Ingest data with Python on Elastic Cloud Enterprise

This guide tells you how to get started with:

If you are an Python application programmer who is new to the Elastic Stack, this content can help you get started more easily.

Time required: 45 minutes

These steps are applicable to your existing application. If you don’t have one, you can use the example included here to create one.

python -m pip install elasticsearch
python -m pip install elasticsearch-async

# Elasticsearch 7.x
elasticsearch>=7.0.0,<8.0.0

When connecting to Elastic Cloud Enterprise you need to use your Cloud ID to specify the connection details. Find your Cloud ID by going to the Kibana main menu and selecting Management > Integrations, and then selecting View deployment details.

To connect to, stream data to, and issue queries with Elastic Cloud Enterprise, you need to think about authentication. Two authentication mechanisms are supported, API key and basic authentication. Here, to get you started quickly, we’ll show you how to use basic authentication, but you can also generate API keys as shown later on. API keys are safer and preferred for production environments.

For basic authentication, use the same deployment credentials (username and password parameters) and Cloud ID you copied down earlier. Find your Cloud ID by going to the Kibana main menu and selecting Management > Integrations, and then selecting View deployment details. (If you did not save the password, you can reset the password .)

You first need to create and edit an example.ini file with your deployment details:

[ELASTIC]
cloud_id = DEPLOYMENT_NAME:CLOUD_ID_DETAILS
user = elastic
password = LONGPASSWORD
ca = /path/to/your/elastic-ece-ca-cert.pem 

The following examples are to be typed into the Python interpreter in interactive mode. The prompts have been removed to make it easier for you to copy the samples, the output from the interpreter is shown unmodified.

❯ python3
Python 3.9.6 (default, Jun 29 2021, 05:25:02)
[Clang 12.0.5 (clang-1205.0.22.9)] on darwin
Type "help", "copyright", "credits" or "license" for more information.

from elasticsearch import Elasticsearch, helpers
import configparser
from ssl import create_default_context

config = configparser.ConfigParser()
config.read('example.ini')

['example.ini']
>>>

context = create_default_context(cafile=config['ELASTIC']['ca'])
es = Elasticsearch(
    cloud_id=config['ELASTIC']['cloud_id'],
    scheme="https",
    use_ssl=True,
    verify_certs=False,
    ssl_show_warn=False,
    http_auth=(config['ELASTIC']['user'], config['ELASTIC']['password'])
)

You can now confirm that you have connected to the deployment by returning some information about the deployment:

es.info()

{'name': 'instance-0000000000',
  'cluster_name': '747ab208fb70403dbe3155af102aef56',
  'cluster_uuid': 'IpgjkPkVQ5efJY-M9ilG7g',
  'version': {'number': '7.15.0', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '79d65f6e357953a5b3cbcc5e2c7c21073d89aa29', 'build_date': '2021-09-16T03:05:29.143308416Z', 'build_snapshot': False, 'lucene_version': '8.9.0', 'minimum_wire_compatibility_version': '6.8.0', 'minimum_index_compatibility_version': '6.0.0-beta1'},
  'tagline': 'You Know, for Search'}

After connecting to your deployment, you are ready to index and search data. Let’s create a new index, insert some quotes from our favorite characters, and then refresh the index so that it is ready to be searched. A refresh makes all operations performed on an index since the last refresh available for search.

es.index(
 index='lord-of-the-rings',
 document={
  'character': 'Aragon',
  'quote': 'It is not this day.'
 })

{'_index': 'lord-of-the-rings',
  '_type': '_doc',
  '_id': 'IanWEnwBg_mH2XweqDqg',
  '_version': 1,
  'result': 'created',
  '_shards': {'total': 2, 'successful': 1, 'failed': 0}, '_seq_no': 34, '_primary_term': 1}

es.index(
 index='lord-of-the-rings',
 document={
  'character': 'Gandalf',
  'quote': 'A wizard is never late, nor is he early.'
 })

{'_index': 'lord-of-the-rings',
  '_type': '_doc',
  '_id': 'IqnWEnwBg_mH2Xwezjpj',
  '_version': 1,
  'result': 'created',
  '_shards': {'total': 2, 'successful': 1, 'failed': 0}, '_seq_no': 35, '_primary_term': 1}

es.index(
 index='lord-of-the-rings',
 document={
  'character': 'Frodo Baggins',
  'quote': 'You are late'
 })

{'_index': 'lord-of-the-rings',
  '_type': '_doc',
  '_id': 'I6nWEnwBg_mH2Xwe_Tre',
  '_version': 1,
  'result': 'created',
  '_shards': {'total': 2, 'successful': 1, 'failed': 0}, '_seq_no': 36, '_primary_term': 1}

es.indices.refresh(index='lord-of-the-rings')

{'_shards': {'total': 2, 'successful': 1, 'failed': 0}}

When using the es.index API, the request automatically creates the lord-of-the-rings index, if it doesn’t exist already, as well as document IDs for each indexed document if they are not explicitly specified.

After creating a new index and ingesting some data, you are now ready to search. Let’s find what different characters have said things about being late:

result = es.search(
 index='lord-of-the-rings',
  query={
    'match': {'quote': 'late'}
  }
 )

result['hits']['hits']

[{'_index': 'lord-of-the-rings',
  '_type': '_doc',
  '_id': '2EkAzngB_pyHD3p65UMt',
  '_score': 0.5820575,
  '_source': {'character': 'Frodo Baggins', 'quote': 'You are late'}},
 {'_index': 'lord-of-the-rings',
  '_type': '_doc',
  '_id': '10kAzngB_pyHD3p65EPR',
  '_score': 0.37883914,
  '_source': {'character': 'Gandalf',
   'quote': 'A wizard is never late, nor is he early.'}}]

The search request returns content of documents containing late in the quote field, including document IDs that were automatically generated.

You can make updates to specific documents using document IDs. Let’s add a birthplace for our character:

es.update(
 index='lord-of-the-rings',
 id='2EkAzngB_pyHD3p65UMt', 
 doc={'birthplace': 'The Shire'}
 )

es.get(index='lord-of-the-rings', id='2EkAzngB_pyHD3p65UMt')
{'_index': 'lord-of-the-rings',
 '_type': '_doc',
 '_id': '2EkAzngB_pyHD3p65UMt',
 '_version': 2,
 '_seq_no': 3,
 '_primary_term': 1,
 'found': True,
 '_source': {'character': 'Frodo Baggins',
  'quote': 'You are late',
  'birthplace': 'The Shire'}}

For frequently used API calls with the Python client, check Examples.

To get started, authentication to Elasticsearch used the elastic superuser and password, but an API key is much safer and a best practice for production.

In the example that follows, an API key is created with the cluster monitor privilege which gives read-only access for determining the cluster state. Some additional privileges also allow create_index, write, read, and manage operations for the specified index. The index manage privilege is added to enable index refreshes.

The easiest way to create this key is in the API console for your deployment. Select the deployment name and go to ☰ > Management > Dev Tools:

POST /_security/api_key
{
  "name": "python_example",
  "role_descriptors": {
    "python_read_write": {
      "cluster": ["monitor"],
      "index": [
        {
          "names": ["test-index"],
          "privileges": ["create_index", "write", "read", "manage"]
        }
      ]
    }
  }
}

{
  "id" : "API_KEY_ID",
  "name" : "python_example",
  "api_key" : "API_KEY_DETAILS"
}

Edit the example.ini file you created earlier and add the id and api_key you just created. You should also remove the lines for user and password you added earlier after you have tested the api_key, and consider changing the elastic password using the Cloud UI.

[DEFAULT]
cloud_id = DEPLOYMENT_NAME:CLOUD_ID_DETAILS
apikey_id = API_KEY_ID
apikey_key = API_KEY_DETAILS

You can now use the API key in place of a username and password. The client connection becomes:

es = Elasticsearch(
    cloud_id=config['DEFAULT']['cloud_id'],
    api_key=(config['DEFAULT']['apikey_id'], config['DEFAULT']['apikey_key']),
)

Check Create API key API to learn more about API Keys and Security privileges to understand which privileges are needed. If you are not sure what the right combination of privileges for your custom application is, you can enable audit logging on Elasticsearch to find out what privileges are being used. To learn more about how logging works on Elastic Cloud Enterprise, check Monitoring Elastic Cloud deployment logs and metrics.

For more information on refreshing an index, searching, updating, and deleting, check the elasticsearch-py examples.