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The Concursus Programming Model: Kotlin

April 29, 2016 | Software Consultancy

The Concursus Programming Model: Kotlin

In this post, I’ll demonstrate an alternative API which uses some of the advanced language features of the new Kotlin language from Jetbrains. As Kotlin is a JVM-based language, it interoperates seamlessly with Concursus’s Java 8 classes; however, it also offers powerful ways to extend their functionality.

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The Concursus Programming Model: Under the Hood

April 29, 2016 | Software Consultancy

The Concursus Programming Model: Under the Hood

In the previous two posts (part 1, and part 2), we looked at how Concursus uses method mapping to generate events from method calls on proxies, and to dispatch events to matching methods on event handlers and state class instances. This approach provides a concise, convenient client API to the Concursus event system; however the core of the system defines events and event-handling mechanisms without reference to any of the reflection-based machinery used to implement this API. It is perfectly possible (if comparatively cumbersome) to use a Concursus event store to read and write events without using reflection. In this post I’ll show how this is done, continuing with the “lightbulb” example introduced previously.

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The Concursus Programming Model: State

April 28, 2016 | Software Consultancy

The Concursus Programming Model: State

In a conventional RDBMS-with-ORM system, we are used to thinking of domain objects as mapped to rows in database tables, and of the database as a repository where the current state of every object exists simultaneously, so that what we get when we query for an object is the state that object was in at the time the query was issued. To perform an update, we can start a transaction, retrieve the current state of the object, modify it, save it back again and commit. Transactions move the global state of the system from one consistent state to another, so that the database transaction log represents a single, linear history of updates. We are therefore able to have a very stable, intuitive sense of what it means to talk about the “current state” of any domain object.

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The Concursus Programming Model: Events

April 27, 2016 | Software Consultancy

The Concursus Programming Model: Events

Concursus is an open source Java 8 framework for building distributed systems using CQRS and event sourcing patterns. One of its major differences from other such frameworks (such as Jdon, Axon and ES4J) is that it eschews a programming model where each event type is represented by a separate Java class, instead mapping event types to methods on interfaces.

 

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The Why’s, What’s and How’s of Kubernetes Operators

September 27, 2023 | Blog, Kubernetes

The Why’s, What’s and How’s of Kubernetes Operators

Learn to create your first Kubernetes operator by checking out our Senior Consultant Michal Tusnio’s latest blog, “Kubernetes Operators – Whys, Hows and Whats” where he takes you on a journey from zero to operator.

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Why should you upgrade your Terraform provider to the latest version of the API

August 17, 2023 | Blog, Terraform Provider

Why should you upgrade your Terraform provider to the latest version of the API

Check out John Sharpe and Will May’s latest blog where they give suggestions for Terraform Provider authors who are thinking about upgrading from SDKv2 to Framework

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HackCredo – An Internal OpenCredo Hackathon

July 5, 2022 | Blog, Culture

HackCredo – An Internal OpenCredo Hackathon

As we are passionate about using technology to solve problems, we are thrilled to share with you our internal competition, “HackCredo.” Read on to learn more about the competition, the groups, and the winners.

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Exploring How Policy-as-Code and OPA Fit into the K8s World

November 4, 2021 | Kubernetes

Exploring How Policy-as-Code and OPA Fit into the K8s World

We always read that ‘security is everyone’s responsibility’. For any organisation, big or small, security should always be the primary concern—not a mere afterthought. In terms of Kubernetes, securing a cluster is challenging because it has so many moving parts and, apart from securing our Kubernetes environment, we also want to control what an end-user can do in our cluster.

To achieve these goals, we can start with the built-in features provided by Kubernetes like Role-Based Access Control (RBAC), Network Policies, Secrets Management, and Pod Security Policies (PSP). But we know these features are not enough. For example, we may want specific policies like ‘all pods must have specific labels’. And even if we have the policies in place, the next big question is how to enforce them on our Kubernetes cluster in an easy and repeatable manner.

In this blog post, we’ll address this challenge and other questions pertaining to OPA and how it can integrate into Kubernetes.

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Kafka vs RabbitMQ: The Consumer-Driven Choice

July 20, 2021 | Blog, Data Engineering, Kafka

Kafka vs RabbitMQ: The Consumer-Driven Choice

Message and event-driven systems provide an array of benefits to organisations of all shapes and sizes. At their core, they help decouple producers and consumers so that each can work at their own pace without having to wait for the other – asynchronous processing at its best.

In fact, such systems enable a whole range of messaging patterns, offering varying levels of guarantees surrounding the processing and consumption options for clients. Take for example the publish/subscribe pattern, which enables one message to be broadcast and consumed by multiple consumers; or the competing consumer pattern, which enables a message to be processed once but with multiple concurrent consumers vying for the honour—essentially providing a way to distribute the load. The manner in which these patterns are actually realised however, depends a lot on the technology used, as each has its own approach and unique tradeoffs. 

In this article we will explore how this all applies to RabbitMQ and Apache Kafka, and how these two technologies differ, specifically from a message consumer’s perspective.

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A Pragmatic Introduction to Machine Learning for DevOps Engineers

January 23, 2018 | Data Engineering, DevOps

A Pragmatic Introduction to Machine Learning for DevOps Engineers

Machine Learning is a hot topic these days, as can be seen from search trends. It was the success of Deepmind and AlphaGo in 2016 that really brought machine learning to the attention of the wider community and the world at large.

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Writing a custom JupyterHub Spawner

January 11, 2018 | Data Engineering

Writing a custom JupyterHub Spawner

The last few years have seen Python emerge as a lingua franca for data scientists. Alongside Python we have also witnessed the rise of Jupyter Notebooks, which are now considered a de facto data science productivity tool, especially in the Python community. Jupyter Notebooks started as a university side-project known as iPython in circa 2001 at UC Berkeley.

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Riak, the Dynamo paper and life beyond Basho

August 8, 2017 | Cassandra

Riak, the Dynamo paper and life beyond Basho

Recently, the sad news has emerged that Basho, which developed the Riak distributed database, has gone into receivership. This would appear to present a problem for those who have adopted the commercial version of the Riak database (Riak KV) supported by Basho.

 

This blog is written exclusively by the OpenCredo team. We do not accept external contributions.

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New Blog Series: Spark – The Pragmatic Bits

April 25, 2017 | Cassandra, Data Analysis, Data Engineering

New Blog Series: Spark – The Pragmatic Bits

Apache Spark is a powerful open source processing engine which is fast becoming our technology of choice for data analytic projects here at OpenCredo. For many years now we have been helping our clients to practically implement and take advantage of various big data technologies including the like of Apache Cassandra amongst others.

 

 

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Programmable Infrastructure Needs Testing Too

March 20, 2017 | DevOps

Programmable Infrastructure Needs Testing Too

DevOps has swept the tech landscape. Now, many are discovering the benefits of programmable infrastructure. I have been lucky to work on many projects where we’ve taken advantage of tools such as Terraform, Ansible, or Chef.

 

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Google Cloud Spanner: our first impressions

March 7, 2017 | Data Analysis, GCP

Google Cloud Spanner: our first impressions

Google has recently made its internal Spanner database available to the wider public, as a hosted solution on Google Cloud. This is a distributed relational/transactional database used inside for various Google projects (including F1, the advertising backend), promising high throughput, low latency and 99.999% availability. As such it is an interesting alternative to many open source or other hosted solutions. This whitepaper gives a good theoretical introduction into Spanner.

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Event Replaying with Hazelcast Jet

February 13, 2017 | Data Engineering

Event Replaying with Hazelcast Jet

One of the stated intentions behind the design of Java 8’s Streams API was to take better advantage of the multi-core processing power of modern computers. Operations that could be performed on a single, linear stream of values could also be run in parallel by splitting that stream into multiple sub-streams, and combining the results from processing each sub-stream as they became available.

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Reactive event processing with Reactor Core: a first look

January 26, 2017 | Data Engineering

Reactive event processing with Reactor Core: a first look

Suppose you are given the task of writing code that fulfils the following contract:

  • You will be given a promise that, at some point in the future, some data – a series of values – will become available.
  • In return, you will supply a promise that, at some point in the future, some data representing the results of processing that data will become available.
  • There may be more values to process than you can fit in memory, or even an infinite series of values.
  • You are allowed to specify what will be done with each individual value, as and when it becomes available; this includes discarding some values.
  • Whenever you want to use some external service to do something with a value, that service can only return you a promise that, at some point in the future, some data representing the result of processing that value will become available.

 

This blog is written exclusively by the OpenCredo team. We do not accept external contributions.

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What I Don’t Like About Error Handling in Go, and How to Work Around It

January 23, 2017 | Data Analysis

What I Don’t Like About Error Handling in Go, and How to Work Around It

More often than not, people who write Go have some sort of opinion on its error handling model. Depending on your experience with other languages, you may be used to different approaches. That’s why I’ve decided to write this article, as despite being relatively opinionated, I think drawing on my experiences can be useful in the debate. The main issues I wanted to cover are that it is difficult to force good error handling practice, that errors don’t have stack traces, and that error handling itself is too verbose.

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How to TDD FizzBuzz with JUnit Theories

January 13, 2017 | Software Consultancy

How to TDD FizzBuzz with JUnit Theories

The notorious FizzBuzz interview test was originally proposed as a way of weeding out candidates for programming jobs who – to put it bluntly – couldn’t program. The task is as follows:

Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”.

It turns out that this problem has just enough subtlety about it to cause headaches to anyone who knows the basics but hasn’t learned how to think in nested structures.

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From Java to Go, and Back Again

October 13, 2016 | Data Analysis

From Java to Go, and Back Again

In Lisp, you don’t just write your program down toward the language, you also build the language up toward your program. As you’re writing a program you may think “I wish Lisp had such-and-such an operator.” So you go and write it. Afterward you realize that using the new operator would simplify the design of another part of the program, and so on. Language and program evolve together…In the end your program will look as if the language had been designed for it. And when language and program fit one another well, you end up with code which is clear, small, and efficient – Paul Graham, Programming Bottom-Up

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Kubernetes from scratch to AWS with Terraform and Ansible (part 1)

August 26, 2016 | Kubernetes

Kubernetes from scratch to AWS with Terraform and Ansible (part 1)

This post is the first of a series of three tutorial articles introducing a sample, tutorial project, demonstrating how to provision Kubernetes on AWS from scratch, using Terraform and Ansible.

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The Destructor Pattern

June 3, 2016 | Software Consultancy

The Destructor Pattern

Complexity warning

In this post, I’m going to take something extremely simple, unfold it into something disconcertingly complex, and then fold it back into something relatively simple again. The exercise isn’t entirely empty: in the process, we’ll derive a more powerful (because more generic) version of the extremely simple thing we started with. I’m describing the overall shape of the journey now, because programmers who don’t love complexity for its own sake often find the initial “unfolding” stage objectionable, and then have trouble regarding the eventual increase in fanciness as worth the struggle.

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Complex Kotlin

May 25, 2016 | Software Consultancy

Complex Kotlin

In this post I’m going to demonstrate the implementation of Complex, a Kotlin class handling complex numbers, which uses operator overloading to provide the usual arithmetic operations for those numbers. In the process, I’ll also demonstrate a Kotlin pattern which I call “complicit conversion”, and show how to implement complicit conversion between two types: Double and Complex.

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Concursus: Event Sourcing for the Internet of Things

May 10, 2016 | Data Engineering, White Paper

Concursus: Event Sourcing for the Internet of Things

In this technical report, we present Concursus, a framework for developing distributed applications using CQRS and event sourcing patterns within a modern, Java 8-centric, programming model. Following a high-level survey of the trends leading towards the adoption of these patterns, we show how Concursus simplifies the task of programming event sourcing applications by providing a concise, intuitive API to systems composed of event processing middleware.

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Kotlin: a new JVM language you should try

March 3, 2016 | Software Consultancy

Kotlin: a new JVM language you should try

JetBrains (the people behind IntelliJ IDEA) have recently announced  the first RC for version 1.0 of Kotlin, a new programming language for the JVM. I say ‘new’, but Kotlin has been in the making for a few years now, and has been used by JetBrains to develop several of their products, including Intellij IDEA. The company open-sourced Kotlin in 2011, and have worked with the community since then to make the language what it is today.

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Is it Time for Your ‘Microservices Checkup’?

March 2, 2016 | DevOps, Microservices

Is it Time for Your ‘Microservices Checkup’?

Many of our clients are currently implementing applications using a ‘microservice’-based architecture. Increasingly we are hearing from organisations that are part way through a migration to microservices, and they want our help with validating and improving their current solution. These ‘microservices checkup’ projects have revealed some interesting patterns, and because we have experience of working in a wide-range of industries (and also have ‘fresh eyes’ when looking at a project), we are often able to work alongside teams to make significant improvements and create a strategic roadmap for future improvements.

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Akka Typed brings type safety to Akka framework

January 18, 2016 | Software Consultancy

Akka Typed brings type safety to Akka framework

Last time in this series I summarised all the Akka Persistence related improvements in Akka 2.4. Since then Akka 2.4.1 has been released with some additional bug fixes and improvements so perhaps now is a perfect time to pick up this mini-series and introduce some other new features included in Akka 2.4.x.

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Facebook’s React, and the Signal:Noise Ratio

January 15, 2016 | Software Consultancy

Facebook’s React, and the Signal:Noise Ratio

So, you’ve started to hear a lot about React, the Javascript library developed by Facebook, but is it something you need to investigate? It’s time to distil the signal from the noise, position React amongst its rivals, and provide an indication of where it currently would – and would not – be a suitable fit.

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The Business Behind Microservices (Redux)

November 24, 2015 | DevOps, Microservices

The Business Behind Microservices (Redux)

It was once again a privilege to present at the annual ‘muCon 2015‘ microservices conference held in London (at the shiny new Skillsmatter CodeNode venue). Based on feedback fro talks I gave earlier in the year, I presented a completely new version of my ‘The Business Behind Microservices‘ talk, which focuses on the organisational and people side of implementing a microservice-based application.

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JavaOne: Debugging Java Applications Running in Docker

November 3, 2015 | Software Consultancy

JavaOne: Debugging Java Applications Running in Docker

My JavaOne experience was rather busy this year, what with three talks presented in a single day! The first of these talks “Debugging Java Apps in Containers: No Heavy Welding Gear Required” was delivered with my regular co-presenter Steve Poole, from IBM, and we shared our combined experiences of working with Java and Docker over the past year.

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Our Thoughts on DevOps and Cloud at JAX London

October 18, 2015 | Cloud, DevOps

Our Thoughts on DevOps and Cloud at JAX London

DevOps, Cloud and Microservices: “All Hail the Developer King/Queen”

Last week Steve Poole and I were once again back at the always informative JAX London conference talking about DevOps and the Cloud. This presentation built upon our previous DevOps talk that was presented last year, and focused on the experiences that Steve and I had encountered over the last year (the slides for our 2014 “Moving to a DevOps” mode talk can be found on SlideShare, and the video on Parleys).

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Terraform Infrastructure Design Patterns

September 14, 2015 | Cloud, DevOps

Terraform Infrastructure Design Patterns

If you are operating in the programmable infrastructure space, you will hopefully have come across Terraform, a tool from HashiCorp which is primarily used to manage infrastructure resources such as virtual machines, DNS names and firewall settings across a number of public and private providers (AWS, GCP, Azure, …).

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Software Circus: Thinking Fast and Slow with Software Development

September 13, 2015 | DevOps

Software Circus: Thinking Fast and Slow with Software Development

Making Good Decisions within Software

Last week I was privileged to be able to present my “Thinking Fast and Slow with Software Development” talk at the inaugural Software Circus conference in Amsterdam. The conference was amazing, and I’ll write more about this later, but in this post I was keen to share the presentation slides and the thinking behind this talk…

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New Tricks With Dynamic Proxies In Java 8 (part 3)

August 18, 2015 | Software Consultancy

New Tricks With Dynamic Proxies In Java 8 (part 3)

In this post, the last in the New Tricks With Dynamic Proxies series (see part 1 and part 2), I’m going to look at using dynamic proxies to create bean-like value objects to represent records. The basic idea here is to have some untyped storage for a collection of property values, such as an array of Objects, and a typed wrapper around that storage which provides a convenient and type-safe access mechanism. A dynamic proxy is used to convert calls on getter and setter methods in the wrapper interface into calls which read and write values in the store.

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OpenCredo: A trusted Typesafe partner
Validating your test strategy

November 6, 2013 | Software Consultancy

Validating your test strategy

In many organisations, development and test teams have a ready answer for this, and that answer is usually wrong. Commonly, teams use test counts and code coverage statistics, which alone are not enough to validate a test approach and run the risk of giving a false sense of security to stakeholders. In practice, we are not able to fully prove the efficacy of our test strategy until after a release. Once software is in use, new defects highlight where our tests are failing to validate the software and where we need to invest effort to improve coverage. This is where many teams fail to learn and improve.

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Trifork & Erlang take a stake in OpenCredo
Model Matters: Graphs, Neo4j and the Future

February 25, 2013 | Neo4j

Model Matters: Graphs, Neo4j and the Future

As part of our work, we often help our customers choose the right datastore for a project. There are usually a number of considerations involved in that process, such as performance, scalability, the expected size of the data set, and the suitability of the data model to the problem at hand.

This blog post is about my experience with graph database technologies, specifically Neo4j. I would like to share some thoughts on when Neo4j is a good fit but also what challenges Neo4j faces now and in the near future.

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A dive into saltstack

January 10, 2013 | DevOps

A dive into saltstack

Recently I have started looking into SaltStack as a solution that does both config management and orchestration. It is a relatively new project started in 2011, but it has a growing fanbase among Sys Admins and DevOps Engineers. In this blog post I will look into Salt as a promising alternative, and comparing it to Puppet as a way of exploring its basic set of features.

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Withstanding the test of time

December 18, 2012 | Software Consultancy

Withstanding the test of time

The first thing most people think of when they start a project with the good intentions of test driven development is: write a test first. That’s great, and something I would fully encourage. However, diving in to writing tests without forethought, especially on large projects with a lot of developers can lead to new problems that TDD is not going to solve. With some upfront thinking (but not big upfront design!) a large team can avoid problems later down the line by considering some important and desirable traits of a large and rapidly changing test suite.

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