Azure Cosmos DB 的重写分区键策略

Question

We're using CosmosDB in production to store HTTP request/response audit data. The structure of this data generally looks as follows:

{
    "id": "5ff4c51d3a7a47c0b5697520ae024769",
    "Timestamp": "2019-06-27T10:08:03.2123924+00:00",
    "Source": "Microservice",
    "Origin": "Client",
    "User": "SOME-USER",
    "Uri": "GET /some/url",
    "NormalizedUri": "GET /SOME/URL",
    "UserAgent": "okhttp/3.10.0",
    "Client": "0.XX.0-ssffgg;8.1.0;samsung;SM-G390F",
    "ClientAppVersion": "XX-ssffgg",
    "ClientAndroidVersion": "8.1.0",
    "ClientManufacturer": "samsung",
    "ClientModel": "SM-G390F",
    "ResponseCode": "OK",
    "TrackingId": "739f22d01987470591556468213651e9",
    "Response": "[ REDACTED ],   <— Usually quite long (thousands of chars)
    "PartitionKey": 45,
    "InstanceVersion": 1,
    "_rid": "TIFzALOuulIEAAAAAACACA==",
    "_self": "dbs/TIFzAA==/colls/TIFzALOuulI=/docs/TIFzALOuulIEAAAAAACACA==/",
    "_etag": "\"0d00c779-0000-0d00-0000-5d1495830000\"",
    "_attachments": "attachments/",
    "_ts": 1561630083
}

We're currently writing around 150,000 - 200,000 of documents similar to the above a day with /PartitionKey as the partition key path that's configured on the container. The value of the PartitionKey is a randomly generated number in C#.net between 0 and 999.

However, we are seeing daily hotspots where a single physical partition can hit a max of 2.5K - 4.5K RU/s and others are very low (around 200 RU/s). This has a knock on to cost implications as we need to provision throughput for our largest utilised partition.

The second factor is we're storing a fair bit of data, close to 1TB of documents, and we add a few GB each day. As a result we have currently have around 40 physical partitions.

Combining these two factors means we end up having to provision for at minimum somewhere between 120,000 - 184,000 RU/s.

I should mention that we barely ever need to query this data; apart from very occasional for ad-hoc manually constructed queries in Cosmos data explorer.

My question is... would we be a lot better off in terms of RU/s required and distribution of data by simply using the “id” column as our partition key (or a randomly generated GUID) - and then setting a sensible TTL so we don't have a continually growing dataset?

I understand this would require us to re-create the collection.

Thanks very much.

Max throughput per physical partition

Answer 1

While using the id or a GUID would give you better cardinality than the random number you have today, any query you run would be very expensive as it would always be cross-partition and over a huge amount of data.

I think a better choice would be to use a synthetic key that combines multiple properties that both have high cardinality and also are used to query for the data. Can learn more about these here, https://docs.microsoft.com/en-us/azure/cosmos-db/synthetic-partition-keys

As far as TTL I would definitely set that for whatever retention you need for this data. Cosmos will TTL the data off with unused throughput so will never get in the way.

Lastly, you should also consider (if you haven't already) using a custom indexing policy and exclude any paths which are never queried for. Especially the "response" property since you say it is thousands of characters long. This can save considerable RU/s in write-heavy scenarios like yours.

Answer 2

From my experience what I see is cosmos tends to degrade with new data. More data mean more physical partitons. So you meed more throughput to be allocated to each of them . Currently we are starting to archive old data into blob storage to avoid this kind of problems and keep the number of physical partition unchangeable. We use cosmos as hot storage and then the old data go to blobs storage as cold storage. We reduce RU allocated to each physical partitions and we save money.