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Load balancing: Rancher vs Swarm

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Rancher has a load balancer built it (HAProxy). Let's compare its performance vs Docker Swarm one. I will use 3 identical nodes:

192GB RAM28-cores i5 Xeon1GBit LANCentOS 7Docker 1.12Rancher 1.1.2 I will benchmark against a hello world HTTP server written in Scala with Akka-HTTP and Spray JSON serialization (I don't think it matters though), sources are on GitHub. I will use Apache AB benchmark tool.
As a baseline, I exposed the web server port outside the container and run the following command:
ab -n 100000 -c 20 -k http://1.1.1.1:29001/person/kot
It shows 22400 requests per second. I'm not sure whether it's a great result for Akka-HTTP, considering that some services written in C can handle hundreds of thousands requests per second, but it's not the main topic of this blog post (I ran the test with 100 concurrent connections (-c 100), and it shows ~50k req/sec. I don't know if this number is good enough either :) )
Now I created a Rancher so-called "stac…

Running computational intensive code outside of Hopac scheduler

Hopac uses a bounded pool of worker threads, number of which is equal to number of CPU cores (by default). A dangerous thing about this design is that a situation is possible where all the threads are busy doing some CPU intensive work and no other Hopac jobs can proceed. A good solution for this is running such a CPU bound computations on the standard .NET thread pool, freeing Hopac pool for more intelligent work. I found a nice code in one of the older Hopac GitHub discussions which schedules a ordinary function on ThreadPool and represents the result as a Hopac job. Here is a test with explanations:

Upcoming F# struct tuples: are they always faster?

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Don Syme has been working on struct tuples for F# language. Let's see if they are more performant than "old" (heap allocated) tuples in simple scenario: returning tuple from function. The code is very simple: Decompiled code in Release configuration:

Everything we need to change to switch to struct tuples, is adding "struct" keyword in front of constructor and pattern matching:


Decompiled code in Release configuration: I don't know about you, but I was surprised with those results. The performance roughly the same. GC is not a bottleneck as no objects were promoted to generation 1.

Conclusions:

Using struct tuples as a faster or "GC-friendly" alternative to return multiple values from functions does not make sense.Building in release mode erases away heap allocated tuples, but not struct tuples. Building in release mode inlines the "foo" function, which makes the code 10x faster. You can fearlessly allocate tens of millions of sh…

Hash maps: Rust, F#, D, Go, Scala

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Let's compare performance of hashmap implementation in Rust, .NET, D (LDC) and Go.
Rust:
F#:
As you can see, Rust is slower at about 17% on insersions and at about 21% on lookups.

Update As @GolDDranks suggested on Twitter, since Rust 1.7 it's possible to use custom hashers in HashMap. Let's try it:
Yes, it's significantly faster: additions is only 5% slower than .NET implementation, and lookups are 32% *faster*! Great.

Update: D addedLDC x64 on windows
It's very slow at insertions and quite fast on lookups.

Update: Go added
Update: Scala added

Compared to Scala all the other languages looks equally fast :) What's worse, Scala loaded all four CPU cores at almost 100% during the test, while others used roughly single core. My guess is that JVM allocated so many objects (each Int is an object, BTW), that 3/4 of CPU time was spend for garbage collecting. However, I'm a Scala/JVM noob, so I just could write the whole benchmark in a wrong way. Scala…

Akka.NET Streams vs Hopac vs AsyncSeq

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Akka.NET Streams is a port of its Scala/Java counterpart and intended to execute complex data processing graphs, optionally in parallel and even distributed. It has quite different semantics compared to Hopac's one and it's wrong to compare them feature-by-feature, but it's still interesting to benchmark them in a scenario which both of them supports well: read lines of a file asynchronously, filter them by a regex in controlled degree of parallelism, then normalize the lines with a simple string manipulation algorithm, also in parallel, then count the number of lines.

Firts, Akka.NET:

Note that I have to use the empty string as indication that the regular expression does not match. I should use `option` of course (just like I do in the Hopac snippet below), but Akka.NET Streams is strict about what is allowed to be returned by its combinators like `Map` or `Filter`, in particular, you cannot return `null`, doing so makes Akka.NET unhappy and it will throw exception at you…