Add blog article about the new collections performance

Author: julienrf

blog/_posts/2018-02-07-collections-performance.md

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+---
+layout: blog-detail
+post-type: blog
+by: Julien Richard-Foy
+title: On Performance of the New Collections
+---
+
+In a [previous blog post](/blog/2017/11/28/view-based-collections.html), I explained
+how [Scala 2.13’s new collections](http://www.scala-lang.org/blog/2017/02/28/collections-rework.html)
+have been designed so that the default implementations of transformation operations work
+with both strict and non-strict types of collections. In essence, we abstract over
+the evaluation mode (strict or non strict) of concrete collection types.
+
+After we published that blog post, the community
+[raised concerns](https://www.reddit.com/r/scala/comments/7g52cy/let_them_be_lazy/dqgol36/)
+about possible performance implications of having more levels of abstraction than before.
+
+This blog article gives more information about the overhead of the
+collections’ view-based design and our solution to remove that
+overhead.
+
+For reference, the source code of the new collections is available in
+[this GitHub repository](https://github.com/scala/collection-strawman).
+
+## Overhead Of View Based Implementations
+
+Let’s be clear: the view based implementations are in general slower than their
+builder based versions. How much slower exactly varies with the type of collection
+(e.g. `List`, `Vector`, `Set`), the operation (e.g. `map`, `flatMap`, `filter`)
+and the number of elements in the collection. In my benchmark on `Vector`, on
+the `map`, `filter` and `flatMap` operations, with 1 element to 7 million of
+elements, I measured an average slowdown of 25%.
+
+## How To Fix That Performance Regression?
+
+Our solution is simply to go back to builder based implementations for strict collections: we
+override the default view based implementations with more efficient builder based
+ones. We actually and up with the same implementations as in the old collections.
+
+In practice these implementations are factored out in traits that can be mixed
+into concrete collection types. Such trait names are always prefixed with
+`StrictOptimized`. For instance, here is an excerpt of the `StrictOptimizedIterableOps`
+trait:
+
+~~~ scala
+trait StrictOptimizedIterableOps[+A, +CC[_], +C] extends IterableOps[A, CC, C] {
+
+  override def map[B](f: A => B): CC[B] = {
+    val b = iterableFactory.newBuilder[B]()
+    val it = iterator()
+    while (it.hasNext) {
+      b += f(it.next())
+    }
+    b.result()
+  }
+
+}
+~~~
+
+Then, to implement the `Vector` collection, we just mix the above trait:
+
+~~~ scala
+trait Vector[+A] extends IndexedSeq[A]
+  with IndexedSeqOps[A, Vector, Vector[A]]
+  with StrictOptimizedSeqOps[A, Vector, Vector[A]]
+~~~
+
+Here we use `StrictOptimizedSeqOps`, which is a specialization of `StrictOptimizedIterableOps`
+for `Seq` collections.
+
+## Is The View Based Design Worth It?
+
+In my previous article I explained that a drawback of the old builder based design was that,
+on non strict collections (e.g. `Stream` or `View`), we had to carefully override all the
+default implementations of transformation operations to make them non strict.
+
+Now it seems that the situation is just reversed: the default implementations work well
+with non strict collections, but we have to override them in strict collections.
+
+So, is the new design worth it? To answer this question I will quote a comment posted
+by Stefan Zeiger [here](https://www.reddit.com/r/scala/comments/7g52cy/let_them_be_lazy/dqixt8d/):
+
+> The lazy-by-default approach is mostly beneficial when you're implementing lazy
+> collections because you don't have to override pretty much everything or get
+> incorrect semantics. The reverse risk is smaller: If you don't override a lazy
+> implementation for a strict collection type you only suffer a small performance
+> impact but it's still correct.
+
+In short: implementations are **correct first** in the new design but you might want to
+override them for performance reasons on strict collections.
+
+## Performance Comparison With 2.12’s Collections
+
+Talking about performance, how performant are the new collections compared to the old ones?
+
+Again, the answer depends on the type of collection, the operations and the number of elements.
+My `Vector` benchmarks show a 20% speedup on average:
+
+![](/resources/img/new-collections-performance-filter.png)
+
+![](/resources/img/new-collections-performance-map.png)
+
+![](/resources/img/new-collections-performance-flatMap.png)
+
+These charts show the execution time (vertically) of the `filter`, `map` and `flatMap`
+operations, according to the number of elements (horizontally). Note that scales are
+logarithmic in both axis. The blue line shows the performance of the old `Vector`,
+the green line shows the performance of the new `Vector` if it used only view based
+implementations, and the red line shows the actual performance of the new `Vector`
+(with strict optimized implementations). Benchmark source code and numbers can be found
+[here](https://gist.github.com/julienrf/f1cb2b062cd9783a35e2f35778959c76).
+
+Since operation implementations end up being the same, why do we get better performance
+at all? Well, these numbers are specific to `Vector`, and are due to the fact that
+we more agressively inlined a few critical methods. I don’t expect the new collections
+to be *always* 20% faster than the old collections. However, there is no reason for
+them to be slower since the execution path, when calling an operation, can be made
+exactly the same as in the old collections.
+
+## Conclusion
+
+This article studied the performance of the new collections. I’ve reported that view
+based operation implementations are about 25% slower than builder based implementations
+and I’ve explained how we restored builder based implementations on strict collections.
+
+I expect the new collections to be as fast or slightly faster than the previous collections.
+Indeed, we took advantage of the rewrite to apply some more optimizations here and
+again.
+
+More significant performance improvements can be achieved by using different
+data structures. For instance, we recently
+[merged](https://github.com/scala/collection-strawman/pull/342)
+a completely new implementation of immutable `Set` and `Map` based on [compressed
+hash-array mapped prefix-trees](https://michael.steindorfer.name/publications/oopsla15.pdf).
+This data structure has a smaller memory footprint than the old `HashSet` and `HashMap`,
+and some operations are an order of magnitude faster.