PyCharm: Webinar recap: Building Search Functionality With Python, Flask, and Elasticsearch

In this webinar, Aravind covered the following topics step by step in a Github branch.

1. Building a basic Python Flask application. (1-basic-flask-app)
2. TailwindCSS to build a beautiful search experience (2-add-tailwindcss)
3. Working with elasticsearch-py and elasticsearch-dsl libraries (3-add-elasticsearch)
4. Working with Elastic App Search client library (4-add-appsearch)
5. Shipping logs, traces from the flask app (5-apm-logs)
6. Building a Docker container (6-deploy)

The entire code is available under this repository.

Questions from the session:

1. What are the ways to build a search experience using Elasticsearch with the existing RDBMS data?

   A: There are two primary ways: Using Logstash plugin – logstash-input-jdbc or Using the client libraries.

   • By using the logstash plugin (input-jdbc-plugin), you can connect to any RDBMS data and effectively sync the data to elasticsearch. You can also do incremental data fetching.
   • By using the client libraries, like elasticsearch-py, Java high-level REST clients directly ship the data to elasticsearch directly from the application.
2. Can we use Elasticsearch like an RDBMS?

   A: Yes, with some caveats. There are several organizations that use Elasticsearch as their primary data store. But, please note that you need to have a disaster recovery strategy for the data stored in the cluster. You might want to snapshot your data to an object store like S3 or GCS.

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