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Tag Archives: Performance

Scaling Scala vs Java

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In my previous post I showed how it makes no sense to benchmark Scala against Java, and concluded by saying that when it comes to performance, the question you should be asking is ‘How will Scala help me when my servers are falling over from unanticipated load?’ In this post I will seek to answer that, and show that indeed ...

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Benchmarking Scala against Java

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A question recently came up at work about benchmarks between Java and Scala. Maybe you came across my blog post because you too are wanting to know which is faster, Java or Scala. Well I’m sorry to say this, but if that is you, you are asking the wrong question. In this post, I will show you that Scala is ...

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Performance Analysis of REST/HTTP Services with JMeter and Yourkit

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My last post described how to accomplish stress- or load-testing of asynchronous REST/HTTP services with JMeter. However, running such tests often reveals that the system under test does not deal well with increasing load. The question is now how to find the bottleneck? Having an in-depth look at the code to detect suspicious parts could be one alternative. But considering ...

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Opinion: Performance Testing

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Performance tuning an application is time consuming, and expensive. Useful tests often need dedicated hardware to run on. It’s specialised and time consuming to prepare the ground work and write the various fixtures needed to run, and whose only perceived benefit is preventing a production issue that you don’t even know will happen yet. Stereotypical Scenarios and Outcomes Here’s some ...

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NUMA architecture and Java

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Time to deploy your application, looking forward to procure hardware that suits best to the load requirements. Boxes with 40 cores or 80 cores are pretty common these days. General conception is more cores, more processing power, more throughput. But I have seen a little contrary results, showing a small cpu-intensive test run performs slower on 80 core box than ...

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Single Writer Principle

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When trying to build a highly scalable system the single biggest limitation on scalability is having multiple writers contend for any item of data or resource. Sure, algorithms can be bad, but let’s assume they have a reasonable Big O notation so we’ll focus on the scalability limitations of the systems design. I keep seeing people just accept having multiple ...

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Smart Batching

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How often have we all heard that “batching” will increase latency? As someone with a passion for low-latency systems this surprises me. In my experience when batching is done correctly, not only does it increase throughput, it can also reduce average latency and keep it consistent. Well then, how can batching magically reduce latency? It comes down to what algorithm ...

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Smart Batching

disruptor-logo

How often have we all heard that “batching” will increase latency? As someone with a passion for low-latency systems this surprises me. In my experience when batching is done correctly, not only does it increase throughput, it can also reduce average latency and keep it consistent. Well then, how can batching magically reduce latency? It comes down to what algorithm ...

Read More »

Java Lock Implementations

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We all use 3rd party libraries as a normal part of development. Generally, we have no control over their internals. The libraries provided with the JDK are a typical example. Many of these libraries employ locks to manage contention. JDK locks come with two implementations. One uses atomic CAS style instructions to manage the claim process. CAS instructions tend to ...

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Java Sequential IO Performance

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Many applications record a series of events to file-based storage for later use. This can be anything from logging and auditing, through to keeping a transaction redo log in an event sourced design or its close relative CQRS. Java has a number of means by which a file can be sequentially written to, or read back again. This article explores ...

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