PauselessHashMap

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/*

• Written by Gil Tene, based on Apache Harmony version of java.util.HashMap. */

PauselessHashMap: A java.util.HashMap compatible Map implementation that performs background resizing for inserts, avoiding the common "resize/rehash" outlier experienced by normal HashMap.

get() and put() operations are "pauseless" in the sense that they do not block during resizing of the map. Other operations, like remove(), putAll(), clear(), and the derivation of keysets and such will block for pending resize operations.

Like HashMap, PauselessHashMap provides no synchronization or thread-safe behaviors on it's own, and MUST be externally synchronized if used by multiple threads. The background resizing mechanism relies on the calling program enforcing serialized access to all methods, and behavior is undefined if concurrent access (for modification or otherwise) is allowed.

And like HashMap, PauselessHashMap is an implementation of Map. All optional operations (adding and removing) are supported. Keys and values can be any objects.

Here is some more background and commentary I included in my posting on the mechanical sympathy group on the subject: https://groups.google.com/forum/#!topic/mechanical-sympathy/DY8vysxdmj4

Pauseless HashMap

Some background: As those of you who have read my various rants may have noticed, I spend a lot of my time thinking about the behavior of latency/response-time/reaction-time. In addition to trying to understand and teach about the behavior better (with monitoring and measurement tools like HdrHistogram, LatencyUtils, and jHiccup), I actually work on things that try to improve bad behavior (for some definitions of "improve" and "bad"). Eliminating pausing behavior in GC was the lowest hanging fruit, but more recent work has focused on eliminating pauses due to things like at-market-open deoptimzations, or lock deflation, or de-basing, or all sorts of TTSP (time to safepoint) issues. I've also learned a lot about how to bring down Linux's contribution to latency spikes.

But the JVM and the OS are not the only things that cause latency spikes. Sometimes your code is just "spiky". In my day job, I keep running into in actual, real-world low latency system code that is typically super-fast, but occasionally spikes in actual work latency due to some rare but huge piece of work that needs to be done, most often due to some state accumulation. After we eliminate GC pauses (which tend to dominate latency spikes, and which simply disappear immediately when Zing is applied), we often see this nice pattern of growing latency spikes at growing intervals, with a near-perfect doubling in both magnitude and interval between the spikes. This happens so often that we've studied the common causes, and (by far) the most common culprits are HashMaps used to accumulate something during the trading day. And it's all about resizing work.

I've had "build a Pauseless HashMap" on my weekend project list for over a year now, but finally got around to actually building it (at the request of someone on this list). There are probably at least 17 ways to skin a HashMap so it won't stall puts and gets when it resizes, but this is my simple take on it:

https://github.com/giltene/PauselessHashMap

Keep in mind that this is a "probably-working draft" that's gone through some bench testing, but is not yet battle hardened (scrutiny is welcome).

I intentionally based this version on Apache Harmony's version of HashMap, and not on OpenJDK's, in order to make it available without GPLv2 license restrictions (for those who may want to include it in non-GPL products). The background resizing concept itself is simple, and can be applied just as easily to the OpenJDK version (e.g. if some future Java SE version wants to use it). You can use (https://svn.apache.org/repos/asf/harmony/enhanced/java/trunk/classlib/modules/luni/src/main/java/java/util/HashMap.java) as a baseline comparison for the code I started with.

This is also a classic example of how GC makes this sort of concurrent programming thing both fast and simple. This is a classic case of an asymmetric speed need between two concurrent actors that share mutable state. I worked hard to make the fast path get() and put() cheap, and managed (I think) to not even use volatiles in the fast path. In doing this, I shifted all the cost I could think of to the background work, where latency doesn't matter nearly as much. This sort of trick would be much harder (or slower) to do if GC wasn't there to safely pick up the junk behind us, as it would (invariably, I think) add a need for additional synchronizing work in the fast path.