Elasticsearch

Course Snapshot 

• Duration: 3 days 
• Skill-level: Foundation-level Elasticsearch skills for Intermediate skilled team members. This is not a basic class. 
• Targeted Audience: This course is geared for those who wants to build scalable search applications using Elasticsearch. 
• Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
• Delivery Format: This course is available for onsite private classroom presentation. 
• Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Elasticsearch teaches you how to write applications that deliver professional quality search. As you read, you’ll learn to add basic search features to any application, enhance search results with predictive analysis and relevancy ranking, and use saved data from prior searches to give users a custom experience. This practical course focuses on Elasticsearch’s REST API via HTTP. Code snippets are written mostly in bash using cURL, so they’re easily translatable to other languages 

Working in a hands-on learning environment, led by our Elasticsearch expert instructor, students will learn about and explore: 

• teaches you how to build scalable search applications using Elasticsearch.  
• You’ll ramp up fast, with an informative overview and an engaging introductory example.  
• you’ll pick up the core concepts you need to implement basic searches and efficient indexing.  
• With the fundamentals well in hand, you’ll go on to gain an organized view of how to optimize your design. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

• What is a great search application? 
• Building scalable search solutions 
• Using Elasticsearch with any language 
• Configuration and tuning 

Audience & Pre-Requisites 

This course is designed for developers and administrators building and managing search-oriented applications 

Pre-Requisites:  Students should have familiar with  

• Basics of Programming  

Course Agenda / Topics 

1. Introducing Elasticsearch 

• Solving search problems with Elasticsearch 
• Exploring typical Elasticsearch use cases 

2. Diving into the functionality 

• Understanding the logical layout: documents, types, and indices 
• Understanding the physical layout: nodes and shards 
• Indexing new data 
• Searching for and retrieving data 
• Configuring Elasticsearch 
• Adding nodes to the cluster 

3. Indexing, updating, and deleting data 

• Using mappings to define kinds of documents 
• Core types for defining your own fields in documents 
• Arrays and multi-fields 
• Using predefined fields 
• Updating existing documents 
• Deleting data 

4. Searching your data 

• Structure of a search request 
• Introducing the query and filter DSL 
• Combining queries or compound queries 
• Beyond match and filter queries 
• Querying for field existence with filters 
• Choosing the best query for the job 

5. Analyzing your data 

• What is analysis? 
• Using analyzers for your documents 
• Analyzing text with the analyze API 
• Analyzers, tokenizers, and token filters, oh my! 
• Ngrams, edge ngrams, and shingles 
• Stemming 

6. Searching with relevancy 

• How scoring works in Elasticsearch 
• Other scoring methods 
• Boosting 
• Understanding how a document was scored with explain 
• Reducing scoring impact with query rescoring 
• Custom scoring with function_score 
• Tying it back together 
• Sorting with scripts 
• Field data detour 

7. Exploring your data with aggregations 

• Understanding the anatomy of an aggregation 
• Metrics aggregations 
• Multi-bucket aggregations 
• Nesting aggregations 

8. Relations among documents 

• Overview of options for defining relationships among documents 
• Having objects as field values 
• Nested type: connecting nested documents 
• Parent-child relationships: connecting separate documents 
• Denormalizing: using redundant data connections 
• Application-side joins 

9. Scaling out 

• Adding nodes to your Elasticsearch cluster 
• Discovering other Elasticsearch nodes 
• Removing nodes from a cluster 
• Upgrading Elasticsearch nodes 
• Using the _cat API 
• Scaling strategies 
• Aliases 
• Routing 

10. Improving performance 

• Grouping requests 
• Optimizing the handling of Lucene segments 
• Making the best use of caches 
• Other performance tradeoffs 

11. Administering your cluster 

• Improving defaults 
• Allocation awareness 
• Monitoring for bottlenecks 
• Backing up your data