rishabhdaim commented on PR #2898:
URL: https://github.com/apache/jackrabbit-oak/pull/2898#issuecomment-4444327116
`SegmentCachePolicyBenchmark` cacheCapacity~=1023 pool=10000 zipf=1.0
- Scenario A: Zipfian steady-state (AbstractTest timed run) ---
> CAFFEINE miss%= 26.0 hits=4,083,694 misses=1,437,306
evictions=1,436,285 evict%= 26.0
> LIRS miss%= 27.5 hits=4,002,834 misses=1,518,166
evictions=3,033,157 evict%= 54.9
> GUAVA miss%= 32.4 hits=3,734,365 misses=1,786,635
evictions=1,785,626 evict%= 32.3
- Scenario B: scan (50,000 segs) then Zipfian (warmup=20,000
measure=200,000 ops) ---
> CAFFEINE miss%= 25.3 hits= 149,331 misses= 50,669 evictions=
50,731 evict%= 25.4
> LIRS miss%= 27.5 hits= 144,914 misses= 55,086 evictions=
110,176 evict%= 55.1
> GUAVA miss%= 32.1 hits= 135,768 misses= 64,232 evictions=
64,244 evict%= 32.1
- Scenario C: cold-start regression (scan=9,000 working-set=3,000
measure=100,000 ops) ---
scan fills TinyLFU sketch at freq=1; working-set entries start at freq=0
> CAFFEINE miss%= 64.1 hits= 35,946 misses= 64,054 evictions=
63,963 evict%= 64.0
> LIRS miss%= 62.9 hits= 37,084 misses= 62,916 evictions=
127,912 evict%=127.9
> GUAVA miss%= 66.0 hits= 33,979 misses= 66,021 evictions=
66,028 evict%= 66.0
- Scenario D: uniform random / cache thrash (pool=25,000 = ~24x cache
measure=200,000 ops) ---
no hot data — uniform access over pool 25x cache; expected miss ~95%%
> CAFFEINE miss%= 95.5 hits= 9,091 misses= 190,909 evictions=
191,026 evict%= 95.5
> LIRS miss%= 93.5 hits= 13,062 misses= 186,938 evictions=
373,890 evict%=186.9
> GUAVA miss%= 95.9 hits= 8,194 misses= 191,806 evictions=
191,787 evict%= 95.9
- Scenario E: burst new content (burst=500 segs × 20 hits warmup=50,000
measure=100,000 ops) ---
warm Zipfian cache hit by burst of new segments; measures working-set miss
rate after burst subsides
> CAFFEINE miss%= 27.1 hits= 72,891 misses= 27,109 evictions=
27,110 evict%= 27.1
> LIRS miss%= 27.8 hits= 72,235 misses= 27,765 evictions=
55,038 evict%= 55.0
> GUAVA miss%= 32.3 hits= 67,699 misses= 32,301 evictions=
32,315 evict%= 32.3
- Scenario F: periodic GC/diff alternation (cycles=10 zipf/cycle=10,000
scan/cycle=2,000 measure=100,000 ops) ---
repeated small scans interleaved with Zipfian; cumulative sketch pollution
vs LRU recency aging
> CAFFEINE miss%= 25.3 hits= 74,680 misses= 25,320 evictions=
25,255 evict%= 25.3
> LIRS miss%= 27.8 hits= 72,235 misses= 27,765 evictions=
56,165 evict%= 56.2
> GUAVA miss%= 32.4 hits= 67,562 misses= 32,438 evictions=
32,446 evict%= 32.4
- Scenario G: write-heavy import then read-back (import=50,000
recent-window=2,000 measure=100,000 ops) ---
large sequential import followed by random reads of recently-imported
segments; recency (LRU) vs frequency (Caffeine) post-import
> CAFFEINE miss%= 47.2 hits= 52,801 misses= 47,199 evictions=
47,445 evict%= 47.4
> LIRS miss%= 46.9 hits= 53,052 misses= 46,948 evictions=
96,454 evict%= 96.5
> GUAVA miss%= 48.5 hits= 51,521 misses= 48,479 evictions=
48,477 evict%= 48.5
- Scenario H: sliding window / temporal locality (window=1,200 ~117% of
cache slide=200 pool=20,000 hits/item=2 measure=150,000 ops) ---
hot window slides forward; pure recency (LRU) is optimal; window > cache
forces evictions on every slide
> CAFFEINE miss%= 63.5 hits= 54,790 misses= 95,210 evictions=
95,218 evict%= 63.5
> LIRS miss%= 56.6 hits= 65,037 misses= 84,963 evictions=
169,800 evict%=113.2
> GUAVA miss%= 73.2 hits= 40,259 misses= 109,741 evictions=
109,758 evict%= 73.2
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