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From Dibyendu Bhattacharya <dibyendu.bhattach...@gmail.com>
Subject Re: How do I present Blur
Date Fri, 06 Jan 2017 12:45:06 GMT
In my previous job, I was working with Blur and even presented Blur in
Lucene Conference . I tried to integrate Blur with Spark Streaming and
presented the same .

http://www.slideshare.net/lucidworks/near-real-time-indexing-kafka-messages-into-apache-blur-presented-by-dibyendu-bhattacharya-pearson-north-america

https://www.youtube.com/watch?v=n7lfYhJgtJo



Regards,
Dibyendu


On Fri, Jan 6, 2017 at 4:49 PM, Lukáš Vlček <lukas.vlcek@gmail.com> wrote:

> Hi Aaron,
>
> question regarding the Blur data ingestion focus:
>
> Do I read it correctly that Blur is not near-real time system (like both ES
> and Solr are)? For example would Blur be a valid candidate for aggregated
> logging use case? How long does it usually take for indexed data to become
> searchable (ms, sec, mins)?
>
> As for retention of the data what are the strategies to drop old data from
> the index? For example is there anything like dropping old data based on
> index name patters (given the index name contains timestamp)?
>
> thanks,
> Lukáš
>
>
> > - Massive data ingestion
> >
> > Basically the focus on ingestion was not on latency but rather having the
> > ability to incrementally add large amounts of data to the index that is
> > likely also very large on it's own.  The project uses Yarn MR for this
> and
> > it is not a quick way to bring data but if your needs are to index large
> > chunks of data incrementally it works very well.  Also if a full reindex
> > was needed this could done easily as well.  Something to point out here
> is
> > that the MR indexing puts very little strain on the running system to
> > perform the updates/reindexes I believe this differs from how ES and Solr
> > are implemented.
>

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