Jvm uses the client vm; make sure to run `java` with the server vm for best performance by adding `-server` to the command line – How to solve related issues

Overview

Elasticsearch has many settings that can cause significant performance problems if not set correctly. To prevent this happening, Elasticsearch carries out “bootstrap checks” to ensure that these important settings have been covered. If any of the checks fail, Elasticsearch will write an error to the logs and will not start. In this guide we cover common bootstrap checks you should know and how to configure your settings correctly to pass the checks successfully.

Bootstrap checks are carried out when the network.host setting in:

network.host: 0.0.0.0

If network host is not set and you use the default localhost, then Elasticsearch will consider the node to be in development mode, and bootstrap checks will not be enforced.

Common issues with bootstrap checks

If you install elasticsearch using RPM or Debian packages (strongly recommended) then most of the important configuration is already done for you, and the only bootstrap checks you are likely to run up against are the following.

Heap size check

The minimum and maximum heap sizes specified in jvm.options (or via environment variables) must be equal to one another.

File descriptors check

Minimum file descriptors must have been set to at least 65535

Memory lock check

There are various methods used to prevent memory swapping, and you must use one of them.  The most common is to set in elasticsearch.yml

bootstrap.memory_lock: true

For this to be effective you must give permission to elasticsearch to enable this. There are various ways to do so, depending upon your operating system.

Discovery configuration checks

At least one of the following properties must be configured in elasticsearch.yml to ensure that your node can form a cluster properly:

• discovery.seed_hosts
• discovery.seed_providers
• cluster.initial_master_nodes

Less common bootstrap check issues

If you are not using RPM or debian packages, you may come across the following issues:

Max number of threads check

You must allow your system to create at least 4096 threads. In linux this is done by editing /etc/security/limits.conf and adjusting the nproc setting.

Max file size check

Your system should be able to create unlimited file sizes. In linux this is done by editing /etc/security/limits.conf and adjusting the fsize setting

Max virtual memory check

The system should be able to create unlimited virtual memory for the elasticsearch user.

This is done by editing /etc/security/limits.conf 

<user> - as unlimited

Max map count check

The system must be able to use mmap effectively.  This is done by running the command 

sysctl vm.max_map_count 262144

Other checks

The following checks are also carried out, but are rarely found:

OsX File Descriptor Check
Client Jvm Check
UseS erial GC Check
System Call Filter Check
Might Fork Check
On Error Check
On Out Of Memory Error Check
Early Access Check
G1GC Check
All Permission Check

Overview

Any application that interfaces with Elasticsearch to index, update or search data, or to monitor and maintain Elasticsearch using various APIs can be considered a client

It is very important to configure clients properly in order to ensure optimum use of Elasticsearch resources.

Examples

There are many open-source client applications for monitoring, alerting and visualization, such as ElasticHQ, Elastalerts, and Grafana to name a few. On top of Elastic client applications such as filebeat, metricbeat, logstash and kibana that have all been designed to integrate with Elasticsearch.

However it is frequently necessary to create your own client application to interface with Elasticsearch. Below is a simple example of the python client (taken from the client documentation):

from datetime import datetime
from elasticsearch import Elasticsearch
es = Elasticsearch()

doc = {
    'author': 'Testing',
    'text': 'Elasticsearch: cool. bonsai cool.',
    'timestamp': datetime.now(),
}
res = es.index(index="test-index", doc_type='tweet', id=1, body=doc)
print(res['result'])

res = es.get(index="test-index", doc_type='tweet', id=1)
print(res['_source'])

es.indices.refresh(index="test-index")

res = es.search(index="test-index", body={"query": {"match_all": {}}})
print("Got %d Hits:" % res['hits']['total']['value'])
for hit in res['hits']['hits']:
    print("%(timestamp)s %(author)s: %(text)s" % hit["_source"])

All of the official Elasticsearch clients follow a similar structure, working as light wrappers around the Elasticsearch rest API, so if you are familiar with Elasticsearch query structure they are usually quite straightforward to implement.

Notes and Good Things to Know

Use official Elasticsearch libraries.

Although it is possible to connect with Elasticsearch using any HTTP method, such as a curl request, the official Elasticsearch libraries have been designed to properly implement connection pooling and keep-alives.   

Official Elasticsearch clients are available for java, javascript, Perl, PHP, python, ruby and .NET. Many other programming languages are supported by community versions.

Keep your Elasticsearch version and client versions in sync.

To avoid surprises, always keep your client versions in line with the Elasticsearch version you are using. Always test clients with Elasticsearch since even minor version upgrades can cause issues due to dependencies or a need for code changes. 

Load balance across appropriate nodes.

Make sure that the client properly load balances across all of the appropriate nodes in the cluster.  In small clusters this will normally mean only across data nodes (never master nodes), or in larger clusters, all dedicated coordinating nodes (if implemented) .

Ensure that the Elasticsearch application properly handles exceptions.

In the case of Elasticsearch being unable to cope with the volume of requests, designing a client application to handle this gracefully (such as through some sort of queueing mechanism) will be better than simply inundating a struggling cluster with repeated requests.