On this submit I discover the right 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 method in distinction to extracting knowledge to a different system like Athena, Spark or Elastic.
Rockset 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 alternative ways during which international secondary indexes (GSI) are used for analytical queries.
DynamoDB shops knowledge below the hood by partitioning it over numerous nodes primarily based on a user-specified partition key area current in every merchandise. This user-specified partition key will be optionally mixed with a kind key to signify 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 kind key if specified. Performing operations that aren’t lined by the above scheme requires the usage of a scan operation, which is usually executed by scanning over the complete DynamoDB desk in parallel. These scans will be gradual and costly by way of Learn Capability Items (RCUs) as a result of they require a full learn of the complete 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 assist 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 approach 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 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 sorts 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 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 nicely with the information. A badly chosen partition key can worsen efficiency and improve 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 will pressure denormalizing of the information to flatten nested objects and arrays with the intention to construct secondary indexes, which might probably explode the variety of writes carried out and related prices. Other than value and adaptability, there are additionally safety and efficiency issues with regards to supporting analytic use instances on an operational knowledge retailer in a manufacturing setting.
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 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 skill make it unsuitable for the broader vary of analytical queries in any other case. Subsequently, for a majority of analytic use instances, it’s value efficient to export the information from DynamoDB into a unique system that permits 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 submit Analytics on DynamoDB: Evaluating Athena, Spark and Elastic, the place I additionally consider which use instances every of them are greatest suited to.
Different DynamoDB assets:
