A single Query operation will read up to the maximum number of items set (if using the Limit parameter) or a maximum of 1 MB of data and then apply any filtering to the results using FilterExpression.
You're 100% right that Limit is applied before FilterExpression. Meaning Dynamo might return some number or documents less than the Limit while other documents that satisfy the FilterExpression still exist in the table but aren't returned.
Its sounds like it would be unacceptable for your api to behave in the same manner. That is going to mean that in some cases, a single request to your service will result in multiple requests to Dynamo. Also, keep in mind that there is no way to predict what the LastEvaluatedKey
will be which would be required to parallelize these requests. So in the case that your service makes multiple requests to Dynamo, they will be serial. To me, this is a rather heavy tradeoff but, if it is a requirement that you satisfy the Limit whenever possible, you have options.
First, Dynamo will automatically page at 1 MB. That means you could simply send your query to Dynamo without a Limit and implement the Limit on your end. You may still need to make multiple requests to ensure that your've satisfied the Limit but this approach will result in the fewest number of requests to Dynamo. The trade off here is the total data being read and transferred. Chances are your Limit will not happen to line up perfectly with the 1 MB limit which means the excess data being read, filtered, and transferred is wasted.
You already mentioned the other extreme of sending a Limit of 1 and pointed out that will result in the maximum number of requests to Dynamo
Another approach along these lines is to create some sort of probabilistic function that takes the Limit given to your service by the client and computes a new Limit for Dynamo. For example, your FilterExpression filters out about half of the documents in the table. That means you can multiply the client Limit by 2 and that would be a reasonable Limit to send to Dynamo. Of the approaches we've talked about so far, this one has the highest potential for efficiency however, it also has the highest potential for complexity. For example, you might find that using a simple linear function is not good enough and instead you need to use machine learning to find a multi-variate non-linear function to calculate the new Limit. This approach also heavily depends on the uniformity of your data in Dynamo as well as your access patterns. Again, you might need machine learning to optimize for those variables.
In any of the cases where you are implementing the Limit on your end, if you plan on sending back the LastEvaluatedKey
to the client for subsequent calls to your service, you will also need to take care to keep track of the LastEvaluatedKey
that you evaluated. You will no longer be able to rely on the LastEvaluatedKey
returned from Dynamo.
The final approach would be to reorganize/regroup your data either with a GSI, a separate table that you keep in sync using Dynamo Streams or a different schema altogether with the goal of not requiring a FilterExpression.
LastEvaluatedKey
(and add count up while doing so). – mewa