Hello ,

My advice would be to keep all the logs in a single index , but apply index
tailing.
That is write logs of a day or hour ( depending upon traffic) to each index
like logstash does.
So name of the index would be of format logs-`yyyy-MM-dd`
This way , you wont be stuck with the fixed shard problem and dynamic
horizontal scaling can be achieved.
Also , it would be a wise idea to remove old logs using TTL facility OR
closing old index or even take a snapshot and remove the index.

TTL -
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-ttl
Index Close -
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-open-close.html#indices-open-close

Thanks
          Vineeth





On Thu, Sep 11, 2014 at 7:39 AM, Konstantin Erman <[email protected]> wrote:

> We use Elasticsearch to aggregate several types of logs - web server logs,
> application logs, windows event logs, statistics, etc.
>
> As far as I understand I can do one of the following:
> 1, Send each log to its own index and when I need to combine them in query
> - specify several indices in Kibana settings;
> 2. Send all logs to the same index (we turn them over every day) and give
> logs from different sources different document types;
> 3. Do more or less nothing, push all documents together without
> distinguishing them explicitly;
>
> My question is - what are advantages and disadvantages of each approach?
> We have substantial amount of logs going in every second, but querying is
> rather rare, at least so far.
>
> Thank you!
> Konstantin
>
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