On this submit I discover the right way 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 method in distinction to extracting knowledge to a different system like Athena, Spark or Elastic.
Rockset lately added support for DynamoDB – which mainly means you possibly can run fast SQL on DynamoDB tables with none ETL. As I spoke to our users, I got here throughout other ways wherein international secondary indexes (GSI) are used for analytical queries.
DynamoDB shops knowledge 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 might be optionally mixed with a form 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 type 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 by way of Learn Capability Items (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 wish to help analytical queries with out encountering prohibitive scan prices, we are able to leverage secondary indexes in DynamoDB. Secondary indexes additionally consist of making partition keys and non-compulsory kind keys over fields that we wish to question over in a lot the identical manner 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 may also be used to energy particular options by 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 another 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 knowledge between knowledge units, and so forth., 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 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 sorts. This may power denormalizing of the information to flatten nested objects and arrays so as 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 issues with regards to supporting analytic use circumstances on an operational knowledge retailer in a manufacturing surroundings.
Benefits
- No extra setup outdoors DynamoDB
- Quick and scalable serving for primary 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 method could also be appropriate if we now have 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 capacity make it unsuitable for the broader vary of analytical queries in any other case. Subsequently, for a majority of analytic use circumstances, 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 knowledge to a different system, there are a number of completely 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 submit Analytics on DynamoDB: Comparing Athena, Spark and Elastic, the place I additionally consider which use circumstances every of them are finest fitted to.
Different DynamoDB sources:
