- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment
WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Preface
editPreface
editThe world is swimming in data. For years we have been simply overwhelmed by the quantity of data flowing through and produced by our systems. Existing technology has focused on how to store and structure warehouses full of data. That’s all well and good—until you actually need to make decisions in real time informed by that data.
Elasticsearch is a distributed, scalable, real-time search and analytics engine. It enables you to search, analyze, and explore your data, often in ways that you did not anticipate at the start of a project. It exists because raw data sitting on a hard drive is just not useful.
Whether you need full-text search, real-time analytics of structured data, or a combination of the two, this book introduces you to the fundamental concepts required to start working with Elasticsearch at a basic level. With these foundations laid, it will move on to more-advanced search techniques, which you will need to shape the search experience to fit your requirements.
Elasticsearch is not just about full-text search. We explain structured search, analytics, the complexities of dealing with human language, geolocation, and relationships. We will also discuss how best to model your data to take advantage of the horizontal scalability of Elasticsearch, and how to configure and monitor your cluster when moving to production.