- 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:
- Painless scripting
- Installing additional script plugins
- Managing scripts
- Sorting data using scripts
- Computing return fields with scripting
- Filtering a search via scripting
- Using scripting in aggregations
- Updating a document using scripts
- Reindexing with a script
Elasticsearch has a powerful way to extend its capabilities with custom scripts that can be written in several programming languages. The most common ones are Painless, Groovy, JavaScript, and Python.
In this chapter, we will see how it's possible to create custom scoring algorithms, special processed return fields, custom sorting, and complex update operations on records.
The scripting concept of Elasticsearch is an advanced stored procedures system in the NoSQL world; so, for an advanced use of Elasticsearch, it is very important to master it.
Elasticsearch natively provides scripting in Java (a Java code compiled in JAR), Painless, Groovy, Express, and Mustache; but a lot of interesting languages are available as plugins, such as JavaScript and Python.
In older Elasticsearch releases, prior to version 5.0, the official scripting language was Groovy, but for better sandboxing and performance, the official language is now Painless, which is provided by default in Elasticsearch.
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