max number of couchbase views per bucket

Question

How many views per bucket is too much, assuming a large amount of data in the bucket (>100GB, >100M documents, >12 document types), and assuming each view applies only to one document type? Or asked another way, at what point should some document types be split into separate buckets to save on the overhead of processing all views on all document types?

I am having a hard time deciding how to split my data into couchbase buckets, and the performance implications of the views required on the data. My data consists of more than a dozen relational DBs, with at least half with hundreds of millions of rows in a number of tables.

The http://www.couchbase.com/docs/couchbase-manual-2.0/couchbase-views-writing-bestpractice.html doc section "using document types" seems to imply having multiple document types in the same bucket is not ideal because views on specific document types are updated for all documents, even those that will never match the view. Indeed, it suggests separating data into buckets to avoid this overhead.

Yet there is a limit of 10 buckets per cluster for performance reasons. My only conclusion therefore is that each cluster can handle a maximum of 10 large collections of documents efficiently. Is this accurate?

Answer 1

Tug's advice was right on and allow me to add some perspective as well.

A bucket can be considered most closely related to (though not exactly) a "database instantiation" within the RDMS world. There will be multiple tables/schemas within that "database" and those can all be combined within a bucket.

Think about a bucket as a logical grouping of data that all shares some common configuration parameters (RAM quota, replica count, etc) and you should only need to split your data into multiple buckets when you need certain datasets to be controlled separately. Other reasons are related to very different workloads to different datasets or the desire to be able to track the workload to those datasets separately.

Some examples:

-I want to control the caching behavior for one set of data differently than another. For instance, many customers have a "session" bucket that they want always in RAM whereas they may have a larger, "user profile" bucket that doesn't need all the data cached in RAM. Technically these two data sets could reside in one bucket and allow Couchbase to be intelligent about which data to keep in RAM, but you don't have as much guarantee or control that the session data won't get pushed out...so putting it in its own bucket allows you to enforce that. It also gives you the added benefit of being able to monitor that traffic separately.

-I want some data to be replicated more times than others. While we generally recommend only one replica in most clusters, there are times when our users choose certain datasets that they want replicated an extra time. This can be controlled via separate buckets.

-Along the same lines, I only want some data to be replicated to another cluster/datacenter. This is also controlled per-bucket and so that data could be split to a separate bucket.

-When you have fairly extreme differences in workload (especially around the amount of writes) to a given dataset, it does begin to make sense from a view/index perspective to separate the data into a separate bucket. I mention this because it's true, but I also want to be clear that it is not the common case. You should use this approach after you identify a problem, not before because you think you might.

Regarding this last point, yes every write to a bucket will be picked up by the indexing engine but by using document types within the JSON, you can abort the processing for a given document very quickly and it really shouldn't have a detrimental impact to have lots of data coming in that doesn't apply to certain views. If you don't mind, I'm particularly curious at which parts of the documentation imply otherwise since that certainly wasn't our intention.

So in general, we see most deployments with a low number of buckets (2-3) and only a few upwards of 5. Our limit of 10 comes from some known CPU and disk IO overhead of our internal tracking of statistics (the load or lack thereof on a bucket doesn't matter here). We certainly plan to reduce this overhead with future releases, but that still wouldn't change our recommendation of only having a few buckets. The advantages of being able to combine multiple "schemas" into a single logical grouping and apply view/indexes across that still exist regardless.

We are in the process right now of coming up with much more specific guidelines and sizing recommendations (I wrote those first two blogs as a stop-gap until we do).

As an initial approach, you want to try and keep the number of design documents around 4 because by default we process up to 4 in parallel. You can increase this number, but that should be matched by increased CPU and disk IO capacity. You'll then want to keep the number of views within each document relatively low, probably well below 10, since they are each processed in serial.

I recently worked with one user who had an fairly large amount of views (around 8 design documents and some dd's with nearly 20 views) and we were able to drastically bring this down by combining multiple views into one. Obviously it's very application dependent, but you should try to generate multiple different "queries" off of one index. Using reductions, key-prefixing (within the views), and collation, all combined with different range and grouping queries can make a single index that may appear crowded at first, but is actually very flexible.

The less design documents and views you have, the less disk space, IO and CPU resources you will need. There's never going to be a magic bullet or hard-and-fast guideline number unfortunately. In the end, YMMV and testing on your own dataset is better than any multi-page response I can write ;-)

Hope that helps, please don't hesitate to reach out to us directly if you have specific questions about your specific use case that you don't want published.

Perry