The DataNucleus AccessPlatform provides persistence and retrieval of data to a range of datastores using a range of APIs, with a range of query languages. We provide a FactSheet for DataNucleus AccessPlatform in PDF and ODF formats.
DataNucleus AccessPlatform Checklist
|3.3 (Galileo)||Planning||-||Free, Commercial||HTML|
|3.2 (Copernicus)||Developed||Mar/2013||Free, Commercial||HTML||HTML | PDF|
|2.2 (Geiger)||Retired||Dec/2010||None||HTML||HTML | PDF|
|2.1 (Thomson)||Retired||Jun/2010||None||HTML||HTML | PDF|
|2.0 (Bohr)||Retired||Jan/2010||None||N/A||HTML | PDF|
|1.1 (Rutherford)||Retired||Feb/2009||None||N/A||HTML | PDF|
|1.0 (Faraday)||Retired||Sep/2008||None||N/A||HTML | PDF|
Current development effort is focussed on maintenance for the current version 3.2 and on planning the next version 3.3. The features currently planned for 3.3 are
Do you want to help us develop this release or the next release of Access Platform? Does your company want to have some control over the direction we take? If so please go to our Forum and register your interest. Please also refer to this page for developing DataNucleus source code further.
In most applications, the performance of the persistence layer is very unlikely to be a bottleneck. More likely the design of the datastore itself, and in particular its indices are more likely to have the most impact, or alternatively network latency. That said, it is the DataNucleus projects' committed aim to provide the best performance possible, though we also want to provide functionality, so there is a compromise with respect to resource.
|What is a benchmark? This is simply a series of persistence operations performing particular things e.g persist n objects, or retrieve n objects. If those operations are representative of your application then the benchmark is valid to you.|
To find (or create) a benchmark appropriate to your project you need to determine the typical persistence operations that your application will perform. Are you interested in persisting 100 objects at once, or 1 million, for example? Then when you have a benchmark appropriate for that operation, compare the persistence solutions.
For performance of DataNucleus Access Platform, please refer to the Performance Tuning Guide and also refer to the following blog entry for our take on performance of DataNucleus AccessPlatform. And then the later blog entry about how to tune for bulk operations
There is an interesting presentation on JPA provider performance that was presented at GeeCon 2012 by Patrycja Wegrzynowicz. This presentation takes the time to look at what operations the persistence provider is performing, and does more than just "persist large number of flat objects into a single table", and so gives you something more interesting to analyse. DataNucleus comes out pretty well in many situations. You can also see the PDF here.
The PolePosition benchmark is a project on SourceForge to provide a benchmark of the write, read and delete of different data structures using the various persistence tools on the market. JPOX was run against this benchmark just before being renamed as DataNucleus and the results are found in the DataNucleus Wiki. The input data used for that benchmark run is found in JPOX SVN. Some comments on the PolePos benchmark :-