1 | -1 | -1 | -1 | | s_city | STRING | -1 | -1 | -1 | -1 | | s_county | STRING | -1 | -1 | -1 | -1 | | s_state | STRING | -1 | -1 | -1 | -1 | | s_zip | STRING | -1 | -1 | -1 | -1 | | s_country | STRING | -1 | -1 | -1 | -1 | | s_gmt_offset | FLOAT | -1 | -1 | 4 | 4 | | s_tax_percentage | FLOAT | -1 | -1 | 4 | 4 | +--------------------+-----------+------------------+--------+----------+----------+ Returned 29 row(s) in 0.04s
With the Hive ANALYZE TABLE statement for column statistics, you had to specify each column for which to gather statistics. The Impala COMPUTE STATS statement automatically gathers statistics for all columns, because it reads through the entire table relatively quickly and can efficiently compute the values for all the columns. This example shows how after running the COMPUTE STATS statement, statistics are filled in for both the table and all its columns:
[localhost:21000] > compute stats store; +------------------------------------------+ | summary | +------------------------------------------+ | Updated 1 partition(s) and 29 column(s). | +------------------------------------------+ Returned 1 row(s) in 1.88s [localhost:21000] > show table stats store; +-------+--------+--------+--------+ | #Rows | #Files | Size | Format | +-------+--------+--------+--------+ | 12 | 1 | 3.08KB | TEXT | +-------+--------+--------+--------+ Returned 1 row(s) in 0.02s [localhost:21000] > show column stats store; +--------------------+-----------+------------------+--------+----------+-------------------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +--------------------+-----------+------------------+--------+----------+-------------------+ | s_store_sk | INT | 12 | -1 | 4 | 4 | | s_store_id | STRING | 6 | -1 | 16 | 16 | | s_rec_start_date | TIMESTAMP | 4 | -1 | 16 | 16 | | s_rec_end_date | TIMESTAMP | 3 | -1 | 16 | 16 | | s_closed_date_sk | INT | 3 | -1 | 4 | 4 | | s_store_name | STRING | 8 | -1 | 5 | 4.25 | | s_number_employees | INT | 9 | -1 | 4 | 4 | | s_floor_space | INT | 10 | -1 | 4 | 4 | | s_hours | STRING | 2 | -1 | 8 | 7.083300113677979 | | s_manager | STRING | 7 | -1 | 15 | 12 | | s_market_id | INT | 7 | -1 | 4 | 4 | | s_geography_class | STRING | 1 | -1 | 7 | 7 | | s_market_desc | STRING | 10 | -1 | 94 | 55.5 | | s_market_manager | STRING | 7 | -1 | 16 | 14 | | s_division_id | INT | 1 | -1 | 4 | 4 | | s_division_name | STRING | 1 | -1 | 7 | 7 | | s_company_id | INT | 1 | -1 | 4 | 4 | | s_company_name | STRING | 1 | -1 | 7 | 7 | | s_street_number | STRING | 9 | -1 | 3 | 2.833300113677979 | | s_street_name | STRING | 12 | -1 | 11 | 6.583300113677979 | | s_street_type | STRING | 8 | -1 | 9 | 4.833300113677979 | | s_suite_number | STRING | 11 | -1 | 9 | 8.25 | | s_city | STRING | 2 | -1 | 8 | 6.5 | | s_county | STRING | 1 | -1 | 17 | 17 | | s_state | STRING | 1 | -1 | 2 | 2 | | s_zip | STRING | 2 | -1 | 5 | 5 | | s_country | STRING | 1 | -1 | 13 | 13 | | s_gmt_offset | FLOAT | 1 | -1 | 4 | 4 | | s_tax_percentage | FLOAT | 5 | -1 | 4 | 4 | +--------------------+-----------+------------------+--------+----------+-------------------+ Returned 29 row(s) in 0.04s
The following example shows how statistics are represented for a partitioned table. In this case, we have set up a table to hold the world's most trivial census data, a single STRING field, partitioned by a YEAR column. The table statistics include a separate entry for each partition, plus final totals for the numeric fields. The column statistics include some easily deducible facts for the partitioning column, such as the number of distinct values (the number of partition subdirectories).
localhost:21000] > describe census; +------+----------+---------+ | name | type | comment | +------+----------+---------+ | name | string | | | year | smallint | | +------+----------+---------+ Returned 2 row(s) in 0.02s [localhost:21000] > show table stats census; +-------+-------+--------+------+---------+ | year | #Rows | #Files | Size | Format | +-------+-------+--------+------+---------+ | 2000 | -1 | 0 | 0B | TEXT | | 2004 | -1 | 0 | 0B | TEXT | | 2008 | -1 | 0 | 0B | TEXT | | 2010 | -1 | 0 | 0B | TEXT | | 2011 | 0 | 1 | 22B | TEXT | | 2012 | -1 | 1 | 22B | TEXT | | 2013 | -1 | 1 | 231B | PARQUET | | Total | 0 | 3 | 275B | | +-------+-------+--------+------+---------+ Returned 8 row(s) in 0.02s [localhost:21000] > show column stats census; +--------+----------+------------------+--------+----------+----------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +--------+----------+------------------+--------+----------+----------+ | name | STRING | -1 | -1 | -1 | -1 | | year | SMALLINT | 7 | -1 | 2 | 2 | +--------+----------+------------------+--------+----------+----------+ Returned 2 row(s) in 0.02s
The following example shows how the statistics are filled in by a COMPUTE STATS statement in Impala.
[localhost:21000] > compute stats census; +-----------------------------------------+ | summary | +-----------------------------------------+ | Updated 3 partition(s) and 1 column(s). | +-----------------------------------------+ Returned 1 row(s) in 2.16s [localhost:21000] > show table stats census; +-------+-------+--------+------+---------+ | year | #Rows | #Files | Size | Format | +-------+-------+--------+------+---------+ | 2000 | -1 | 0 | 0B | TEXT | | 2004 | -1 | 0 | 0B | TEXT | | 2008 | -1 | 0 | 0B | TEXT | | 2010 | -1 | 0 | 0B | TEXT | | 2011 | 4 | 1 | 22B | TEXT | | 2012 | 4 | 1 | 22B | TEXT | | 2013 | 1 | 1 | 231B | PARQUET | | Total | 9 | 3 | 275B | | +-------+-------+--------+------+---------+ Returned 8 row(s) in 0.02s [localhost:21000] > show column stats census; +--------+----------+------------------+--------+----------+----------+ | Column | Type | #Distinct Values | #Nulls | Max Size | Avg Size | +--------+----------+------------------+--------+----------+----------+ | name | STRING | 4 | -1 | 5 | 4.5 | | year | SMALLINT | 7 | -1 | 2 | 2 | +--------+----------+------------------+--------+----------+----------+ Returned 2 row(s) in 0.02s
For examples showing how some queries work differently when statistics are available, see Examples of Join Order Optimization. You can see how Impala executes a query differently in each case by observing the EXPLAIN output before and after collecting statistics. Measure the before and after query times, and examine the throughput numbers in before and after SUMMARY or PROFILE output, to verify how much the improved plan speeds up performance.