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On this publish I discover methods to help 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 not too long ago added help for DynamoDB – which mainly means you may run quick SQL on DynamoDB tables with none ETL. As I spoke to our customers, I got here throughout other ways during which world secondary indexes (GSI) are used for analytical queries.
DynamoDB shops knowledge underneath the hood by partitioning it over a lot of nodes primarily based on a user-specified partition key area current in every merchandise. This user-specified partition key might be optionally mixed with a form key to signify 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 your entire DynamoDB desk in parallel. These scans might be sluggish and costly when it comes to Learn Capability Models (RCUs) as a result of they require a full learn of your entire desk. Scans additionally are likely to decelerate when the desk dimension grows as there’s extra knowledge to scan to supply outcomes.
If we need to help 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 manner as the first key. Secondary indexes are sometimes used to enhance software efficiency by indexing fields that are queried fairly often. Question operations on secondary indexes will also be used to energy particular options by 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 every 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 secondary indexes. Secondary indexes that may be created are additionally restricted in quantity and require some planning to make sure that they scale effectively with the info. A badly chosen partition key can worsen efficiency and enhance prices considerably. Knowledge 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 as a way to construct secondary indexes, which might doubtlessly explode the variety of writes carried out and related prices. Other than price and suppleness, there are additionally safety and efficiency issues 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 help for analytical queries over indexes; no SQL queries, grouping, or joins
- Can’t arrange indexes on nested fields with out denormalizing knowledge and exploding out writes
- Safety and efficiency implications of operating analytical queries on an operational database
This strategy could also be appropriate if we now have an software that requires a selected function that’s easy sufficient to be realized utilizing a question over an index. The elevated storage and I/O price and the restricted question skill 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 price efficient to export the info from DynamoDB into a unique system that permits us to question with greater 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 examine every of those when it comes to 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:
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