Transform finished indexing all data. initiating stop. – How to solve related issues

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.

Index and indexing in Elasticsearch - 3 min

Overview

Indexing is the process of adding documents to and updating documents on an Elasticsearch index.

Examples

In its simplest form, you can index a document like this:

POST /test/_doc
{
  "message": "Opster Rocks Elasticsearch Management"
}

This will create the index “test” (if it doesn’t already exist) and add a document with the source equal to the body of the POST call.  In this case, the ID will be created automatically. If you repeat this command, a second document will be created with an identical source but a different ID.
Alternatively, you can do this: 

PUT /test/_doc/1
{
  "message": "Opster Elasticsearch Management and Troubleshooting"
}

This is almost the same, but in this case, the call sets the ID of the document to 1. If you repeat the command modifying the message, you will modify the original document, replacing the previous source with the latest source.

However note that this is NOT the same as an UPDATE operation, which is a different API and allows us to modify certain fields of the document while leaving others unchanged.

Notes and good things to know

You can set your own ID if necessary (especially if you later need to update the same ID) but this comes at a performance penalty.  If you don’t need to update documents, then let Elasticsearch set its own ID automatically.

If you need to index many documents at once, it is much more efficient to use the BULK API to carry out these operations with a single call.

Indexing is not an immediate automatic process. Documents will not be available for search until the index has refreshed. Refresh time by default is 1 second. Increasing this time reduces the burden on the cluster of indexing, increasing indexing speed. It is possible to modify the refresh time in the index settings.  

You can apply version control by setting the version parameter (?version=3) and indicating version_type=external.  By doing this Elasticsearch will reject any index requests where the version specified is less than the current version.  This can be useful when running distributed processes and you cannot guarantee that updated documents arrive in the correct order.

PUT test/_doc/1?version=20&version_type=external
{
	"message" : "using external version the document will be modified only  if version is greater than previous!"
}

The process of indexing is as follows

The index request is sent to the primary shard. Once the primary shard is updated, then the replication process request will be relayed to the replica shards. The command will not return until the primary shard (at least) has been updated. For greater resilience, you can specify a minimum number of shard replicas to be available before proceeding with the operation by using the parameter ?wait_for_active_shards=2

You can also specify which specific shard the index operation is sent to by using the “routing” command. There are 2 reasons that this might be done:  

• Certain Elasticsearch functions (parent-child documents) that require that the parent and child documents be held on the same shard.  
• Secondly, it may be possible to increase search speeds and reduce load on Elasticsearch by storing similar documents together on the same shard and then specifying the routing for both indexing and searching.  Although this can be done explicitly during indexing, it is not recommended. It would be preferable to set this up using the index mapping, so that the routing is determined by an ID value on the source document.