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https://issues.apache.org/jira/browse/HBASE-15560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15643265#comment-15643265
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Ben Manes commented on HBASE-15560:
-----------------------------------
{{quote}
Would you want the same dataset loaded too?
{{quote}}
That can't hurt, so unless its more work might as well.
---
In my [simulator|https://github.com/ben-manes/caffeine/wiki/Simulator], I tried
to emulate {{workload c}} using the following configuration,
* maximum-size = (below)
* source = "synthetic"
* distribution = "zipfian"
* zipfian.items = 1000
I then ran it with small caches to emulate your observation. {{LruBlockCache}}
is an SLru variant, so I'm assuming it behaves similar to the theoretical
version.
||Policy||max=5||max=10||max=25||
|Lru|13.10%|20.70%|35.60%|
|SLru|25.90%|29.30|45.00%|
|Caffeine|24.40%|32.30%|46.00%|
|Optimal|35.20%|42.10%|45.50%|
We see that at the smallest size, 5, Caffeine slightly under performs. However
whether its slightly lower, equal, or higher varies on the run. This is due to
the distribution generation and Caffeine's hashing having randomness, so across
runs we see it pretty much on par. As the size increases we see them all stay
pretty close. Since SLru is known to be optimal for Zipf, this at least is a
good sign but does not explain your observations.
> TinyLFU-based BlockCache
> ------------------------
>
> Key: HBASE-15560
> URL: https://issues.apache.org/jira/browse/HBASE-15560
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache
> Affects Versions: 2.0.0
> Reporter: Ben Manes
> Assignee: Ben Manes
> Attachments: HBASE-15560.patch, HBASE-15560.patch, HBASE-15560.patch,
> HBASE-15560.patch, HBASE-15560.patch, HBASE-15560.patch, HBASE-15560.patch,
> bc.hit.count, bc.miss.count, branch-1.tinylfu.txt, gets, tinylfu.patch
>
>
> LruBlockCache uses the Segmented LRU (SLRU) policy to capture frequency and
> recency of the working set. It achieves concurrency by using an O( n )
> background thread to prioritize the entries and evict. Accessing an entry is
> O(1) by a hash table lookup, recording its logical access time, and setting a
> frequency flag. A write is performed in O(1) time by updating the hash table
> and triggering an async eviction thread. This provides ideal concurrency and
> minimizes the latencies by penalizing the thread instead of the caller.
> However the policy does not age the frequencies and may not be resilient to
> various workload patterns.
> W-TinyLFU ([research paper|http://arxiv.org/pdf/1512.00727.pdf]) records the
> frequency in a counting sketch, ages periodically by halving the counters,
> and orders entries by SLRU. An entry is discarded by comparing the frequency
> of the new arrival (candidate) to the SLRU's victim, and keeping the one with
> the highest frequency. This allows the operations to be performed in O(1)
> time and, though the use of a compact sketch, a much larger history is
> retained beyond the current working set. In a variety of real world traces
> the policy had [near optimal hit
> rates|https://github.com/ben-manes/caffeine/wiki/Efficiency].
> Concurrency is achieved by buffering and replaying the operations, similar to
> a write-ahead log. A read is recorded into a striped ring buffer and writes
> to a queue. The operations are applied in batches under a try-lock by an
> asynchronous thread, thereby track the usage pattern without incurring high
> latencies
> ([benchmarks|https://github.com/ben-manes/caffeine/wiki/Benchmarks#server-class]).
> In YCSB benchmarks the results were inconclusive. For a large cache (99% hit
> rates) the two caches have near identical throughput and latencies with
> LruBlockCache narrowly winning. At medium and small caches, TinyLFU had a
> 1-4% hit rate improvement and therefore lower latencies. The lack luster
> result is because a synthetic Zipfian distribution is used, which SLRU
> performs optimally. In a more varied, real-world workload we'd expect to see
> improvements by being able to make smarter predictions.
> The provided patch implements BlockCache using the
> [Caffeine|https://github.com/ben-manes/caffeine] caching library (see
> HighScalability
> [article|http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html]).
> Edward Bortnikov and Eshcar Hillel have graciously provided guidance for
> evaluating this patch ([github
> branch|https://github.com/ben-manes/hbase/tree/tinylfu]).
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