3/13/2023 0 Comments Athena vs redshiftnot to mention setting up file transfers. Too small files (less than 128 MB) means the execution engine may take a lot of time opening S3 files, and accessing details like metadata, listing directories, reading file headers and compression dictionaries etc. Make sure the file size on S3 is optimal – between 200 MB to 1GB. It is recommended to use data in Apache Parquet or Apache ORC formats since they are splittable and also by default compress data. A single, unsplit file means only one reader can be deployed and the others remain idle. Splitting a file means the Athena execution engine can use multiple readers to process it in parallel. For this method your object key names must be in accordance with a specific pattern.ĭid you know BryteFlow partitions data for you automatically as it loads to S3? (Use BryteFlow to create an S3 Data Lake in minutes) Tip 2: Compression and splitting of files Use a single MSCK REPAIR TABLE statement to create all partitions.Use the ALTER TABLE statement for individual partitions.To start, you need to load the partitions into the table before you start querying the data. This makes query performance faster and reduces costs. Partitions create focus on the actual data you need and lower the data volume required to be scanned for each query. You can improve the performance with these 7 tips: Tip 1: Partition your dataīy partitioning your data, you can divide tables based on column values like date, timestamps etc. Build a Data Lakehouse on Amazon S3 How to tune your Amazon Athena query performance: 7 easy tipsĪmazon Athena’s performance is strongly dependent on how data is organized in S3. You simply point Athena to your data stored on Amazon S3 and you’re good to go. Setting up Amazon AthenaĪmazon Athena is easy to set up it is a serverless service which can be accessed directly from the AWS Management Console with a few clicks. Please read our blog Face off: AWS Athena vs Redshift Spectrum – which service you should use and when. Your AWS ETL options with AWS Glue are explained in our blog if you need to ingest or transform your data on S3. You may need to access data on S3 via an API or via Redshift Spectrum or other means. Hence, if you have several users needing to do interactive queries or using dashboards on data on Amazon S3, this may not be the solution for you. There are service limits imposed by AWS – you need to refer to these before deciding if this works for you. There are certain restrictions imposed by AWS on user access to Athena, which you should be aware of. Read about BryteFlow for AWS ETL Things to know regarding user access on Amazon Athena Create an S3 Data Lake in Minutes (turorial with vidoes) You also can run queries in parallel, Athena simply scales up without a fuss and results are lightning-fast even with huge datasets. And you pay only for the queries you run which makes it extremely cost-effective. Being a serverless service, you can use Athena without setting up or managing any infrastructure. You can query data on Amazon Simple Storage Service (Amazon S3) with Athena using standard SQL. Need to query data on Amazon S3 directly? Amazon Athena is the interactive AWS service that makes it possible. Let’s first understand about Athena and then dive into performance tuning.ĭownload our eBook: How to get siloed data to AWS Athena in just a few clicks. It is important to understand how Amazon Athena works, and the tweaks you can make now, so that you can derive the best performance and lower your costs. How do you tune your Amazon Athena query performance?
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