【Practical Exercise】Data Storage and Analysis: Storing Scraped Data into Elasticsearch and Performing Full-Text Search

# 2.1.1 Distributed Storage and Indexing Mechanism

Elasticsearch employs a distributed storage architecture, storing data across multiple nodes to ensure high availability and scalability. It utilizes Lucene as its underlying search engine, organizing data into immutable data structures known as segments. Each segment contains an inverted index for rapid lookup and document retrieval.

The indexing mechanism of Elasticsearch is based on Lucene's inverted index. This index maps each term in every document to a list of documents containing that term. This structure allows for quick retrieval of documents containing specific terms, even when the documents are distributed across multiple nodes. Additionally, Elasticsearch supports various index types, such as standard indices, analytics indices, and geospatial indices, to optimize queries for specific types of data.

# 2. Elasticsearch Fundamentals and Practice

Elasticsearch is an open-source, distributed search and analytics engine built on Lucene, providing a robust platform to store, search, and analyze large volumes of data. This section will introduce the fundamental architecture and principles of Elasticsearch, as well as its practical use for data storage and analysis.

### 2.1 Elasticsearch Architecture and Principles

#### 2.1.1 Distributed Storage and Indexing Mechanism

Elasticsearch adopts a distributed architecture, storing data across multiple nodes, each acting as an independent server. Data is divided into smaller units called shards, with each shard stored on different nodes. This provides high availability and scalability, as services can continue even if a node fails.

Elasticsearch uses inverted indices to store data. An inverted index is a data structure that associates each term with a list of documents containing that term. During a search, Elasticsearch consults the inverted index to quickly find documents containing the search terms.

#### 2.1.2 Data Model and Query Language

Elasticsearch uses JSON (JavaScript Object Notation) as its data model. JSON is a lightweight format capable of representing complex data structures. Elasticsearch also provides its own query language, known as Elasticsearch Query Language (ESQL), for querying and analyzing data.

ESQL is a language similar to SQL, but optimized for Elasticsearch's data model and indexing mechanisms. It offers a wide range of query features, including Boolean queries, range queries, and aggregation queries.

### 2.2 Elasticsearch Data Storage Practice

#### 2.2.1 Data Indexing and Document Management

In Elasticsearch, data is stored in containers called indices. An index is a logical concept that contains a set of documents. Each document is a JSON object representing a data entity.

When creating an index, one must specify a mapping that defines the types and attributes of the fields in the document. The mapping determines how fields are indexed and stored, as well as how they are searched and aggregated.

#### 2.2.2 Data Querying and Aggregation Analysis

Elasticsearch offers powerful querying capabilities, allowing users to query data using ESQL. Queries can be filtered based on field values, ranges, Boolean expressions, and more.

Furthermore, Elasticsearch provides aggregation functionality for grouping, counting, summing, and other operations on data. Aggregations help users quickly summarize and analyze large datasets, extracting meaningful insights.

# Query Example
GET /my-index/_search
{
   "query": {
     "match": {
       "title": "Elasticsearch"
     }
   }
}

# Aggregation Example
GET /my-index/_search
{
   "aggs": {
     "authors": {
       "terms": {
         "field": "author"
       }
     }
   }
}

# 3.1 Data Scraping Technologies and Tools

#### 3.1.1 Web Scraping Principles and Tools

Web scraping, also known as web crawling, is a software program used to automatically retrieve data from the internet. Its working principle is typically as follows:

1. **URL Queue:** The crawler starts with a seed URL and places it into the URL queue.
2. **Fetching Pages:** The crawler retrieves a URL from the queue and sends a request to that URL, fetching page content.
3. **Parsing Pages:** The crawler parses the page content, extracting the required data, such as text, images, links, etc.
4. **Storing Data:** The crawler stores the extracted data in a database or other storage systems.
5. **Updating Queue:** The crawler extracts new URLs from the parsed page and adds them to the URL queue.
6. **Repeat Steps 2-5:***

***mon web scraping tools include:

- **Scrapy:** An open-source Python framework that provides various crawling functionalities and extensions.
- **Beautiful Soup:** A Python library for parsing HTML and XML documents.
- **Selenium:** A tool for automating web browsers that supports JavaScript rendering.
- **Jsoup:** A Java library for pa