Performance Isolation in Scylla¶
A Scylla shard does many different things in parallel. For example, as a coordinator it processes the user’s CQL requests and sends them to replicas. As a replica, it executes read and write requests. Write requests are stored in memory (memtables) which is eventually flushed to sstables in a background process. When these sstables become too numerous, we compact them. And there are various other background operations, such as repair, gossip, etc.
The performance isolation mechanisms described in this document have two goals. The first goal is to isolate the performance of each component from the others. In other words, the throughput and latency of one component should not depend on implementation details of another component such as its degree of parallelism or the amount of I/O it does. The second goal is that Scylla be able to control this isolation, and determine how big a share of the resources is given to each of these components.
As an example of such control consider compaction. Scylla wants to ensure that the background compaction work receives enough resources to complete previous compaction tasks before new ones appear (as additional data gets written). On the other hand, it doesn’t want to do compactions faster than necessary, because this will cause performance fluctuations for user queries (query performance is low while a compaction is proceeding quickly, and then becomes high when there is nothing left to compact).
To implement performance isolation, Scylla uses existing Seastar isolation features such as scheduling groups (for CPU-time isolation) and I/O priority classes (for disk work isolation). These Seastar features will not be described in detail here, and the reader is assumed to be familiar with them or to refer to Seastar’s documentation. The focus of this document is how Scylla chose to use these features to implement isolation.
Scheduling groups (CPU scheduler)¶
Scylla defines the following Seastar scheduling groups. The groups have a global scope (i.e., not per-table, per-keyspace, etc.). They are created in
main.cc, and saved in a
database_config structure of the database object (see
The groups currently defined are:
TODO: explain the purpose each of each of these scheduling groups, and what they are used for. E.g., “streaming” is also called maintenance and used also used for repair. memtable is used for memtable flushes (?). default is used for gossip, etc.
The “Default shares” is the initial number of shares given to each scheduling group. They can be later modified by controllers, which aim to discover when a certain component needs to run faster because it is not keeping up - or run slower because it is finishing more quickly than it needs and causing performance to fluctuate. See the “Controllers” section below.
RPC: TODO: explain commit 8c993e0728508728c2d5cb0c4323728831d84890.
I/O Priority classes (I/O scheduler)¶
Ideally, we would have liked to use the same list of I/O priority classes (which is Seastar’s term for disk-I/O isolation groups) as the aforementioned list of CPU scheduling groups. But because Seastar separates CPU and I/O groups, and because of historical accident, we actually have a different list. This list is defined in
service/priority_manager.hh. It currently includes:
TODO: explain each of these priority classes and what it is used for. TODO: Do controllers also modify these shares? RPC: TODO: are I/O priority classes also inherited there?
TODO: mention that the more groups we have, the higher worst-case latency. To mitigate this, we should switch to nested groups, where two nested groups in statement compete with each other but not with compaction (for example).
TODO: list the controllers we have, and which shares each one changes, and how.
See also: [compaction_controller.md]
Per user performance isolation¶
We do not yet support multi-tenancy, in the sense that different tenants of the same server get isolated performance guarantees. When we do support this, it will need to be documented here.