Goldman Sachs gave a talk about how they're using Elasticsearch to index
5TB of log data per day. I can't find the video of the talk, but from a
blogpost about it:

Next was Indy Tharmakumar from our hosts Goldman
Sachs<http://www.goldmansachs.com/>,
showing how his team have built powerful support systems using
ElasticSearch to index log data. Using 32 1 core CPU instances the system
they have built can store 1.2 billion log lines with a throughput up to
40,000 messages a second (the systems monitored produce 5TB of log data
every day). Log data is queued up in Redis <http://redis.io/>, distributed
to many Logstash <http://logstash.net/> processes, indexed by Elasticsearch
with a Kibana <http://rashidkpc.github.io/Kibana/> front end. They learned
that Logstash can be particularly CPU intensive but Elasticsearch itself
scales extremely well. Future plans include considering Apache
Kafka<http://kafka.apache.org/> as
a data backbone.


On 19 April 2014 06:46, 熊贻青 <xiong.jag...@gmail.com> wrote:

> We have a cluster with 10 nodes, 48g heap for each ES process. The total
> indexing rate is about 25000 doc per second, about 20 indices actively
> receiving new data. I'm really courious to compare and evaluate the
> indexing performance numers.
>
> Thanks!
>
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