The Schedulers present in Scylla–namely the I/O Scheduler and the CPU scheduler are fully capable of enforcing resource usage between classes, like compaction and foreground reads, for instance. Classes with more shares will use more of the resources while classes with less shares, will use less.

Before the advent of the Controllers, those shares were statically determined at startup. And while that would guarantee fairness between classes, it would not guarantee that the classes were progressing at optimal speed.

Compactions could be too fast: this could be seen in compaction graphs when compaction was a visible peak. While fast compactions are nice, ee take a lot of resources away from the foreground requests to achieve that speed depressing latencies momentaneously. It would be better to run compactions more slowly in that case, turning the fast peak into a better distributed plateau.

But blindly making compactions slower can create huge compaction backlogs, depressing future reads. So we need to try to stay in the sweet spot.

We will keep track of the current backlog for compaction work. The backlog is an estimate of the amount of work that we still have to do in the future to compact everything. Because the various compaction strategies compact things very differently and have very different ideas about what it means for the data to be “fully compacted”, the backlogs are strategy-dependent.

Each Table (Column Family) keeps track of the backlog that it has, and those backlogs are then added together to create a system-wide backlog.

It is expected that the backlog measurement is consistent accross strategies. What this means is that if a strategy, at a certain point in time, has more work to do than another strategy, it should return a higher backlog.

Currently unimplemented strategy will return a fixed backlog. Our aim is to soon implement backlog controls for all classes.

Unimplemented

Unimplemented

Unimplemented

Backlog for one SSTable under STCS:

```
(1) Bi = Ei * log4 (T / Si),
```

where Ei is the effective size of the SStable, Si is the Size of this SSTable, and T is the total size of the Table.

To calculate the backlog, we can use the logarithm in any base, but we choose 4 as that is the historical minimum for the number of SSTables being compacted together. Although now that minimum could be lifted, this is still a good number of SSTables to aim for in a compaction execution.

T, the total table size, is defined as

```
(2) T = Sum(i = 0...N) { Si }.
```

Ei, the effective size, is defined as

```
(3) Ei = Si - Ci,
```

where Ci is the total amount of bytes already compacted for this table. For tables that are not under compaction, Ci = 0 and Si = Ei.

Using the fact that log(a / b) = log(a) - log(b), we rewrite (1) as:

```
Bi = Ei log4(T) - Ei log4(Si)
```

For the entire Table, the Aggregate Backlog (A) is

```
A = Sum(i = 0...N) { Ei * log4(T) - Ei * log4(Si) },
```

which can be expressed as a sum of a table component and a SSTable component:

```
A = Sum(i = 0...N) { Ei } * log4(T) - Sum(i = 0...N) { Ei * log4(Si) },
```

and if we define C = Sum(i = 0…N) { Ci }, then we can write

```
A = (T - C) * log4(T) - Sum(i = 0...N) { (Si - Ci)* log4(Si) }.
```

Because the SSTable number can be quite big, we’d like to keep iterations to a minimum. We can do that if we rewrite the expression above one more time, yielding:

```
(4) A = T * log4(T) - C * log4(T) - (Sum(i = 0...N) { Si * log4(Si) } - Sum(i = 0...N) { Ci * log4(Si) }
```

When SSTables are added or removed, we update the static parts of the equation, and every time we need to compute the backlog we use the most up-to-date estimate of Ci to calculate the compacted parts, having to iterate only over the SSTables that are compacting, instead of all of them.

For SSTables that are being currently written, we assume that they are a full SSTable in a certain point in time, whose size is the amount of bytes currently written. So all we need to do is keep track of them too, and add the current estimate to the static part of (4).