eBook - ePub
Mastering Elasticsearch - Second Edition
Rafal Kuc, Marek Rogozinski
This is a test
Share book
- 434 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Mastering Elasticsearch - Second Edition
Rafal Kuc, Marek Rogozinski
Book details
Book preview
Table of contents
Citations
Frequently asked questions
How do I cancel my subscription?
Can/how do I download books?
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
What is the difference between the pricing plans?
Both plans give you full access to the library and all of Perlegoās features. The only differences are the price and subscription period: With the annual plan youāll save around 30% compared to 12 months on the monthly plan.
What is Perlego?
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, weāve got you covered! Learn more here.
Do you support text-to-speech?
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Is Mastering Elasticsearch - Second Edition an online PDF/ePUB?
Yes, you can access Mastering Elasticsearch - Second Edition by Rafal Kuc, Marek Rogozinski in PDF and/or ePUB format, as well as other popular books in Computer Science & Web Development. We have over one million books available in our catalogue for you to explore.
Information
Mastering Elasticsearch Second Edition
Table of Contents
Mastering Elasticsearch Second Edition
Credits
About the Author
Acknowledgments
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Introduction to Elasticsearch
Introducing Apache Lucene
Getting familiar with Lucene
Overall architecture
Getting deeper into Lucene index
Norms
Term vectors
Posting formats
Doc values
Analyzing your data
Indexing and querying
Lucene query language
Understanding the basics
Querying fields
Term modifiers
Handling special characters
Introducing Elasticsearch
Basic concepts
Index
Document
Type
Mapping
Node
Cluster
Shard
Replica
Key concepts behind Elasticsearch architecture
Workings of Elasticsearch
The startup process
Failure detection
Communicating with Elasticsearch
Indexing data
Querying data
The story
Summary
2. Power User Query DSL
Default Apache Lucene scoring explained
When a document is matched
TF/IDF scoring formula
Lucene conceptual scoring formula
Lucene practical scoring formula
Elasticsearch point of view
An example
Query rewrite explained
Prefix query as an example
Getting back to Apache Lucene
Query rewrite properties
Query templates
Introducing query templates
Templates as strings
The Mustache template engine
Conditional expressions
Loops
Default values
Storing templates in files
Handling filters and why it matters
Filters and query relevance
How filters work
Bool or and/or/not filters
Performance considerations
Post filtering and filtered query
Choosing the right filtering method
Choosing the right query for the job
Query categorization
Basic queries
Compound queries
Not analyzed queries
Full text search queries
Pattern queries
Similarity supporting queries
Score altering queries
Position aware queries
Structure aware queries
The use cases
Example data
Basic queries use cases
Searching for values in range
Simplified query for multiple terms
Compound queries use cases
Boosting some of the matched documents
Ignoring lower scoring partial queries
Not analyzed queries use cases
Limiting results to given tags
Efficient query time stopwords handling
Full text search queries use cases
Using Lucene query syntax in queries
Handling user queries without errors
Pattern queries use cases
Autocomplete using prefixes
Pattern matching
Similarity supporting queries use cases
Finding terms similar to a given one
Finding documents with similar field values
Score altering queries use cases
Favoring newer books
Decreasing importance of books with certain value
Pattern queries use cases
Matching phrases
Spans, spans everywhere
Structure aware queries use cases
Returning parent documents having a certain nested document
Affecting parent document score with the score of nested documents
Summary
3. Not Only Full Text Search
Query rescoring
What is query rescoring?
An example query
Structure of the rescore query
Rescore parameters
Choosing the scoring mode
To sum up
Controlling multimatching
Multimatch types
Best fields matching
Cross fields matching
Most fields matching
Phrase matching
Phrase with prefixes matching
Significant terms aggregation
An example
Choosing significant terms
Multiple values analysis
Significant terms aggregation and full text search fields
Additional configuration options
Controlling the number of returned buckets
Background set filtering
Minimum document count
Execution hint
More options
There are limits
Memory consumption
Shouldn't be used as top-level aggregation
Counts are approximated
Floating point fields are not allowed
Documents grouping
Top hits aggregation
An example
Additional parameters
Relations between documents
The object type
The nested documents
Parentāchild relationship
Parentāchild relationship in the cluster
A few words about alternatives
Scripting changes between Elasticsearch versions
Scripting changes
Security issues
Groovy ā the new default scripting language
Removal of MVEL language
Short Groovy introduction
Using Groovy as your scripting language
Variable definition in scripts
Conditionals
Loops
An example
There is more
Scripting in full text context
Field-related information
Shard level information
Term level information
More advanced term information
Lucene expressions explained
The basics
An example
There is more
Summary
4. Improving the User Search Experience
Correcting user spelling mistakes
Testing data
Getting into technical details
Suggesters
Using the _suggest REST endpoint
Understanding the REST endpoint suggester response
Including suggestion requests in query
The term suggester
Configuration
Common term suggester options
Additional term suggester options
The phrase suggester
Usage example
Configuration
Basic configuration
Configuring smoothing models
Configuring candidate generators
Configuring direct generators
The completion suggester
The logic behind the completion suggester
Using the completion suggester
Indexing data
Querying data
Custom weights
Additional parameters
Improving the query relevance
Data
The quest for relevance improvement
The standard query
The multi match query
Phrases comes into play
Let's throw the garbage away
Now, we boost
Performing a misspelling-proof search
Drill downs with faceting
Summary
5. The Index Distribution Architecture
Choosing the right amount of shards and replicas
Sharding and overallocation
A positive example of overallocation
Multiple shards versus multiple indices
Replicas
Routing explained
Shards and data
Let's test routing
Indexing with routing
Routing in practice
Querying
Aliases
Multiple routing values
Altering the default shard allocation behavior
Allocation awareness
Forcing allocation awareness
Filtering
What include, exclude, and require mean
Runtime allocation updating
Index level updates
Cluster level updates
Defining total shards allowed per node
Defining total shards allowed per physical server
Inclusion
Re...