Making ZIO-Kafka Safer And Faster

My talk "Making ZIO-Kafka Safer And Faster" at Functional Scala 2023 went online!

Explore Erik van Oosten's presentation on improving ZIO-Kafka for better safety and performance. Learn about the modifications introduced in 2023, get insights into the library's internal workings, and uncover useful ZIO techniques and Kafka's lesser-known challenges.

Contents in the video:

2:07 Improvements
9:06 Results
10:29 Rebalances
18:10 The Future

Tips for running Roundcube for years

I have been running a Roundcube instance for about 8 years now. At the beginning I only used it as a backup email client that can be invoked from anywhere. Nowadays, is it so good that I didn't even bother installing Thunderbird on my work laptop.

Unfortunately, I discovered that the docker backup of Roundcube had become quite large, many GBs. This was quite unexpected for a service that is used by only 2 people. The reason was quickly found: the sqlite database was huge!

What did I know? I though ony Postgresql needed scheduled cleanups. Turns out sqlite needs it too! Would this be the reason Android phones tend to fill up over time?

Anyways, the fix was simple: run the vacuum command! So, now I have the following run daily using cron. Problem solved!

sqlite3 /paht/to/roundcubemail.sqlite 'VACUUM;'

Scheduling tasks and sharing state with streams

Recently we built a system that needs to perform 2 tasks. Taks 1 runs every 15 minutes, task 2 runs every 2 minutes. Task 1 kicks off some background jobs (an upload to BigQuery), task 2 checks upon the results of these background jobs and does some cleanup when they are done (delete the uploaded files). The two tasks need to share information back and forth about what jobs are running in the background.

Now think to yourself (ignore the title for now 😉), what would be the most elegant way to implement this? I suspect that most developers will come with a solution that involves some locking, synchronisation and global state. For example by sharing the information through a transactional database, or by using a semaphore to prevent the two tasks from running at the same time plus sharing information in a global variable. This is understandable, most programming environments do not provide better techniques for these kinds of problems at all!

However, if your environment supports streams and has some kind of scheduling, here are two tricks you can use: one for the scheduling of the tasks, the second for sharing information without a global variable.

Here is an example for the first written in Scala using the ZIO streams library. Read on for an explanation.

import zio._ import zio.stream._ def performTask1: Task[Unit] = ??? def performTask2: Task[Unit] = ??? // An enumeration (scala 2 style) for our task. sealed trait BusinessTask object Task1 extends BusinessTask object Task2 extends BusinessTask ZStream.mergeAllUnbounded()( ZStream.fromSchedule(Schedule.fixed(15.minutes)).as(Task1), ZStream.fromSchedule(Schedule.fixed(2.minutes)).as(Task2) ) .mapZIO { case Task1 => performTask1 case Task2 => performTask2 } .runDrain

We create 2 streams, each stream contains sequential numbers, emitted upon a schedule. As you can see, the schedule corresponds directly with the requirements. We do not really care for the sequential numbers, so with stream operator as we convert the stream's emitted values to a value from the BusinessTask enumeration.

Then we merge the two streams. We now have a stream that emits the two enumeration values at the time the corresponding task should run. This is already a big win! Even when the two schedules produce an item at the same time, the tasks will run sequentially. This is because by default streams are evaluated without parallelism.

We are not there yet though. The tasks need to share information. They could access a shared variable but then we still have tightly coupled components and no guarantees that the shared variable is used correctly.

Also, wouldn't it be great if performTask1 and performTask2 are functions that can be tested in isolation? With streams this is possible.

Here is the second part of the idea. Again, read on for an explanation.

case class State(...) val initialState = State(...) def performTask1(state: State): Task[State] = ??? def performTask2(state: State): Task[State] = ??? ZStream.mergeAllUnbounded()( ZStream.fromSchedule(Schedule.fixed(15.minutes)).as(Task1), ZStream.fromSchedule(Schedule.fixed(2.minutes)).as(Task2) ) .scanZIO(initialState) { (state, task) => task match { case Task1 => performTask1(state) case Task2 => performTask2(state) } } .runDrain

We have changed the signatures of the performTask* methods. Also, the mapZIO operator has been replaced with scanZIO. The stream operator scanZIO works much like foldLeft on collections. Like foldLeft, it accepts an initial state, and a function that combines the accumulated state plus the next stream element (of type BusinessTask) and converts those into the next state.

Stream operator scanZIO also emits the new states. This allows further common processing. For example we can persist the state to disk, or collect custom metrics about the state.

Conclusion

Using libraries with higher level constructs like streams, we can express straightforward requirements in a straightforward way. With a few lines of code we have solved the scheduling requirement, and showed an elegant way of sharing information between tasks without global variables.