I'm struggling with a performance issue when adding or deleting entities that belong to a one-to-many collection set on another entity.
Tuning application containers Tuning underlying systems including databases and OS. Tuning all of them without a well planned methodology is very time consuming and may well be ineffective. An important part of a good tuning methodology is prioritizing tuning areas.
Memory and CPU based access can provide much lower latency and greater throughput than disk and network based access. Example 1 We tuned an HQL query to select electricity deals from about 30 seconds to less than 1 second. If we had worked on the Garbage Collector the improvements are likely to have been much smaller — perhaps a few milliseconds or several seconds at best, which would have been unnoticeable to traders compared to the HQL improvement.
Another important part of a good tuning methodology is deciding when to optimize . Proactive tuning advocates promote tuning right from the start, ie in the business rules and the design phase, and carrying on tuning across the whole SDLC because they think it is often very costly to change business rules and redesign later on.
Reactive advocates promote tuning at the end of the SDLC because they complain that early tuning can often complicate the design and coding. A trade-off is required to balance the tuning and coding.
The authors' experience suggests that proper early tuning results in more prudent design and careful coding. However, it is not always feasible to carry out a lot of early tuning because you don't know exactly where the application's bottlenecks are without profiling it first, on top of which the application is often still evolving.
A practical approach is as follows: Identify the main bottlenecks most of which will predictably fall into Hibernate, and the business rules and design the number depends on your tuning goal; but three to five is a good start.
Modify your application to eliminate them. Test your application and repeat from Step 1 again until you have achieved your tuning goal.
In the following sections we explain specific tuning techniques in an approximate order of tuning impact that is, those listed first generally have the highest impact.
You can find a configuration sample from this web site. However, most commercial tools such as JProbe  are very expensive.
Compared to its commercial counterparts, it is fairly basic. It does, however, provide a lot of debug and tuning information.
Accordingly we highlight some points related to Hibernate tunings. Through business requirements collection and tunings, you should know: Data retrieval characteristics including reference data, read-only data, read groups, read size, searching criteria, and data grouping and aggregation.
Data modification characteristics including data changes, change groups, change size, false modification allowance, databases are all changes in one database or in more than one? Data relationships such as associations, generalization, realization and dependency.
Based on business requirements, you come up with an optimal design where you decide your application type On-line Transaction Processing OLTP or data warehouse, or close to either and tier structure separate persistence and service tiers or combinedcreate domain objects usually as POJO and decide data aggregation location aggregation in the database takes advantage of powerful database functions and saves you network bandwidth; however it is usually not portable except for such standard aggregations as COUNT, SUM, AVG, MIN and MAX.
Aggregation in the application server allows you to apply more complex business logic; however you need to load detailed data to your application tier first. Initially they wanted to show most table columns.
The application took as much as 30 minutes due to loading up to 1 million rows to the front UI, even though the DB responded within about 1 minute.
After reanalysis, analysts removed all but 14 columns. Because many optional high cardinality columns were gone, the aggregation grouping from the remaining columns returned far less data than before and the data loading time was cut to be acceptable in most cases.
Example 3 Electricity Hourly traders often modify 24 shaped hours that include 2 properties:READ_WRITE: should be used when an entity can be modified and there are great chances that changes in their condition occur simultaneously, is the strategy that consumes more resources. NON_STRICT_READ_WRITE: strategy should be used when an entity can be modified but it is unusual that the changes occur at the same time.
Hi,We have succesfully implemented EHCache with Hibernate ,JBoss and Sping x in a standalone server timberdesignmag.comr we intend . Oct 14, · Re: Wildfly 10 - Interceptor stack is not supported in simple cache Michael Feldman Oct 10, PM (in response to Paul Ferraro) If I switch all of the entities using NONSTRICT_READ_WRITE to READ_WRITE, I don't get any errors.
Jul 20, · My understanding of these terms is that Hibernate defines 4 cache types -- read-only, read-write, non-strict-read-write, and transactional. Transactional allows reads and writes and uses JTA transactions for coordinating access to cache data.
Jul 22, · Hi, I've spent this day trying to understand the Hibernate CacheConcurrencyStrategies NONSTRICT_READ_WRITE, READ_WRITE and TRANSACTIONAL, when used with EhCache.
Although I do have a good understanding of database transaction semantics, as well as Hibernate (/JPA) Session (/EntityManager) semantics, the combination with the second level cache. Introduction. In my previous post, I introduced the READ_ONLY CacheConcurrencyStrategy, which is the obvious choice for immutable entity timberdesignmag.com cached data is changeable, we need to use a read-write caching strategy and this post will describe how NONSTRICT_READ_WRITE second-level cache works..