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This documentation is no longer maintained and may be removed.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.
Marvel for Monitoringedit
Marvel enables you to easily monitor Elasticsearch through Kibana. You can view your cluster’s health and performance in real time as well as analyze past cluster, index, and node metrics.
While you can access a large number of statistics through the APIs described in this chapter, they only show you what’s going on at a single point in time. Knowing memory usage at this instant is helpful, but knowing memory usage over time is much more useful. Marvel queries and aggregates the metrics so you can visualize your cluster’s behavior over time, which makes it easy to spot trends.
As your cluster grows, the output from the stats APIs can get truly hairy. Once you have a dozen nodes, let alone a hundred, reading through stacks of JSON becomes very tedious. Marvel lets you explore the data interactively and makes it easy to zero in on what’s going on with particular nodes or indices.
Marvel uses the same stats APIs that are available to you—it does not expose any statistics that you can’t access through the APIs. However, Marvel greatly simplifies the collection and visualization of those statistics.
Marvel is free to use (even in production!), so you should definitely try it out! For installation instructions, see Getting Started with Marvel.
- 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