Workshop Elasticsearch / Kibana 7.4
A presentation at Workshop Ecole 42 (private event) in in Paris, France by David Pilato
Workshop Elasticsearch @Ecole 42 David Pilato Elastic, @dadoonet
Elastic Stack SOLUTIONS Kibana Visualize & Manage Elasticsearch Store, Search, & Analyze Elastic Stack Beats APM Logstash SaaS Elastic Cloud SELF-MANAGED Elastic Cloud Enterprise Standalone Ingest
Deployment in the Enterprise Beats Elasticsearch Custom UI FILEBEAT WINGLOGBEAT HEARTBEAT METRICBEAT Master (3) Ingest (X) Logstash Coordinating (X) PACKETBEAT Elasticsearch Clients AUDITBEAT Data – Hot (X) Kafka Kibana Data – Warm (X) Data store Web APIs Redis Workers (2+) Alerting (X) Messaging Queue Social Machine Learning (2+) Sensors LDAP ES-Hadoop 3 AD Authentication SSO Notification
4 https://www.elastic.co/fr/subscriptions
Agenda • Elasticsearch overview • Workshop 0: getting started • Workshop 1: let’s index some documents • Workshop 2: let’s search them • Workshop 3: let’s pull some analytics • Workshop 4: let’s add a powerful live UI on top 5
think document! • Change your mindset: ‒ Forget SQL! ‒ Index what you want to find • A document ‒ A JSON object ‒ Core field types (string, numbers, booleans, dates) ‒ Complex field types (arrays, objects) ‒ Additional field types (dates, geo points, geo shapes) 7
A document { } 8 “name” : “elastic”, “website” : “http://www.elastic.co”, “category” : “software”, “founded_year” : 2012, “overview” : “The company behind elasticsearch”, “tags” : [“search”, “datastore”, “analytics”], “location” : { “city” : “Amsterdam”, “country_code” : “NL”, “geo” : { “lat” : 52.370176, “lon” : 4.895008 } }
workshop 0 setup 9
docker compose (elasticsearch) —version: ‘3’ services: elasticsearch: image: docker.elastic.co/elasticsearch/elasticsearch:$ELASTIC_VERSION environment: - bootstrap.memory_lock=true - discovery.type=single-node - cluster.routing.allocation.disk.threshold_enabled=false ulimits: memlock: soft: -1 hard: -1 ports: - 9200:9200 networks: [‘stack’] 10 https://gist.github.com/dadoonet/f3c67bebf2cf604df02f78ac2cb2fbde
docker compose (kibana) kibana: image: docker.elastic.co/kibana/kibana:$ELASTIC_VERSION ports: [‘5601:5601’] networks: [‘stack’] links: [‘elasticsearch’] depends_on: [‘elasticsearch’] networks: stack: {} 11 https://gist.github.com/dadoonet/f3c67bebf2cf604df02f78ac2cb2fbde
start • run docker-compose up • open Kibana open http://0.0.0.0:5601/ 12
workshop 1 we index persons 13
workshop 1: index some documents • Load demo-console.txt file in Kibana dev console 14 https://gist.github.com/dadoonet/f3c67bebf2cf604df02f78ac2cb2fbde
workshop 1: 500 000 persons • use injector script java -jar injector-7.0.jar —debug —nb 500000 • see effect with _cat API GET _cat/indices/person?v https://ela.st/injector 15
workshop 2 we search for persons 16
workshop 3 we compute persons 17
workshop 4 clic and play! 18
Vous cherchez toujours dans vos données avec des SELECT * FROM person WHERE name like '%david%pilato%" ?
Au delà des performances obtenues, êtes-vous certain de renvoyer les résultats les plus pertinents pour vos utilisateurs d’abord ?
Venez découvrir comment un moteur de recherche vous aidera à répondre aux questions posées par vos utilisateurs, de manière pertinente et efficace, tout en apportant des fonctionnalités d’analyse des résultats et ce, quelque soit le volume…
Au programme de cet atelier :
- Introduction au principe du moteur de recherche
- Lancement d’elasticsearch et de Kibana
- CRUD avec Elasticsearch
- Search
- Aggregations
- Création de visualisation, dashboards dans Kibana
Resources
The following resources were mentioned during the presentation or are useful additional information.
