Does Redshift Spectrum supports caching and how it's helpful in cost?

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  1. I want to know whether redshift spectrum supports caching if yes how it could be useful in cost reduction. I know that redshift spectrum incur cost of about 5$ for TB of data scanning from S3. If it supports caching does the scanning of data will be avoided right so is that helpful in cost reduction?
  2. Also I want to know the costs would incur for redshift spectrum apart from data scanning.

Thank you for any insight you can provide.

MSkalai
asked 9 months ago462 views
1 Answer
1
Accepted Answer

To my research, Amazon Redshift Spectrum does not natively support caching of query results. Redshift Spectrum is designed to directly query data stored in Amazon S3, and each query reads the data from S3 without maintaining a cache of the results. This is different from Amazon Redshift itself, which has its own query optimization and caching mechanisms.

The cost structure of Redshift Spectrum is primarily based on the amount of data scanned from S3. You are correct that you're charged approximately $5 per terabyte of data scanned. This cost is one of the key considerations when using Redshift Spectrum, and it's important to optimize your queries and data organization to minimize the amount of data scanned.

Caching query results can be helpful in cost reduction for systems where queries are repeatedly executed, as the cached results can be retrieved without the need to rescan the underlying data. However, since Redshift Spectrum doesn't have native caching, the cost reduction through caching isn't directly applicable here.

To optimize cost and performance when using Redshift Spectrum:

  1. Partitioning and Data Organization: Store your data in an organized manner in S3, partitioned by relevant columns. This can significantly reduce the amount of data scanned when querying specific partitions.

  2. Columnar Formats: Use columnar data formats like Parquet and ORC when storing data in S3. These formats are highly compressed and optimized for query performance, which can reduce the amount of data scanned and improve query speed.

  3. Query Optimization: Write efficient queries that only request the columns and data you actually need. Minimize the use of wildcard queries that scan large portions of the data.

  4. Compression: Compress your data in S3 using supported compression algorithms. Compressed data requires less I/O, leading to reduced scan costs.

  5. Use Redshift Properly: For frequently queried data or data that requires more complex querying, consider loading it into Amazon Redshift tables (not Redshift Spectrum) where caching and more advanced query optimization can be used.

In terms of costs for Redshift Spectrum, aside from the data scanning cost, you should also consider the costs associated with using Amazon S3 to store your data. S3 storage costs depend on the amount of data stored and any additional features you might use, like data replication or request costs for accessing the data. Keep in mind that the pricing structures for AWS services can change, so it's important to refer to the official AWS pricing documentation or contact AWS support for the most up-to-date information regarding Redshift Spectrum costs.

Hope this helps

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answered 9 months ago

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