Note: This is a follow up of What is the overhead of Entity Framework tracking? which asks about a rather old version of Entity Framework.
One of the team I am working with have used no tracking behavior at global level rather than at context or query level. This lead to strange code in the repositories since all injected context instances are not tracked: adding requires manual attaching, update has to explicit and some other explicit code related to navigation properties.
When asked about this, they invoked performance reasons, but failed to indicate a source.
My solution would be to use no tracking at context instance level. This would remove virtually all cumbersome code related to saving changes and the only issue would be when the programmer forgets to set no tracking for the instance. My assumption is that would not be such a big deal under normal circumstances (trees of several hundreds models).
This article suggests a big difference (both memory and CPU) between systematic usage of no tracking and everything tracked, but it is not clear if this only related to this (the difference seems much bigger than just the cloned data required for tracking).
Question: What is the overhead of tracking in Entity Framework 3.1?
What is the overhead of tracking in Entity Framework 3.1?
It's exactly the same as attaching every entity you query to the change tracker manually.
You can also opt-in to tracking for a single query with .AsTracking().
I am trying the new ehcache version but I noticed that, apparently, there are no search APIs.
Is this normal or am I missing something? Documentation says nothing about that.
The main reason is that searching in a cache has very strange semantics in the face of expiry and eviction. Since Ehcache 3 is focusing on what it is good at - the caching use case - we decided to shave some features that are more targeted at the storage use case.
We're going to migrate our application from eXist-db 1.4.1 to ~2.2 (probably RC2).
I`m wondering if anybody already did such a migration and what impediments they met?
I already found some documentation about this on official website and tried to Google, but didn't find much. For now I know that there were significant changes in the security model and some APIs are also changed. But still I want to know if somebody investigated further or maybe can share success story.
The main difference between 1.4.1 and 2.1/2.2 is that stored XQueries now need the executable flag to be set. You can fix your permissions automatically using a query as described in the documentation.
It is also possible that some of your existing queries report errors on 2.1/2.2, which they did not before. In nearly all cases this happens because 1.4 was less strict about the XQuery specification and processed expressions which should not be allowed (the standard as well as the implementation evolved). Also, the query engine may now do additional checks to prevent potential issues. Usually the error messages by the compiler should directly lead to the code you have to fix. This may cost a few minutes, but it's worth the effort. Apart from this, no particular migration issues have been reported.
I noticed that Spring reference application (Sagan) uses the SimpleCacheManager implementation. See here for source code of Sagan.
I was surprised by this choice because I thought that all but small applications running on a single node would use something like a Redis cache manager and not the simple cache manager.
How can a large application like Sagan -which I assume runs on cloudfoundry- use this simple implementation?
Any comment welcome.
Well, the SimpleCacheManager choice has been made because it was the simplest solution that could possibly work. Note that Sagan is, at least for now, not storing a lot of data in that cache and merely using it to respect various APIs rate-limiting and get better performance on some parts of the application.
Yes, Sagan is running on CloudFoundry (see this presentation) and is using CF marketplace services.
Even if cache consistency between instances is not a constraint for now, we could definitely add another marketplace service, here a Redis Cloud instance, and use this as a central cache repository.
Now that we're considering using that cache for more features, it even makes sense to at least consider that use case, since it could lower our monthly bill (pay a small fee for a redis service and use less memory for our CF instances).
In any case, thanks a lot balteo for this insightful question, we've created a Github issue for that.
Question is clear as you see in the title, it would be appreciated to hear your ideas about adv./disadv. differences between them.
UPDATE:
I have decided to use Hazelcast because of the advantages like distributed caching/locking mechanism as well as the extremely easy configuration while adapting it to your application.
We tried both of them for one of the largest online classifieds and e-commerce platform. We started with ehcache/terracotta(server array) cause it's well-known, backed by Terracotta and has bigger community support than hazelcast. When we get it on production environment(distributed,beyond one node cluster) things changed, our backend architecture became really expensive so we decided to give hazelcast a chance.
Hazelcast is dead simple, it does what it says and performs really well without any configuration overhead.
Our caching layer is on top of hazelcast for more than a year, we are quite pleased with it.
Even though Ehcache has been popular among Java systems, I find it less flexible than other caching solutions. I played around with Hazelcast and yes it did the job, it was easy to get running etc and it is newer than Ehcache. I can say that Ehcache has much more features than Hazelcast, is more mature, and has big support behind it.
There are several other good cache solutions as well, with all different properties and solutions such as good old Memcache, Membase (now CouchBase), Redis, AppFabric, even several NoSQL solutions which provides key value stores with or without persistence. They all have different characteristics in the sense they implement CAP theorem, or BASE theorem along with transactions.
You should care more about, which one have the functionality you want in your application, again, you should consider CAP theorem or BASE theorem for your application.
This test was done very recently with Cassandra on the cloud by Netflix. They reached to million writes per second with about 300 instances. Cassandra is not a memory cache but you data model is like a cache, which is consist of key value pairs. You can as well use Cassandra as a distributed memory cache.
Hazelcast has been a nightmare to scale and stability is still a major issue.
The dedicated client to grid component choices are
The messy version that cant survive node loss anywhere, negating the point of backups (superclient), or
An incredibly slow native client option that does not allow for any type of load balancing to processing nodes in the grid.
If any host could request records from this data grid it would be a sweet design, but you are stuck with those two lackluster option to get anything out of it.
Also multiple issues with database thread pools locking up on individual members and not writing anything to the databases, causing permanent records loss is a frequent issue and we often have to take the whole thing down for hours to refresh any of the JVM's. Split brain is also still an issue, although in 1.9.6 it seems to have calmed down a little.
Rallying to move to Ehcache and improving the database layer instead of using this as a band-aid.
Hazelcast serializes everything whenever there is a node (standard-one), so the data you will save to Hazelcast must implement serialization.
http://open.bekk.no/efficient-java-serialization/
Hazelcast has been a nightmare for me. I was able to get it "working" in a clustered Websphere environment. I use the term "working" loosely. First, all of Hazelcast's documentation is out of date and only shows examples using deprecated method calls. Trying to use the new code without comments in the Javadocs and no examples in the documentation is very hard. Also, the J2EE container code simply does not work at this point because it does not support XA transactions in Websphere. An error is thrown calling code that follows their only J2EE example explicitly(it does look like Milestone 3.0 is addressing this). I had to forget about joining Hazelcast to a J2EE transaction. It does seem Hazelcast is definitely geared to a non EJB/Non-J2EE container environment. Making calls to Hazelcast.getAllInstances() fails to retain any information about Hazelcast's state when switching from one enterprise java bean to another. That forces me to create a new Hazelcast instance just to run calls that give me access to my data. That causes many Hazelcast Instances to start up on the same JVM. Also,retrieving data from Hazelcast is not fast. I tried retrieving data using both the Native Client and directly as a member of the cluster. I stored 51 lists, each containing only 625 objects in Hazelcast. I could not perform a query directly on a list and did not want to store a map just to get access to that feature (SQL operations can be performed on a map). It took about a half second to retrieve each list of 625 objects because Hazelcast Serializes the entire list and sends it over the wire rather than just giving me the delta (what has changed). Another thing, I had to switch to a TCPIP configuration and explicitly list the ip addresses of the servers I wanted to be in the cluster. The default Multicast configuration did not work and from the group discussions in google, other people are experiencing that difficulty as well. To sum up; I did eventually get 8 machines communicating in a cluster through many hours of torturous programmatic configuration and trial and error (the documentation will be little help) but when I did, I still had no control over the number of instances and partitions being created on each JVM due to the half finished nature of Hazelcast for EJB/J2EE and it was VERY SLOW. I implemented a real use case in the unemployment insurance application I work on and the code was much faster making direct calls to the database. It would have been cool if Hazelcast worked as advertised because I really did not want to use a separate service to implement what I am trying to do. I have used MongoDB extensively so I may skip the whole in memory cache and just serialize my objects as documents in a separate repository.
One advantage of Ehcache is that it is backed by a company (Terracotta) that does extensive performance, failover, and platform testing in a large performance lab. Terracotta provides support, indemnity, etc. For many companies, that sort of thing is important.
I have not used Hazelcast but I've heard that it is easy to use and that it works. I haven't heard anything with respect to scalability or performance of Hazelcast vs Terracotta/Ehcache but given the amount of scalability and failover testing that Terracotta does, it's hard for me to imagine that Hazelcast would be competitive in a production deployment. But I presume it would work fine for smaller uses.
[Bias: I'm a former employee of Terracotta.]
Developers describe Ehcache as "Java's Most Widely-Used Cache". Ehcache is an open-source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches. On the other hand, Hazelcast is detailed as "Clustering and highly scalable data distribution platform for Java". With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
Ehcache and Hazelcast are primarily classified as "Cache" and "In-Memory Databases" tools respectively.