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The Java maps are known to be non-storage friendly. It's assumed that often 80%+ of the map size is used for maintenance, where only 20% are real data stored in the map. Note that we use ConcurrentHashMap.
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Current size
A test was performed to check the amount of indexed elements in the maps as well as the size of the complete indexing tree. The test was executed with normal load pace and invocations having ~50-100 children. Max size of buffer is 335MB. The results are:
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2014-11-14 15:44:18,125: 1239302 [indexing-thread] DEBUG it.cmr.cache.impl.AtomicBuffer - Indexing tree new size: 32934038
2014-11-14 15:44:18,125: 1239302 [indexing-thread] DEBUG it.cmr.cache.impl.AtomicBuffer - Indexing occupancy percentage: 10.049% |
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2014-11-14 15:49:13,842: 1535019 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 17245
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 300778
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 4554
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 32965
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 644
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 10604
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 598
2014-11-14 15:49:13,843: 1535020 [pool-2-thread-1] INFO ctit.indexing.buffer.impl.Leaf - Leaf map size prior to clean = 10604 |
As seen, indexing tree in this situation occupies 10% of buffer, totaling to over 30MB. In my opinion this is ideal situation and under 10% we can not go.
We have total of 8 maps, with different sizes, biggest one having 300,778 indexed elements.
ConcurrentHashMap compared with other thread-safe map
We furthermore tested the ConcurrentHashMap with two other map implementations: NonBlockingHashMapLong from Cliff's Click high scale lib and synchronized Long2ObjectMap from FastUtil lib. The results shows that NonBlockingHashMap beats heavily other maps in both size and performance measurements:
Size
| Number of elements | ConcurrentHashMap | NonBlockingHashMapLong | Long2ObjectMap | |||
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| Size | Ch. (%) | Size | Ch. (%) | Size | Ch. (%) | |
| Empty | 224 | - | 712 | +317% | 568 | +253% |
| 8 | 800 | - | 824 | +3% | 696 | -13% |
| 32 | 2720 | - | 1400 | -48% | 1496 | -45% |
| 256 | 20640 | - | 7672 | -63% | 10904 | -47% |
| 1024 | 82080 | - | 29176 | -64% | 43160 | -47% |
| 4096 | 327840 | - | 115192 | -65% | 172184 | -47% |
As seen in the table there are three stages of elements numbers:
- empty map makes Java implementation the smallest
- with 8 elements have maps similar size
- from 256 elements the max saving for NonBlickingHashMapLong and Long2ObjectMap is achieved
Performance
Martin Thompson's style of PerfTest
2 consumers, 2 producers. Producers putting with key value pair that is their id and random object. Consumers removing with key that is their id. Basically a bunch of <1,O> or <2, O> putting and removing with <1> or <2>.
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Java
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0 - 2 producers 2 consumers: 4,782,614 ops/sec - MapsPerfTest
1 - 2 producers 2 consumers: 4,194,545 ops/sec - MapsPerfTest
2 - 2 producers 2 consumers: 4,726,670 ops/sec - MapsPerfTest
3 - 2 producers 2 consumers: 5,339,190 ops/sec - MapsPerfTest
4 - 2 producers 2 consumers: 4,952,414 ops/sec - MapsPerfTest
Cliff's
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0 - 2 producers 2 consumers: 23,030,573 ops/sec - MapsPerfTest
1 - 2 producers 2 consumers: 31,244,208 ops/sec - MapsPerfTest
2 - 2 producers 2 consumers: 30,630,539 ops/sec - MapsPerfTest
3 - 2 producers 2 consumers: 28,825,699 ops/sec - MapsPerfTest
4 - 2 producers 2 consumers: 26,588,300 ops/sec - MapsPerfTest
Long2ObjectMap (FastUtil)
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0 - 2 producers 2 consumers: 5,569,578 ops/sec - MapsPerfTest
1 - 2 producers 2 consumers: 5,940,526 ops/sec - MapsPerfTest
2 - 2 producers 2 consumers: 5,883,532 ops/sec - MapsPerfTest
3 - 2 producers 2 consumers: 5,798,396 ops/sec - MapsPerfTest
4 - 2 producers 2 consumers: 5,537,185 ops/sec - MapsPerfTest |
Using JMH Approach 1
2 consumers, 2 producers. Map is initially empty. Producers execute put with random long 0-10. Consumer are removing with random long 0-10.
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[java] Benchmark Mode Samples Score Error Units
[java] i.n.i.ThreadSafeMapsPerfTest.concurrentHashMap thrpt 5 17205.845 ± 1549.392 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.concurrentHashMap:putChm thrpt 5 8031.802 ± 627.409 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.concurrentHashMap:removeChm thrpt 5 9174.043 ± 938.380 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.nonBlockingHashMapLong thrpt 5 22120.230 ± 1674.090 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.nonBlockingHashMapLong:putNbhml thrpt 5 10369.226 ± 505.723 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.nonBlockingHashMapLong:removeNbhml thrpt 5 11751.004 ± 1391.759 ops/ms |
Using JMH Approach 2
3 threads.. Map is filled with 1000 elements. Each tread removes the element with random 0-1000 remove and then puts back the removed element to the map right away with same key.
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[java] Benchmark Mode Samples Score Error Units
[java] i.n.i.ThreadSafeMapsPerfTest.concurrentHashMap thrpt 5 16033.299 ± 642.117 ops/ms
[java] i.n.i.ThreadSafeMapsPerfTest.nonBlockingHashMapLong thrpt 5 27084.222 ± 323.981 ops/ms |
Saving With NonBlockingHashMapLong
Taking the map sizes reported by the indexing structure, we can easily check what is the expected size of indexing structure if the map would be changed to NonBlockingHashMapLong:
| Map # | Number of elements | App. CocnurrentHashMap size | App. NonBlockingHashMapSize |
|---|---|---|---|
| 1 | 17245 | 1,380,273 | 478,677 |
| 2 | 300778 | 24,073,989 | 8,348,829 |
| 3 | 4554 | 364,497 | 126,407 |
| 4 | 32965 | 2,638,487 | 915,024 |
| 5 | 644 | 51,721 | 18,348 |
| 6 | 10604 | 848,734 | 294,339 |
| 7 | 598 | 48,026 | 17,038 |
| 8 | 10604 | 848,734 | 294,339 |
Total | 377992 | 30,254,461 | 10,493,001 |
As seen with change to high scale lib we would gain the storage optimization of almost 20MB (for given example), or decrease of 67%.
Further options to optimize indexing tree size
Simplest option would be to decrease the number of elements indexed. We can re-think on our use-cases and real need to index every single element that is in the buffer (timers and SQLs we often need in aggregated mode only).
The other option to optimize the indexing tree size would be to go away from the maps and implement different structure.
The additional option would be not to map the element ID with the element it self (that's what we store in maps). Because the element ID is only needed for direct element retrieval and this use case we have only for invocation sequences. Thus for everything else we could spare that space by holding the elements in the array list for example.