I added an objectify sample into the performance samples:
http://slim3demo.appspot.com/performance/

One result:
The number of entities: 10000
low-level API:get: 1276 millis
Slim3: 1327 millis
Objectify: 3028 millis
JDO: 3222 millis

I have not profiled yet.
But I know java runtime reflections are very very slow on production server,
so slim3 creates the mapping logic between a model and an entity
as the source code when compiling.

Yasuo Higa

On Wed, Jun 8, 2011 at 10:18 AM, Ikai Lan (Google) <ika...@google.com> wrote:
> I doubt the low-level API is significantly faster than JDO (have not
> profiled, so can't tell you for sure). JDO just dispatches to low-level and
> does serialization/deserialization. That should really be a very small part
> of the entire operation.
> Reasons to use the low-level API include:
>  - it maps best to how the datastore works (schemaless)
>  - you always get new features the fastest (async datastore API)
> That being said, the Slim3 and Objectify projects move pretty quickly and
> add features almost as quickly as we do. They're one level removed from the
> schemaless nature of the datastore, but this fits better with the 95% use
> case.
>
> Ikai Lan
> Developer Programs Engineer, Google App Engine
> Blog: http://googleappengine.blogspot.com
> Twitter: http://twitter.com/app_engine
> Reddit: http://www.reddit.com/r/appengine
>
>
> On Wed, Jun 8, 2011 at 8:07 AM, Dennis Peterson <dennisbpeter...@gmail.com>
> wrote:
>>
>> That makes more sense, thanks.
>> I also found this online benchmark of JDO and LL, which has similar
>> results:
>> http://gaejava.appspot.com/
>>
>> On Tue, Jun 7, 2011 at 8:03 PM, Anders <blabl...@gmail.com> wrote:
>>>
>>> Yeah, that's what I suspected. Lazy loading. With the modification Slim3
>>> is almost as fast as the native API. Strange that JDO is so slow. I thought
>>> most of the time was for accessing the datastore, not for running the Java
>>> bytecode.
>>>
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