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Low loading/searching-as-you-type performance with large (1000+ entries) DBs #6044

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ytzemih opened this issue Feb 28, 2020 · 3 comments · Fixed by #6270
Closed
1 task done

Low loading/searching-as-you-type performance with large (1000+ entries) DBs #6044

ytzemih opened this issue Feb 28, 2020 · 3 comments · Fixed by #6270

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@ytzemih
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ytzemih commented Feb 28, 2020

JabRef 5.0-pull-5945-merge.440--2020-02-19--fcaeef1
Linux 5.4.19-050419-generic amd64
Java 13.0.2

Steps to reproduce the behavior:

  1. Open a large BIB file (say 1000-2000+ entries)
  2. You might have to wait up to 2-3 seconds. The issue gets a real one once you have 5-6 files from the last session that altogether form several 1000 entries to be loaded.
  3. Type keyword into search field, you might be required to wait a bit after each letter entered

Loading a larger DB (1000+ entries) takes unusually long (even on my i7 8th gen laptop).
JR5 to me shows to be significantly slower than JR3/4, which was faster even on my old machine, an i7 4th gen. This way, search renders to be difficult to use because typed-in letters don't show in the text field before the search procedure has returned.

JabRef 3/4 still had the option to enter the search term and hit enter to start the search.

Hence, it would be highly useful to at least have a switch to toggle between "old-school" and "as-you-type" search.

My observation might be linked to that issue.

@AEgit
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AEgit commented Feb 29, 2020

As mentioned by @ytzemih this might be a duplicate of:
#5071

See also #5735 and #5510 (comment) for possibly related performance problems.

@tobiasdiez
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This should be fixed in the latest development version. Could you please check the build from http://builds.jabref.org/master/. Thanks! Please remember to make a backup of your library before trying-out this version.

@ytzemih
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ytzemih commented Apr 13, 2020

@tobiasdiez Thanks for the fix. I tried
JabRef 5.1--2020-04-12--771af5d
Linux 5.4.19-050419-generic amd64
Java 14
with a file having >3500 entries and search-as-you-type works now much more smoothly.

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4 participants