Elasticsearch Index for the The Movie Database.

Install Elasticsearch w/ Dependencies

Using Docker

Build containers

With Docker installed, build the containers as below. You only need to do this once.

cd es-docker
docker build --tag=elasticsearch-tlre .
cd ../kb-docker
docker build --tag=kibana-tlre .
cd ..

Run containers

docker-compose up

Browse to http://localhost:9200 and http://localhost:5601 to confirm ES / Kibana running

Install Manually

Elasticsearch

1. Download Elasticsearch 7.5.1
2. Unzip to where you'd like to run Elasticsearch
3. Add the following to config/elasticsearch.yml

http.cors.allow-origin: "/https?:\\/\\/(.*?\\.)?(quepid\\.com|splainer\\.io)/"
http.cors.enabled: true
indices.query.bool.max_clause_count: 10240

4. Install the Elasticsearch LTR plugin for 7.5.1:

bin/elasticsearch-plugin install -b http://es-learn-to-rank.labs.o19s.com/ltr-1.1.2-es7.5.1.zip

5. Run Elasticsearch

bin/elasticsearch

6. In your browser, navigate to http://localhost:9200 to confirm Elasticsearch is running

Kibana

1. Download Kibana 7.5.1

2. Unzip to where you'd like to run Kibana

3. Install the matching version of the Kibana Analyze Plugin

bin/kibana-plugin install https://github.com/johtani/analyze-api-ui-plugin/releases/download/7.5.1/analyze_api_ui-7.5.1.zip

4. Run Kibana

bin/kibana

6. In your browser, navigate to http://localhost:5601 to confirm Kibana is running with the Analyze plugin included

Index TMDB movies

Once installed, grab TMDB data and index into Elasticsearch

1. Download tmdb.json, run curl -o tmdb.json http://es-learn-to-rank.labs.o19s.com/tmdb.json
2. Install [Python 3.6] or greater (https://www.python.org/downloads/)
3. Install elasticsearch Python libraries library, run pip install elasticsearch

• Optional set-up a virtual ennvironment before install python3 -m venv . source bin/activate

4. Run python3 indexTmdb.py to index movies

Confirm Elasticsearch has TMDB movies

Run a wildcard search and confirm you get results.

Postman

Postman is an API development tool, that helps build, run and manage API requests. The examples from the TLRE slides exist here too as a Postman Collection (es-TLRE-postman_collection.json). We like using Postman becasue it makes tinkering with query parameters nicer and we think it is a useful way to follow along as you learn about tuning search relevance.

If you want to use Postman during the TLRE class:

1. Download Postman for your OS
2. Open Postman and Import (top-menu >> File) es-TLRE-postman_collection.json
3. Define a global variable (grey eye icon in the upper-right) es-host to point to your running Elasticsearch instance (default is localhost:9200)
4. Tinker with the base URL, Params or JSON Body (optional)
5. Press 'Send' (blue rectangle button right of URL bar)

This collection is also valuable for testing examples against new versions of Elasticsearch. Using Postman's command line tool Newman you can check all of the requests in the collection:

newman run --global-var "es_host=localhost:9200" es-TLRE-postman_collection.json