Elasticsearch in Action
Elasticsearch in Action
1.
Radu Gheorghe, Matthew Lee Hinman, and Roy Russo
2.
Copyright
3.
Brief Table of Contents
4.
Table of Contents
5.
序言
6.
致谢
7.
About This Book
7.1.
Roadmap
7.2.
Code conventions and downloads
7.3.
Author Online
8.
About the Cover Illustration
9.
Chapter 1. Introducing Elasticsearch
9.1.
1.1. Solving search problems with Elasticsearch
9.2.
1.2. Exploring typical Elasticsearch use cases
9.3.
1.3. Summary
10.
Chapter 2. Diving into the functionality
10.1.
2.1. Understanding the logical layout: documents, types, and indices
10.2.
2.2. Understanding the physical layout: nodes and shards
10.3.
2.3. Indexing new data
10.4.
2.4. Searching for and retrieving data
10.5.
2.5. Configuring Elasticsearch
10.6.
2.6. Adding nodes to the cluster
10.7.
2.7. Summary
11.
Chapter 3. Indexing, updating, and deleting data
11.1.
3.1. Using mappings to define kinds of documents
11.2.
3.2. Core types for defining your own fields in documents
11.3.
3.3. Arrays and multi-fields
11.4.
3.4. Using predefined fields
11.5.
3.5. Updating existing documents
11.6.
3.6. Deleting data
11.7.
3.7. Summary
12.
Chapter 4. Searching your data
12.1.
Searchable data
12.2.
4.1. Structure of a search request
12.3.
4.2. Introducing the query and filter DSL
12.4.
4.3. Combining queries or compound queries
12.5.
4.4. Beyond match and filter queries
12.6.
4.5. Querying for field existence with filters
12.7.
4.6. Choosing the best query for the job
12.8.
4.7. Summary
13.
Chapter 5. Analyzing your data
13.1.
5.1.1. Character filtering
13.2.
5.1.2. Breaking into tokens
13.3.
5.1.3. Token filtering
13.4.
5.1.4. Token indexing
13.5.
Analysis while executing a search
13.6.
5.2. Using analyzers for your documents
13.7.
5.3. Analyzing text with the analyze API
13.8.
5.5. Ngrams, edge ngrams, and shingles
13.9.
5.6. Stemming
13.10.
5.7. Summary
14.
Chapter 6. Searching with relevancy
14.1.
6.1. How scoring works in Elasticsearch
14.2.
6.2. Other scoring methods
14.3.
6.3. Boosting
14.4.
6.4. Understanding how a document was scored with explain
14.5.
6.5. Reducing scoring impact with query rescoring
14.6.
6.6. Custom scoring with function_score
14.7.
6.7. Tying it back together
14.8.
6.8. Sorting with scripts
14.9.
6.9. Field data detour
14.10.
6.10. Summary
15.
Chapter 7. Exploring your data with aggregations
15.1.
7.1. Understanding the anatomy of an aggregation
15.2.
7.2. Metrics aggregations
15.3.
7.3. Multi-bucket aggregations
15.4.
7.4. Nesting aggregations
15.5.
7.5. Summary
16.
Chapter 8. Relations among documents
16.1.
8.1. Overview of options for defining relationships among documents
16.2.
8.2. Having objects as field values
16.3.
8.3. Nested type: connecting nested documents
16.4.
8.4. Parent-child relationships: connecting separate documents
16.5.
8.5. Denormalizing: using redundant data connections
16.6.
8.6. Application-side joins
16.7.
8.7. Summary
17.
Chapter 9. Scaling out
17.1.
9.1. Adding nodes to your Elasticsearch cluster
17.2.
9.2. Discovering other Elasticsearch nodes
17.3.
9.3. Removing nodes from a cluster
17.4.
9.4. Upgrading Elasticsearch nodes
17.5.
9.5. Using the _cat API
17.6.
9.6. Scaling strategies
17.7.
9.7. Aliases
17.8.
9.8. Routing
17.9.
9.9. Summary
18.
Chapter 10. Improving performance
18.1.
10.1. Grouping requests
18.2.
10.2. Optimizing the handling of Lucene segments
18.3.
10.3. Making the best use of caches
18.4.
10.4. Other performance tradeoffs
18.5.
10.5. Summary
19.
Chapter 11. Administering your cluster
19.1.
11.1. Improving defaults
19.2.
11.2. Allocation awareness
19.3.
11.3. Monitoring for bottlenecks
19.4.
11.4. Backing up your data
19.5.
11.5. Summary
20.
Appendix A. Working with geospatial data
20.1.
A.1. Points and distances between them
20.2.
A.2. Adding distance to your sort criteria
20.3.
A.3. Filter and aggregate based on distance
20.4.
A.5. Shape intersections
21.
Appendix B. Plugins
21.1.
B.1. Working with plugins
21.2.
B.2. Installing plugins
21.3.
B.3. Accessing plugins
21.4.
B.4. Telling Elasticsearch to require certain plugins
21.5.
B.5. Removing or updating plugins
22.
Appendix C. Highlighting
22.1.
C.1. Highlighting basics
22.2.
C.2. Highlighting options
22.3.
C.3. Highlighter implementations
23.
Appendix D. Elasticsearch monitoring plugins
23.1.
D.1. Bigdesk: visualize your cluster
23.2.
D.2. ElasticHQ: monitoring with management
23.3.
D.3. Head: advanced query building
23.4.
D.4. Kopf: snapshots, warmers, and percolators
23.5.
D.5. Marvel: fine-grained analysis
23.6.
D.6. Sematext SPM: the Swiss Army knife
24.
Appendix E. Turning search upside down with the percolator
24.1.
E.1. Percolator basics
24.2.
E.2. Performance tips
24.3.
E.3. Functionality tricks
25.
Appendix F. Using suggesters for autocomplete and did-you-mean functionality
25.1.
F.1. Did-you-mean suggesters
25.2.
F.2. Autocomplete suggesters
26.
Index
26.1.
SYMBOL
27.
List of Figures
28.
List of Tables
29.
List of Listings
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Elasticsearch in Action
Elasticsearch in Action
Elasticsearch in Action