Use and optimization of Java lightweight full-text search engine Lucene

1. Introduction

1. Introduction to Lucene

Lucene is an open source full-text search engine toolkit written by Doug Cutting. It was designed to implement full-text search functionality, i.e. read in a bunch of text files and convert them into easily searchable data structures. Lucene provides a set of simple and powerful APIs that make the indexing and searching process very convenient.

2. Lucene application areas and usage scenarios

Lucene is widely used in the background technology of search engines such as Internet search from 12 million sites, full-text indexing of news information, internal enterprise document management systems, email servers, and limited-space full-text search of scientific and technological documents and patent documents.

3. What kind of tool is Lucene?

Lucene is mainly used to create document collection indexes, and quickly search these document collections according to keywords. It uses inverted index technology, which can quickly retrieve qualified data when searching, and can support multiple retrieval methods.

Two, Lucene quick start

1. The basic principles and architecture of Lucene

The workflow of Lucene is as follows:

1. Create and build an indexer (IndexWriter) to read the text content that needs to be indexed full-text.

2. Use the tokenizer (Tokenizer) and analyzer (Analyzer) to process the text, process the text into index terms one by one, then build the document (Document) and add it to the index. The collection of documents is the text that needs to be indexed in full text.

3. After the index library (Index) is established, a query (Query) is constructed during retrieval, the index library is searched, and matching results are returned.

The Lucene architecture is as follows:

• Directory: Index data storage location.
• Document: The data unit for indexing.
• Field: A component of a data unit.
• Analyzer: Data analyzer.
• Query: A query statement including keywords and logical operations.
• IndexSearcher: searcher.
• ScoreDoc: Information such as the document score, document ID, and scoring domain calculated using the scoring algorithm.

2. Common APIs of Lucene

• IndexWriter: write index.
• IndexReader: Index reader.
• TermQuery: term query.
• BooleanQuery: Boolean query.
• PhraseQuery: Phrase query.
• QueryParser: query parser.

3. Create an index and perform a retrieval operation

create index

// 定义索引存储目录
Directory directory = FSDirectory.open(Paths.get(indexPath)); 
// 定义分析器
Analyzer analyzer = new StandardAnalyzer(); 
// 配置索引写入器
IndexWriterConfig indexWriterConfig = new IndexWriterConfig(analyzer); 
IndexWriter indexWriter = new IndexWriter(directory, indexWriterConfig);
// 清空索引库
indexWriter.deleteAll(); 
// 创建文档
Document document = new Document(); 
// 添加字段
document.add(new TextField("fileName", file.getName(), Field.Store.YES)); 
document.add(new TextField("content", new String(Files.readAllBytes(file.toPath())), Field.Store.NO));
// 添加文档到索引库
indexWriter.addDocument(document); 
// 提交索引
indexWriter.commit(); 
// 关闭writer
indexWriter.close(); 

perform search

// 定义索引存储目录
Directory directory = FSDirectory.open(Paths.get(indexPath)); 
// 打开索引
IndexReader indexReader = DirectoryReader.open(directory);
// 创建搜索器
IndexSearcher indexSearcher = new IndexSearcher(indexReader);
// 定义分析器 
Analyzer analyzer = new StandardAnalyzer(); 
// 关键词解析器
QueryParser queryParser = new QueryParser("content", analyzer); 
// 解析查询关键词
Query query = queryParser.parse(keywords); 
// 进行搜索
TopDocs topDocs = indexSearcher.search(query, 10); 
// 获取搜索结果
ScoreDoc[] scoreDocs = topDocs.scoreDocs; 
for (ScoreDoc scoreDoc : scoreDocs) {
    
    
	// 获取文档
    Document document = indexSearcher.doc(scoreDoc.doc); 
    // 获取文件名
    String fileName = document.get("fileName"); 
    // 获取内容
    String content = document.get("content"); 
    // 获取文档得分
    float score = scoreDoc.score; 
    System.out.println(fileName + " " + content + " " + score);
}
// 关闭reader
indexReader.close(); 

3. Detailed explanation of Lucene use

1. Data type support and data preprocessing

The data types supported in Lucene include text, numbers, dates, etc. When storing text data, Lucene performs preprocessing operations such as standardization and word segmentation on the text.

2. Tokenizer and Filter

Lucene's tokenizer is used to decompose the text into words, and the filter is used to filter the decomposed words, such as removing stop words, converting case and so on.

3. Advanced query syntax

In addition to the basic query syntax, Lucene also provides a variety of advanced query syntax, such as wildcard query, fuzzy query, range query, etc.

4. Sort paginated aggregates

The search results can be sorted by fields such as relevance score and time, and displayed in pagination. In addition, Lucene also supports aggregation operations, such as grouping based on a certain field.

4. Lucene performance optimization

1. Index optimization

Index structure analysis

In order to achieve efficient search Lucene uses an inverted index structure. When using Lucene to build an index, it is necessary to consider whether the index structure is reasonable, including the selection and setting of fields, word segmentation and filtering, etc.

Practical tips for index optimization

There are many ways to optimize indexes, such as increasing memory cache, adjusting flush strategy, using doc values, etc. Also, you should avoid dirty data, unnecessary fields, etc. in the index.

2. Search optimization

Search Algorithm Analysis

The search algorithms used by Lucene include vector space model and BM25 algorithm. When retrieving, it is necessary to consider the construction of the query statement, the choice of the query parser, and so on.

Practical Tips for Search Optimization

There are also many ways to optimize retrieval, such as using caches, avoiding frequent opening of new IndexReaders, and choosing faster sorting algorithms.

3. Memory optimization

JVM tuning

Memory usage efficiency can be improved by adjusting JVM parameters such as -Xms, -Xmx, etc. Frequent GC operations should also be avoided to reduce problems such as memory leaks.

Caching Mechanism Optimization

Reasonable use of cache during retrieval can greatly improve retrieval efficiency. You can use LRU cache algorithm, SoftReference cache, etc. to improve the effect of cache and avoid problems such as OOM.

5. Lucene stored procedure and index maintenance

1. Document and index structure stored procedure

1.1 Document storage process

When we need to store data in Lucene, we need to store the data in the form of documents first. Here is an example of storing documents using Lucene:

// 创建一个文档对象
Document doc = new Document();

// 添加文档字段
doc.add(new StringField("id", "001", Field.Store.YES));
doc.add(new TextField("title", "Java程序设计", Field.Store.YES));
doc.add(new TextField("content", "Java程序设计入门到精通", Field.Store.YES));

// 将文档添加到索引中
indexWriter.addDocument(doc);

In the above code, we first created a document object doc, and then added three fields to it: id, titleand content, representing the number, title and content of the document respectively. StringFieldFields of type will not be processed by tokenizers, while fields of TextFieldtype will be divided into multiple terms according to the specified tokenizer.

Finally, we add the document to the Lucene index, thus completing a stored procedure for a document.

1.2 Index structure stored procedure

Lucene's index structure is composed of segments, and each segment contains index information for a part of the document. Operations such as creating new indexes, merging multiple segments, and optimizing indexes all involve Lucene's index maintenance mechanism.

Here is an example of using Lucene to store index structures:

// 创建索引目录
Directory directory = FSDirectory.open(Paths.get("index"));

// 创建分词器
Analyzer analyzer = new StandardAnalyzer();

// 创建索引写入器
IndexWriterConfig config = new IndexWriterConfig(analyzer);
IndexWriter indexWriter = new IndexWriter(directory, config);

// 创建文档对象
Document doc = new Document();
doc.add(new StringField("id", "001", Field.Store.YES));
doc.add(new TextField("title", "Java程序设计", Field.Store.YES));
doc.add(new TextField("content", "Java程序设计入门到精通", Field.Store.YES));

// 将文档添加到索引中
indexWriter.addDocument(doc);

// 提交索引
indexWriter.commit();

// 关闭索引写入器
indexWriter.close();

In the above code, an index directory is first created directory, which is used to store all index data. Then a standard tokenizer was created analyzerto divide the text content into words. Then create an index writer indexWriter, which is used to write document index information to the index directory.

Next a document object is created docand three fields are added to it. Then, add the document object to the index writer, and call commit()the method to submit the index. Finally the index writer is closed.

2. Index maintenance and update strategy

Index maintenance is a very important link in Lucene, which involves index update strategies, merging mechanisms, and data compression. Here are some common index maintenance and update strategies:

2.1 Index optimization

Index optimization in Lucene refers to optimizing and compressing indexes to improve search performance and reduce storage space. During index optimization, segments are merged into fewer segments and obsolete documents are deleted.

// 创建索引目录
Directory directory = FSDirectory.open(Paths.get("index"));

// 创建分词器
Analyzer analyzer = new StandardAnalyzer();

// 创建索引写入器
IndexWriterConfig config = new IndexWriterConfig(analyzer);
IndexWriter indexWriter = new IndexWriter(directory, config);

// 进行索引优化
indexWriter.forceMerge(1);

// 关闭索引写入器
indexWriter.close();

In the above code an index optimizer is created forceMergewhich is used to combine multiple segments into a single segment. The parameter 1indicates that only one segment is reserved, which means that the index file can be compressed to the minimum as possible.

2.2 Documentation update

Lucene supports operations such as adding, updating, and deleting documents. When updating a document, IndexWriter.updateDocument()the method to update the original document.

// 创建索引目录
Directory directory = FSDirectory.open(Paths.get("index"));

// 创建分词器
Analyzer analyzer = new StandardAnalyzer();

// 创建索引写入器
IndexWriterConfig config = new IndexWriterConfig(analyzer);
IndexWriter indexWriter = new IndexWriter(directory, config);

// 更新文档
Term term = new Term("id", "001");
indexWriter.updateDocument(term, doc);

// 关闭索引写入器
indexWriter.close();

In the above code, we first create an index directory, tokenizer and index writer. Then, use Termthe object to specify the document to be updated, and use IndexWriter.updateDocument()the method to perform the update operation. Finally, close the index writer.

6. Comparison between Lucene and Solr Elasticsearch

1. Comparison of Solr and Elasticsearch features

Both Solr and Elasticsearch are search engine systems based on Lucene, and they both provide functions such as full-text search, distributed search, data aggregation and analysis. The following are some basic introductions and feature comparisons between Solr and Elasticsearch:

Solr

Apache Solr is an open source search engine project based on Lucene, providing rich search and aggregation functions. Solr supports HTTP/JSON interface and integrates functions such as distributed search, multi-tenancy, data import and result analysis.

Advantages of Solr:

• Easy to install, use and maintain.
• Provides a visual management interface to monitor index and query performance.
• Features such as batch import and incremental update are provided.
• It supports distributed architecture and naturally supports high availability and load balancing.

Solr Disadvantages:

• The customizability is poor, and it is difficult to meet some special scenarios.
• The query syntax is relatively complicated, and you need to learn Solr's query syntax to take advantage of it.

Elasticsearch

Elasticsearch is a distributed search engine built on Lucene. Elasticsearch provides a RESTful interface for easy document retrieval, aggregation, and analysis. Its data modeling method is similar to that of traditional relational databases, and supports JSON-based queries and multi-tenant environments. Elasticsearch also provides client library support for non-Java development languages ​​such as Node.js.

Advantages of Elasticsearch:

• Easy to use and deploy, enabling rapid iteration and development of projects.
• It supports multiple data types and formats, and has good scalability.
• It supports rich functions such as real-time search and aggregation analysis.
• Integrates common NoSQL features, supports clustering and high availability.

Elasticsearch Disadvantages:

• The performance of high-concurrency queries is poor, and a certain technical architecture and load balancing support are required.
• Indexing and query syntax is simpler than Solr's, but can be confusing and error-prone.

2. Analysis of the advantages and disadvantages of Solr and Elasticsearch

Both Solr and Elasticsearch have their own unique features and advantages. Here is a comparative analysis of them:

Advantages of Solr

• Modular design, high flexibility.
• Support asynchronous import, distributed retrieval and other features, suitable for massive data, fast processing speed.
• With a visual management interface, it is easy to operate and maintain.
• More friendly to multi-tenant support.

Disadvantages of Solr

• The user experience is not as strong as Elasticsearch.
• The customization is poor, and more secondary development and code writing are required to meet some special scenarios.
• After the stress test, the query delay is large and there are problems such as crashes.

Advantages of Elasticsearch

• Widely popular, rich in information, easy to learn, and wide in application scenarios.
• The support for JSON data is more friendly, and the document structure makes it easier for users to understand.
• The data storage method is more suitable for innovation, and supports complex type data and dynamic field construction.
• Additional functionality can be provided through plugins.

Disadvantages of Elasticsearch

• The speed of processing massive data is slow, and the query is slow.
• Data migration functions such as hot backup and update are not supported.

3. Practical recommendations

There may be differences in performance between Solr and Elasticsearch in different application scenarios. When choosing a search engine, comprehensive consideration needs to be made according to the specific business situation. Generally speaking, Elasticsearch is more suitable for searching, aggregating and analyzing data in JSON format, and Solr is more suitable for processing text data and massive data. At the same time, in practical applications, we need to pay attention to the following points:

• First, choose a search engine based on your business needs. If you need full-text search and aggregation analysis, you can give priority to Elasticsearch; if you need to process massive text and document data, you can give priority to Solr.
• Second, the performance and resource consumption of the search engine needs to be evaluated. You can use stress testing tools such as JMeter to stress test the search engine cluster to understand its performance in terms of query latency, correctness of results, and load balancing.
• Finally, adequate testing and verification should be carried out during the development phase to ensure that the search engine can meet business needs and be deployed and maintained in accordance with best practices.

7. Common problems and solutions

1. Index lock exception

When multiple threads access the same Lucene index at the same time, an index lock exception may occur. The solution is to share the IndexWriter object between multiple threads, or use multiple IndexWriter instances but open different index directories.

2. Inefficient search

There may be many reasons for the low search efficiency, such as unreasonable index structure design, unreasonable text word segmentation, etc. Solutions include redesigning the index structure, using better tokenizers, etc.

3. Index performance drops

As the amount of indexed data increases, the indexing performance will gradually degrade. Solutions include using a better hardware environment, using SSD hard drives, etc.

4. Memory overflow problem

When Lucene handles large amounts of data, memory overflow problems may arise. Solutions include adjusting JVM memory parameters, optimizing code, and more.

5. Optimization suggestions for sharding and cluster environments

In sharded and clustered environments, each node handles part of the index, requiring more coordination and communication. In order to improve efficiency, the number of communications can be reduced as much as possible, and a caching mechanism can be added. At the same time, it is necessary to design load balancing and fault tolerance mechanisms.