Google’s App Engine is one of the best services the company provides to developers. The constant updates to the core framework of the engine definitely helps that good will to go a long way. Good news then that only after a month, a new version of App Engine is up and ready.

The Google App Engine blog Google+App+Engine+Blog%29″>announced the release of App Engine 1.6.4 today. Unlike other incremental numbered updates, App Engine receives quite the hefty update this time with plenty of features that should make developers happy.

The new update brings with it system wide changes. These changes include:

Logs – Now that the new settings for log storage have been available for one month, logs over the limit you specify will be deleted.

Datastore Index Stats – The Datastore Statistics page in the Admin Console now displays the storage used by your Datastore Indexes in addition to your Datastore Entities.

Blobstore Migration – The Datastore Migration tool now includes an experimental option which allows you to migrate your Blobstore objects during the migration process from M/S to HRD. We strongly encourage all applications to migrate to HRD.

Datastore Backup to Google Cloud Storage – In 1.6.3, we launched backup and restore to Blobstore, and in this release we’ve added the ability to backup your data to Google Cloud Storage.

Memcache viewer – We’ve introduced the ability to view Memcache statistics and examine memcache entries by key.

Serve objects from Google Cloud Storage – You can now serve blobs directly from Google Cloud Storage as well as Blobstore.

Runtime and datastore framework saw less changes, but they are important nonetheless. These changes include:

Threads – Both Java and Python now offer background threads when running on backends as an experimental feature. Additionally, we’ve added the ability to use threads for frontend requests in Java to match Python 2.7.

NDB for Python – The NDB API has graduated from experimental and is now a fully supported feature. This next-generation datastore API improves data modeling and querying and has been built from the ground up to support an asynchronous computing model.

JPA 2 and JDO 3 for Java – We have made significant improvements to App Engine’s DataNucleus plugin. This experimental release of version 2.0 of the plugin adds support for JPA 2, JDO 3, and contains over 40 bug fixes. Check out the full release notes here.

If you want to know more about these updates and the bug fixes in the release, check out the release notes for both Java and Python.