How to setup ElasticSearch cluster with auto-scaling on Amazon EC2?

Question

There is a great tutorial elasticsearch on ec2 about configuring ES on Amazon EC2. I studied it and applied all recommendations.

Now I have AMI and can run any number of nodes in the cluster from this AMI. Auto-discovery is configured and the nodes join the cluster as they really should.

The question is How to configure cluster in way that I can automatically launch/terminate nodes depending on cluster load?

For example I want to have only 1 node running when we don't have any load and 12 nodes running on peak load. But wait, if I terminate 11 nodes in cluster what would happen with shards and replicas? How to make sure I don't lose any data in cluster if I terminate 11 nodes out of 12 nodes?

I might want to configure S3 Gateway for this. But all the gateways except for local are deprecated.

There is an article in the manual about shards allocation. May be I'm missing something very basic but I should admit I failed to figure out if it is possible to configure one node to always hold all the shards copies. My goal is to make sure that if this would be the only node running in the cluster we still don't lose any data.

The only solution I can imagine now is to configure index to have 12 shards and 12 replicas. Then when up to 12 nodes are launched every node would have copy of every shard. But I don't like this solution cause I would have to reconfigure cluster if I might want to have more then 12 nodes on peak load.

Answer 1

I can give you an alternative approach using aws elastic search service(it will cost little bit more than normal ec2 elasticsearch).Write a simple script which continuously monitor the load (through api/cli)on the service and if the load goes beyond the threshold, programatically increase the nodes of your aws elasticsearch-service cluster.Here the advantage is aws will take care of the scaling(As per the documentation they are taking a snaphost and launching a completely new cluster).This will work for scale down also.

Regarding Auto-scaling approach there is some challenges like shard movement has an impact on the existing cluster, also we need to more vigilant while scaling down.You can find a good article on scaling down here which I have tested.If you can do some kind of intelligent automation of the steps in the above link through some scripting(python, shell) or through automation tools like Ansible, then the scaling in/out is achievable.But again you need to start the scaling up well before the normal limits since the scale up activities can have an impact on existing cluster.

Question: is possible to configure one node to always hold all the shards copies?

Answer: Yes,its possible by explicit shard routing.More details here

Answer 2

I would be tempted to suggest solving this a different way in AWS. I dont know what ES data this is or how its updated etc... Making a lot of assumptions I would put the ES instance behind a ALB (app load balancer) I would have a scheduled process that creates updated AMI's regularly (if you do it often then it will be quick to do), then based on load of your single server I would trigger more instances to be created from the latest instance you have available. Add the new instances to the ALB to share some of the load. As this quiet down I would trigger the termination of the temp instances. If you go this route here are a couple more things to consider

• Use spot instances since they are cheaper and if it fits your use case
• The "T" instances dont fit well here since they need time to build up credits
• Use lambdas for the task of turning things on and off, if you want to be fancy you can trigger it based on a webhook to the aws gateway
• Making more assumptions about your use case, consider putting a Varnish server in front of your ES machine so that you can more cheaply provide scale based on a cache strategy (lots of assumptions here) based on the stress you can dial in the right TTL for cache eviction. Check out the soft-purge feature for our ES stuff we have gotten a lot of good value from this.
• if you do any of what i suggest here make sure to make your spawned ES instances report any logs back to a central addressable place on the persistent ES machine so you don't lose logs when the machines die

Answer 3

I think this is a concern in general when it comes to employing auto-scalable architecture to meet temporary demands, but data still needs to be saved. I think there is a solution that leverages EBS

• map shards to specific EBS volumes. Lets say we need 15 shards. We will need 15 EBS Volumes

• amazon allows you to mount multiple volumes, so when we start we can start with few instances that have multiple volumes attached to them

• as load increase, we can spin up additional instance - upto 15.

The above solution is only advised if you know your max capacity requirements.

Answer 4

In cases where the elasticity of your application is driven by a variable query load you could setup ES nodes configured to not store any data (node.data = false, http.enabled = true) and then put them in for auto scaling. These nodes could offload all the HTTP and result conflation processing from your main data nodes (freeing them up for more indexing and searching).

Since these nodes wouldn't have shards allocated to them bringing them up and down dynamically shouldn't be a problem and the auto-discovery should allow them to join the cluster.

Answer 5

Auto scaling doesn't make a lot of sense with ElasticSearch.

Shard moving and re-allocation is not a light process, especially if you have a lot of data. It stresses IO and network, and can degrade the performance of ElasticSearch badly. (If you want to limit the effect you should throttle cluster recovery using settings like cluster.routing.allocation.cluster_concurrent_rebalance, indices.recovery.concurrent_streams, indices.recovery.max_size_per_sec . This will limit the impact but will also slow the re-balancing and recovery).

Also, if you care about your data you don't want to have only 1 node ever. You need your data to be replicated, so you will need at least 2 nodes (or more if you feel safer with a higher replication level).

Another thing to remember is that while you can change the number of replicas, you can't change the number of shards. This is configured when you create your index and cannot be changed (if you want more shards you need to create another index and reindex all your data). So your number of shards should take into account the data size and the cluster size, considering the higher number of nodes you want but also your minimal setup (can fewer nodes hold all the shards and serve the estimated traffic?).

So theoretically, if you want to have 2 nodes at low time and 12 nodes on peak, you can set your index to have 6 shards with 1 replica. So on low times you have 2 nodes that hold 6 shards each, and on peak you have 12 nodes that hold 1 shard each.

But again, I strongly suggest rethinking this and testing the impact of shard moving on your cluster performance.