Learning is a fundamental ability. However, "My life has a limit, and my knowledge has no limit." If you don't pay attention to the method of learning, you will "follow the limit with the limit, and you will be at risk".
Learning is like eating and sleeping. It is a human instinct, and everyone has the ability to learn. When we were just born, we didn’t know anything, it was a real blank piece of paper. We learned to walk, talk, and dress by relying on the instinct of learning. Later, when we went to school, the teacher took the knowledge from the book one by one. Instilled in our brains, we have more and more knowledge, but at the same time, our learning ability seems to be getting worse and worse, we are used to being fed by others, and seem to forget how to feed ourselves.
Learning is originally just an instinct, not an ability. However, after more than 20 years of continuous learning, learning has become a real ability, because we gradually lose it, it is even more precious.
As a programmer, continuous learning is more important. If you don't learn new knowledge, you will be eliminated sooner or later.
Search is an essential skill for software engineers. Elasticsearch is a powerful open-source distributed search and analysis engine with almost no rivals in the same field. ES is also widely used in near real-time analysis of big data, including log analysis, indicator monitoring, information security and other fields.
With the urgent demand of enterprises for near real-time search, Elasticsearch has received more and more attention. Whether it is Internet companies such as Ali, Tencent, JD.com, or traditional companies such as Ping An and SF Express, Elasticsearch is widely used!
**However, in learning, I suffer from lack of reference materials, lack of expert guidance when encountering problems, a lot of time and energy spent looking at the source code and falling into the details, and the lack of best practices for guidance. But don't panic, this time I specially organized and shared this Elasticsearch from basic to advanced study notes to friends in need! **Introduces documents, indexing, search, aggregation, and clustering from the bottom layer of es, and shows the charm of es from search and aggregation analysis examples. How Logstash collects data from the inside to the specified place to show its data collection function. Kibana shows the function of data visualization from data drawing.
Elasticsearch from basic to advanced study notes directory
Basics
- Introduction to the Elastic Stack
- What is Elasticsearch
This part is an introduction to learning Elasticsearch, building an Elasticsearch service. Building a service is the foundation of learning Elasticsearch. This article will explain in detail and comprehensively how to build a standard-compliant Elasticsearch service, as well as some practical building skills.
- Elasticsearch related software installation
- esquick start
- Getting started with documentation
- Java api implements document management
- Graphical es internal mechanism
- Graphical document storage mechanism
- Getting Started with Mapping
- Getting Started with Index
Searching with Elasticsearch is easy, doing well is hard! A good index design plays a pivotal role in the entire cluster planning, and the index design directly affects the quality, complexity and work efficiency of the cluster design. This part will first lead you to deeply interpret the importance of Elasticsearch index design, and then interpret the key issues of index design one by one based on nearly 10 million-level practical experience, to help you improve index design skills, avoid unnecessary detours, and avoid development. Rework due to design defects at the end of the stage improves development efficiency.
Advanced
- Chinese tokenizer IK tokenizer
- Java api implements index management
- searchGetting started
- java api implements search
- Detailed explanation of the scoring mechanism
This part is mainly divided into the actual application of the Elasticsearch search engine, the Chinese tokenizer and advanced complex query of Elasticsearch, and the application of the log analysis system ELK .
Actual combat
- Getting Started with Aggregation
- java api implements aggregation
- es7 sql new features
- Logstash learning
- kibana learning
- Cluster deployment
- Project combat
Content display
Summarize
Now that big data is rampant in the world, how to quickly store and view application data has become indispensable, and search engines are born. The bottom layer of ElasticSearch relies on Lucene for a lot of simplified encapsulation on it, and has the characteristics of high availability and distribution. The most important thing is the improvement of the Logstash Kibana Beats suite with rich peripheral products, which has allowed ElasticSearch to achieve rapid and rapid development, which is a must for every company today. Fewer infrastructure components.
The above ElasticSearch study notes can help friends in need to gain the following:
- Using the latest ElasticSearch features
- ElasticSearch business core skills such as Kibana business analysis methods, these methods will meet the needs of most companies, pie charts, line charts, histograms, aggregation analysis, bucketing, etc.
- ElasticSearnch big data search and analysis advanced gameplay, such as the basic and advanced usage of DSL query syntax, learn how to use DSL for full-text retrieval services, learn how to use ES as a database, and use DSL to implement various SQL operations.
- ElasticSearch environment deployment and construction, such as cluster construction, understand the concept of sharding, instance, node role, what is an inverted index, and have an overall grasp of ES.
At the same time, with the above study notes, a **ElasticSearch learning idea map was handwritten for those who need it.