Type refinement is all about making the types more precise. But why would do that? Because using the correct types makes your program safer as you reduce the possibility to introduce bugs.
First let’s think about types. For instance
String is a type we use all the time. A variable of type
String can have many different values (in theory an infinity of values) but it’s quite unlikely that all these values make sense in our application. Continue reading “Refined types, what are they good for?”
As we’ve seen in this comparison with Apache Cassandra, DynamoDB may be a valuable choice for storing data. It’s fully maintained by AWS you just have to configure your instance and you can start using it straight away.
Being one of the Amazon web services DynamoDB offers a JSON/HTTP interface and Amazon provides a set of client for a variety of language. Unfortunately Scala is not one of them but there is a Java client available. Sadly it suffers from big performance issues due to poor design choices.
So what are the alternatives? This is what we’re going to see in this post: How can we get the most out of the official driver and are there better alternatives both in terms of performance or integration with the Scala ecosystem. Continue reading “Querying DynamoDB from Scala”
Cassandra and DynamoDB both origin from the same paper: Dynamo: Amazon’s Highly Available Key-value store. (By the way – it has been a very influential paper and set the foundations for several NoSQL databases).
Of course it means that DynamoDB and Cassandra have a lot in common! (They have the same DNA). However both AWS DynamoDB and Apache Cassandra have evolved quite a lot since this paper was written back in 2007 and there are now some key differences to be aware of when choosing between the two.
This post aims at comparing these 2 distributed databases so that you can choose the one that best matches your requirements. Continue reading “Amazon DynamoDB vs Apache Cassandra”
Back from holidays let’s continue with some of my favourite topics: AkkaStreams and gRPC.
We’ve already seen how can take advantage of the ScalaPB code generation tool to generate new interfaces (GRPCMonix) on top of the grpc-java implementation or to create new tools to integrate gRPC with other services (GRPCGateway).
Similarly to GRPCMonix which provides a Monix interface – Task, Observable – on top of gRPC, it’s possible to develop an AkkaStream interface on top of gRPC. Continue reading “Akka stream interface for gRPC”
In this previous post we’ve seen that before using Scala’s Future it might be worth taking some time to think of the use cases (especially error cases), the execution model we need, … as it might be more advantageous to choose a solution like Monix’s
Task (although not available in standard library) to gain finer control over the execution. However some might not be able to make a decision at this stage and like to keep their options open. Let’s see how we can revisit our product repository in such way that we don’t have to make a decision too early.
Continue reading “Introduction to Tagless final”
In this previous post we saw how Scala Futures work and why they need an implicit ExecutionContext to run their computation. While there is some trick to pass the ExecutionContext, it’s usually cumbersome and clutter the code.
So the question really is: Do you need an ExecutionContext everywhere? Well, you do as long as you use Futures, but is there any alternatives? Continue reading “Scala Futures vs Monix Tasks”
Viktor Klang recently published a set of useful tips on Scala Futures. While being widespread and heavily used, people (especially newcomers) are still experiencing problems working with Scala Futures.
In my opinion many of the problems come from a misunderstanding or misconception on how Futures work. (e.g. the strict nature of Futures and the way they interact with Execution Contexts to name a few).
That’s enough of an excuse to dive into the Scala
ExecutionContext implementation. Continue reading “Understanding Scala Futures and Execution Contexts”
In the previous post we’ve seen how to implement a gRPC service in Scala. While gRPC is a great way to implement remote services, many client/server interactions are still implemented using REST/HTTP nowadays.
So the question is: Is it possible to use gRPC to define and implement services and make them available over REST at the same time?
Well, it turns out it’s possible. The translation from protobuf to JSON format is quite straightforward. The only thing left is to define associated endpoints for each of the gRPC calls. Here again it can be easily done along with the gRPC definition using the HTTP annotations from Google.
This is exactly what GRPC-Gateway provides. While GRPC-Gateway is implemented in Go it can run along with any gRPC implementation as it only relies on the protobuf/gRPC definitions (.proto files). It then spawns up an HTTP proxy that translates and forwards REST (HTTP/JSON) calls to a gRPC server.
Fortunately ScalaPB‘s code generation makes it possible to implement the same functionalities in Scala. Continue reading “Publishing gRPC services over REST/HTTP with gRPC Gateway”
As many might think, gRPC doesn’t stand for “google Remote Procedure Call” but is a recursive acronym meaning “gRPC Remote Procedure Call”. I don’t know if you buy it but the truth is that is was originally developed by Google and then open-sourced.
If you’ve been in the IT for a while RPC doesn’t necessarily bring back happy memories. On the JVM it all started with RMI in the 90s. RMI was inspired by CORBA and suffered from a lack of interoperability as both the client and the server had to be implemented in Java. RMI was also particularly slow as Java serialisation is not a very efficient protocol.
Later in the 2000s came XML based RPC with XML-RPC and especially SOAP. Both of these formats address the interoperability as it no longer matters how the client/server are implemented. They only need to speak XML. However XML is still not an efficient protocol and communications remain slow.
SOAP provides an interesting definition language (WSDL – Web Service Definition Language) that can be used to generate service implementations.
gRPC addresses all these drawbacks. By default, it uses protobuf (Protocol buffers) for the service definitions and as its serialisation mechanism, which allows it to interoperate with many different languages while providing an efficient serialisation protocol. gRPC also takes advantage of HTTP/2 to add streaming capabilities.
Unfortunately Scala is not in the list! … but we have scalaPB (and sbt-protoc) to save the day! Continue reading “gRPC in Scala”
Many people see Kafka as a messaging system but in reality it’s more than that. It’s a distributed streaming platform. While it can be used as a traditional messaging platform it also means that it’s more complex.
In this post we’ll introduce the main concepts present in Kafka and see how they can be used to build different application from the traditional publish/subscribe all the way up to streaming applications. Continue reading “Kafka concepts and common patterns”