In the most recent announcement
the following caught my eye:
"Another example is a company which uses Logstash and Elasticsearch not
only for all their application logs, but also for all of their application
metrics. The ability to tie metrics indicating high CPU usage to a log
message of “mmm, we shouldn’t really get here” has proven to be invaluable
more than once."
I've been looking a lot at dedicated multi-server timeseries metrics stores
(opentsdb, kairosdb, blueflood etc) and I wonder how ES compares.
Specifically, I'm looking for a system that does:
- load distribution across servers (reads and writes),
- HA (data in+out should be 100% available in case of node failures) and
self healing (replication) in case nodes go down.
- the ability to aggregate data in the storage system itself (i.e. given
minutely datapoints, give me the per-hour max, or min, or avg, or mean. or
histograms across a certain timerange)
- easy or trivial to deploy (I know ES kicks ass here)
nice to have:
- balance data to prevent nodes with unequal disk capacities running full.
I'm not sure how appropriate ES is because:
- there's no need for flexible schema. datapoints are basically (timestamp,
value) pairs. not "documents". no need for unique "id's". I'm concerned
about storage space and performance here. (value could be float or int)
- fixed intervals means it should be easy to seek to the location of the
data, no real indices needed, and in fact this means timestamps don't need
to be stored, they are implicit. does this work with ES?
I noticed a lot of the information about ES is "top down" (what the
features are and how to use them). is there any "bottom-up" material that
conveys the internal design of ES, and what makes it applicable (or not)
for certain use cases?
- If I wanted to build a metrics system with ES, how would I go about it?
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