Preface
by Saurabh Chhajed, Marek Rogoziński, Rafał Kuć, Bharvi Dixit
Elasticsearch: A Complete Guide
- Elasticsearch: A Complete Guide
- Table of Contents
- Elasticsearch: A Complete Guide
- Elasticsearch: A Complete Guide
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Elasticsearch
- Introducing Elasticsearch
- Installing and configuring Elasticsearch
- Basic operations with Elasticsearch
- Summary
- 2. Understanding Document Analysis and Creating Mappings
- 3. Putting Elasticsearch into Action
- CRUD operations using elasticsearch-py
- CRUD operations using Java
- Creating a search database
- Elasticsearch Query-DSL
- Understanding Query-DSL parameters
- Search requests using Python
- Search requests using Java
- Sorting your data
- Document routing
- Summary
- 4. Aggregations for Analytics
- 5. Data Looks Better on Maps: Master Geo-Spatiality
- 6. Document Relationships in NoSQL World
- 7. Different Methods of Search and Bulk Operations
- 8. Controlling Relevancy
- 9. Cluster Scaling in Production Deployments
- Node types in Elasticsearch
- Introducing Zen-Discovery
- Multicasting discovery
- Unicasting discovery
- Configuring unicasting discovery
- Minimum number of master nodes: preventing split-brain
- 10. Backups and Security
- II. Module 2
- 1. Introduction to Elasticsearch
- 2. Power User Query DSL
- Default Apache Lucene scoring explained
- Query rewrite explained
- Query templates
- Handling filters and why it matters
- Choosing the right query for the job
- Query categorization
- The use cases
- Summary
- 3. Not Only Full Text Search
- Query rescoring
- Controlling multimatching
- Significant terms aggregation
- Documents grouping
- Relations between documents
- Scripting changes between Elasticsearch versions
- Summary
- 4. Improving the User Search Experience
- Correcting user spelling mistakes
- Improving the query relevance
- Summary
- 5. The Index Distribution Architecture
- 6. Low-level Index Control
- Altering Apache Lucene scoring
- Choosing the right directory implementation – the store module
- NRT, flush, refresh, and transaction log
- Segment merging under control
- When it is too much for I/O – throttling explained
- Understanding Elasticsearch caching
- The filter cache
- The field data cache
- The shard query cache
- Using circuit breakers
- Clearing the caches
- Index, indices, and all caches clearing
- Summary
- 7. Elasticsearch Administration
- Discovery and recovery modules
- The human-friendly status API – using the Cat API
- Backing up
- Federated search
- Summary
- 8. Improving Performance
- Using doc values to optimize your queries
- Knowing about garbage collector
- Benchmarking queries
- Very hot threads
- Scaling Elasticsearch
- Vertical scaling
- Horizontal scaling
- Using Elasticsearch for high load scenarios
- Summary
- 9. Developing Elasticsearch Plugins
- Creating the Apache Maven project structure
- Understanding the basics
- Creating custom REST action
- Creating the custom analysis plugin
- Implementation details
- Implementing TokenFilter
- Implementing the TokenFilter factory
- Implementing the class custom analyzer
- Implementing the analyzer provider
- Implementing the analysis binder
- Implementing the analyzer indices component
- Implementing the analyzer module
- Implementing the analyzer plugin
- Informing Elasticsearch about our custom analyzer
- Testing our custom analysis plugin
- Implementation details
- Summary
- III. Module 3
- 1. Introduction to ELK Stack
- The need for log analysis
- Challenges in log analysis
- The ELK Stack
- ELK data pipeline
- ELK Stack installation
- Installing Elasticsearch
- Running Elasticsearch
- Elasticsearch configuration
- Elasticsearch plugins
- Installing Logstash
- Running Logstash
- Logstash with file input
- Logstash with Elasticsearch output
- Configuring Logstash
- Installing Logstash forwarder
- Logstash plugins
- Installing Kibana
- Configuring Kibana
- Running Kibana
- Kibana interface
- Summary
- 2. Building Your First Data Pipeline with ELK
- 3. Collect, Parse and Transform Data with Logstash
- Configuring Logstash
- Logstash plugins
- Summary
- 4. Creating Custom Logstash Plugins
- 5. Why Do We Need Elasticsearch in ELK?
- 6. Finding Insights with Kibana
- 7. Kibana – Visualization and Dashboard
- 8. Putting It All Together
- 9. ELK Stack in Production
- 10. Expanding Horizons with ELK
- A. Bibliography
- 1. Introduction to ELK Stack
- Index
- Configuring unicasting discovery
- 1. Getting Started with Elasticsearch
Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, open source search and analytics engine. It provides a new level of control over how you can index and search even huge sets of data. This course will take you from basics of Elasticsearch to using Elasticsearch in the Elastic stack, and in production. You will start with very basics of understanding Elasticsearch terminologies and installation & configuration. After this, you will understand the basic analytics and indexing, search, and querying. You will also learn about creating various maps and visualization. You will also get a quick understanding of cluster scaling, search and bulk operations, and more. You will also learn about backups and security. After this, you will dig your teeth deeper into Elasticsearch's internal functionalities including caches, Apache Lucene library, and its monitoring capabilities. You'll learn about practical usage of Elasticsearch configuration parameters and how to use the monitoring API. You will learn how to improve user search experience, index distribution, segment statistics, merging, and more. Once you are a master, it would be time to move on. You will dive into end-to-end visualize-analyze-log techniques with Elastic Stack (also known as the ELK stack). You will look at Elasticsearch, Logstash, and Kibana, and how to make them work together to build amazing insights and business metrics out of data. You will know how to effectively use Elasticsearch with other De facto components and get the most out of Elasticsearch. You will have developed a full-fledged data pipeline by the end of this course.
Module 1, Elasticsearch Essentials, this module provides a complete coverage of working with Elasticsearch using Python and as well as Java APIs to perform CRUD operations, aggregation-based analytics, handling document relationships, working with geospatial data, and controlling search relevancy.
Module 2, Mastering Elasticsearch, in this module we start with an introduction to the world of Lucene and Elasticsearch. We will discuss topics such as different scoring algorithms, choosing the right store mechanism, what the differences between them are, and why choosing the proper one matters. We touch the administration part of Elasticsearch by discussing discovery and recovery modules and the human-friendly Cat API.
Module 3, Learning ELK Stack, this module is aimed at introducing building your own ELK Stack data pipeline using the open source technologies stack of Elasticsearch, Logstash, and Kibana. This module covers the core concepts of each of the components of the stack and quickly using them to build your own log analytics solutions.
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