Introduction to Elasticsearch

Elasticsearch is an open source distributed RESTful search and analysis engine built on Apache Lucene. It has a flexible data model and powerful distributed performance, which can quickly and safely store, search, and analyze massive amounts of data. The initial development of Elasticsearch began in 2010, with Elasticsearch Company (formerly Elastic) founded by Shay Banon. It later developed into an open source project and officially launched Elasticsearch version 1.0 in 2014. Elasticsearch is suitable for various scenarios, especially those that require large-scale data analysis and real-time search. It can be used for log and indicator analysis, full-text search, security intelligence, business analysis, geographic information systems, and more. Advantages: 1. Powerful full-text search function: Elasticsearch supports complex query syntax and multiple search methods, providing efficient full-text search and fuzzy search. 2. Distributed architecture: Elasticsearch uses a distributed architecture that can scale horizontally and automatically handle data sharding, replication, and fault recovery. 3. High performance: Elastic search adopts Inverted index and distributed search algorithm, with fast write and read performance. 4. Real time: Elasticsearch can process real-time data within milliseconds and provide real-time search and analysis capabilities. 5. Easy to use: Elasticsearch provides a simple RESTful interface and rich client libraries, making it easy to integrate and operate. Disadvantages: 1. The Learning curve is steep: the configuration and use of Elasticsearch require certain learning costs, especially in complex scenarios. 2. Data consistency: Due to the distributed nature, Elasticsearch may experience data consistency issues due to node failures or network issues. Technical principles: Elasticsearch is built internally based on Apache Lucene, and efficient data storage and search are realized through Inverted index and distributed search algorithm. It adopts sharding and replication mechanisms to disperse data and store it on multiple nodes in the cluster, achieving horizontal data expansion and fault recovery. Performance analysis: Elasticsearch has excellent performance and can complete search and analysis operations at the millisecond level. Its performance is mainly affected by the following factors: hardware configuration, data volume, query complexity, network latency, etc. By optimizing hardware, data model design, and query statements, performance can be further improved. Official website: https://www.elastic.co/ Summary: Elasticsearch is a powerful open source search and analysis engine suitable for various large-scale data analysis and real-time search scenarios. It has advantages such as flexible data models, high performance, distributed architecture, and ease of use, but also requires a certain learning cost. Overall, Elasticsearch is a powerful and widely used database engine.

Introduction to Apache Solr

Apache Solr is an open source full-text search platform built on Apache Lucene. It provides powerful full-text search, hit highlighting, distributed search, document oriented search, scalability, and ease of use functions. Solr was founded in 2004, initially developed by Yonik Seeley, and became a top-level project of the Apache Software Foundation in 2006. Solr is maintained and developed by the Apache community, with a wide user base and good ecosystem support. Solr is widely used in various types of document retrieval scenarios, especially for scenarios that require complex search and filtering. It can be used to create and search document collections containing unstructured text, such as web pages, text fields in databases, files, and emails. The advantages of Solr include: 1. Fast: Solr is based on Lucene's powerful search engine, with efficient indexing and search functions, and can quickly process large-scale datasets. 2. Scalability: Solr supports horizontal scaling and can handle larger data volumes and more concurrent requests by adding more nodes. 3. Ease of use: Solr provides a RESTful API and rich Query language, enabling developers to easily build and execute complex query operations. 4. Highly customizable: Solr supports custom analyzers, query parsers, and plugins, which can be highly customized according to specific needs. However, Solr also has some drawbacks: 1. The Learning curve is steep: Solr may have a certain Learning curve for beginners, especially for complex queries and the use of advanced functions. 2. Memory usage: Due to Solr's need to load index data into memory to improve query performance, memory usage may be higher for large-scale datasets. The core technical principles of Solr include: 1. Index construction: Solr divides the process of document analysis and index construction into multiple stages, including text parsing, tokenization, lexicalization, word frequency calculation, Inverted index construction, etc. 2. Query processing: After Solr receives a query request, it first performs query parsing and converts the query into an internal data structure. Then sort the matching results based on the query scoring algorithm and return the most relevant search results. In terms of performance analysis, Solr can conduct performance analysis and optimization by monitoring performance indicators such as query response time, throughput, and resource utilization. The official website of Solr is: https://lucene.apache.org/solr/ Summary: Apache Solr is a powerful full-text search platform that provides efficient full-text search, distributed search, and document oriented search functions. It is suitable for scenarios that require complex search and filtering, and has advantages such as speed, scalability, and ease of use. However, for beginners, the Learning curve may be steep. The core technical principles of Solr include index construction and query processing. Perform performance analysis and optimization by monitoring performance indicators.

Introduction to Lucene

Lucene is a Java based full-text search engine library that provides indexing and search functionality. It can be used in various fields such as building search applications, text analysis, and intelligent machines. Here is a detailed introduction to Lucene: 1. Database Introduction: Lucene is an open source full-text search engine library written in Java, originally created by Doug Cutting in 1999 and officially opened in 2000. Unlike traditional relational databases, Lucene is not a database, but a library used to build full-text search applications. 2. Date of establishment, founder or company: The founder of Lucene is Doug Cutting, who founded Lucene in 1999 and opened it up in 2000. Currently, Lucene is managed by the Apache Software Foundation. 3. Applicable scenarios: Lucene is widely used in various fields, such as website search engines, document management systems, e-commerce website product search, log analysis tools, intelligent machines, etc. It is suitable for scenarios that require full-text search, sorting, and filtering, and can quickly process large-scale text data. 4. Advantages: -High performance: Lucene uses the data structure of Inverted index and the optimization algorithm for search scenarios to quickly index and search operations, with excellent performance. -Scalability: Lucene supports horizontal scalability and can improve search performance by adding nodes in the face of large-scale data and high concurrency. -Flexibility: Lucene provides multiple query methods and configuration of search parameters, supporting advanced search functions such as Boolean queries, fuzzy queries, range queries, etc., which can meet complex search needs. -Support for Chinese search: Lucene has built-in support for Chinese word segmentation function, which can index and search Chinese words. 5. Disadvantages: -Complex query syntax: Lucene's query syntax is relatively complex and requires a certain learning cost. Familiarity with various query methods, search parameters, and query syntax is required to optimize query operations. -Real time updates are not supported: Lucene's index is static and cannot be updated in real time once created. If you need to update the index in real-time, you need to use other tools or frameworks to achieve it. -High learning cost: Although Lucene provides rich functions and flexible configuration options, learning and understanding Lucene's usage and internal principles may require some time and effort for beginners. 6. Technical principles: Lucene's core technical principle is Inverted index, which segments the content of documents and establishes the data structure of Inverted index to achieve rapid search and sorting. The Inverted index maps each word segment to the document containing the word segment. When searching, Lucene segments the query and quickly locates the document containing the query segmentation in the Inverted index. 7. Performance analysis: Lucene is a high-performance search engine library with excellent performance for indexing and searching large-scale text data. The specific performance depends on factors such as data volume, query complexity, and system hardware. By reasonably setting shards and adding nodes, search performance can be further improved. 8. Official website: Lucene's official website is: https://lucene.apache.org/ 9. Summary: Lucene is a powerful full-text search engine library widely used in various fields. It has the advantages of high performance, scalability, and flexibility, but also has drawbacks such as complex query syntax and not supporting real-time updates. By understanding and using Lucene's query syntax and configuration options reasonably, it is possible to fully utilize its search capabilities and provide efficient full-text search capabilities for applications.

Introduction to Sphinx

Database Introduction: Sphinx is an open source full-text search engine and index database that can be used for efficient storage, retrieval, and management of text data. The main purpose of Sphinx is to implement full-text search functionality in large websites or applications, and it is very suitable for processing massive amounts of data. Date of establishment, founder or company: The Sphinx database was created by Russian programmer Andrew Aksyonoff in 2001. Initially, it started as a project similar to MySQL, but after several version iterations, Sphinx transformed into a full-text search and became its main use. Applicable scenario: Sphinx has a wide range of applications in many different scenarios. Especially for large websites and applications that require fast and efficient full-text search, Sphinx is a very ideal solution. It can handle a large amount of text data and provide high-performance search and filtering capabilities. Advantages: 1. High performance: Sphinx can quickly index and search massive text data, and has high query performance. It uses a series of optimized algorithms and data structures to support fast full-text search operations. 2. Powerful features: Sphinx provides rich functionality and flexible configuration options to meet various complex search needs. It supports search based on keywords and phrases, and can also perform field matching, sorting, grouping, and other operations. 3. Scalability: Sphinx has good scalability and can easily handle large-scale datasets. It supports distributed indexing and querying, and can perform parallel operations on multiple nodes to improve performance and reliability. Disadvantages: 1. The Learning curve is steep: the configuration and use of Sphinx are relatively complex, requiring a certain learning cost. For beginners, getting started may have some difficulties. 2. Relatively limited functionality: Although Sphinx provides many powerful search functions, its functionality still has some limitations compared to other full-text search engines such as Elasticsearch. Technical principles: The technical principles of Sphinx mainly include two aspects: index construction and query processing. In the index construction phase, Sphinx will scan the text data, preprocess, segment and encode it, and then build inverted indexes and other necessary data structures for efficient query operations. In the query processing phase, Sphinx will quickly locate the matching documents through the Inverted index according to the keywords entered by the user, and return the results according to the sorting rules specified by the user. Performance analysis: Sphinx has excellent performance. When processing Big data sets, it can perform fast search and filtering operations with very low latency. In addition, Sphinx has also highly optimized queries, using various technical means to improve query efficiency, such as Boolean operations, deduplication, etc. Official website: The official website of Sphinx is https://www.sphinxsearch.com/ Summary: Sphinx is a powerful and high-performance full-text search engine and index database. It is suitable for large websites and applications, which can efficiently process massive amounts of text data and provide flexible search and filtering functions. Although Sphinx has a steep Learning curve and relatively limited functions, its excellent performance and scalability make it a very reliable solution.

Elasticsearch installation and use

Elasticsearch is an open source real-time distributed search and analysis engine that can be used to quickly search, analyze, and store large amounts of data. The following is an introduction to the installation and use of Elasticsearch, including the installation process of the database and how to create data tables, insert, modify, query, and delete data: 1. Install Elasticsearch: -Firstly, you need to download the Elasticsearch installation package first. You can find the installation package suitable for your operating system on the download page of Elasticsearch official website. -After downloading, unzip the installation package to the path you want to install. -Enter the extracted directory, locate the bin folder, and run Elasticsearch. bat (Windows) or Elasticsearch (Linux/Mac) to start Elasticsearch. 2. Create a data table (index): -Open a terminal or command line window and initiate a request to Elasticsearch through the curl command or other tools. -Create an index (data table) using the PUT request, which can specify some configuration parameters and field mappings. -For example, using the curl command to create a file called my_ Index: ``` curl -X PUT "http://localhost:9200/my_index" ``` 3. Data insertion: -Insert data into the index using POST requests. -The data is provided in JSON format, including the fields to be inserted and corresponding values. -For example, using the curl command to_ Insert a document with "id", "name", and "age" fields into the index index: ``` curl -X POST "http://localhost:9200/my_index/_doc" -d '{"id": "1", "name": "John", "age": 30}' ``` 4. Data modification: -Use POST or PUT requests to modify data. -Use the ID of the document to determine which document to modify. -Provide new field values to update the document. -For example, using the curl command to modify the name field of a document with id 1: ``` curl -X POST "http://localhost:9200/my_index/_doc/1/_update" -d '{"doc": {"name": "Jane"}}' ``` 5. Data Query: -Use GET requests to query data. -You can use simple query statements, or you can use query DSL (Domain-specific language) to build complex queries. -For example, using the curl command to query a document with a name field of "John": ``` curl -X GET "http://localhost:9200/my_index/_search?q=name:John" ``` 6. Data deletion: -Use DELETE to request the deletion of a document or entire index. -For example, using the curl command to delete the name 'my'_ Index: ``` curl -X DELETE "http://localhost:9200/my_index" ``` The above is an introduction to the installation and use of Elasticsearch, including the installation process of the database and how to create data tables, insert, modify, query, and delete data. With these basic operations, you can start using Elasticsearch to search and analyze a large amount of data.

Apache Solr Installation and Usage

Apache Solr is a search platform based on the open source search engine library Lucene, which provides powerful full-text search and real-time analysis capabilities for applications. The following is an introduction to the installation and use of Apache Solr. 1. Download and install: Firstly, from the official Apache website( http://lucene.apache.org/solr/ )Download the latest version of Solr compressed package. After decompression, enter the directory after decompression. 2. Start Solr: In the extracted directory, use the command line to enter the bin directory and execute the following command to start Solr: ``` ./solr start ``` This will start a Solr server, which defaults to listening on port 8983. 3. Create a data table (Core): Solr uses a data table (Core) to organize index data. Execute the following command in the bin directory to create a data table: ``` ./solr create -c example ``` This will create a data table called 'example'. The name of the data table can be customized according to actual needs. 4. Add document: Use Solr's API to add, modify, query, and delete documents. Firstly, we need to define the document structure and fields. In the Solr management interface, select the corresponding data table (such as example), click the "Schema" tab on the left, and on the right, you can define the document structure and fields. Then, you can use Solr's API to add documents. The following is an example POST request that adds a document to the data table: ``` POST http://localhost:8983/solr/example/update/json/docs Content-Type: application/json { "id": "1", "title": "Solr Tutorial", "content": "This is a tutorial on how to use Apache Solr." } ``` You can add more fields and content to the above request. 5. Modify the document: The operation of modifying a document is similar to adding, just use the same API interface and modify the corresponding document content. 6. Query documents: Using Solr's API for query operations, the following is an example GET request to query documents in the data table: ``` GET http://localhost:8983/solr/example/select?q=title:Solr ``` In this example, we queried documents with titles containing 'Solr'. The query results will be returned in JSON format. 7. Delete Document: The operation of deleting documents is also carried out through Solr's API. Here is an example POST request that deletes documents with specified conditions: ``` POST http://localhost:8983/solr/example/update?commit=true Content-Type: application/json { "delete": { "query": "title:Solr" } } ``` In this example, we removed all documents with "Solr" in the title. The above is an introduction to the installation and use of Apache Solr, including the process and examples of installing, creating data tables, inserting, modifying, querying, and deleting data. Corresponding operations and adjustments can be made based on actual needs and document structure.

Lucene installation and use

Lucene is an open source Full-text search library that provides efficient text indexing and search functions. It is not a database, but a tool library for building full-text indexes. Installing Lucene requires the following steps: 1. Download Lucene: On Lucene's official website( https://lucene.apache.org/ )Download the latest Lucene version from. After downloading, extract the file to the specified directory. 2. Import Lucene library: In your Java project, add Lucene's jar file to the project's classpath. 3. Create index: Firstly, you need to create an index directory to store Lucene's indexes. Create a new Java class named IndexDemo, and then write the following code in that class: ```java import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.document.Document; import org.apache.lucene.document.Field; import org.apache.lucene.index.IndexWriter; import org.apache.lucene.index.IndexWriterConfig; import org.apache.lucene.store.Directory; import org.apache.lucene.store.FSDirectory; import java.io.IOException; import java.nio.file.Paths; public class IndexDemo { public static void main(String[] args) { //Create an index directory String indexPath = "path/to/index"; Directory indexDirectory; try { indexDirectory = FSDirectory.open(Paths.get(indexPath)); } catch (IOException e) { e.printStackTrace(); return; } //Create an IndexWriter object and configure its parser StandardAnalyzer analyzer = new StandardAnalyzer(); IndexWriterConfig config = new IndexWriterConfig(analyzer); IndexWriter indexWriter; try { indexWriter = new IndexWriter(indexDirectory, config); //Create a Document object and add a Field to it Document document = new Document(); document.add(new Field("id", "1", Field.Store.YES, Field.Index.NO)); document.add(new Field("title", "Lucene Introduction", Field.Store.YES, Field.Index.ANALYZED)); document.add(new Field("content", "This is a Lucene tutorial.", Field.Store.YES, Field.Index.ANALYZED)); //Using IndexWriter to Write Document Objects to an Index indexWriter.addDocument(document); //Submit index and close IndexWriter indexWriter.commit(); indexWriter.close(); } catch (IOException e) { e.printStackTrace(); } } } ``` In the above code, we first created an index directory, indexDirectory, and then created an IndexWriterConfig to configure the analyzer and other parameters. Next, we created a Document object and added Fields to it to represent the different properties of the document. Finally, we use IndexWriter to write the Document object to the index, submit the index, and close IndexWriter. 4. Search index: The following is an example code for using Lucene search index: ```java import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.document.Document; import org.apache.lucene.index.DirectoryReader; import org.apache.lucene.index.IndexReader; import org.apache.lucene.queryparser.classic.QueryParser; import org.apache.lucene.search.IndexSearcher; import org.apache.lucene.search.Query; import org.apache.lucene.search.ScoreDoc; import org.apache.lucene.search.TopDocs; import org.apache.lucene.store.Directory; import org.apache.lucene.store.FSDirectory; import org.apache.lucene.util.Version; import java.io.IOException; import java.nio.file.Paths; public class SearchDemo { public static void main(String[] args) { //Open index directory String indexPath = "path/to/index"; Directory indexDirectory; try { indexDirectory = FSDirectory.open(Paths.get(indexPath)); } catch (IOException e) { e.printStackTrace(); return; } //Create a search object IndexReader indexReader; try { indexReader = DirectoryReader.open(indexDirectory); } catch (IOException e) { e.printStackTrace(); return; } IndexSearcher searcher = new IndexSearcher(indexReader); //Create a query object StandardAnalyzer analyzer = new StandardAnalyzer(); QueryParser parser = new QueryParser("content", analyzer); Query query; try { query = parser.parse("Lucene tutorial"); } catch (Exception e) { e.printStackTrace(); return; } //Execute Query TopDocs topDocs; try { topDocs = searcher.search(query, 10); } catch (IOException e) { e.printStackTrace(); return; } //Process search results for (ScoreDoc scoreDoc : topDocs.scoreDocs) { try { Document document = searcher.doc(scoreDoc.doc); System.out.println("Score: " + scoreDoc.score); System.out.println("Title: " + document.get("title")); System.out.println("Content: " + document.get("content")); System.out.println(); } catch (IOException e) { e.printStackTrace(); } } } } ``` In the above code, we first open the index directory indexDirectory and create an IndexSearcher object to perform the search operation. Then we use StandardAnalyzer and QueryParser to create a query object that represents the content to be searched for. Next, we perform a search operation and process the search results. 5. Delete index: To delete an index, you need to first create an IndexWriter object and then use IndexWriter's deleteDocuments method to delete the specified document. The example code is as follows: ```java import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.document.Document; import org.apache.lucene.document.Field; import org.apache.lucene.document.IntPoint; import org.apache.lucene.document.StringField; import org.apache.lucene.document.TextField; import org.apache.lucene.index.DirectoryReader; import org.apache.lucene.index.IndexWriter; import org.apache.lucene.index.IndexWriterConfig; import org.apache.lucene.index.Term; import org.apache.lucene.search.IndexSearcher; import org.apache.lucene.search.Query; import org.apache.lucene.search.ScoreDoc; import org.apache.lucene.search.TopDocs; import org.apache.lucene.store.Directory; import org.apache.lucene.store.FSDirectory; import java.io.IOException; import java.nio.file.Paths; public class DeleteDemo { public static void main(String[] args) { //Create an index directory String indexPath = "path/to/index"; Directory indexDirectory; try { indexDirectory = FSDirectory.open(Paths.get(indexPath)); } catch (IOException e) { e.printStackTrace(); return; } //Create an IndexWriter object and configure its parser StandardAnalyzer analyzer = new StandardAnalyzer(); IndexWriterConfig config = new IndexWriterConfig(analyzer); IndexWriter indexWriter; try { indexWriter = new IndexWriter(indexDirectory, config); //Create a Document object and add a Field to it Document document1 = new Document(); document1.add(new StringField("id", "1", Field.Store.YES)); document1.add(new TextField("content", "This is document 1.", Field.Store.YES)); Document document2 = new Document(); document2.add(new StringField("id", "2", Field.Store.YES)); document2.add(new TextField("content", "This is document 2.", Field.Store.YES)); //Using IndexWriter to Write Document Objects to an Index indexWriter.addDocument(document1); indexWriter.addDocument(document2); //Delete document with id 1 indexWriter.deleteDocuments(new Term("id", "1")); //Submit index and close IndexWriter indexWriter.commit(); indexWriter.close(); } catch (IOException e) { e.printStackTrace(); } } } ``` In the above code, we first created an index directory, indexDirectory, and then created an IndexWriter object and configured its parser. Next, we created two Document objects and added Fields to them. Then, we use IndexWriter to write the Document object to the index, and use the deleteDocuments method to delete the document with id 1. Finally, we submit the index and close IndexWriter. Through the above steps, you can achieve Lucene's installation, index creation, search, deletion, and other functions. In most cases, Lucene should be integrated into your Java application for use, rather than being used as a standalone database.

Sphinx installation and use

Sphinx is an open source full-text search engine that can be used to create high-performance search functions. The following is an introduction to the installation and use of Sphinx, including the installation process and examples of how to create data tables, insert, modify, query, and delete data. **Installation process:** 1. Preparation environment: First, ensure that your operating system has installed the MySQL database and is started. 2. Download Sphinx: Go to the Sphinx official website( http://sphinxsearch.com/downloads/ )Download the latest Sphinx software package. Choose the version that is suitable for your operating system to download. 3. Unzip software package: Unzip the downloaded software package to the directory where you want to install Sphinx. 4. Configure Sphinx: Enter the decompression directory, copy the configuration file 'sphinx. conf. dist' and rename it to 'sphinx. conf'. Open the 'sphinx. conf' file and modify several configuration items, such as database connection information, search engine index configuration, etc. 5. Create Index: Execute the following command from the command line to create an index: ```shell /path/to/sphinx/bin/indexer --config /path/to/sphinx.conf --all ``` 6. Start Sphinx: Execute the following command to start Sphinx search: ```shell /path/to/sphinx/bin/searchd --config /path/to/sphinx.conf ``` **Create a data table:** In Sphinx, data is stored in an index. Here is an example of creating a data table: ```sql CREATE TABLE my_index ( Id integer primary key, -- primary key Title varchar (255), -- Title Content text - Content ) ENGINE = sphinx ``` **Data insertion:** Sphinx's data insertion operation is actually inserting data into an index. Here is an example of inserting data into 'my'_ Example in index table: ```sql INSERT INTO my_index (id, title, content) VALUES (1, 'Example Title', 'This is an example content'); ``` **Data modification:** To modify the data in the Sphinx index, you need to recreate the index. Before modifying the data, you can use the following command to delete the index file: ```shell /path/to/sphinx/bin/indexer --config /path/to/sphinx.conf --rotate --all ``` Then, perform the data insertion operation again. **Data Query:** The following is an example in 'my'_ Example of searching in the index table: ```sql SELECT * FROM my_index WHERE MATCH('example'); ``` This query will return records containing the keyword 'example'. **Data deletion:** To delete data from the Sphinx index, you need to recreate the index and exclude the data to be deleted. The following is an example of deleting a record with ID 1: ```shell /path/to/sphinx/bin/indexer --config /path/to/sphinx.conf --rotate --rotate-index my_index --exclude id 1 ``` This command will create a new index, excluding records with ID 1. The above is an introduction to the installation and use of Sphinx, including examples of the installation process, creating data tables, and inserting, modifying, querying, and deleting data. I hope it can be helpful to you!

Using Java to Operate Elasticsearch

Using Java to operate Elasticsearch can be achieved through the Elasticsearch client library provided by Java. The following are the steps to use Java to operate Elasticsearch: 1. Ensure that the Elasticsearch server has been installed and started. 2. Create a Java project and add Maven dependencies for the Elasticsearch client library. The following dependencies can be added to the pom.xml file of the project: ```xml <dependencies> <dependency> <groupId>org.elasticsearch.client</groupId> <artifactId>elasticsearch-rest-client</artifactId> <version>7.15.1</version> </dependency> <dependency> <groupId>org.elasticsearch.client</groupId> <artifactId>elasticsearch-rest-high-level-client</artifactId> <version>7.15.1</version> </dependency> </dependencies> ``` 3. Create an Elasticsearch client instance and specify the host and port number when connecting to the Elasticsearch service: ```java import org.elasticsearch.client.RestClient; import org.elasticsearch.client.RestHighLevelClient; RestHighLevelClient client = new RestHighLevelClient( RestClient.builder(new HttpHost("localhost", 9200, "http"))); ``` 4. Insert data. You can use the Index API to insert data: ```java import org.elasticsearch.action.index.IndexRequest; import org.elasticsearch.action.index.IndexResponse; IndexRequest request = new IndexRequest("index_name"); request.id("document_id"); request.source("field_name", "field_value"); IndexResponse response = client.index(request); ``` 5. Modify data. You can use the Update API to modify data: ```java import org.elasticsearch.action.update.UpdateRequest; import org.elasticsearch.action.update.UpdateResponse; UpdateRequest request = new UpdateRequest("index_name", "document_id"); request.doc("field_name", "new_field_value"); UpdateResponse response = client.update(request); ``` 6. Query data. You can use the Search API to query data: ```java import org.elasticsearch.action.search.SearchRequest; import org.elasticsearch.action.search.SearchResponse; import org.elasticsearch.index.query.QueryBuilders; import org.elasticsearch.search.builder.SearchSourceBuilder; SearchRequest request = new SearchRequest("index_name"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); searchSourceBuilder.query(QueryBuilders.matchAllQuery()); request.source(searchSourceBuilder); SearchResponse response = client.search(request); ``` 7. Delete data. You can use the Delete API to delete data: ```java import org.elasticsearch.action.delete.DeleteRequest; import org.elasticsearch.action.delete.DeleteResponse; DeleteRequest request = new DeleteRequest("index_name", "document_id"); DeleteResponse response = client.delete(request); ``` Finally, remember to close the Elasticsearch client connection at the end of the program: ```java client.close(); ``` The above are the basic steps and sample code for using Java to operate Elasticsearch. According to specific business requirements, other APIs can also be used to achieve more functions.

Using Java to Operate Apache Solr

Apache Solr is an open source search platform used to provide high-performance Full-text search and analysis functions. It is built on the foundation of Apache Lucene and provides a simple and easy-to-use HTTP interface that can be operated through Java. The following are the basic steps for using Java to operate Apache Solr: 1. Add Maven dependency: Add the following dependencies in the pom.xml file of the project: ```xml <dependency> <groupId>org.apache.solr</groupId> <artifactId>solr-solrj</artifactId> Version number </dependency> ``` This will add Apache Solr's Java client library, Solr Solrj, to your project. 2. Establish a SolrServer connection: First, you need to establish a connection to the Solr server. You can use the 'HttpSolrClient' class to implement it, which provides methods for interacting with Solr servers. ```java import org.apache.solr.client.solrj.impl.HttpSolrClient; String solrUrl=“ http://localhost:8983/solr URL of Solr server SolrClient solr = new HttpSolrClient.Builder(solrUrl).build(); ``` In the above code, we created a client that connects to the Solr server using 'HttpSolrClient. Builder'. 3. Data Insertion: The following is an example code that demonstrates how to insert data into a Solr server. ```java import org.apache.solr.common.SolrInputDocument; //Create a SolrInputDocument object SolrInputDocument document = new SolrInputDocument(); document.addField("id", "1"); document.addField("title", "Example Document"); document.addField("content", "This is an example document for Solr"); //Adding Documents to Solr Server solr.add(document); //Submit Changes solr.commit(); ``` In the above code, we first created a 'SolrInputDocument' object and added fields and corresponding values to it. Then, use the 'solr. add()' method to add the document to the Solr server and submit the changes using the 'solr. commit()' method. 4. Data modification: The following is an example code that demonstrates how to modify data in the Solr server. ```java import org.apache.solr.client.solrj.SolrQuery; import org.apache.solr.client.solrj.response.UpdateResponse; //Create a query object SolrQuery query = new SolrQuery("id:1"); //Query and obtain documents QueryResponse response = solr.query(query); SolrDocumentList results = response.getResults(); //Get the first document SolrDocument document = results.get(0); document.setField("title", "Updated Document"); //Update Document UpdateResponse updateResponse = solr.add(document); //Submit Changes solr.commit(); ``` In the above code, we first create a query object and then execute the query using the 'solr. query()' method. The 'response. getResults()' method can be used to obtain a list of documents with query results. Next, we take the first document and modify its field values, then use the 'solr. add()' method to update the document to the Solr server and submit the changes using the 'solr. commit()' method. 5. Data Query: The following is an example code that demonstrates how to query data from a Solr server. ```java import org.apache.solr.client.solrj.SolrQuery; import org.apache.solr.client.solrj.response.QueryResponse; //Create a query object SolrQuery query = new SolrQuery("content:example"); //Query and obtain results QueryResponse response = solr.query(query); SolrDocumentList results = response.getResults(); //Output query results for (SolrDocument document : results) { System.out.println("id: " + document.get("id")); System.out.println("title: " + document.get("title")); System.out.println("content: " + document.get("content")); } ``` In the above code, we first create a query object and then execute the query using the 'solr. query()' method. The 'response. getResults()' method can be used to obtain a list of documents with query results. Then, we can output the query results by traversing the document list. 6. Data deletion: The following is an example code that demonstrates how to delete data from the Solr server. ```java import org.apache.solr.client.solrj.response.UpdateResponse; //Delete the document with the specified ID UpdateResponse updateResponse = solr.deleteById("1"); //Submit Changes solr.commit(); ``` In the above code, we used the 'solr. deleteById()' method to delete the document with the specified id and submitted the changes using the 'solr. commit()' method. These are the basic steps and sample code for using Java to operate Apache Solr. You can further expand and optimize the code according to your own needs.