Librería Portfolio

Whether you need full-text search or real-time analytics of structured data-or both-the Elasticsearch distributed search engine is an ideal way to put your data to work. This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships.

If you're a newcomer to both search and distributed systems, you'll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you'll follow a problem-based approach to learn why, when, and how to use Elasticsearch features.

Understand how Elasticsearch interprets data in your documents
Index and query your data to take advantage of search concepts such as relevance and word proximity
Handle human language through the effective use of analyzers and queries
Summarize and group data to show overall trends, with aggregations and analytics
Use geo-points and geo-shapes-Elasticsearch's approaches to geolocation
Model your data to take advantage of Elasticsearch's horizontal scalability
Learn how to configure and monitor your cluster in production


Getting Started
Chapter 1: You Know, for Search.
Installing Elasticsearch
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
Chapter 2: Life Inside a Cluster
An Empty Cluster
Cluster Health
Add an Index
Add Failover
Scale Horizontally
Coping with Failure
Chapter 3: 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
Chapter 4: Distributed Document Store
Routing a Document to a Shard
How Primary and Replica Shards Interact
Creating, Indexing, and Deleting a Document
Retrieving a Document
Partial Updates to a Document
Multidocument Patterns
Chapter 5: Searching-The Basic Tools
The Empty Search
Multi-index, Multitype
Pagination
Search Lite
Chapter 6: Mapping and Analysis
Exact Values Versus Full Text
Inverted Index
Analysis and Analyzers
Mapping
Complex Core Field Types
Chapter 7: Full-Body Search
Empty Search
Query DSL
Queries and Filters
Most Important Queries and Filters
Combining Queries with Filters
Validating Queries
Chapter 8: Sorting and Relevance
Sorting
String Sorting and Multifields
What Is Relevance?
Fielddata
Chapter 9: Distributed Search Execution
Query Phase
Fetch Phase
Search Options
scan and scroll
Chapter 10: Index Management
Creating an Index
Deleting an Index
Index Settings
Configuring Analyzers
Custom Analyzers
Types and Mappings
The Root Object
Dynamic Mapping
Customizing Dynamic Mapping
Default Mapping
Reindexing Your Data
Index Aliases and Zero Downtime
Chapter 11: Inside a Shard
Making Text Searchable
Dynamically Updatable Indices
Near Real-Time Search
Making Changes Persistent
Segment Merging
Search in Depth
Chapter 12: Structured Search
Finding Exact Values
Combining Filters
Finding Multiple Exact Values
Ranges
Dealing with Null Values
All About Caching
Filter Order
Chapter 13: Full-Text Search
Term-Based Versus Full-Text
The match Query
Multiword Queries
Combining Queries
How match Uses bool
Boosting Query Clauses
Controlling Analysis
Relevance Is Broken!
Chapter 14: Multifield Search
Multiple Query Strings
Single Query String
Best Fields
Tuning Best Fields Queries
multi_match Query
Most Fields
Cross-fields Entity Search
Field-Centric Queries
Custom _all Fields
cross-fields Queries
Exact-Value Fields
Chapter 15: Proximity Matching
Phrase Matching
Mixing It Up
Multivalue Fields
Closer Is Better
Proximity for Relevance
Improving Performance
Finding Associated Words
Chapter 16: Partial Matching
Postcodes and Structured Data
prefix Query
wildcard and regexp Queries
Query-Time Search-as-You-Type
Index-Time Optimizations
Ngrams for Partial Matching
Index-Time Search-as-You-Type
Ngrams for Compound Words
Chapter 17: 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
Chapter 18: Getting Started with Languages
Using Language Analyzers
Configuring Language Analyzers
Pitfalls of Mixing Languages
One Language per Document
One Language per Field
Mixed-Language Fields
Chapter 19: Identifying Words
standard Analyzer
standard Tokenizer
Installing the ICU Plug-in
icu_tokenizer
Tidying Up Input Text
Chapter 20: Normalizing Tokens
In That Case
You Have an Accent
Living in a Unicode World
Unicode Case Folding
Unicode Character Folding
Sorting and Collations
Chapter 21: Reducing Words to Their Root Form
Algorithmic Stemmers
Dictionary Stemmers
Hunspell Stemmer
Choosing a Stemmer
Controlling Stemming
Stemming in situ
Chapter 22: Stopwords: Performance Versus Precision
Pros and Cons of Stopwords
Using Stopwords
Stopwords and Performance
Divide and Conquer
Stopwords and Phrase Queries
common_grams Token Filter
Stopwords and Relevance
Chapter 23: Synonyms
Using Synonyms
Formatting Synonyms
Expand or contract
Synonyms and The Analysis Chain
Multiword Synonyms and Phrase Queries
Symbol Synonyms
Chapter 24: Typoes and Mispelings
Fuzziness
Fuzzy Query
Fuzzy match Query
Scoring Fuzziness
Phonetic Matching
Aggregations
Chapter 25: High-Level Concepts
Buckets
Metrics
Combining the Two
Chapter 26: Aggregation Test-Drive
Adding a Metric to the Mix
Buckets Inside Buckets
One Final Modification
Chapter 27: Building Bar Charts
Chapter 28: Looking at Time
Returning Empty Buckets
Extended Example
The Sky's the Limit
Chapter 29: Scoping Aggregations
Chapter 30: Filtering Queries and Aggregations
Filtered Query
Filter Bucket
Post Filter
Recap
Chapter 31: Sorting Multivalue Buckets
Intrinsic Sorts
Sorting by a Metric
Sorting Based on "Deepö Metrics
Chapter 32: Approximate Aggregations
Finding Distinct Counts
Calculating Percentiles
Chapter 33: Significant Terms
significant_terms Demo
Chapter 34: Controlling Memory Use and Latency
Fielddata
Aggregations and Analysis
Limiting Memory Usage
Fielddata Filtering