Using Kibana to Analyze NYC Restaurant Inspection Data

This example demonstrates how to analyze & visualize New York City restaurant inspection data using Kibana. The NYC Restaurant Inspection data analyzed in this example is from the NYC Open Data initiative.

This example was used as a part of the Kibana 101: Getting Started with Visualization webinar. Please check out the webinar for more context on the data and the story.

Version

Example has been tested in following versions:

• Elasticsearch 2.3.0
• Kibana 4.5.0

Contents

• Installation & Setup
• Download
• Run Example
• Feedback

Installation & Setup

• Follow the Installation & Setup Guide to install and test the elastic stack (you can skip this step if you already have a working installation of the Elastic stack)

• Run Elasticsearch & Kibana

  <path_to_elasticsearch_root_dir>/bin/elasticsearch
  <path_to_kibana_root_dir>/bin/kibana

• Check that Elasticsearch and Kibana are up and running.

  • Open localhost:9200 in web browser -- should return status code 200
  • Open localhost:5601 in web browser -- should display Kibana UI.

  Note: By default, Elasticsearch runs on port 9200, and Kibana run on ports 5601. If you changed the default ports, change the above calls to use appropriate ports.

  Download & Ingest Data

  You have 2 options to index the data into Elasticsearch. You can either use the Elasticsearch snapshot and restore API to directly restore the nyc_restaurants index from a snapshot. OR, you can download the raw data from the data.cityofnewyork.us website and then use the scripts in the Scripts - Python folder to process the raw files and index the data.

  Option 1. Load data by restoring index snapshot

  (Learn more about snapshot / restore here )

  Using this option involves 4 easy steps:

  • Download and uncompress the index snapshot .tar.gz file into a local folder
    

  # Create snapshots directory
  mkdir elastic_restaurants
  cd elastic_restaurants
  # Download index snapshot to elastic_restaurants directory
  wget http://download.elasticsearch.org/demos/nyc_restaurants/nyc_restaurants.tar.gz .
  # Uncompress snapshot file
  tar -xf nyc_restaurants.tar.gz

  This adds a nyc_restaurants subfolder containing the index snapshots.

  • Add nyc_restaurants dir to path.repo variable in the elasticsearch.yml in the path_to_elasticsearch_root_dir/config/ folder. See example here.. Restart elasticsearch for the change to take effect.

  • Register a file system repository for the snapshot (change the value of the “location” parameter below to the location of your restaurants_backup directory)

  curl -XPUT 'http://localhost:9200/_snapshot/restaurants_backup' -d '{
      "type": "fs",
      "settings": {
          "location": "<path_to_nyc_restaurants>/",
          "compress": true,
          "max_snapshot_bytes_per_sec": "1000mb",
          "max_restore_bytes_per_sec": "1000mb"
      }
  }'

  • Restore the index data into your Elasticsearch instance:

    curl -XPOST "localhost:9200/_snapshot/restaurants_backup/snapshot_1/_restore"

  Option 2: Process and load data using Python script

  The raw NYC Restaurants Inspection data is provided as a CSV file. For this example, we ingested the data into Elasticsearch using the Python Elasticsearch client. See Scripts - Python

  We are providing this option in case you want to modify how the data is processed before ingest. Follow the ReadMe in the Scripts - Python folder if you want to try this option.

  Check data availability

  Once the index is created using either of the above options, you can check to see if all the data is available in Elasticsearch. If all goes well, you should get a count response of approximately 473039 when you run the following command.

  curl -XGET localhost:9200/nyc_restaurants/_count -d '{
  	"query": {
  		"match_all": {}
  	}
  }'

  Visualize Data in Kibana

  • Access Kibana by going to http://localhost:5601 in a web browser
  • Connect Kibana to the nyc_restaurants index in Elasticsearch
    • Click the Settings tab >> Indices tab >> Create New. Specify nyc_restaurants as the index pattern name, select Inspection_Date as the Time-field name, and click Create to define the index pattern. (Leave the Use event times to create index names box unchecked)
  • Load sample dashboard into Kibana
    • Click the Settings tab >> Objects tab >> Import, and select restaurants_kibana.json
  • Open dashboard
    • Click on Dashboard tab and open DOHMH: Overview dashboard. Voila! You should see the following dashboard. Happy Data Exploration!

  

  We would love to hear from you!

  If you run into issues running this example or have suggestions to improve it, please use Github issues to let us know. Have an easy fix? Submit a pull request. We will try our best to respond in a timely manner!

  Have you created interesting examples using the ELK stack? Looking for a way to share your amazing work with the community? We would love to include your awesome work here. For more information on how to contribute, check out the Contribution section!