Performance Tuning in Athena - Amazon Athena
Physical LimitsQuery Optimization TechniquesAdditional Resources

Performance Tuning in Athena

This topic provides general information and specific suggestions for improving the performance of Athena when you have large amounts of data and experience memory usage or performance issues.

Physical Limits

By default, Athena limits the runtime of DML queries to 30 minutes and DDL queries to 600 minutes. Queries that run beyond these limits are automatically cancelled without charge. If a query runs out of memory or a node crashes during processing, errors like the following can occur: 

INTERNAL_ERROR_QUERY_ENGINE
EXCEEDED_MEMORY_LIMIT: Query exceeded local memory limit
Query exhausted resources at this scale factor
Encountered too many errors talking to a worker node. The node may have crashed or be under too much load.

Query Optimization Techniques

For queries that require resources beyond existing limits, you can either optimize the query or restructure the data being queried. To optimize your queries, consider the suggestions in this section.

Data Size

Avoid single large files – If your file size is extremely large, try to break up the file into smaller files and use partitions to organize them.

Read a smaller amount of data at once – Scanning a large amount of data at one time can slow down the query and increase cost. Use partitions or filters to limit the files to be scanned.

Avoid having too many columns – The message GENERIC_INTERNAL_ERROR: io.airlift.bytecode.CompilationException can occur when Athena fails to compile the query to bytecode. This exception is usually caused by having too many columns in the query. Reduce the number of the columns in the query or create subqueries and use a JOIN that retrieves a smaller amount of data.

Avoid large query outputs – A large amount of output data can slow performance. To work around this, try using CTAS to create a new table with the result of the query or INSERT INTO to append new results into an existing table.

Avoid CTAS queries with a large output – CTAS queries can also use a large amount of memory. If you are outputting large amount of data, try separating the task into smaller queries.

If possible, avoid having a large number of small files – Amazon S3 has a limit of 5500 requests per second. Athena queries share the same limit. If you need to scan millions of small objects in a single query, your query can be easily throttled by Amazon S3. To avoid excessive scanning, use AWS Glue ETL to periodically compact your files or partition the table and add partition key filters. For more information, see Reading Input Files in Larger Groups in the AWS Glue Developer Guide or How can I configure an AWS Glue ETL job to output larger files? in the AWS Knowledge Center.

Avoid scanning an entire table – Use the following techniques to avoid scanning entire tables:

  • Limit the use of "*". Try not to select all columns unless necessary.

  • Avoid scanning the same table multiple times in the same query

  • Use filters to reduce the amount of data to be scanned.

  • Whenever possible, add a LIMIT clause.

Avoid referring to many views and tables in a single query – Because queries with many views and/or tables must load a large amount of data, out of memory errors can occur. If possible, avoid referring to an excessive number of views or tables in a single query.

Avoid large JSON strings – If data is stored in a single JSON string and the size of the JSON data is large, out of memory errors can occur when the JSON data is processed.

File Formats

Use an efficient file format such as Parquet or ORC – To dramatically reduce query running time and costs, use compressed Parquet or ORC files to store your data. To convert your existing dataset to those formats in Athena, you can use CTAS. For more information, see Using CTAS and INSERT INTO for ETL and Data Analysis.

Switch between ORC and Parquet formats – Experience shows that the same set of data can have significant differences in processing time depending on whether it is stored in ORC or Parquet format. If you are experiencing performance issues, try a different format.

Hudi queries – Because Hudi queries bypass the native reader and split generator for files in parquet format, they can be slow. Keep this in mind when querying Hudi datasets.

Joins, Grouping, and Unions

Reduce the usage of memory intensive operations – Operations like JOIN, GROUP BY, ORDER BY, and UNION all require loading large amount of data into memory. To speed up your query, find other ways to achieve the same results, or add a clause like LIMIT to the outer query whenever possible.

Consider using UNION ALL – To eliminate duplicates, UNION builds a hash table, which consumes memory. If your query does not require the elimination of duplicates, consider using UNION ALL for better performance.

Use CTAS as an intermediary step to speed up JOIN operations – Instead of loading and processing intermediary data with every query, use CTAS to persist the intermediary data into Amazon S3. This can help speed up the performance of operations like JOIN.

Partitioning

Limit the number of partitions in a table – When a table has more than 100,000 partitions, queries can be slow because of the large number of requests sent to AWS Glue to retrieve partition information. To resolve this issue, try one of the following options:

Remove old partitions even if they are empty – Even if a partition is empty, the metadata of the partition is still stored in AWS Glue. Loading these unneeded partitions can increase query runtimes. To remove the unneeded partitions, use ALTER TABLE DROP PARTITION.

Look up a single partition – When looking up a single partition, try to provide all partition values so that Athena can locate the partition with a single call to AWS Glue. Otherwise, Athena must retrieve all partitions and filter them. This can be costly and greatly increase the planning time for your query. If you have a predictable partition pattern, you can use partition projection to avoid the partition look up calls to AWS Glue.

Set reasonable partition projection properties – When using partition projection, Athena tries to create a partition object for every partition name. Because of this, make sure that the table properties that you define do not create a near infinite amount of possible partitions.

To add new partitions frequently, use ALTER TABLE ADD PARTITION – If you use MSCK REPAIR TABLE to add new partitions frequently (for example, on a daily basis) and are experiencing query timeouts, consider using ALTER TABLE ADD PARTITION. MSCK REPAIR TABLE is best used when creating a table for the first time or when there is uncertainty about parity between data and partition metadata.

Avoid using coalesce() in a WHERE clause with partitioned columns – Under some circumstances, using the coalesce() or other functions in a WHERE clause against partitioned columns might result in reduced performance. If this occurs, try rewriting your query to provide the same functionality without using coalesce().

Window Functions

Minimize the use of window functions – Window functions such as rank() are memory intensive. In general, window functions require an entire dataset to be loaded into a single Athena node for processing. With an extremely large dataset, this can risk crashing the node. To avoid this, try the following options:

  • Filter the data and run window functions on a subset of the data.

  • Use the PARTITION BY clause with the window function whenever possible.

  • Find an alternative way to construct the query.

Use More Efficient Functions

Replace row_number() OVER (...) as rnk ... WHERE rnk = 1 – To speed up a query with a row_number() clause like this, replace this syntax with a combination of GROUP BY, ORDER BY, and LIMIT 1.

Use regular expressions instead of LIKE on large strings – Queries that include clauses such as LIKE '%string%' on large strings can be very costly. Consider using the regexp_like() function and a regular expression instead.

Use max() instead of element_at(array_sort(), 1) – For increased speed, replace the nested functions element_at(array_sort(), 1) with max().

Additional Resources

For additional information on performance tuning in Athena, consider the following resources: