On this submit 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 lately added assist for DynamoDB – which principally means you may run quick SQL on DynamoDB tables with none ETL. As I spoke to our customers, I got here throughout other ways wherein world secondary indexes (GSI) are used for analytical queries.
DynamoDB shops information underneath 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 will 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 type key if specified. Performing operations that aren’t coated by the above scheme requires using a scan operation, which is usually executed by scanning over the whole DynamoDB desk in parallel. These scans will be sluggish and costly when it comes to Learn Capability Items (RCUs) as a result of they require a full learn of the whole desk. Scans additionally are inclined to decelerate when the desk measurement grows as there’s 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 optionally available type 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 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.
Nevertheless, it’s infeasible to make use of this method for a wider vary of analytical queries due to the restricted forms 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 forth., 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 effectively with the information. A badly chosen partition key can worsen efficiency and improve prices considerably. Information in DynamoDB can have a nested construction together with arrays and objects, however indexes can solely be constructed on sure primitive sorts. This could power denormalizing of the information to flatten nested objects and arrays with a purpose to construct secondary indexes, which might probably explode the variety of writes carried out and related prices. Other than price and adaptability, there are additionally safety and efficiency concerns on the subject of supporting analytic use circumstances on an operational information retailer in a manufacturing surroundings.
Benefits
- No extra setup exterior DynamoDB
- Quick and scalable serving for primary 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 working analytical queries on an operational database
This method could also be appropriate if we’ve got 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 price and the restricted question capacity make it unsuitable for the broader vary of analytical queries in any other case. Due to this fact, for a majority of analytic use circumstances, it’s price efficient to export the information from DynamoDB into a special system that enables us to question with larger constancy.
In case you are contemplating extracting information 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 when it comes to 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 circumstances every of them are finest suited to.
Different DynamoDB sources:
