AWS Athena is a fully managed and completely cloud-hosted data warehouse service, meaning that there are no challenges where you need to download any software at all. It simplifies the process for AWS Athena queries if you want to run SQL queries on your data for specific purposes like statistical analysis. In this article, I’m going to give you 12 tips on how to make your AWS Athena queries fly through the clouds to find out how fast and exceptional it is, compared to competitors.
What is AWS Athena?
1. AWS Athena is a cloud-based query service that makes it easy to analyze data stored in Amazon S3.
2. AWS Athena is serverless, so there is no need to set up or manage any servers. You can point AWS Athena at your data stored in S3 and begin querying it using SQL.
3. AWS Athena is highly scalable and can handle queries against huge data sets.
4. AWS Athena is pay-per-query, so you only pay for the queries that you run. There is no minimum fee and no upfront costs.
5. To get started with AWS Athena, you need an Amazon S3 bucket where your data is stored.
Tip 1: Use an efficient file format such as Parquet or ORC
You will often store your data in a very inefficient file format such as CSV or JSON. When this is the case, your AWS Athena queries will take much longer to run because AWS Athena has to process more data.
To fix this, you can convert your data into a more efficient file format such as Parquet or ORC. Doing this will make your AWS Athena queries run faster because AWS Athena won’t have to process as much data.
You can use the Apache Hive Convert tool to convert your data into the Parquet or ORC file format. This tool is available for free, and it’s straightforward to use.
Once you’ve converted your data into the Parquet or ORC file format, you should see a significant improvement in the performance of your AWS Athena queries.
Tip 2: Avoid single large files
An excellent way to improve the performance of your AWS Athena queries is to avoid having too many large files in your data source. Single large files can take a long time to process, so it’s best to break them up into smaller chunks.
One way to do this is to use partitioning. Partitioning is a way of grouping data into separate folders based on specific criteria. For example, you could partition your data by date or by location.
Partitioning can help improve query performance because AWS Athena can skip over partitions that don’t match the criteria specified in the query. It means that AWS Athena doesn’t have to scan through all of the data in the data source, saving a lot of time.
Tip 3: Read a smaller amount of data at once
One of the best ways to make your AWS Athena queries run faster is to read a smaller amount of data at once. You can accomplish this by using the LIMIT keyword in your SQL query.
For example, let’s say you’re querying a large table with millions of rows. But you only need to retrieve 1000 rows from the table. In this case, you would use the following query:
SELECT * FROM large_table LIMIT 1000;
You’re telling AWS Athena only to read the first 1000 rows from the table by doing this. It can save time since AWS Athena won’t need to scan through the entire table.
Of course, you’ll need to be careful when using the LIMIT keyword. Make sure that you’re only retrieving the data that you need. Otherwise, you might not get the results you’re expecting.
Tip 4: Avoid having a large number of small files
One of the most significant performance killers for AWS Athena is having a large number of small files. When you have too many small files, AWS Athena has to scan through them, which takes up a lot of time and resources.
The best way to avoid this problem is to compress your files into fewer, larger files. You can do this with a tool like gzip. Compressing your files will make your queries run faster and save you money on AWS Athena fees.
Tip 5: Avoid scanning an entire table
One of the most common mistakes people make when writing AWS Athena queries is scanning an entire table when they only need a small subset of data. This can be a considerable performance drag and significantly increase your AWS costs.
To avoid this, always specify the columns you need in your query. For example, if you only need to know customers’ names in a specific state, you would only query the “name” and “state” columns instead of select *.
Also, take advantage of partitioning your data. By partitions, we mean dividing your data by dates or other criteria. This allows AWS Athena only to scan the partitions containing the data you need, giving your queries a significant performance boost.
Tip 6: Avoid large JSON strings
When working with JSON data in AWS Athena, it’s important to avoid loading large JSON strings into memory. If your JSON data is too large, you’ll likely see out-of-memory errors.
Instead, you can use the Amazon S3 Select feature to retrieve only the data you need from your JSON files. This will help reduce the amount of memory required to process your data and improve performance.
Tip 7: Reduce the usage of memory intensive operations
One way to optimize your AWS Athena queries is to reduce the usage of memory-intensive operations. Some of the most common memory-intensive functions include ORDER BY, DISTINCT, and GROUP BY.
If you can avoid using these operations, your queries will run faster and use less memory. However, sometimes they are necessary, so you must weigh the trade-offs.
Another tip is to use the correct data type for your columns. For example, if you have a column that only contains integers, you should use the INT data type instead of the STRING data type. This will reduce the amount of memory used for each value in the column.
Tip 8: Limit the number of partitions in a table
Partitioning your data is a great way to improve query performance. However, too many partitions can hurt performance.
When you have too many partitions, the query optimizer has to scan more sections to find the data it needs. This can lead to longer query times and increased CPU usage.
It’s essential to strike a balance when partitioning your data. Make sure you have enough partitions to improve query performance but don’t go overboard, or you’ll have worse performance than before.
If you’re unsure how many partitions to use, start with a smaller number and increase it until you see a measurable improvement in query performance.
Tip 9: Remove old partitions even if they are empty
If you’re using AWS Athena to query data stored in Amazon S3, keeping your data partitions up to date is essential. Partitions that are no longer needed can be removed, even if they are empty. This helps to keep your AWS Athena query performance optimal.
You can use the ALTER TABLE … DROP PARTITION command to remove an old partition. For example, say you have a table named ‘events’ partitioned by year and month. To drop the partition for events that occurred in January 2018, you would run the following SQL query:
ALTER TABLE events DROP PARTITION (year=2018, month=1);
You can also use the AWS Athena console to drop partitions. Select the table you want to modify, then choose the partitions you want to delete. Once you’ve made your selections, click the ‘Drop’ button to remove the partitions.
Tip 10: To add new partitions frequently, use ALTER TABLE ADD PARTITION
You’ll need to use the ALTER TABLE ADD PARTITION command to add new partitions. This command will allow you to specify the location of the new partition and the name of the partition.
By adding new partitions frequently and rebuilding the table regularly, you’ll be able to keep your AWS Athena queries running smoothly and efficiently.
Tip 11: Minimize the use of window functions
Are you using window functions in your AWS Athena queries? If so, you may want to reconsider.
Window functions are often very resource-intensive and can lead to significant performance degradation. In many cases, it’s possible to rewrite your query in a way that doesn’t use window functions and will therefore be much faster.
If you absolutely must use window functions, there are a few things you can do to minimize the impact on performance:
– Use LIMIT when possible: This will limit the number of rows that need to be processed by the window function and make a big difference in performance.
– Use Partition By clause wherever possible
– Use a smaller window frame: The larger the window frame, the more resources are required to process it. If you can use a smaller frame, do it.
Tip 12: Use regular expressions instead of LIKE on large strings
LIKE is very powerful for simple string matching, but it can be pretty slow on larger strings. A common workaround is to use a regular expression instead.
Congratulations on making it to the end of this blog post! You now know how to improve the performance of your AWS Athena queries. We hope you found this blog post helpful. If you have any questions or feedback, please let us know in the comments below.