On this publish I discover how you can assist analytical queries with out encountering prohibitive scan prices, by leveraging secondary indexes in DynamoDB. I additionally consider the professionals and cons of this method in distinction to extracting information to a different system like Athena, Spark or Elastic.

Rockset just lately added assist for DynamoDB – which mainly means you possibly can run quick SQL on DynamoDB tables with none ETL. As I spoke to our customers, I got here throughout alternative ways during which world secondary indexes (GSI) are used for analytical queries.

DynamoDB shops information beneath the hood by partitioning it over a lot of nodes primarily based on a user-specified partition key subject current in every merchandise. This user-specified partition key could be optionally mixed with a kind key to symbolize a main 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 type key if specified. Performing operations that aren’t lined by the above scheme requires the usage of a scan operation, which is often executed by scanning over the complete DynamoDB desk in parallel. These scans could be gradual and costly when it comes to Learn Capability Items (RCUs) as a result of they require a full learn of the complete desk. Scans additionally are inclined to decelerate when the desk dimension grows as there may be extra information to scan to provide outcomes.

If we wish 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 wish to question over in a lot the identical method 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 means of analytic queries which have clearly outlined necessities—like computing a leaderboard in a recreation. One clear benefit of this method of performing analytical queries is that there isn’t any want for some other system.

Nonetheless, it’s infeasible to make use of this method for a wider vary of analytical queries due to the restricted varieties of queries it helps. The total gamut of analytics requires filtering on a number of fields, grouping, ordering, becoming a member of information between information units, and so on., which can’t be achieved merely by means of secondary indexes. Secondary indexes that may be created are additionally restricted in quantity and require some planning to make sure that they scale nicely with the information. 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 could drive denormalizing of the information to flatten nested objects and arrays to be able to construct secondary indexes, which might probably explode the variety of writes carried out and related prices. Aside from value and adaptability, there are additionally safety and efficiency concerns in relation to supporting analytic use instances on an operational information retailer in a manufacturing atmosphere.

Benefits

• No extra 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’t arrange indexes on nested fields with out denormalizing information and exploding out writes
• Safety and efficiency implications of operating analytical queries on an operational database

This method could also be appropriate if we have now an utility that requires a particular 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 information from DynamoDB into a unique system that enables us to question with greater constancy.

If you’re contemplating extracting information to a different system, there are a number of totally different choices for real-time analytics:

1. DynamoDB + Glue + S3 + Athena
2. DynamoDB + Hive/Spark
3. DynamoDB + AWS Lambda + Elasticsearch
4. 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 assets: