
Deploy a simple elastic stack on your local laptop by running a single node Elasticsearch and Kibana without editing any single configuration file, Docker, or cloud services.
Explore Elasticsearch query types and the domain specific language for defining queries, mastering leaf and compound queries, leveraging term, range, and bool queries, with aggregations, highlights, and type-ahead support.
Explain the difference between query context and filter context, where query context scores documents and filter context enables cacheable, score-free filtering using numbers, dates, booleans, and enums.
Explore reverse search with the percolate query to implement notifications, replacing pooling with matching documents against stored queries; deploy dedicated percolation indices for scalable classification and enrichment.
Explore metric aggregations in Elasticsearch, including average, max, min, value count, top hits, and scripted metrics; learn caching, Kibana querying, and runtime mappings.
Explore Elasticsearch's suggest feature for spelling correction with term and phrase suggesters, and for fast typeahead with completion or context suggesters. Learn dictionary design and memory-latency trade-offs.
Explore source filtering in Elasticsearch to control what fields are returned and indexed, compare query and indexing scenarios, and understand its effects on disk usage, partial updates, and recovery source.
Use the scroll API to retrieve large Elasticsearch result sets by creating a scroll context and fetching pages with a scroll identifier.
Understand Elasticsearch custom scoring for relevance using tf-idf, bm25 factors, term frequency, and idf, with function score and script score, with boosting and explain for better ranking.
This course will guide you how to properly and effectively use Elasticsearch Query DSL (Domain Specific Language) based on JSON to define queries. Additionally I present most commonly used Search APIs that will help you fully understand how Elasticsearch works and how to use it to build modern search applications, like Google, Bing, Yahoo!, DuckDuckGo etc. Course contains a lot of practical knowledge, examples and hands-on lectures.
If you are a beginner, don't worry, course guides you from very generic concept of lucene inverted index and role of search engines like Elasticsearch) in the system architecture to more advanced features.
If you have no data to play with, don't worry we import sample datasets at the very beginning of this course.
If you already have experience with Elasticsearch, you will enjoy the advanced part of it. Maybe you wonder if the way that use use Elasticsearch is the proper way and maybe your queries can return results faster ? If so, then course will help you find answers to that questions, optionally grounding and strengthening your exiting experience. No matter what is your existing level of knowledge, after completing this course, you will be ready to become a true professional in the Elasticsearch community.
In this course, I will show you how to properly use Elasticsearch product. We will start by explaining basic terms and role of Elasticsearch in the system architecture. Then, after importing sample data, we will go through term based queries, range queries, specialized queries, geo queries, nested queries and so on. We will get to know how to build effective notifications by using percolate queries or aggregate and analyze results using aggregations.
I’ll show you how to do highlighting, suggestions, spell corrections, and template your queries. At the end we will cover tuning and optimization best practices, query profiling, performance testing and customize default routing and scoring.
Overall, you'll learn how to properly and effectively query Elasticsearch in the easy way, without spending hours reading manuals.
I hope to see you in the first lecture.