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ConcurrentHashMap scalability

As mentioned in our introduction to ConcurrentHashMap, the purpose of the class is to reduce lock contention that occurs when several threads access the same synchronized hash map simultaneously.

To measure the effectiveness of ConcurrentHashMap— and specifically its scalability— we consider an experiment where a number of threads simultaneously "hit" a map, adding and removing items at random. Each thread sits in a tight loop choosing a random key and seeing if that key is in the map. If not, the thread has an approximately 10% chance of adding a mapping of that key to the map; if the key is already mapped, there is a 1% chance that the mapping will be removed. After running 2,000 iterations, each thread sleeps for a small random number of milliseconds before running the loop again. Thus, the program aims to simulate a moderate-ish amount of contention on the map, as might be experienced by an important cache map on a busy server.

Despite each thread performing enough accesses to create contention on the synchronization lock, very little CPU is consumed by each one. This means that in theory, with each new thread added, we would expect almost the same additional throughput.

Figure 1 below shows the actual behaviour of (a) an ordinary synchronized hash map and (b) a ConcurrentHashMap being concurrently accessed by a number of threads, running on a machine with an Intel i7-720QM processor (4 cores hyperthreading, effectively supporting a maximum of 8 hardware threads). The throughput measurement is the total number of accesses (actually, the number of blocks of 2,000 accesses) to the map performed by all threads together in two seconds1.

We see that scalability of the synchronized hash map rapidly tails off shortly after the number of threads reaches the number of supported hardware threads. On the other hand, ConcurrentHashMap remains scalable: even with a relatively high number of threads, ConcurrentHashMap behaves close to the ideal: that is, having n threads gives an almost n times increase in throughput.

Graph: scalability or synchronized HashMap vs ConcurrentHashMap

Figure 1 The scalability of ConcurrentHashMap over a regular synchronized HashMap, illustrated by the relative throughput of a contended map implemented with each. Measurements made under on an Intel i7-720QM (Quad Core Hypterthreading) machine running Hotspot version 1.6.0_18 under Windows 7.

The concurrency parameter

When constructing a ConcurrentHashMap, you can pass in a concurrency parameter which is intended to indicate "the estimated number of concurrently updating threads". In the test described above, we always pass as this parameter the maximum number of threads used in the experiment.

Note that we didn't reproduce in this test the results of a previous experiment in which overestimating the concurrency parameter gave a slight increase in throughput. That previous experiment was conducted under somewhat different conditions: a dual processor Xeon server (i.e. a dual processor hyperthreading machine), an older version of the JVM (Java 1.5.0_08) and a different operating system (Linux) and for reference the results are given in Figure 2 below.

Figure 2: Results of the map throughout test on Hotspot version 1.5.0_08 under Linux on a dual processor hyperthreading (Xeon) machine.

In a similar experiment, but with more contention on the map, Goez et al Java Concurrency in Practice (pp. 242-3) report essentially similar results on an 8-processor Sparc V880, but in their case the throughput of the synchronized HashMap plateaus even more quickly (and throughput with ConcurrentHashMap also plateaus eventually).

More on using ConcurrentHashMap

Now we have seen the performance benefits of ConcurrentHashMap, on the next page, we'll look further at the functionality of ConcurrentHashMap, with details of how atomic updates to the map are performed.


1. Of course, as generally with all the experiments reported on this site, we actually take the mean time over a number of runs of the test (in this case, the mean measurement over ten runs is taken in each case).

Article written by Neil Coffey (@BitterCoffey).

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