Understanding Impala Query Performance - EXPLAIN Plans and Query Profiles

To understand the high-level performance considerations for Impala queries, read the output of the EXPLAIN statement for the query. You can get the EXPLAIN plan without actually running the query itself.

For an overview of the physical performance characteristics for a query, issue the SUMMARY statement in impala-shell immediately after executing a query. This condensed information shows which phases of execution took the most time, and how the estimates for memory usage and number of rows at each phase compare to the actual values.

To understand the detailed performance characteristics for a query, issue the PROFILE statement in impala-shell immediately after executing a query. This low-level information includes physical details about memory, CPU, I/O, and network usage, and thus is only available after the query is actually run.

Also, see Performance Considerations for the Impala-HBase Integration and Understanding and Tuning Impala Query Performance for S3 Data for examples of interpreting EXPLAIN plans for queries against HBase tables and data stored in the Amazon Simple Storage System (S3).

Parent topic: Tuning Impala for Performance

Using the EXPLAIN Plan for Performance Tuning

The [EXPLAIN]($d3039e00eb17a82a.md#explain) statement gives you an outline of the logical steps that a query will perform, such as how the work will be distributed among the nodes and how intermediate results will be combined to produce the final result set. You can see these details before actually running the query. You can use this information to check that the query will not operate in some very unexpected or inefficient way.

  1. [impalad-host:21000] > EXPLAIN SELECT COUNT(*) FROM customer_address;
  2. +----------------------------------------------------------+
  3. | Explain String                                           |
  4. +----------------------------------------------------------+
  5. | ...                                                      |
  6. |                                                          |
  7. | 03:AGGREGATE [FINALIZE]                                  |
  8. | |  output: sum(count(*))                                 |
  9. | |                                                        |
  10. | 02:EXCHANGE [UNPARTITIONED]                              |
  11. | |                                                        |
  12. | 01:AGGREGATE                                             |
  13. | |  output: count(*)                                      |
  14. | |                                                        |
  15. | 00:SCAN HDFS [default.customer_address]                  |
  16. |    partitions=1/1 size=5.25MB                            |
  17. +----------------------------------------------------------+

Read the EXPLAIN plan from bottom to top:

  • The last part of the plan shows the low-level details such as the expected amount of data that will be read, where you can judge the effectiveness of your partitioning strategy and estimate how long it will take to scan a table based on total data size and the size of the cluster.
  • As you work your way up, next you see the operations that will be parallelized and performed on each Impala node.
  • At the higher levels, you see how data flows when intermediate result sets are combined and transmitted from one node to another.
  • See EXPLAIN_LEVEL Query Option for details about the EXPLAIN_LEVEL query option, which lets you customize how much detail to show in the EXPLAIN plan depending on whether you are doing high-level or low-level tuning, dealing with logical or physical aspects of the query.

The EXPLAIN plan is also printed at the beginning of the query profile report described in Using the Query Profile for Performance Tuning, for convenience in examining both the logical and physical aspects of the query side-by-side.

The amount of detail displayed in the EXPLAIN output is controlled by the EXPLAIN_LEVEL query option. You typically increase this setting from standard to extended (or from 1 to 2) when doublechecking the presence of table and column statistics during performance tuning, or when estimating query resource usage in conjunction with the resource management features.

Using the SUMMARY Report for Performance Tuning

The [SUMMARY]($6b18a9d73d7cd1ad.md#shell_commands) command within the impala-shell interpreter gives you an easy-to-digest overview of the timings for the different phases of execution for a query. Like the EXPLAIN plan, it is easy to see potential performance bottlenecks. Like the PROFILE output, it is available after the query is run and so displays actual timing numbers.

The SUMMARY report is also printed at the beginning of the query profile report described in Using the Query Profile for Performance Tuning, for convenience in examining high-level and low-level aspects of the query side-by-side.

When the MT_DOP query option is set to a value larger than 0, the #Inst column in the output shows the number of fragment instances. Impala decomposes each query into smaller units of work that are distributed across the cluster, and these units are referred as fragments.

When the MT_DOP query option is set to 0, the #Inst column in the output shows the same value as the #Hosts column, since there is exactly one fragment for each host.

For example, here is a query involving an aggregate function, on a single-node cluster. The different stages of the query and their timings are shown (rolled up for all nodes), along with estimated and actual values used in planning the query. In this case, the AVG() function is computed for a subset of data on each node (stage 01) and then the aggregated results from all nodes are combined at the end (stage 03). You can see which stages took the most time, and whether any estimates were substantially different than the actual data distribution.

  1. > SELECT AVG(ss_sales_price) FROM store_sales WHERE ss_coupon_amt = 0;
  2. > SUMMARY;
  3. +--------------+--------+--------+----------+----------+-------+------------+----------+---------------+-----------------+
  4. | Operator     | #Hosts | #Inst  | Avg Time | Max Time | #Rows | Est. #Rows | Peak Mem | Est. Peak Mem | Detail          |
  5. +--------------+--------+--------+----------+----------+-------+------------+----------+---------------+-----------------+
  6. | 03:AGGREGATE | 1      | 1      | 1.03ms   | 1.03ms   | 1     | 1          | 48.00 KB | -1 B          | MERGE FINALIZE  |
  7. | 02:EXCHANGE  | 1      | 1      | 0ns      | 0ns      | 1     | 1          | 0 B      | -1 B          | UNPARTITIONED   |
  8. | 01:AGGREGATE | 1      | 1      |30.79ms   | 30.79ms  | 1     | 1          | 80.00 KB | 10.00 MB      |                 |
  9. | 00:SCAN HDFS | 1      | 1      | 5.45s    | 5.45s    | 2.21M | -1         | 64.05 MB | 432.00 MB     | tpc.store_sales |
  10. +--------------+--------+--------+----------+----------+-------+------------+----------+---------------+-----------------+

Notice how the longest initial phase of the query is measured in seconds (s), while later phases working on smaller intermediate results are measured in milliseconds (ms) or even nanoseconds (ns).

Using the Query Profile for Performance Tuning

The PROFILE command, available in the impala-shell interpreter, produces a detailed low-level report showing how the most recent query was executed. Unlike the EXPLAIN plan described in Using the EXPLAIN Plan for Performance Tuning, this information is only available after the query has finished. It shows physical details such as the number of bytes read, maximum memory usage, and so on for each node. You can use this information to determine if the query is I/O-bound or CPU-bound, whether some network condition is imposing a bottleneck, whether a slowdown is affecting some nodes but not others, and to check that recommended configuration settings such as short-circuit local reads are in effect.

By default, time values in the profile output reflect the wall-clock time taken by an operation. For values denoting system time or user time, the measurement unit is reflected in the metric name, such as ScannerThreadsSysTime or ScannerThreadsUserTime. For example, a multi-threaded I/O operation might show a small figure for wall-clock time, while the corresponding system time is larger, representing the sum of the CPU time taken by each thread. Or a wall-clock time figure might be larger because it counts time spent waiting, while the corresponding system and user time figures only measure the time while the operation is actively using CPU cycles.

The EXPLAIN plan is also printed at the beginning of the query profile report, for convenience in examining both the logical and physical aspects of the query side-by-side. The EXPLAIN_LEVEL query option also controls the verbosity of the EXPLAIN output printed by the PROFILE command.

In Impala 3.2, a new Per Node Profiles section was added to the profile output. The new section includes the following metrics that can be controlled by the [RESOURCE_TRACE_RATIO]($025402dbc95c89e6.md#resource_trace_ratio) query option.

  • CpuIoWaitPercentage
  • CpuSysPercentage
  • CpuUserPercentage
  • HostDiskReadThroughput: All data read by the host as part of the execution of this query (spilling), by the HDFS data node, and by other processes running on the same system.
  • HostDiskWriteThroughput: All data written by the host as part of the execution of this query (spilling), by the HDFS data node, and by other processes running on the same system.
  • HostNetworkRx: All data received by the host as part of the execution of this query, other queries, and other processes running on the same system.
  • HostNetworkTx: All data transmitted by the host as part of the execution of this query, other queries, and other processes running on the same system.