See also Elasticsearch: administration
Indexes: store all the JSON documents and are stored in a shard
Shards: a complete Lucene database
Indices can be stored in multiple shards.
i.e. if we have multiple nodes then shards will migrate across nodes – aka rebalancing.
Replicas: an exact duplicate of a shard (except designated as a Replica). Can configure as many replicas per shard as you like.
E.g. here you have 2 shards plus 2 replicas. Each contain the one index (I01).
http://www.snowcrash.eu/wp-content/uploads/2018/10/Screen-Shot-2018-10-16-at-11.05.46-AM-300x176.png 300w, http://www.snowcrash.eu/wp-content/uploads/2018/10/Screen-Shot-2018-10-16-at-11.05.46-AM-768x449.png 768w, http://www.snowcrash.eu/wp-content/uploads/2018/10/Screen-Shot-2018-10-16-at-11.05.46-AM-588x344.png 588w" sizes="(max-width: 815px) 100vw, 815px" />
Replicas are Read-only and can serve data thereby increasing scale.
https://www.elastic.co/guide/en/elasticsearch/guide/current/replica-shards.html
Node Roles
Can run all on a single node but makes it more efficient if you separate them out.
Data:
- most hard working
- contains all shards
- don’t typically receive service queries
- tend to be the beefiest
Client
- gateway to the cluster
- big increase in performance
- handle all query requests and redirects them to the data nodes
Master
- brains of cluster
- maintains cluster state
- all nodes have a copy of the state but only the master can update cluster state
Capacity Planning
Data Nodes
To test, set up a load test on one node until node is completely saturated.
E.g. 1M documents on 1 node = 4.0 seconds then probably need 4 nodes to get to 1.0 second response time.
Master Nodes
To avoid split brain scenario: set minimum_master_nodes to (number of master nodes / 2 ) + 1]
Should have at least 3.
Client Nodes
Could exist behind a load balancer.
E.g. in summary, a setup could be:
4 data nodes, 3 master nodes, 2 client nodes – i.e. a total of 9 nodes.
Server Requirements
- CPU: more cores the better (favour over clock speed) – i.e. better to run more processes concurrently rather than run them faster
- RAM: 64GB for Data nodes is ideal (e.g. in AWS an
i3.2xlarge– https://aws.amazon.com/ec2/instance-types/i3/ ) - Disks: fastest disks possible. Safe to use RAID 0 for more speed though not fault tolerant but Elasticsearch has shards. Avoid using NAS as performance will drop drastically
- Networking: keep clustering within same data centre as shard rebalancing requires fast networking
- VMs: don’t use for data nodes in production
Running on AWS
Data: i3.2xlarge
Master:
Client:
See also https://www.elastic.co/guide/en/elasticsearch/plugins/master/cloud-aws-best-practices.html
and https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing