Yep, exactly.

It was not an issue before but the implementation changed and there were
some places that have not yet been fully converted.
That's why you see a slow-down if both the in-transaction-state as well as
the on-disk/persisted index state have to be consulted.

Will def. be fixed in 2.1 most certainly earlier.


On Mon, Jan 27, 2014 at 2:48 AM, Javad Karabi <karabija...@gmail.com> wrote:

> ah, would you mind elaborating a bit? i am just curious.
> is this because any index updates would not have been committed to the
> actual index until the transaction is committed, therefore there are now
> _2_ indexes that must be consulted when looking up an index? The index that
> is already committed, and the one that is soon to be committed?
>
>
> On Sun, Jan 26, 2014 at 7:41 PM, Michael Hunger <
> michael.hun...@neotechnology.com> wrote:
>
>> Yep, definitely. You'd batch your transaction to sizes like 1k elements
>> at a time.
>>
>> Usually it's more (20k-50k) but there is an issue in 2.0 that does makes 
>> findNodesByLabelAndProperty
>> slower when working in larger transactions :(
>>
>>
>> On Mon, Jan 27, 2014 at 2:27 AM, Javad Karabi <karabija...@gmail.com>wrote:
>>
>>> i should clarify that the arguement to firstOrNull is
>>> findNodesByLabelAndProperty, so i am assuming that most of the time is
>>> actually in that function, that findNodesByLabelAndProperty may be whats
>>> taking longer as the transaction grows
>>>
>>>
>>> On Sunday, January 26, 2014 7:10:55 PM UTC-6, Javad Karabi wrote:
>>>>
>>>> im importing data from a csv file, and when i wrap the whole thing in
>>>> one transaction, the performance is actually slower than multiple
>>>> transactions.
>>>> when i use one transaction, most of the time is spent in the closing of
>>>> the transaction, but when i wrap the whole thing in one transaction, most
>>>> of the time is spent in IteratorUtil.firstOrNull, (although, again, using
>>>> multiple transactions is faster)
>>>>
>>>> does the performance of firstOrNull degrade as the transaction it is in
>>>> gets larger?
>>>>
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