I have a message driven bean running on Glassfish. It receives hundreds of messages per second. After receiving a message, it needs to read the values in the JDBC database and process.
However, the values in database will only be updated one time a day or less. So what the MDB will read is consistent most of the time. So, is it there a good way to cache the content into the memory in order to increase the performance?
Update: is it posible to configure a in-memory database JDBC Connection Pool in Glassfish for the MDB?
You may get inspired by the identity map pattern and implement your own cache mechanism (with your own expiration policy), if you think that using a third party solution (like memcached) would be overkill.
The most obvious answer is to define the table as a MEMORY table type. If the underlying hardware is not prone to crashing and the OS is stable and there's a UPS attached, you might want to think about this. It also depends on the consequences of losing a number of transactions since the last backup whenever this fails. But performance-wise, this is lightning fast. More information can be found here for MySQL. (YMMV)
I've implemented multiple tables that way, and it worked great for me.
You can use a simple SoftReference HashMap based cache. Here is the complete implementation example SoftReference Cache In addition you can clear the complete Map periodically to bring in fresh data.
Or in case you can use 3rd party library, you might use ReferenceMap provided as part of Apache Collections.
Related
I have millions of rows to be read from database and multiple users come in a day to read the same data. so I want to create a cache. so that I don't have to go to database again for same data.
I have seen many option but couldn't find figure out which approach to use.
Creating my own cache I am thinking saving the data of a query result and writing in a file or
use some third party in memory caches?
Guava CacheBuilder ,LRUMap caching,whirlycache ,cache4j.
You are not the first person to have requirements like this, which is why there are dozens of cache implementations available as open source projects, and even a standard set of Java APIs for caching (JCache). If your needs go beyond those solutions, there are even commercial solutions that handle tens of terabytes of data transparently across RAM, flash, database, etc. If none of those are sufficient, then you should definitely write your own.
Its totally dependent on multiple factors. and i think answer will be based on environment, Size of data etc. here is the main points
You want to keep the cache in ram as much as possible because its faster to access than being in file system.
You can also use OS memory mapped files which does balance access vs utilization. I suggest any proven solution than creating your own
If you are running low on memory then you might need to ask question on what is more important like caching the top access data as they are most likely to be asked by client.
So there is not a sure or definite answer but you have to decide based on your constraints. Hope this helps
I think you are overengineering the problem, it isn't trivial to write a performant, transparent cache, unless you only need a simple HashMap to hold some values. You should focus on writing code to solve your domain problem and not writing too much framework code.
Stop reinventing the wheel, use either an in-memory cache (e.g. infinispan or redis) or a database (e.g. postgres). You will have less pain and better performance.
I'm part of a team architecting a Java web application wherein users will search for results in a relational database and then view them in tabular fashion in a browser. Users will then also have the option to subsequently view the same result set (or a subset of those results) in a separate browser window, using for example a charting tool. In other words, we need to give the user the ability to visualize the same result set records later (up to a limit of 24 hours).
Since searches on the system will be resource-intensive and just out of good common sense, we would like a clean way to cache each result set so that it can be pulled later from memory (RAM or disk). We are looking for a good approach to doing this caching, we believe others have done this before, and we prefer to use a best-practice or framework rather than building such a thing from scratch. The server will have plenty of RAM but since there could be hundreds of people using the system, we may need an approach that stores to RAM first but then can also cache to hard disk if RAM is getting full.
I believe it makes most sense to persist as Java objects but I'm open to better advice. We would like a vendor-neutral approach, so that if the database team chooses to switch vendors later we aren't stuck with a proprietary solution. Thanks.
I think what you might be looking for is Terracotta Ehcache. This does everything you mentioned and more. It is a free product that can be used to cache things in memory, overflow to disk, specify max cache sizes by either MB or # of items, and expire based on last access time or entry time.
I've seen http://www.jboss.org/infinispan/ used to do exactly that. It can cache to memory, disk and or database. I wouldn't say I love it (the configuration is not super easy and documentation is somewhat lacking) but it most certainly works and is actively maintained.
Being vendor neutral is all about writing an abstraction layer that is native to your application, then plugging in the cache service you would like to use behind this layer, while keeping your layer that exposes these operations to your main code the same.
There are plenty of ways to cache. Look into using various NoSql solutions.
Redis
Memcached
Most of the time you will serialize your object and persist it to your cache layer.
It just occurred to me why not to have most of the objects in a cache(memory) when an application start.
if it's not that large web application. Or to have a settings for how much I want to put in the cache/memory.
I just guess it could require to have something like below 1 GB RAM or a lot less.
Everything in order to speed up the application even more by not querying database.
Is it good idea?
Caching is definitely a good idea and is widely used, but it has to be implemented correctly. There are plenty of pitfalls if done incorrectly. Try looking into one of the big proven systems, like memcached.
Caching is definitely a good idea.
Databases are also not a catch-all solution, though you have to be careful about consistency between runs of your program. What if you change the data but your program crashes before you update it to the database?
There are also lightweight memory resident databases that can let you keep your current queries for now, but run much stuff from memory. Using an ORM tool instead of SQL is particularly effective for this since the switch is almost transparent.
Quickly becomes Not so good idea, when some other node starts updating database.
In that case your cache will be holding stale data.
You can maintain a cache of Frequently Used objects in the memory, just don't forget to add methods to refresh the cache when the underlying database state changes.
Eg: If you have a user's table and you need user names in many many pages, then load the entire table in cache at time of Application Startup, just make sure to update the cache when you are adding new users online or modifying / deleting entries from user table
You don't persist objects to database. What you persist is object's state. So that you can have exactly the same state even after your app stops/closes/restarts. If you want to keep states of your objects persisted, you have no choice, but to use db (or anything else, that allows you to write data to file system).
The details are beyond the scope of an answer here, but we have had good experience of using ehCache ( http://ehcache.org/ )
The combination of support for distributed caches, and overflow to disk has allowed us to keep large numbers of computationally heavy, but fairly unchanging pages in the cache for a site being served from multiple tomcats.
Distribution addresses the question of staleness (if you invalidate your items correctly) and the disk overflow allows us to basically cache everything which was just not feasible with an in-memory cache.
Of course the implementation is not trivial for a real world application, but it improved our performance significantly once the caches were bubbling.
I'm doing a school software project with my class mates in Java.
We store the info on a remote db.
When we start the application we pull all the information from the database and transform it into objects to use in our application (using java sql statemens).
In the application we edit some of these objects and then when we exit the application
we save or update information in the database using Hibernate.
As you see we dont use Hibernate for pulling in information, we use it just for saving and updating.
We have 2, but very similar problems.
The loading of object(when we start the app) and the saving of objects(with Hibernate) in the db(when closing the app) is taking too much time.
And our project its not a huge enterprise application, its a quite small app, we just manage some students, teachers, homeworks and tests. So our db is also very very small.
How could we increase performance ?
later edit: if we use a local database it runs very quick, it just runs slow on remote databases
Are you saying you are loading the entire database into memory and then manipulating it? If that is the case, why don't you instead simply use the database as a storage device, and do lookups and manipulation as necessary (using Hibernate if you like, or something else if you don't)? The key there is to make sure that you are using connection pooling, as that will reduce the connection time.
If this is what you are doing, then you could be running into memory issues as well - first, by not caching the entire database in memory, you will reduce memory and will spread out the network load from the beginning/end to the times when it needs to happen.
These 2 sentences are red flags for me :
When we start the application we pull
all the information from the database
and transform it into objects to use
in our application (using java sql
statemens). In the application we edit
some of these objects and then when we
exit the application we save or update
information in the database using
Hibernate.
Is there a requirements reason that you are loading all the information from the database into memory at startup, or why you're waiting until shutdown to save changes back in the database?
If not, I'd suggest a design change. If you've already got Hibernate mappings for the tables in the DB, I'd use Hibernate for both all of your CRUD (create, read, update, delete) operations. And, I'd only load the data that each page in your app needs, as it needs it.
If you can't make that kind of design change at this point, I think you've got to look closely at how you're managing the database connections. Are you using connection pools? Are you opening up multiple connections? Forgetting to release them?
Something else to look at. How are you using Hibernate to save the entities to the db? Are you doing a getHibernateTemplate().get on each one and then doing an entity.save or entity.update on each one? If so, that means you are also causing Hibernate to run a select query for each database object before it does a save or update. So, essentially, you'd be loading each database object twice (once at the beginning of the program, once before saving). To see if that's what's happening, you can turn on the show_sql property or use P6Spy to see exactly what queries Hibernate is running.
For what you are doing, you may very well be better off serializing your objects and writing them out to a flat file.
But, much more likely, you should just read / update objects directly from your database as needed instead of all at once, for all the reasons aperkins gives.
Also, consider what happens if your application crashes? If all of your updates are saved only in memory until the application is closed, everything would be lost if the app closes unexpectedly.
The difference in loading everything from a remote DB server versus loading everything from a local DB server is the network latency / pipe size. The network is a much smaller pipe than anything else. Two questions: first, how much data are we really talking about? Second, what is your network speed? 10/100/1000? Figure between 10 and 20% of your pipe size is going to be overhead due to everything from networking protocols to the actual queries themselves.
As others have stated, the way you've architected is usually high on the list of "don't do". When starting, pull only enough data to initialize the app. As the user works through it, pull what you need for that task.
The ONLY time you pull everything is when they are working in a disconnected state. In that case, you still don't load everything as objects in the application, you just work from a local data store which gets sync'ed with the remote server every so often.
The project its pretty much complete. we cant do large refactoring on it now.
I tried to use a second level cache for Hibernate when saving. EhCacheProvider.
in hibernate.xml:
net.sf.ehcache.hibernate.EhCacheProvider
i have done a config for the cache, ehcache.xml:
i have put the cache.jar in the project build path
and i have set the hibernate property for every class and set in the mapping.
But this cache doesn't seem to have an effect. I dont know if it works(if it is used).
Try minimising number of SQL queries, since every query has its own overhead.
You can enable database compression, which should speed things up when there is a lot of data.
Maybe you are connecting to the database many times?
Check the ping time of remote database server - it might be the problem.
As your application is just slow when running on a remote database server, I'd assume that the performance loss is due to:
Connecting to the server: try to reuse connections (pass the instance around) or use connection pooling
Query round-trip time: use as few queries as possible, see here in case of a hand-written DAL:
Preferred way of retrieving row with multiple relating rows
For hibernate you may use its batch functionality and adjust hibernate.batch_size.
In all cases, especially when you can't refactor larger parts of the codebase, use a profiler (method time or sql queries) to find the bottleneck. I bet you'll find thousands of queries, each taking 10ms RTT) which could be merged into one.
Some other things you can look into:
You can allocate more memory to the JVM
Use the jconsole tool to investigate what the bottlenecks are.
Why dont you have two separate threads?
Thread 1 will load your objects one by one.
Thread 2 will process objects as they are loaded.
Your app will seem more interactive at startup.
It never hurts to review the basics:
Improving speed means reducing time (obviously), and to do that, you find activities that take significant time but can be eliminated or replaced with something that uses less time. What I mean by activity is almost always a function call, method call, or property call, performed on a specific line of code for a specific purpose. If may invoke I/O or it may invoke computation, or both. If its purpose is not essential, then it can be optimized.
Many people use profilers to try to find these time-wasting lines of code, but most profilers miss the target because they look at functions, not lines, they go to sleep during I/O, and they worry about "self time".
Many more people try to guess what could be the problem, or they ask others to guess, such as by asking on SO. Such guesses, in the nature of guesses, are sometimes right - more often not, but people still invest time and resources in them.
There's a very simple way to find out for sure, without guessing, what could fruitfully be optimized, and here is one way to do it in Java.
Thanks for your answers. Their were more than helpful.
We completely solved this problem like so:
Refactored the LOAD code. Now it uses Hibernate with Lazy Fetching.
Refactored the SAVE code. Now it saves, just the data that was modified and right after the time it was modified. This way we dont have a HUGE save an the end.
Im amazed of how good it all went. The amount of new code we had to write was very very small.
I am currently in need of a high performance java storage mechanism.
This means:
1) I have 10,000+ objects with 1 - Many Relationship.
2) The objects are updated every 5 seconds, with the most recent updates persistent in the case of system failure.
3) The objects need to be queryable in a reasonable time (1-5 seconds). (IE: Give me all of the objects with this timestamp or give me all of the objects within these location boundaries).
4) The objects need to be available across various Glassfish installs.
Currently:
I have been using JMS to distribute the objects, Hibernate as an ORM, and HSQLDB to provide the needed recoverablity.
I am not exactly happy with the performance. Especially the JMS part of this.
After doing some Stack Overflow research, I am wondering if this would be a better solution. Keep in mind that I have no experience with what Terracotta gives me.
I would use Terracotta to distribute objects around the system, and something else need to give the ability to "query" for attributes of those objects.
Does this sound reasonable? Would it meet these performance constraints? What other solutions should I consider?
I know it's not what you asked, but, you may want to start by switching from HSQLDB to H2. H2 is a relatively new, pure Java DB. It is written by the same guy who wrote HSQLDB and he claims the performance is much better. I'm using it for some time now and I'm very happy with it. It should be a very quick transition (add a Jar, change the connection string, create the database) so it's worth a shot.
In general, I believe in trying to get the most of what I have before rewriting the application in a different architecture. Try profiling it to identify the bottleneck first.
At first, Lucene isn't your friend here. (read only)
Terracotta is to scale around at the Logical layer! Your problem seems not to be related to the processing logic. It's more around the Storage/Communication point.
Identify your bottleneck! Benchmark the Storage/Logic/JMS processing time and overhead!
Kill JMS issues with a good JMS framework (eg. ActiveMQ) and a good/tuned configuration.
Maybe a distributed key=>value store is your friend. Try Project Voldemort!
If you like to stay at Hibernate and HSQL, check out the Hibernate 2nd level cache and connection pooling (c3po, container driven...)!
Several Terracotta users have built systems like this in the past, so I can you tell you by proof of existence that it can be done. :)
Compass does have support for clustering with Terracotta so that might help you. I suspect you might get further faster by just being careful with how you create your clustered data structures.
Regarding your requirements and Terracotta:
1) 10k objects is quite small from a Terracotta perspective
2) 5 sec update rate doesn't seem like an issue. Might depend how many nodes there are and whether there is any natural partitioning you can take advantage of. All updates will be persistent.
3) 1-5 second query time seems quite easy. Building your own well-organized data structures for lookup is the tricky part. Obviously you want to avoid scanning all the data.
4) Terracotta currently supports Glassfish v1 and v2.
If you post on the Terracotta forums, you could probably get more Terracotta eyeballs on the problem.
I am currently working on writing the client for a very (very) fast Key/Value distributed hash DB that provides set + list semantics. The DB is C99 and requires GCC and right now I'm battling with good old Java network IO to break my current 30,000 get/sets per/sec barrier. Hope to be done within the week. Drop me a line through my account and I'll get back when its show time.
With such a high update rate, Lucene is almost definitely not what you're looking for, since there is no way to update a document once it's indexed. You'd have to keep all the object versions in the index and select the one with the latest time stamp, which will kill your performance.
I'm no DB expert, but I think you should look into any one of the distributed DB solutions that's been on the news lately. (CouchDB, Cassandra)
Maybe you should take a look to: Prevayler.
Your objects are always in mem.
The "changes" to your objects are persisted.
From time to time you are able to take a snapshot: every object is persisted.
You don't say what vendor you are using for JMS, but I wouldn't surprise me if you have some bottle neck there. I couldn't get more than 100 messages a second from ActiveMq, and whatever I tried in terms of configuration of acknowledgment, queue size, etc we were unable to soak the CPU beyond a few percent.
The solution was to batch many queries into one JMS message. We had a simple class that either sent a batch of messages when it got to 200 queries or reached a timeout (we used 20ms), which gave us a dramatic increase in message throughput.
Guaranteed messaging is going to be much slower than volatile messaging. Given every object is updated every few second, you might consider batching your updates (into say 500 changes or by time say 1-10 ms' worth), sending over volatile messaging, and batching your transactions. In this case you are more likely to be limited by bandwidth. Tuning your use case you may find smaller batch sizes also work efficiently. If bandwidth is critical (say you have a 10 MB connection or slower, then you could use compression over JMS)
You can achieve much higher performance with a custom solution (which also might be simpler) e.g. Hazelcast & JGroups are free (you can add a node(s) which does the database synchronization so your main app doesn't slow down). There are commercial products which handle in the order of half a million durable messages/sec.
Terracotta + jofti = queryable persistent clustered data structures
Search google for terracotta querymap or visit tusharkhairnar.blogspot.com for querymap blog
You may want to integrate timasync as well to update your database. Database is is your system of record use terracotta as caching and database offloading mechanism you can even batch async updates to make it faster so that I'd db contains fairly recent data
Tushar
tusharkhairnar.blogspot.com