- Elasticsearch 5.x Cookbook Third Edition
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Dedication
- Preface
- 1. Getting Started
- 2. Downloading and Setup
- Introduction
- Downloading and installing Elasticsearch
- Setting up networking
- Setting up a node
- Setting up for Linux systems
- Setting up different node types
- Setting up a client node
- Setting up an ingestion node
- Installing plugins in Elasticsearch
- Installing plugins manually
- Removing a plugin
- Changing logging settings
- Setting up a node via Docker
- 3. Managing Mappings
- Introduction
- Using explicit mapping creation
- Mapping base types
- Mapping arrays
- Mapping an object
- Mapping a document
- Using dynamic templates in document mapping
- Managing nested objects
- Managing child document
- Adding a field with multiple mapping
- Mapping a GeoPoint field
- Mapping a GeoShape field
- Mapping an IP field
- Mapping an attachment field
- Adding metadata to a mapping
- Specifying a different analyzer
- Mapping a completion field
- 4. Basic Operations
- Introduction
- Creating an index
- Deleting an index
- Opening/closing an index
- Putting a mapping in an index
- Getting a mapping
- Reindexing an index
- See also
- Refreshing an index
- Flushing an index
- ForceMerge an index
- Shrinking an index
- Checking if an index or type exists
- Managing index settings
- Using index aliases
- Rollover an index
- Indexing a document
- Getting a document
- Deleting a document
- Updating a document
- Speeding up atomic operations (bulk operations)
- Speeding up GET operations (multi GET)
- 5. Search
- Introduction
- Executing a search
- Sorting results
- Highlighting results
- Executing a scrolling query
- Using the search_after functionality
- Returning inner hits in results
- Suggesting a correct query
- Counting matched results
- Explaining a query
- Query profiling
- Deleting by query
- Updating by query
- Matching all the documents
- Using a boolean query
- 6. Text and Numeric Queries
- Introduction
- Using a term query
- Using a terms query
- Using a prefix query
- Using a wildcard query
- Using a regexp query
- Using span queries
- Using a match query
- Using a query string query
- Using a simple query string query
- Using the range query
- The common terms query
- Using IDs query
- Using the function score query
- Using the exists query
- Using the template query
- 7. Relationships and Geo Queries
- 8. Aggregations
- Introduction
- Executing an aggregation
- Executing stats aggregations
- Executing terms aggregation
- Executing significant terms aggregation
- Executing range aggregations
- Executing histogram aggregations
- Executing date histogram aggregations
- Executing filter aggregations
- Executing filters aggregations
- Executing global aggregations
- Executing geo distance aggregations
- Executing children aggregations
- Executing nested aggregations
- Executing top hit aggregations
- Executing a matrix stats aggregation
- Executing geo bounds aggregations
- Executing geo centroid aggregations
- 9. Scripting
- 10. Managing Clusters and Nodes
- 11. Backup and Restore
- 12. User Interfaces
- 13. Ingest
- 14. Java Integration
- Introduction
- Creating a standard Java HTTP client
- Creating an HTTP Elasticsearch client
- Creating a native client
- Managing indices with the native client
- Managing mappings
- Managing documents
- Managing bulk actions
- Building a query
- Executing a standard search
- Executing a search with aggregations
- Executing a scroll search
- 15. Scala Integration
- 16. Python Integration
- 17. Plugin Development
- 18. Big Data Integration
In this chapter we will cover the following recipes:
- Installing and using Cerebro
- Installing Kibana and X-Pack
- Managing Kibana dashboards
- Monitoring with Kibana
- Using Kibana console
- Visualizing data with Kibana
- Installing Kibana plugins
- Generating Graph with Kibana
In Elasticsearch ecosystem, it can be immensely useful to monitor nodes and cluster to manage and improve their performance and state. There are several issues that can arise at cluster level, such as:
- Node overheads, where some nodes can have too many shards allocated and can become a bottleneck for the entire cluster
- Node shutdown can happen due to many reasons, for example, full disks, hardware failures, and power problems
- Shard relocation problems or corruptions, in which some shards are unable to get an online status
- Too large shards happens when a shard is too big; the index performance decreases due to Lucene massive segments merging
- Empty indices and shards waste memory and resources, but because every shard has a lot of active threads if there is a huge number of unused indices and shards, the general cluster performance is degraded
- Node problems such as high CPU usage or disk full
Detecting malfunction or bad performances can be done via API or via some frontends that are designed to be used in Elasticsearch.
Some of the frontends introduced in this chapter allow the readers to have a working web dashboard on their Elasticsearch data, monitoring the cluster health, backuping/restoring your data and allow testing queries before implementing them in the code. In this chapter, we will have an overview of these frontends due to their complexity and the large number of features that will require a book to themselves. For an in-depth description I suggest the reader to have a look at the official documentation of Kibana at https://www.elastic.co/guide/en/kibana/current/index.html or dedicated books.
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