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”
Today’s focus is on scalameta. In this introduction post we’re going to see how to create a macro annotation to generate protobuf formats for case classes.
The idea is to be able to serialise any case classes to protobuf just by adding a
@PBSerializable annotation to the case class declaration.
Then behind the scene the macro will generate implicit formats in the companion object. These implicit formats can then be used to serialise the case class to/from protobuf binary format.
This is quite similar to Json formats of play-json.
In this post we’re going to cover the main principles of scalameta and how to apply them to create our own macros. Continue reading “Generating protobuf formats with scala.meta macros”
Akka actors fits nicely with DDD (Domain Driven Design) to design an application. E.g. It’s quite natural to model entities as individual actors who can be persisted, …
One of the key aspect in DDD is the notion of bounded context. A bounded context is simply a “self-content” component. It can interact with other components but it is coherent on its own. Each bounded context has its own domain model which belongs only to itself and should not leaked or be influenced by other bounded context.
Anti-corruption layers (aka translation layers or adapter layers) are used to enforce this principle. Basically their role is to translate the core domain objects into/from another domain that is used for communication or persistence.
In this blog post we’re going to try to follow the DDD principles to build a small (contrived) application using Akka and try to figure out the best way to build efficient anticorruption layers. Continue reading “Building anti-corruption layers with Akka”
Protocol Buffer (aka Protobuf) is an efficient and fast way to serialise data into a binary format. It is much more compact than Java serialisation or any text-based format (Json, XML, CSV, …).
Protobuf is schema based – it needs a description (in a .proto file) of the data structures to be serialised/deserialised.
On the JVM, protoc (the Protobuf compiler) reads the .proto description files and generates corresponding classes.
For Scala there is a very good sbt plugin “scalaPB” that follows the same process and generates case classes corresponding to the .proto files definitions.
The .proto files are an easy way to describe a protocol between 2 components (e.g. services). However there are some cases (e.g. writing to persistent storage) where the .proto files definition are just unnecessary and add superfluous complexity. (Who likes to read auto-generated code?).
In such cases it would be much easier to serialise an object directly into protobuf (using its class definition as a schema). Afterall this is what the protobuf java binding does: it serialises (auto-generated) java classes into protobuf binary format.
To that matter, let me introduce – PBDirect – a scala library to directly encode scala objects into protobuf. Continue reading “PBDirect – Protobuf without the .proto files”
Logging has been around on the JVM for a while now. It all started with Log4J back in 2001. Log4J was the first logging framework and it is still around today (in its version 2). It provides a simple and efficient API (compare to
System.out.println that was in use before).
- Get a logger for a class
- Use that logger to log messages
val logger = Logger.getLogger(classOf[MyClass])
logger.log(Level.DEBUG, "I am doing something right now")
logger.error("Oops, something went wrong", theException)
Today there are a few more frameworks on the JVM but they all provide similar APIs as Log4J:
- JUL(2002): java.util.logging provides a standardisation of Log4J and of course provides a similar API
- Commons-logging (2002): Apache project providing a façade over Log4J, JUL, … still the same API
- SLF4J (2005): Another façade over Log4J (1&2), JUL, JCL, … no much changes in the API
- Logback (2006): Brings structured logging with an API compatible (and similar) to SLF4J (and Log4J)
- Log4J2 (2012): Rewrite of Log4J inspired by Log4J and Logback with improved performances. The API does not change much though.
As you can see the logging APIs available on the JVM haven’t changed much over the last 15 years. The most interesting additions are structured logging and the Mapped Dependent Context (MDC) as we shall see later.
In this post I am going to look at the current limitations of these APIs and see how we can overcome them while still relying on this frameworks to actually write the logs. Continue reading “Rethinking logging on the JVM with Logoon”
As promised in my previous post we’re going to explore to internal of Fluent and how it uses Shapeless and implicit resolution to transform case classes.
Fluent started as an experiment (and still is), the code is rather small (about 300 lines of code) and yet I am still impressed by the variety of cases it can handle.
Before working with Shapeless I’ve often heard that is pure magic and I got the impression that most people (including me) don’t really know how it works. It turns out that the principles used in Shapeless are not really difficult to understand – especially if you read the well-written Type Astronaut’s guide to Shapeless.
Understanding how Shapeless works doesn’t mean it’s easy to work with. Actually Shapeless makes a heavy use of implicits and working with implicits is hard. Remember that implicits resolution is performed at compile time so when it fails, there is nothing to debug, no log messages or stack trace. We are just left with rather blunt messages like
could not find implicit value for parameter ...
In this post I am going to explain the concept used in Fluent, the problem I faced during implementation and hopefully by the end of the post, you’ll know enough to understand and edit the code (Pull requests welcomed!). Continue reading “Fluent – A deep dive into Shapeless and implicit resolution”
In Domain Driven Design (DDD) it is recommended to introduce a translation layer (aka anticorruption layer) between 2 bounded contexts. The role of the anticorruption layer is to avoid any concepts to leak from one domain into the other.
This is a sound idea as it keeps the domains isolated from each other ensuring they can evolve independently. After having implemented several anticorruption layers I realised that, although useful, they also introduced a lot of boilerplate code that doesn’t add much value to the business.
To this extent, let me introduce Fluent, a library that aims at getting rid of this boilerplate code by leveraging all the power of Shapeless and its generic programming. Continue reading “Introducing Fluent – the seamless translation layer”
If you followed our previous post on forging a DSL using type classes, you surely notice that writing type class instances is a rather repetitive task.
In today’s post we’re going to get rid of this by derivating type class instances automatically using Shapeless. We also use this post as an excuse to experiment with Shapeless and try to understand all the “magic” that’s happening. Continue reading “Reducing type class boilerplate with Shapeless”
In this post we’re going to explore how to build a DSL (Domain Specific Language) with a user-friendly syntax while maintaining as much type-safety as possible. We want that any operations that is not allowed by the business rules fail at compile time. This would be really nice as it makes sure that no one writes such forbidden logic (even by mistake).
More over Scala provides really nice syntactic sugar that can make a DSL syntax pretty neat.
If you don’t know what type classes or don’t feel very comfortable with this concept, follow along as we’ll also explore how we can use them to dissociate data and behaviours (always a good practice). Continue reading “Forging a DSL using Scala type classes”
The free monad is really neat for creating DSL as it allows to completely separate the business logic from the implementation.
It leaves a great freedom for the implementation choices and should you change your implementation you just need to rewrite your interpreter without changing any of the business logic.
That’s great! However after playing a little around with the free monad I was a bit skeptical about the amount of boilerplate required to plug everything together. Writing smart constructors and injectors is not the most trivial thing (depending on how your team is familiar with functional programming).
To conclude this serie on the free monad we’ll have a look at some solutions to remove this boilerplate code, especially freasy and freek.
Continue reading “The free monad without the boilerplate”