Hi Jon,

Could you please post an EXPLAIN of that query?

Thanks

Luigi

Il giorno mar 7 mag 2019 alle ore 11:23 'Jon' via OrientDB <
orient-database@googlegroups.com> ha scritto:

>
> Hi Luigi,
>
> thanks for your fast reply. I' am doing performance tests in OrientDB
> 3.0.18 locally and 3.0.3 in our test environment. The index type is
> UNIQUE(SBTREE). The index is a composite key composed of an id and the
> timestamp. From the logs I can definitely confirm that the query above
> takes the most of the time (per attached vertex) of all remaining
> operations (like attaching edge etc.).  In orient studio if I execute:
>
> SELECT FROM ANYVERTEX WHERE t < 99999999999 AND id=... ORDER BY t DESC LIMIT 1
>
>
> it takes 0.133 sec (which improves with caching) and with
>
> SELECT FROM ANYVERTEX WHERE t < 1 AND id=... ORDER BY t DESC LIMIT 1
>
>
> only 0.054.
>
>
> On Tuesday, May 7, 2019 at 8:44:54 AM UTC+2, Luigi Dell'Aquila wrote:
>
>> Hi Jon
>>
>> Using an index should definitely solve the problem, so it's strange that
>> it didn't work in your case.
>> Which OrientDB version are you using? And what kind of index did you
>> define?
>>
>> Thanks
>>
>> Luigi
>>
>> Il giorno lun 6 mag 2019 alle ore 13:44 'Jon' via OrientDB <
>> orient-...@googlegroups.com> ha scritto:
>>
>>> Let's say I have the following list of vertices (connected by edges) in
>>> the orient database:
>>>
>>>
>>>  [t=1] --> [t=2] --> [t=3] --> [t=4] --> [t=5] --> [t=6] --> [t=7]
>>>
>>>
>>> Each vertex has a timestamp t. I now want to receive the last vertex
>>> before a given date. Example: give me the last vertex before t=5, which is
>>> t=4.
>>>
>>> Currently I'am using the following query to do this:
>>>
>>>
>>>  SELECT FROM ANYVERTEX WHERE t < 5 ORDER BY t DESC LIMIT 1
>>>
>>>
>>> This is working fine when having up to let's say 1000 elements but the
>>> performance of that query drops with the number of elements inserted in the
>>> list. I already tried using an index, which improved the overall
>>> performance, but the problem, that the performance drops with the amount of
>>> elements still persists.
>>>
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