- 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
One of the main characteristics of Elasticsearch is schema-less indexing capability. Records in Elasticsearch can have missing values. Due to its schema-less nature, two kinds of queries are required:
- Exists field: This is used to check if a field exists in a document
- Missing field: This is used to check if a field is missing
You need an up-and-running Elasticsearch installation as we described in the Downloading and installing Elasticsearch recipe in Chapter 2, Downloading and Setup.
To execute curl via the command line, you need to install curl for your operative system.
To correctly execute the following commands, you need an index populated with the chapter_05/populate_query.sh script available in the online code.
For executing existing and missing filters, we will perform the following steps:
- To search all the test-type documents that have a field called
parsedtext, the query will be:curl -XPOST 'http://127.0.0.1:9200/test-index/test- type/_search?pretty=true' -d '{ "query": { "exists": { "field":"parsedtext" } } }' - To search all the test-type documents that do not have a field called
parsedtext, the query will be as follows:curl -XPOST 'http://127.0.0.1:9200/test-index/test- type/_search?pretty=true' -d '{ "query": { "bool": { "must_not": { "exists": { "field": "parsedtext" } } } } }'
The exists and missing filters take only a field parameter, which contains the name of the field to be checked.
Using simple fields, there are no pitfalls; but if you are using a single embedded object or a list of them, you need to use a subobject field due to how Elasticsearch/Lucene works.
An example helps you to understand how Elasticsearch maps JSON objects to Lucene documents internally. If you are trying to index a JSON document:
{
"name":"Paul",
"address":{
"city":"Sydney",
"street":"Opera House Road",
"number":"44"
}
}
Elasticsearch will internally index it as follows:
name:paul address.city:Sydney address.street:Opera House Road address.number:44
As we can see, there is no field address indexed, so exists filter on address fails. To match documents with an address, you must search for a subfield (that is, address.city).
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