Background

The single desk design for DynamoDB simplifies the structure required for storing knowledge in DynamoDB. As an alternative of getting a number of tables for every report kind you’ll be able to mix the several types of knowledge right into a single desk. This works as a result of DynamoDB is ready to retailer very vast tables with various schema. DynamoDB additionally helps nested objects. This permits customers to mix PK because the partition key, SK as the type key with the mix of the 2 turning into a composite major key. Widespread columns can be utilized throughout report sorts like a outcomes column or knowledge column that shops nested JSON. Or the completely different report sorts can have completely completely different columns. DynamoDB helps each fashions, and even a mixture of shared columns and disparate columns. Oftentimes customers following the one desk mannequin will use the PK as a major key inside an SK which works as a namespace. An instance of this:

Discover that the PK is similar for each information, however the SK is completely different. You might think about a two desk mannequin like the next:

and

Whereas neither of those knowledge fashions is definitely a very good instance of correct knowledge modeling, the instance nonetheless represents the concept. The one desk mannequin makes use of PK as a major Key throughout the namespace of an SK.

How one can use the one desk mannequin in Rockset

Rockset is a real-time analytics database that’s usually used along side DynamoDB. It syncs with knowledge in DynamoDB to supply a simple strategy to carry out queries for which DynamoDB is much less suited. Study extra in Alex DeBrie’s weblog on DynamoDB Filtering and Aggregation Queries Utilizing SQL on Rockset.

Rockset has 2 methods of making integrations with DynamoDB. The primary is to use RCUs to scan the DynamoDB desk, and as soon as the preliminary scan is full Rockset tails DynamoDB streams. The opposite technique makes use of DynamoDB export to S3 to first export the DynamoDB desk to S3, carry out a bulk ingestion from S3 after which, after export, Rockset will begin tailing the DynamoDB streams. The primary technique is used for when tables are very small, < 5GB, and the second is far more performant and works for bigger DynamoDB tables. Both technique is acceptable for the one desk technique.

Reminder: Rollups can’t be used on DDB.

As soon as the mixing is ready up you’ve got a couple of choices to think about when configuring the Rockset collections.

Technique 1: Assortment and Views

The primary and easiest is to ingest all the desk right into a single assortment and implement views on high of Rockset. So within the above instance you’d have a SQL transformation that appears like:

-- new_collection
choose i.* from _input i

And you’d construct two views on high of the gathering.

-- person view
Choose c.* from new_collection c the place c.SK = 'Person';

and

--class view
choose c.* from new_collection c the place c.SK='Class';

That is the only strategy and requires the least quantity of data in regards to the tables, desk schema, sizes, entry patterns, and many others. Usually for smaller tables, we begin right here. Reminder: views are syntactic sugar and won’t materialize knowledge, so that they have to be processed like they’re a part of the question for each execution of the question.

Technique 2: Clustered Assortment and Views

This technique is similar to the primary technique, besides that we’ll implement clustering when making the gathering. With out this, when a question that makes use of Rockset’s column index is run, your entire assortment have to be scanned as a result of there isn’t any precise separation of knowledge within the column index. Clustering may have no impression on the inverted index.

The SQL transformation will appear like:

-- clustered_collection
choose i.* from _input i cluster by i.SK

The caveat right here is that clustering does eat extra assets for ingestion, so CPU utilization might be larger for clustered collections vs non-clustered collections. The benefit is queries could be a lot quicker.

The views will look the identical as earlier than:

-- person view
Choose c.* from new_collection c the place c.SK = 'Person';

and

--class view
choose c.* from new_collection c the place c.SK='Class';

Technique 3: Separate Collections

One other technique to think about when constructing collections in Rockset from a DynamoDB single desk mannequin is to create a number of collections. This technique requires extra setup upfront than the earlier two strategies however provides appreciable efficiency advantages. Right here we’ll use the the place clause of our SQL transformation to separate the SKs from DynamoDB into separate collections. This permits us to run queries with out implementing clustering, or implement clustering inside a person SK.

-- Person assortment
Choose i.* from _input i the place i.SK='Person';

and

-- Class assortment
Choose i.* from _input i the place i.SK='Class';

This technique doesn’t require views as a result of the information is materialized into particular person collections. That is actually useful when splitting out very giant tables the place queries will use mixes of Rockset’s inverted index and column index. The limitation right here is that we’re going to must do a separate export and stream from DynamoDB for every assortment you wish to create.

Technique 4: Mixture of Separate Collections and Clustering

The final technique to debate is the mix of the earlier strategies. Right here you’d get away giant SKs into separate collections and use clustering and a mixed desk with views for the smaller SKs.

Take this dataset:

You may construct two collections right here:

-- user_collection
choose i.* from _input i the place i.SK='Person';

and

-- combined_collection
choose i.* from _input i the place i.SK != 'Person' Cluster By SK;

After which 2 views on high of combined_collection:

-- class_view
choose * from combined_collection the place SK='Class';

and

-- transportation_view
choose * from combined_collection the place SK='Transportation';

This provides you the advantages of separating out the massive collections from the small collections, whereas holding your assortment measurement smaller, permitting different smaller SKs to be added to the DynamoDB desk with out having to recreate and re-ingest the collections. It additionally permits probably the most flexibility for question efficiency. This feature does include probably the most operational overhead to setup, monitor, and preserve.

Conclusion

Single desk design is a well-liked knowledge modeling method in DynamoDB. Having supported quite a few DynamoDB customers by way of the event and productionization of their real-time analytics purposes, we have detailed a number of strategies for organizing your DynamoDB single desk mannequin in Rockset, so you’ll be able to choose the design that works finest to your particular use case.