On this publish I discover the way to assist analytical queries with out encountering prohibitive scan prices, by leveraging secondary indexes in DynamoDB. I additionally consider the professionals and cons of this strategy in distinction to extracting knowledge to a different system like Athena, Spark or Elastic.
Rockset just lately added assist for DynamoDB – which principally means you’ll be able to run quick SQL on DynamoDB tables with none ETL. As I spoke to our customers, I got here throughout other ways wherein international secondary indexes (GSI) are used for analytical queries.
DynamoDB shops knowledge underneath the hood by partitioning it over numerous nodes based mostly on a user-specified partition key area current in every merchandise. This user-specified partition key could be optionally mixed with a kind key to symbolize a major key. The first key acts as an index, making question operations on it cheap. A question operation can do equality comparability (=) on the partition key and comparative operations (>, <, =, BETWEEN) on the kind key if specified. Performing operations that aren’t coated by the above scheme requires using a scan operation, which is often executed by scanning over the whole DynamoDB desk in parallel. These scans could be gradual and costly by way of Learn Capability Models (RCUs) as a result of they require a full learn of the whole desk. Scans additionally are likely to decelerate when the desk measurement grows as there’s extra knowledge to scan to supply outcomes.
If we need to assist analytical queries with out encountering prohibitive scan prices, we will leverage secondary indexes in DynamoDB. Secondary indexes additionally consist of making partition keys and elective kind keys over fields that we need to question over in a lot the identical means as the first key. Secondary indexes are sometimes used to enhance utility efficiency by indexing fields that are queried fairly often. Question operations on secondary indexes will also be used to energy particular options by way of analytic queries which have clearly outlined necessities—like computing a leaderboard in a sport. One clear benefit of this strategy of performing analytical queries is that there isn’t a want for some other system.
Nonetheless, it’s infeasible to make use of this strategy for a wider vary of analytical queries due to the restricted varieties of queries it helps. The complete gamut of analytics requires filtering on a number of fields, grouping, ordering, becoming a member of knowledge between knowledge units, and so on., which can’t be achieved merely by way of secondary indexes. Secondary indexes that may be created are additionally restricted in quantity and require some planning to make sure that they scale properly with the info. A badly chosen partition key can worsen efficiency and enhance prices considerably. Information in DynamoDB can have a nested construction together with arrays and objects, however indexes can solely be constructed on sure primitive varieties. This may pressure denormalizing of the info to flatten nested objects and arrays with the intention to construct secondary indexes, which may doubtlessly explode the variety of writes carried out and related prices. Other than value and suppleness, there are additionally safety and efficiency concerns with regards to supporting analytic use instances on an operational knowledge retailer in a manufacturing atmosphere.
Benefits
- No further setup exterior DynamoDB
- Quick and scalable serving for fundamental analytical queries over listed fields
Disadvantages
- Costly when queries require scans over DynamoDB
- Very restricted assist for analytical queries over indexes; no SQL queries, grouping, or joins
- Can not arrange indexes on nested fields with out denormalizing knowledge and exploding out writes
- Safety and efficiency implications of working analytical queries on an operational database
This strategy could also be appropriate if we have now an utility that requires a selected function that’s easy sufficient to be realized utilizing a question over an index. The elevated storage and I/O value and the restricted question potential make it unsuitable for the broader vary of analytical queries in any other case. Due to this fact, for a majority of analytic use instances, it’s value efficient to export the info from DynamoDB into a unique system that enables us to question with increased constancy.
If you’re contemplating extracting knowledge to a different system, there are a number of totally different choices for real-time analytics:
- DynamoDB + Glue + S3 + Athena
- DynamoDB + Hive/Spark
- DynamoDB + AWS Lambda + Elasticsearch
- DynamoDB + Rockset
I evaluate every of those by way of ease of setup, upkeep, question functionality, latency in my different weblog publish Analytics on DynamoDB: Evaluating Athena, Spark and Elastic, the place I additionally consider which use instances every of them are finest fitted to.
Different DynamoDB sources:
