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”
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”
It’s been a while we haven’t covered any machine learning algorithm. Last time we discussed the Markov Decision Process (or MDP).
Today we’re going to build our knowledge on top of the MDP and see how we can generalise our MDP to solve more complex problems.
Reinforcement learning really hit the news back in 2013 when a computer learned how to play a bunch of old Atari games (like Breakout) just by observing the pixels on the screen. Let’s find out how this is possible! Continue reading “Reinforcement learning”
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”