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This documentation is no longer maintained and may be removed.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.
Modeling Your Dataedit
Elasticsearch is a different kind of beast, especially if you come from the world of SQL. It comes with many benefits: performance, scale, near real-time search, and analytics across massive amounts of data. And it is easy to get going! Just download and start using it.
But it is not magic. To get the most out of Elasticsearch, you need to understand how it works and how to make it work for your needs.
Handling relationships between entities is not as obvious as it is with a dedicated relational store. The golden rule of a relational database—normalize your data—does not apply to Elasticsearch. In Handling Relationships, Nested Objects, and Parent-Child Relationship we discuss the pros and cons of the available approaches.
Then in Designing for Scale we talk about the features that Elasticsearch offers that enable you to scale out quickly and flexibly. Scale is not one-size-fits-all. You need to think about how data flows through your system, and design your model accordingly. Time-based data like log events or social network streams require a very different approach than more static collections of documents.
And finally, we talk about the one thing in Elasticsearch that doesn’t scale.
- 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