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.
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Life Inside a Clusteredit
Elasticsearch is built to be always available, and to scale with your needs. Scale can come from buying bigger servers (vertical scale, or scaling up) or from buying more servers (horizontal scale, or scaling out).
While Elasticsearch can benefit from more-powerful hardware, vertical scale has its limits. Real scalability comes from horizontal scale—the ability to add more nodes to the cluster and to spread load and reliability between them.
With most databases, scaling horizontally usually requires a major overhaul of your application to take advantage of these extra boxes. In contrast, Elasticsearch is distributed by nature: it knows how to manage multiple nodes to provide scale and high availability. This also means that your application doesn’t need to care about it.
In this chapter, we show how you can set up your cluster, nodes, and shards to scale with your needs and to ensure that your data is safe from hardware failure.
- 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