Background
The single table design for DynamoDB simplifies the structure required for storing information in DynamoDB. As a substitute of getting a number of tables for every file kind you’ll be able to mix the several types of information 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 enables customers to mix PK because the partition key, SK as the kind key with the mixture of the 2 changing into a composite major key. Widespread columns can be utilized throughout file varieties like a outcomes column or information column that shops nested JSON. Or the completely different file varieties 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 may think about a two desk mannequin like the next:
and
Whereas neither of those information fashions is definitely an excellent instance of correct information 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.
The way to use the one desk mannequin in Rockset
Rockset is a real-time analytics database that’s typically used along with DynamoDB. It syncs with information in DynamoDB to supply a straightforward solution 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 make use of RCUs to scan the DynamoDB desk, and as soon as the preliminary scan is full Rockset tails DynamoDB streams. The opposite methodology 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 methodology is used for when tables are very small, < 5GB, and the second is rather more performant and works for bigger DynamoDB tables. Both methodology is acceptable for the one desk methodology.
Reminder: Rollups can’t be used on DDB.
As soon as the combination is ready up you may have a couple of choices to think about when configuring the Rockset collections.
Methodology 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’ll have a SQL transformation that appears like:
-- new_collection
choose i.* from _input i
And you’ll construct two views on high of the gathering.
-- consumer view
Choose c.* from new_collection c the place c.SK = 'Consumer';
and
--class view
choose c.* from new_collection c the place c.SK='Class';
That is the best method and requires the least quantity of data in regards to the tables, desk schema, sizes, entry patterns, and so on. Usually for smaller tables, we begin right here. Reminder: views are syntactic sugar and won’t materialize information, so that they should be processed like they’re a part of the question for each execution of the question.
Methodology 2: Clustered Assortment and Views
This methodology is similar to the primary methodology, 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, the whole assortment should be scanned as a result of there isn’t a precise separation of knowledge within the column index. Clustering could 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 will likely be greater for clustered collections vs non-clustered collections. The benefit is queries might be a lot sooner.
The views will look the identical as earlier than:
-- consumer view
Choose c.* from new_collection c the place c.SK = 'Consumer';
and
--class view
choose c.* from new_collection c the place c.SK='Class';
Methodology 3: Separate Collections
One other methodology to think about when constructing collections in Rockset from a DynamoDB single desk mannequin is to create a number of collections. This methodology requires extra setup upfront than the earlier two strategies however offers 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 enables us to run queries with out implementing clustering, or implement clustering inside a person SK.
-- Consumer assortment
Choose i.* from _input i the place i.SK='Consumer';
and
-- Class assortment
Choose i.* from _input i the place i.SK='Class';
This methodology doesn’t require views as a result of the info 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 need to do a separate export and stream from DynamoDB for every assortment you need to create.
Methodology 4: Mixture of Separate Collections and Clustering
The final methodology to debate is the mixture of the earlier strategies. Right here you’ll escape 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='Consumer';
and
-- combined_collection
choose i.* from _input i the place i.SK != 'Consumer' 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 protecting your assortment dimension 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 essentially the most flexibility for question efficiency. This feature does include essentially the most operational overhead to setup, monitor, and preserve.
Conclusion
Single desk design is a well-liked information modeling approach in DynamoDB. Having supported quite a few DynamoDB customers via 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 in your particular use case.
