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This documentation is no longer maintained and may be removed.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.
Clusters Are Living, Breathing Creaturesedit
Once you get a cluster into production, you’ll find that it takes on a life of its own. Elasticsearch works hard to make clusters self-sufficient and just work. But a cluster still requires routine care and feeding, such as routine backups and upgrades.
Elasticsearch releases new versions with bug fixes and performance enhancements at a very fast pace, and it is always a good idea to keep your cluster current. Similarly, Lucene continues to find new and exciting bugs in the JVM itself, which means you should always try to keep your JVM up-to-date.
This means it is a good idea to have a standardized, routine way to perform rolling restarts and upgrades in your cluster. Upgrading should be a routine process, rather than a once-yearly fiasco that requires countless hours of precise planning.
Similarly, it is important to have disaster recovery plans in place. Take frequent snapshots of your cluster—and periodically test those snapshots by performing a real recovery! It is all too common for organizations to make routine backups but never test their recovery strategy. Often you’ll find a glaring deficiency the first time you perform a real recovery (such as users being unaware of which drive to mount). It’s better to work these bugs out of your process with routine testing, rather than at 3 a.m. when there is a crisis.
- 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