On Jun 27, 2013, at 1:25 PM, Manik Surtani <msurt...@redhat.com> wrote:
> Good work, Radim. > > I presume you're collaborating with Galder on this? Yeah, we're collaborating. We came up with the test plan and cache stores to test together :). > As for Karsten's FCS implementation, I too have issues with the key set and > value offsets being solely in memory. However I think that could be improved > by storing only a certain number of keys/offsets in memory, and flushing the > rest to disk again into an index file. ^ Karsten's implementation makes this relatively easy to achieve because it already keeps this mapping in a LinkedHashMap (with a given max entries limit [1]) assuming removeEldestEntry() is overriden to flush to disk older entries. Some extra logic would be needed to bring back data from the disk too… but your suggestion below is also quite interesting... > I believe LevelDB follows a similar design, but I think Karsten's FCS will > perform better than LevelDB since it doesn't attempt to maintain a sorted > structure on disk. ^ In-memory, the structure can optionally be ordered if it's bound [1], otherwise it's just a normal map. How would be store it at the disk level? B+ tree with hashes of keys and then linked lists? > One approach to maintaining keys and offsets in memory could be a > WeakReference that points to the key stored in the in-memory DataContainer. > Once evicted from the DC, then the CacheStore impl would need to fetch the > key again from the index file before looking up the value in the actual > store. ^ Hmmm, interesting idea… has the potential to safe the memory space by not having to keep that extra data structure in the cache store. > This way we have hot items always in memory, semi-hot items with offsets in > memory and values on disk, and cold items needing to be read off disk > entirely (both offset and value). Also for write-through and write-behind, > as long as the item is hot or warm (key and offset in memory), writing will > be pretty fast. My worry about Karsten's impl is writing actually. If you look at the last performance numbers in [2], where we see the performance difference of force=true and force=false in Karsten's cache store compared with LevelDB JNI, you see that force=false is fastest, then JNI LevelDB, and the force=true. Me wonders what kind of write guarantees LevelDB JNI provides (and the JAVA version)... > WDYT? [1] http://goo.gl/rPYp2 > > - M > > On 27 Jun 2013, at 10:33, Radim Vansa <rva...@redhat.com> wrote: > >> Oops, by the cache store I mean the previously-superfast >> KarstenFileCacheStore implementation. >> >> ----- Original Message ----- >> | From: "Radim Vansa" <rva...@redhat.com> >> | To: "infinispan -Dev List" <infinispan-dev@lists.jboss.org> >> | Sent: Thursday, June 27, 2013 11:30:53 AM >> | Subject: Re: [infinispan-dev] Cachestores performance >> | >> | I have added FileChannel.force(false) flushes after all write operations in >> | the cache store, and now the comparison is also updated with these values. >> | >> | Radim >> | >> | ----- Original Message ----- >> | | From: "Radim Vansa" <rva...@redhat.com> >> | | To: "infinispan -Dev List" <infinispan-dev@lists.jboss.org> >> | | Sent: Thursday, June 27, 2013 8:54:25 AM >> | | Subject: Re: [infinispan-dev] Cachestores performance >> | | >> | | Yep, write-through. LevelDB JAVA used FileChannelTable implementation >> | | (-Dleveldb.mmap), because Mmaping is not implemented very well and causes >> | | JVM crashes (I believe it's because of calling non-public API via >> | | reflection >> | | - I've found post from the Oracle JVM guys discouraging the particular >> | | trick >> | | it uses). After writing the record to the log, it calls >> | | FileChannel.force(true), therefore, it should be really on the disc by >> that >> | | moment. >> | | I have not looked into the JNI implementation but I expect the same. >> | | >> | | By the way, I have updated [1] with numbers when running on more data (2 >> GB >> | | instead of 100 MB). I won't retype it here, so look there. The >> performance >> | | is much lower. >> | | I may try also increase JVM heap size and try with a bit more data yet. >> | | >> | | Radim >> | | >> | | [1] https://community.jboss.org/wiki/FileCacheStoreRedesign >> | | >> | | ----- Original Message ----- >> | | | From: "Erik Salter" <an1...@hotmail.com> >> | | | To: "infinispan -Dev List" <infinispan-dev@lists.jboss.org> >> | | | Sent: Wednesday, June 26, 2013 7:40:19 PM >> | | | Subject: Re: [infinispan-dev] Cachestores performance >> | | | >> | | | These were write-through cache stores, right? And with LevelDB, this >> was >> | | | through to the database file itself? >> | | | >> | | | Erik >> | | | >> | | | -----Original Message----- >> | | | From: infinispan-dev-boun...@lists.jboss.org >> | | | [mailto:infinispan-dev-boun...@lists.jboss.org] On Behalf Of Radim >> Vansa >> | | | Sent: Wednesday, June 26, 2013 11:24 AM >> | | | To: infinispan -Dev List >> | | | Subject: [infinispan-dev] Cachestores performance >> | | | >> | | | Hi all, >> | | | >> | | | according to [1] I've created the comparison of performance in >> | | | stress-tests. >> | | | >> | | | All setups used local-cache, benchmark was executed via Radargun >> | | | (actually >> | | | version not merged into master yet [2]). I've used 4 nodes just to get >> | | | more >> | | | data - each slave was absolutely independent of the others. >> | | | >> | | | First test was preloading performance - the cache started and tried to >> | | | load >> | | | 1GB of data from harddrive. Without cachestore the startup takes about >> 2 >> | | | - >> | | | 4 >> | | | seconds, average numbers for the cachestores are below: >> | | | >> | | | FileCacheStore: 9.8 s >> | | | KarstenFileCacheStore: 14 s >> | | | LevelDB-JAVA impl.: 12.3 s >> | | | LevelDB-JNI impl.: 12.9 s >> | | | >> | | | IMO nothing special, all times seem affordable. We don't benchmark >> | | | exactly >> | | | storing the data into the cachestore, here FileCacheStore took about 44 >> | | | minutes, while Karsten about 38 seconds, LevelDB-JAVA 4 minutes and >> | | | LevelDB-JNI 96 seconds. The units are right, it's minutes compared to >> | | | seconds. But we all know that FileCacheStore is bloody slow. >> | | | >> | | | Second test is stress test (5 minutes, preceded by 2 minute warmup) >> where >> | | | each of 10 threads works on 10k entries with 1kB values (~100 MB in >> | | | total). >> | | | 20 % writes, 80 % reads, as usual. No eviction is configured, therefore >> | | | the >> | | | cache-store works as a persistent storage only for case of crash. >> | | | >> | | | FileCacheStore: 3.1M reads/s 112 writes/s // on one node the >> | | | performance was only 2.96M reads/s 75 writes/s >> | | | KarstenFileCacheStore: 9.2M reads/s 226k writes/s // yikes! >> | | | LevelDB-JAVA impl.: 3.9M reads/s 5100 writes/s >> | | | LevelDB-JNI impl.: 6.6M reads/s 14k writes/s // on one node the >> | | | performance was 3.9M/8.3k - about half of the others >> | | | Without cache store: 15.5M reads/s 4.4M writes/s >> | | | >> | | | Karsten implementation pretty rules here for two reasons. First of all, >> | | | it >> | | | does not flush the data (it calls only RandomAccessFile.write()). Other >> | | | cheat is that it stores in-memory the keys and offsets of data values >> in >> | | | the >> | | | database file. Therefore, it's definitely the best choice for this >> | | | scenario, >> | | | but it does not allow to scale the cache-store, especially in cases >> where >> | | | the keys are big and values small. However, this performance boost is >> | | | definitely worth checking - I could think of caching the disk offsets >> in >> | | | memory and querying persistent index only in case of missing record, >> with >> | | | part of the persistent index flushed asynchronously (the index can be >> | | | always >> | | | rebuilt during the preloading for case of crash). >> | | | >> | | | The third test should have tested the scenario with more data to be >> | | | stored >> | | | than memory - therefore, the stressors operated on 100k entries (~100 >> MB >> | | | of >> | | | data) but eviction was set to 10k entries (9216 entries ended up in >> | | | memory >> | | | after the test has ended). >> | | | >> | | | FileCacheStore: 750 reads/s 285 writes/s // one >> node >> | | | had >> | | | only 524 reads and 213 writes per second >> | | | KarstenFileCacheStore: 458k reads/s 137k writes/s >> | | | LevelDB-JAVA impl.: 21k reads/s 9k writes/s // a bit >> | | | varying >> | | | performance >> | | | LevelDB-JNI impl.: 13k-46k reads/s 6.6k-15.2k writes/s // the >> | | | performance varied a lot! >> | | | >> | | | 100 MB of data is not much, but it takes so long to push it into >> | | | FileCacheStore that I won't use more unless we exclude this loser from >> | | | the >> | | | comparison :) >> | | | >> | | | Radim >> | | | >> | | | [1] https://community.jboss.org/wiki/FileCacheStoreRedesign >> | | | [2] https://github.com/rvansa/radargun/tree/t_keygen >> | | | >> | | | ----------------------------------------------------------- >> | | | Radim Vansa >> | | | Quality Assurance Engineer >> | | | JBoss Datagrid >> | | | tel. +420532294559 ext. 62559 >> | | | >> | | | Red Hat Czech, s.r.o. >> | | | Brno, Purkyňova 99/71, PSČ 612 45 >> | | | Czech Republic >> | | | >> | | | >> | | | _______________________________________________ >> | | | infinispan-dev mailing list >> | | | infinispan-dev@lists.jboss.org >> | | | https://lists.jboss.org/mailman/listinfo/infinispan-dev >> | | | >> | | | >> | | | _______________________________________________ >> | | | infinispan-dev mailing list >> | | | infinispan-dev@lists.jboss.org >> | | | https://lists.jboss.org/mailman/listinfo/infinispan-dev >> | | >> | | _______________________________________________ >> | | infinispan-dev mailing list >> | | infinispan-dev@lists.jboss.org >> | | https://lists.jboss.org/mailman/listinfo/infinispan-dev >> | >> | _______________________________________________ >> | infinispan-dev mailing list >> | infinispan-dev@lists.jboss.org >> | https://lists.jboss.org/mailman/listinfo/infinispan-dev >> >> _______________________________________________ >> infinispan-dev mailing list >> infinispan-dev@lists.jboss.org >> https://lists.jboss.org/mailman/listinfo/infinispan-dev > > -- > Manik Surtani > ma...@jboss.org > twitter.com/maniksurtani > > Platform Architect, JBoss Data Grid > http://red.ht/data-grid > > > _______________________________________________ > infinispan-dev mailing list > infinispan-dev@lists.jboss.org > https://lists.jboss.org/mailman/listinfo/infinispan-dev -- Galder Zamarreño gal...@redhat.com twitter.com/galderz Project Lead, Escalante http://escalante.io Engineer, Infinispan http://infinispan.org _______________________________________________ infinispan-dev mailing list infinispan-dev@lists.jboss.org https://lists.jboss.org/mailman/listinfo/infinispan-dev
