[ElasticSearch] Inverted index of ElasticSearch

ElasticSearch's inverted index principle

introduction

What is ElasticSearch

ElasticSearch is an open source search engine built on Apache Lucene, which provides powerful full-text search and analysis capabilities. It can not only quickly search and retrieve large amounts of structured and unstructured data, but also has the characteristics of horizontal expansion and high availability.

1. Elasticsearch and distributed features

ElasticSearch is designed to be distributed and can store and process data on multiple nodes. It uses the concepts of sharding and replicas to disperse and store data on different nodes, achieving horizontal data expansion and load balancing. This enables ElasticSearch to handle large-scale data sets with high availability, even if there is a node failure, it will not cause data loss.

2. Real-time and reliability

ElasticSearch has real-time indexing and searching capabilities, it can quickly respond to user query requests and return accurate results. Its distributed architecture and data replication mechanism ensure data reliability and durability, even in the event of node failure or network interruption, data can be protected.

3. Diversified search and analysis functions

ElasticSearch provides rich search and analysis functions, enabling users to query and analyze data in a variety of ways. It supports multiple query methods such as full-text search, exact match, fuzzy search, and multi-field search, and has powerful filtering and aggregation functions, which can filter, sort, and summarize statistics on search results.

4. Multilingual support and scalability

ElasticSearch supports client libraries in multiple programming languages, such as Java, Python, JavaScript, etc., enabling developers to easily interact and integrate with ElasticSearch. In addition, ElasticSearch also provides a wealth of plug-ins and extension mechanisms, which can be extended and customized according to requirements.

5. Document-oriented and flexible data model

ElasticSearch adopts a document-oriented data model, the data is stored in JSON format, and each document has a unique ID and custom fields. This flexible data model makes ElasticSearch suitable for various types of data, including structured data, semi-structured data and unstructured data. At the same time, ElasticSearch provides a wealth of indexing and mapping configuration options, enabling users to define their own data structures and indexing rules according to their needs.

Inverted index

Inverted index is a common index structure, which plays an important role in the field of information retrieval. Different from the traditional forward index, the inverted index is based on terms and maps the content of documents to terms, providing more efficient text search and retrieval capabilities. Here are the importance of inverted index:

• Efficient text search: Inverted indexes can quickly locate documents containing specific terms by mapping terms to document lists. Compared with sequentially scanning the entire document collection, the inverted index can greatly improve the search efficiency.

• Support complex query operations: the inverted index can not only perform simple term matching, but also support complex query operations such as Boolean operations, range queries, fuzzy searches, and wildcard searches. This allows users to flexibly combine and filter search criteria to obtain more precise search results.

• Provides relevance ranking: Each term in the inverted index contains information about where and how often it occurs in each document. This enables the search engine to calculate the matching degree of documents according to the relevance algorithm, and sort the search results according to the relevance, so that users can find the most relevant documents more quickly.

• Support real-time update and incremental index: Inverted index can support real-time data update and incremental index. When new documents are added or old documents are modified, only the corresponding inverted index items need to be updated without rebuilding the entire index structure. This enables search engines to quickly respond to changes in data and provide the latest search results in real time.

Application of inverted index in ES

• Text search: ElasticSearch uses an inverted index to implement full-text search. It maps each term in the document to the corresponding document list to support fast retrieval of keywords.

• Relevance sorting: The inverted index stores the occurrence position and frequency information of the term in the document. ElasticSearch can use this information to calculate the relevance score of the document and sort the search results according to the relevance.

• Multi-field search: ElasticSearch's inverted index supports multi-field search. Users can specify which fields to search in, and obtain more accurate search results by combining and filtering conditions.

• Aggregate query: ElasticSearch's inverted index also supports aggregate query, users can group, count and summarize search results according to custom aggregation rules to meet different data analysis needs.

• Efficient distributed search: The inverted index structure of ElasticSearch is distributed and stored on multiple nodes, which can realize horizontal data expansion and load balancing. This enables ElasticSearch to handle large-scale datasets and perform distributed search and retrieval in an efficient manner.

Inverted index data structure

In an inverted index, there are several basic data structures used to organize and store index information, including inverted lists and term lists. These are discussed in detail below:

1. Inverted Index:

   • The inverted list is the core data structure of the inverted index. It is based on terms and maps each term to a list of documents containing the term.
   • For each term, a posting list (Posting List) is stored in the posting list, which contains information of all documents containing the term.
   • Posting lists usually contain document identifiers (such as document IDs) and other relevant information, such as the occurrence and frequency of terms in the document.

2. Term Dictionary:

   • The term list is a data structure used to store terms in the inverted index. It maintains the dictionary information of the term, including the term itself and a pointer to the corresponding inverted list.
   • The term list is used to speed up the search process. When a user initiates a query, he can first look up the term in the term list, and find the corresponding posting list through the pointer, so as to quickly locate the document containing the term.

3. Document Identifier Table:

   • The document identification table is used to store document identifiers, such as document IDs, which are associated with document identifiers in the posting list.
   • An inverted index needs to know the identifier of each document in order to return relevant document information when searching. The document identifier table provides a mapping relationship that associates document identifiers with actual documents.

These basic data structures together form the main components of the inverted index. They work together to enable search engines to quickly locate and retrieve documents containing specific terms and provide relevant document information. In addition, these data structures can be further optimized and extended according to specific search engine implementations and requirements.

Build an inverted index

In ElasticSearch, building an inverted index is an automated process that happens automatically during document indexing. The following is the basic process of ElasticSearch building an inverted index:

1. Prepare data:

   • First, the data to be indexed needs to be prepared, which can be a collection of documents, such as data in JSON format.

2. Create an index:

   • In ElasticSearch, an index needs to be created first to store data. An index is a logical data container that contains a set of documents and defines the structure and properties of the documents.

3. Define the mapping:

   • When creating an index, you can define a mapping (Mapping), which specifies the fields and their attributes in the document. The mapping describes information such as the data type, tokenizer, indexing options, etc. of each field.
   • The field definition in the mapping will instruct ElasticSearch how to handle the contents of the field when building the inverted index.

4. Document index:

   • Send the prepared document to ElasticSearch for indexing through the indexing API. Documents can be indexed one at a time, or multiple documents can be indexed in batches.
   • During the indexing process, ElasticSearch automatically parses the content of the document and builds an inverted index based on the definitions of the fields in the map.

5. Inverted index construction:

   • When ElasticSearch indexes a document, it automatically extracts the terms in the document and builds an inverted index.
   • For each field, ElasticSearch applies the corresponding tokenizer, breaks the text into terms, and adds the terms to the corresponding posting list.
   • The posting list contains relevant information such as the position information of the term in the document, word frequency and so on.

6. Index refresh:

   • ElasticSearch temporarily stores index operations in memory. In order to make data persistent, index refresh operations can be performed. Flushing writes in-memory index operations to disk, making index changes visible to searches.
   • Refresh operations can be automatically scheduled, or manually triggered.

Through the above process, ElasticSearch will automatically build and update the inverted index to support efficient text search and retrieval. When indexing a large number of documents, the distributed nature of ElasticSearch can achieve parallel processing and horizontal expansion, improving the speed and performance of indexing.

Inverted index search process

In ElasticSearch, the search process of the inverted index mainly includes the following steps:

1. Query parsing:

   • The user sends a search request and provides a query string.
   • ElasticSearch parses the query string and converts it into an internal query data structure.

2. Inverted list matching:

   • According to the query conditions, ElasticSearch searches the inverted index for the inverted list that matches the query conditions.
   • An inverted list contains information about documents that contain a specific term.

3. Boolean operations and filtering:

   • ElasticSearch combines and filters the postings list based on the Boolean operators in the query (such as AND, OR, NOT) to obtain documents that meet the query criteria.
   • Filtering can exclude or include specific terms, fields, or other criteria.

4. Relevance scoring and ranking:

   • ElasticSearch uses a relevance algorithm to calculate a relevance score for each document based on how well it matches the query.
   • The relevance score takes into account factors such as the frequency of the query term in the document, the weight of the field, etc.
   • Search results are sorted by relevance score so that the most relevant documents are returned first.

5. Return search results:

   • ElasticSearch returns search results to the user, including documents matching the query criteria and their relevance scores.
   • Users can further process the search results according to their needs, such as pagination, filtering or other operations.

The entire search process is highly optimized. With the data structure and algorithm of the inverted index, ElasticSearch can quickly locate and retrieve documents containing specific terms, and sort them according to their relevance. At the same time, the support of inverted index enables ElasticSearch to handle large-scale data sets and real-time data updates, providing efficient search and analysis functions.

Optimization techniques for inverted index

• Explore how to optimize an inverted index to improve search performance.
• Including compression algorithm, merge strategy, bit set and other optimization techniques.

Inverted index application case in ElasticSearch

Case: E-commerce product search and recommendation
Suppose we have an e-commerce platform that contains a large amount of product data. Using the inverted index function of ElasticSearch, we can achieve the following functions:

1. Text search: Users can enter keywords through the search box to search in text information such as product titles, descriptions, and labels. ElasticSearch uses the inverted index to quickly locate products containing keywords and return relevant search results.

2. Filtering and Aggregation: Users can filter and aggregate based on product attributes, such as brand, price range, product category, etc. ElasticSearch can efficiently filter and aggregate eligible products through the attribute information in the inverted index.

3. Sorting and relevance scoring: ElasticSearch uses the inverted index and relevance algorithm to calculate the relevance score according to the matching degree of the product and the search query, and sort the search results according to the relevance. In this way, users can see the most relevant products ranked first, improving the accuracy of search results and user experience.

4. Recommendations based on user behavior: By analyzing data such as user search behavior and purchase history, we can use ElasticSearch's inverted index and aggregation functions to implement product recommendations based on users' personalized interests. The inverted index can quickly retrieve the user's historical behavior data and recommend products according to user preferences.

The above case shows how ElasticSearch uses inverted index to realize e-commerce product search and recommendation functions. The efficient retrieval, filtering and sorting capabilities of the inverted index, as well as the combination with other data analysis functions, make ElasticSearch a powerful tool for processing large-scale commodity data and real-time user behavior.

in conclusion

An inverted index is a data structure used to quickly locate documents containing a specific term. Its principle is to map each term in the document collection with the documents containing the term, so as to quickly find related documents during the search process. The following is a summary of the principles and importance of inverted indexes:

principle:

1. An inverted index is built on terms, mapping each term to a list of documents that contain that term.
2. Inversion lists store document identifiers and other relevant information, such as the position and frequency of terms in documents.
3. Through the inverted list, you can quickly locate documents containing specific terms, and support efficient text search and retrieval.

importance:

1. Fast search: The inverted index provides efficient search capabilities, which can quickly locate and retrieve documents containing specific terms in large-scale document collections, speeding up search speed and response time.
2. Relevance sorting: Inverted index supports correlation scoring, calculates the degree of matching between documents and queries based on information such as the frequency and position of terms in documents, and provides correlation sorting to make search results more accurate and useful.
3. Multi-field search: Inverted index can handle multi-field search at the same time, enabling users to conduct compound queries in multiple fields, improving the flexibility and accuracy of search.
4. Data aggregation: The inverted index can be used for data aggregation operations, such as counting the frequency of occurrence of a term in a document collection, calculating the minimum and maximum values ​​of a field, etc., and supporting rich data analysis and aggregation functions.
5. Scalability and real-time update: The inverted index supports horizontal expansion and real-time data update, making it suitable for large-scale data sets and real-time data processing scenarios, and capable of handling highly concurrent search and index operations.

As the core technology of search engine and text analysis, inverted index plays an important role. It enables search engines to quickly and accurately locate and retrieve documents through efficient data structures and algorithms, and provides users with high-quality search experience and data analysis functions.