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The documents written in the Segment cannot be modified, but they can be deleted. Data is written as Batch or as an Append and achieves a high throughput. Every segment has its own independent index and are independently searchable, but the data can never be changed. When the data in the Buffer reaches a certain amount, it will be flushed to become a Segment. When Lucene writes data it first writes to an in-memory buffer (similar to MemTable in LSM, but not readable). They inherit the data writing advantages of LSM but only provide near real-time and not real-time queries. The conceptual design of Segments in Lucene is similar to LSM but there are some differences. SegmentĪn index is composed of one or more sub-indexes. A term dictionary is the basic index used to perform conditional searches on terms. A term is produced when a field is put through an Analyzer (tokenizer). The smallest unit of index and search in Lucene.

Field and FieldType will be discussed in detail later on. Lucene determines the type of indexing (such as Invert Index, Store Field, DocValues or N-dimensional) to use for the data depending on the type of field (FieldType). Lucene provides many different types of field, including StringField, TextField and NumericDocValuesField. A field is the smallest definable unit of a data index in Lucene. One document is made up of one or more fields. Sequence numbers are discussed in detail later on. A document written into an index is assigned a unique ID, that is, a sequence number (more usually called a DocId). Similar to a row in a relational database or a document in a document database, an index can contain multiple documents. You can put all sorts of documents in the folder, and Lucene can index the document whatever the content. You can put a new document into the folder or you can take one out, but if you want to modify one of the documents, you must first take it out, modify it and then put it back in the folder. A Lucene index can be understood as a document folder. However, there are no such constraints on a Lucene index. There are some clear constraints on the definition of the primary key, columns and the like. The table in a traditional relational database or NoSQL database needs to at least have its scheme defined when it is created. This is similar conceptually to a database table, but differs in some important ways. The data in a segment is represented abstractly rather than as a realistic representation of the actual data structure. The figure shows the basic internal structure of an index.

Basic Conceptsīefore going into the details of Lucene, it is good to know a few basic concepts as well as some hidden aspects behind these concepts. Some background knowledge is required before reading this article, including an understanding of basic searching and index principles, concepts such as inverted indexes, tokenizing and relevance, as well as an understanding the basic uses of Lucene, for example, Directory, IndexWriter and IndexSearcher. The whole analysis is based on Lucene 7.2.1.

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We hope that through this series of articles, readers will come to an understanding of how Lucene achieves these goals.

Powerful, Accurate and Efficient Search Algorithms.

Lucene officially summarizes its advantages in several points:

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After this article, we plan a series of articles on the principles of Lucene and its source code to give a complete analysis of Lucene's data model and data read/write path. We have previously published many articles in this column analyzing Elasticsearch's data model, read/write path, distributed architecture and data/meta consistency.

We need to study its engine in minute detail to tune the car to give a faster and more stable ride. If we compare Elasticsearch and Solr to an exquisitely designed, high-performance sports car, then Lucene would be the engine providing the powerful motive force. The best known are Elasticsearch and Solr. Many open-source projects are based on Lucene. It currently supports whole-text indexing as well as providing many other types of indexing to satisfy the requirements of different types of query. Lucene has been developed over many years, providing more powerful features and an increasingly refined architecture. Apache Lucene is an open-source, high-performance, scalable data retrieval engine with powerful data retrieval capabilities.

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