在没有全表扫描的情况下在 Dynamo DB 中随机抽样大小 N

Question

I am new to dynamodb & was having some trouble in finding a way to randomly getting items without a full table scan,most of the algorithms that i found consist of full table scans I am also taking the case where we don't have additional information of the table(Like columns and column Type such info is unknown) Is there a way exist to do so

Answer 1

You can randomly sample by using a randomly generated exclusive start key for the scan or query operation. The exclusive start key does not have to match a record in the table. It just needs to follow the key structure of the table/index.

Answer 2

As with most questions about queries in DynamoDB, how you structure your data depends on how you want to query it.

For something like a random sampling, you have to make it confirm to the following core constraint of DynamoDB:

• You have to provide a partition key
• You can provide a sort key

So with a "single table" type design, you could structure your data something like this:

PK	SK	myVal
my_dict	6caaf1e3-eb8d-404a-a2ae-97d6682b0224	foo
my_dict	1c5496e8-c660-4b4e-980f-4abfb1942863	bar
my_dict	56551340-fff8-4824-a5be-70fcaece2e1a	baz
my_other_dict	520a7b37-233c-49dd-87da-77d871d98c92	test1
my_other_dict	65ccd54e-72c3-499d-a3a7-0cd989252607	test2

The PK is the identifier for your collection of random things to look up. The SK is a random UUID. And myVal contains the value you want to be returned.

You can query this db the following way:

SELECT * FROM "my-table" WHERE PK = 'my_dict' AND SK < '06a04e20-b239-48f2-a205-552eb61fef35'

By querying with an UUID as the SK, you'll get the first item in the table with an UUID close to the one you query for. By using a random uuid each time you query, you'll get a random result back.

The particular query above actually returns nothing, so you need to retry until you get a result.

Also, I haven't done the math (who has?), but I'd imagine that periodic queries like this won't generate perfectly random distributions, especially for small data sets.