Infinispan embedded off-heap cache
by yavuz gokirmak
Hi all,
Is it possible to use infinispan as embedded off-heap cache.
As I understood it is not implemented yet.
If this is the case, we are planning to put effort for off-heap embedded
cache development.
I really need to hear your advices,
best regards
12 years, 4 months
Design change in Infinispan Query
by Sanne Grinovero
Hello all,
currently Infinispan Query is an interceptor registering on the
specific Cache instance which has indexing enabled; one such
interceptor is doing all what it needs to do in the sole scope of the
cache it was registered in.
If you enable indexing - for example - on 3 different caches, there
will be 3 different Hibernate Search engines started in background,
and they are all unaware of each other.
After some design discussions with Ales for CapeDwarf, but also
calling attention on something that bothered me since some time, I'd
evaluate the option to have a single Hibernate Search Engine
registered in the CacheManager, and have it shared across indexed
caches.
Current design limitations:
A- If they are all configured to use the same base directory to
store indexes, and happen to have same-named indexes, they'll share
the index without being aware of each other. This is going to break
unless the user configures some tricky parameters, and even so
performance won't be great: instances will lock each other out, or at
best write in alternate turns.
B- The search engine isn't particularly "heavy", still it would be
nice to share some components and internal services.
C- Configuration details which need some care - like injecting a
JGroups channel for clustering - needs to be done right isolating each
instance (so large parts of configuration would be quite similar but
not totally equal)
D- Incoming messages into a JGroups Receiver need to be routed not
only among indexes, but also among Engine instances. This prevents
Query to reuse code from Hibernate Search.
Problems with a unified Hibernate Search Engine:
1#- Isolation of types / indexes. If the same indexed class is
stored in different (indexed) caches, they'll share the same index. Is
it a problem? I'm tempted to consider this a good thing, but wonder if
it would surprise some users. Would you expect that?
2#- configuration format overhaul: indexing options won't be set on
the cache section but in the global section. I'm looking forward to
use the schema extensions anyway to provide a better configuration
experience than the current <properties />.
3#- Assuming 1# is fine, when a search hit is found I'd need to be
able to figure out from which cache the value should be loaded.
3#A we could have the cache name encoded in the index, as part
of the identifier: {PK,cacheName}
3#B we actually shard the index, keeping a physically separate
index per cache. This would mean searching on the joint index view but
extracting hits from specific indexes to keep track of "which index"..
I think we can do that but it's definitely tricky.
It's likely easier to keep indexed values from different caches in
different indexes. that would mean to reject #1 and mess with the user
defined index name, to add for example the cache name to the user
defined string.
Any comment?
Cheers,
Sanne
12 years, 5 months
singleton @Listeners
by Mircea Markus
This is a problem that pops up constantly:
User: "I add a listener to my distributed/replicated cache but this gets invoked numOwners times - can I make that to be invoked only once cluster wise?"
Developer: "Yes, you can! You have to do that and that..."
What about a "singleton" attribute on the Listener? Would make the reply shorter:
Developer: "Use @Listener(singleton=true)"
Cheers,
Mircea
12 years, 11 months
L1 Consistency with Sync Caches
by William Burns
First off I apologize for the length.
There have been a few Jiras recently that have identified L1 consistency
issues with both TX and non TX sync caches. Async caches with L1 have
their own issues as well, but I only wanted to talk about sync caches.
https://issues.jboss.org/browse/ISPN-3197
https://issues.jboss.org/browse/ISPN-2965
https://issues.jboss.org/browse/ISPN-2990
I have proposed a solution in
https://github.com/infinispan/infinispan/pull/1922 which should start L1
consistency down the right track. There are quite a few comments on it if
you want to look into it more, but because of that I am moving this to the
dev mailing list.
The key changes in the PR are the following (non-tx):
1. Concurrent reads for a key that can retrieve a remote value are
"corralled" into a single thread of execution for that given key. This
would reduce network traffic with concurrent gets for the same key. Note
the "corralling" only happens on a per key basis.
2. The single thread that is doing the remote get would update the L1 if
able (without locking) and make available the value to all the requests
waiting on the get.
3. Invalidations that are received would first check to see if there is a
current remote get occurring for it's keys. If there is it will attempt to
cancel the L1 write(s) before it occurs. If it cannot cancel the L1 write,
then it must also wait on the current remote get completion and
subsequently run the invalidation. Note the cancellation would fail when
the remote get was done and it is in the middle of updating the L1, so this
would be very small window.
4. Local writes will also do the same thing as the invalidation with
cancelling or waiting. Note that non tx local writes only do L1
invalidations and don't write the value to the data container. Reasons why
I found at https://issues.jboss.org/browse/ISPN-3214
5. Writes that require the previous value and don't have it in the L1 would
also do it's get operations using the same "corralling" method.
4/5 are not currently implemented in PR.
This approach would use no locking for non tx caches for all L1 operations.
The synchronization point would be done through the "corralling" method
and invalidations/writes communicating to it.
Transactional caches would do almost the same thing as non-tx. Note these
changes are not done in any way yet.
1. Gets would now update the L1 immediately after retrieving the value
without locking, but still using the "corralling" technique that non-tx
does. Previously the L1 update from a get was transactional. This
actually would remedy issue [1]
2. Writes currently acquire the remote lock when committing, which is why
tx caches are able to update the L1 with the value. Writes would do the
same cancellation/wait method as non-tx.
3. Writes that require the previous value and don't have it in the L1 would
also do it's get operations using the same method.
4. For tx cache [2] would also have to be done.
[1] -
https://issues.jboss.org/browse/ISPN-2965?focusedCommentId=12779780&page=...
[2] - https://issues.jboss.org/browse/ISPN-1540
Also rehashing is another issue, but we should be able to acquire the state
transfer lock before updating the L1 on a get, just like when an entry is
committed to the data container.
Any comments/concerns would be appreciated.
Thanks,
- Will
13 years, 1 month
protobuf as a marshalling format for infinispan remote-query
by Adrian Nistor
Hi all,
as some probably know protobuf was chosen as the serialization format
for the remote query [1]. Them main reason for choosing it was that is
/simple/, time tested, has good support for schema evolution, is nearly
ubiquitous (some people say google uses it :)), has multiple language
support and most importantly it mandates the existence of a schema for
our objects - the proto file. We need that schema on the server side to
be able to extract indexable fields from those binary cache values and
index them without the need to unmarshall them into plain java domain
objects.
And I need to stress that the format was chosen, rather than the actual
API/library provided by Google [2]. While the protobuf wire format is
superb, Google's approach to create a library for marshaling objects
to/from a protobuf stream is heavily based on code generation via the
protoc tool. Both the marshalling code /and the entities/ to be
marshaled (your beloved domain model) are generated. This does not work
well if you want to bring your own domain classes to the party.
So what we did is create a small set of support classes on top of
google's low level wire format classes to assist users in marshaling
their own domain model to the protobuf wire format without using
google's code generator. These attempts are hosted on a small github
project experiment [3] that will be moved to infinispan once we have a
conclusion. This project contains several modules demonstrating the
attempts. [4] explains the purpose of each module. So far, the approach
in module /stream-like/ offers the best user experience. I would name
this plan A, which is going to be implemented for infinispan 6.0.
Plan A can also use the classes generated by google's protoc code
generator tool. So if somebody prefers to go old school it can still
work well. Also, the amount of new support code we added on top of
google's library is small, so I don't foresee any nightmare in porting
this to another language.
I would like to get some feedback from anyone who has some time to have
a look at [3], specifically at the /stream-like/ module.
Stream-like is about 89.9% implemented. There are still some
unimplemented methods but do not mind them for the review.
And finally, if anyone could suggest a better name for the stream-like
package? I can't think of any other option except /streamlike/ :) (which
might be trade-marked) So any other options? If no options then we'll
just call it /marshaling/. I'll move this to the ispn branch once I know
the package name :)
Have a nice weekend guys!
--------------
[1] https://community.jboss.org/wiki/RemoteQueryDesignInInfinispan
[2] https://developers.google.com/protocol-buffers/
[3] https://github.com/anistor/protobuf-playground
[4] https://github.com/anistor/protobuf-playground/blob/master/README.md
13 years, 1 month
L1 Data Container
by William Burns
All the L1 data for a DIST cache is stored in the same data container as
the actual distributed data itself. I wanted to propose breaking this out
so there is a separate data container for the L1 cache as compared to the
distributed data.
I thought of a few quick benefits/drawbacks:
Benefits:
1. L1 cache can be separately tuned - L1 maxEntries for example
2. L1 values will not cause eviction of real data
3. Would make https://issues.jboss.org/browse/ISPN-3229 an easy fix
4. Could add a new DataContainer implementation specific to L1 with
additional optimizations
5. Help with some concurrency issues with L1 without requiring wider
locking (such as locking a key for an entire ClusteredGet rpc call) -
https://issues.jboss.org/browse/ISPN-3197.
Drawbacks:
1. Would require, depending on configuration, an additional thread for
eviction
2. Users upgrading could have double memory used up due to 2 data containers
Both?:
1. Additional configuration available
a. Add maxEntires just like the normal data container (use data
container size if not configured?)
b. Eviction wakeup timer? We could just reuse the task cleanup
frequency?
c. Eviction strategy? I would think the default data container's would
be sufficient.
I was wondering what you guys thought.
Thanks,
- Will
13 years, 1 month
Cachestores performance
by Radim Vansa
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
13 years, 1 month
New bundler performance
by Radim Vansa
Hi,
I was going through the commits (running tests on each of them) to seek the performance regression we've recently discovered and it seems that our test (replicated udp non-transactional stress test on 4 nodes) experiences a serious regression on the commit
ISPN-2848 Use the new bundling mechanism from JGroups 3.3.0 (73da108cdcf9db4f3edbcd6dbda6938d6e45d148)
The performance drops from about 7800 writes/s to 4800 writes/s, and from 1.5M reads/s to 1.2M reads/s (having slower reads in replicated mode is really odd).
It seems that the bundler is not really as good as we hoped for - it may be the bottleneck. I have tried to create another bundler which shares the queue between 4 instances of TransportQueueBundler (so, 4 threads are actually sending the messages which go into one queue) and the performance mildly improved - to 5200 writes/s, but that's not enough.
Radim
Note: you may have seen my conversation with Pedro Ruivo on IRC about the bundler several days ago, in that time our configuration had old bundler. This was fixed, but as I have not built Infinispan properly (something got cached), I have not noticed the regression between these builds.
-----------------------------------------------------------
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
13 years, 1 month
