Hey thanks for clarifying! I actually ended up setting it up as 1x
master-only, 2x master-eligible data-nodes, realizing that I would need 3
eligible masters while putting it all together.
On the heap problems, could you be more specific about what you are
referring to, or maybe point me towards
Three master nodes are enough, for as many data nodes as you wish to add.
You can search this mailing list for discussions where kimchy explained the
dedicated master nodes, and how it fits for split-brain situations
For example
https://groups.google.com/forum/#!topic/elasticsearch/dxjpMd4vNXQ
Hey all!
Background: I am using elasticsearch with logstash to do some log analysis.
My use-case is write-heavy, and I have configured ES accordingly. After
experimenting with different setups, I am considering the following
implementation:
*separate log processing from ES cluster*
1x
If you have an index per (some time period) then you can create the new indexes
with more shards when you have more hardware and leave the old ones with their
old number. You can also allocate more shards then noses in preparation for
getting more nodes. I believe those are the standard tactics
Do not put single-node cluster (no replicas) in production. Never use
single nodes except for development and demo. Always use replica and at
least 3 nodes with minimum master nodes = 2 to avoid splitbrain in
production.
Having 17G RAM on a master-only server is more than enough of a beefy
server
Thanks for the responses! After reading up on the split brain problem, I
am moving to a three-node cluster with one master-only (on logstash
server), one master/data, and one data-only server
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That is a misconception. To avoid split-brain, it is a good idea to set up
master-only (data-less) nodes so they can not face heap problems. But that
is not all, more important is to have at least three master eligible nodes,
in case you get network disruptions. Additionally, you should set up at
