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.
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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).
Using the EXPLAIN Plan for Performance Tuning
The 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.
[impalad-host:21000] > explain select count(*) from customer_address; +----------------------------------------------------------+ | Explain String | +----------------------------------------------------------+ | Estimated Per-Host Requirements: Memory=42.00MB VCores=1 | | | | 03:AGGREGATE [MERGE FINALIZE] | | | output: sum(count(*)) | | | | | 02:EXCHANGE [PARTITION=UNPARTITIONED] | | | | | 01:AGGREGATE | | | output: count(*) | | | | | 00:SCAN HDFS [default.customer_address] | | partitions=1/1 size=5.25MB | +----------------------------------------------------------+
- 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 in CDH 5.
Using the SUMMARY Report for Performance Tuning
The SUMMARY 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.
For example, here is a query involving an aggregate function, on a single-node VM. 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. (When examining the time values, be sure to consider the suffixes such as us for microseconds and ms for milliseconds, rather than just looking for the largest numbers.)
[localhost:21000] > select avg(ss_sales_price) from store_sales where ss_coupon_amt = 0; +---------------------+ | avg(ss_sales_price) | +---------------------+ | 37.80770926328327 | +---------------------+ [localhost:21000] > summary; +--------------+--------+----------+----------+-------+------------+----------+---------------+-----------------+ | Operator | #Hosts | Avg Time | Max Time | #Rows | Est. #Rows | Peak Mem | Est. Peak Mem | Detail | +--------------+--------+----------+----------+-------+------------+----------+---------------+-----------------+ | 03:AGGREGATE | 1 | 1.03ms | 1.03ms | 1 | 1 | 48.00 KB | -1 B | MERGE FINALIZE | | 02:EXCHANGE | 1 | 0ns | 0ns | 1 | 1 | 0 B | -1 B | UNPARTITIONED | | 01:AGGREGATE | 1 | 30.79ms | 30.79ms | 1 | 1 | 80.00 KB | 10.00 MB | | | 00:SCAN HDFS | 1 | 5.45s | 5.45s | 2.21M | -1 | 64.05 MB | 432.00 MB | tpc.store_sales | +--------------+--------+----------+----------+-------+------------+----------+---------------+-----------------+
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).
Here is an example from a more complicated query, as it would appear in the PROFILE output:
Operator #Hosts Avg Time Max Time #Rows Est. #Rows Peak Mem Est. Peak Mem Detail ------------------------------------------------------------------------------------------------------------------------ 09:MERGING-EXCHANGE 1 79.738us 79.738us 5 5 0 -1.00 B UNPARTITIONED 05:TOP-N 3 84.693us 88.810us 5 5 12.00 KB 120.00 B 04:AGGREGATE 3 5.263ms 6.432ms 5 5 44.00 KB 10.00 MB MERGE FINALIZE 08:AGGREGATE 3 16.659ms 27.444ms 52.52K 600.12K 3.20 MB 15.11 MB MERGE 07:EXCHANGE 3 2.644ms 5.1ms 52.52K 600.12K 0 0 HASH(o_orderpriority) 03:AGGREGATE 3 342.913ms 966.291ms 52.52K 600.12K 10.80 MB 15.11 MB 02:HASH JOIN 3 2s165ms 2s171ms 144.87K 600.12K 13.63 MB 941.01 KB INNER JOIN, BROADCAST |--06:EXCHANGE 3 8.296ms 8.692ms 57.22K 15.00K 0 0 BROADCAST | 01:SCAN HDFS 2 1s412ms 1s978ms 57.22K 15.00K 24.21 MB 176.00 MB tpch.orders o 00:SCAN HDFS 3 8s032ms 8s558ms 3.79M 600.12K 32.29 MB 264.00 MB tpch.lineitem l
Using the Query Profile for Performance Tuning
The PROFILE statement, 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.
See Query Details for the steps to download the query profile in the text format in Cloudera Manager.