On Jun 27, 2013, at 1:52 PM, Galder Zamarreño <galder(a)redhat.com> wrote:
On Jun 27, 2013, at 1:25 PM, Manik Surtani <msurtani(a)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)…
^ Oh, Radim mentioned this topic already in a previous post. LevelDB JAVA library seems to
provide force=true equivalent logic.
> WDYT?
[1]
http://goo.gl/rPYp2
>
> - M
>
> On 27 Jun 2013, at 10:33, Radim Vansa <rvansa(a)redhat.com> wrote:
>
>> Oops, by the cache store I mean the previously-superfast KarstenFileCacheStore
implementation.
>>
>> ----- Original Message -----
>> | From: "Radim Vansa" <rvansa(a)redhat.com>
>> | To: "infinispan -Dev List" <infinispan-dev(a)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" <rvansa(a)redhat.com>
>> | | To: "infinispan -Dev List" <infinispan-dev(a)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" <an1310(a)hotmail.com>
>> | | | To: "infinispan -Dev List"
<infinispan-dev(a)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-bounces(a)lists.jboss.org
>> | | | [mailto:infinispan-dev-bounces@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
>> | | |
>> | | |
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>
> --
> Manik Surtani
> manik(a)jboss.org
>
twitter.com/maniksurtani
>
> Platform Architect, JBoss Data Grid
>
http://red.ht/data-grid
>
>
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--
Galder Zamarreño
galder(a)redhat.com
twitter.com/galderz
Project Lead, Escalante
http://escalante.io
Engineer, Infinispan
http://infinispan.org
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