# 0001 - Streaming Writes¶

## Context¶

To write streaming data (e.g. RDD[(K, V)]) to an S3 backend it is necessary to map over rdd partitions and to send multiple async PUT requests for all elements of a certain partition, it is important to synchronize these requests in order to be sure, that after calling a writer function all data was ingested (or at least attempted). Http status error 503 Service Unavailable requires resending a certain PUT request (with exponential backoff) due to possible network problems this error was caused by. Accumulo and Cassandra writers work in a similar fashion.

To handle this situation we use the Task abstraction from Scalaz, which uses it’s own Future implementation. The purpose of this research is to determine the possibility of removing the heavy Scalaz dependency. In a near future we will likely depend on the Cats library, which is lighter, more modular, and covers much of the same ground as Scalaz. Thus, to depend on Scalaz is not ideal.

## Decision¶

We started by a moving from Scalaz Task to an implementation based on the scala standard library Future abstraction. Because List[Future[A]] is convertable to Future[List[A]] it was thought that this simpler home-grown solution might be a workable alternative.

Every Future is basically some calculation that needs to be submitted to a thread pool. When you call (fA: Future[A]).flatMap(a => fB: Future[B]), both Future[A] and Future[B] need to be submitted to the thread pool, even though they are not running concurrently and could run on the same thread. If Future was unsuccessful it is possible to define recovery strategy (in case of S3 it is neccesary).

We faced two problems: difficulties in Future synchronization (Future.await) and in Future delay functionality (as we want an exponential backoff in the S3 backend case).

We can await a Future until it’s done (Duration.Inf), but we can not be sure that Future was completed exactly at this point (for some reason - this needs further investigation - it completes a bit earlier/later).

Having a threadpool of Futures and having some List[Future[A], awaiting of these Futures does not guarantees completeness of each Future of a threadpool. Recovering a Future we produce a new Future, so that recoved Futures and recursive Futures are new Futures in the same threadpool. It isn’t obvious how to await all necessary Futures. Another problem is delayed Futures, in fact such behaviour can only be achieved by creating blocking Futures. As a workaround to such a situation, and to avoid blocking Futures, it is possible to use a Timer, but in fact that would be a sort of separate Future pool.

Let’s observe Scalaz Task more closely, and compare it to native scala Futures. With Task we recieve a bit more control over calculations. In fact Task is not a concurrently running computation, it’s a description of a computation, a lazy sequence of instructions that may or may not include instructions to submit some of calculations to thread pools. When you call (tA: Task[A]).flatMap(a => tB: Task[B]), the Task[B] will by default just continue running on the same thread that was already executing Task[A]. Calling Task.fork pushes the task into the thread pool. Scalaz Tasks operates with their own Future implementation. Thus, having a stream of Tasks provides more control over concurrent computations.

Some implementations were written, but each had synchronization problems. This attempt to get rid of the Scalaz dependency is not as trival as we had anticipated.

This is not a critical decision and, if necessary, we can come back to it later.

## Consequences¶

All implementations based on Futures are non-trival, and it requires time to implement a correct write stream based on native Futures. Here are the two simplest and most transparent implementation variants, but both have synchronization problems.

Scalaz Tasks seem to be better suited to our needs. Tasks run on demand, and there is no requirement of instant submission of Tasks into a thread pool. As described above, Task is a lazy sequence of intructions and some of them could submit calculations into a thread pool. Currently it makes sense to depend on Scalaz.