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Updated on 2025-03-08

Implementation Guide for Storing Vector Data with Java and PostgreSQL

introduction

In today's digital age, the methods and technologies of data storage are becoming increasingly complex and diverse. With the development of machine learning and data science, the storage and management of vector data has become particularly important. This article will introduce in detail how to use Java and PostgreSQL databases to store vector data, explore its application scenarios, advantages, and specific implementation steps.

Vector data and its application scenarios

What is vector data?

A vector is a mathematical object that can be represented as an ordered sequence. Vector data is usually used to represent feature vectors, coordinates, image data, audio data, etc. Vector data is widely used in the fields of machine learning, image processing, natural language processing, etc.

Application scenarios of vector data

  1. Recommended system: By representing users and items as vectors, the similarity between them can be calculated to achieve personalized recommendations.
  2. Image recognition: After converting the image into a vector, the distance between the vectors can be used for image classification and recognition.
  3. Natural Language Processing: Denote text as a vector (such as word embedding), and can perform tasks such as text classification and sentiment analysis.
  4. Exception detection: By analyzing the distribution of vector data, abnormal data points can be detected.

PostgreSQL database introduction

PostgreSQL is a powerful open source relational database management system known for its high scalability and rich functionality. It supports a variety of data types and advanced queries, especially suitable for handling complex data structures and large-scale data.

PostgreSQL's vector data storage support

PostgreSQL provides support for vector data through extensions and plugins. Common vector data storage methods include:

  • Array type: PostgreSQL has built-in array data type that can store vector data.
  • PostGIS: A geospatial database extension that supports the storage and query of geo coordinate vectors.
  • H3, Citus: Some plug-ins and extensions provide efficient vector data storage and query functions.

Project Settings

Environmental preparation

Before you start, make sure you have installed the following software:

  • JDK（Java Development Kit）
  • Maven (Java build tool)
  • PostgreSQL database

Create Spring Boot Project

Use Spring Initializr to create a new Spring Boot project. Add the following dependencies to the project:

<dependency>
    <groupId></groupId>
    <artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>
<dependency>
    <groupId></groupId>
    <artifactId>postgresql</artifactId>
    <version>42.2.5</version>
</dependency>

Configure database connections

existIn the file, configure PostgreSQL database connection information:

=jdbc:postgresql://localhost:5432/yourdatabase
=yourusername
=yourpassword
-auto=update
=

Create a vector data model

Define vector entity class

Create a name calledVectorDataThe entity class used to store vector data:

import .*;
import ;

@Entity
public class VectorData {
    @Id
    @GeneratedValue(strategy = )
    private Long id;

    @Column
    private String name;

    @Column
    private double[] vector;

    // Getters and Setters
    // toString() method}

Create a vector data table

Automatically generate database table structures using JPA and Hibernate. The vector field of the VectorData class will store vector data.

Writing vector data storage and query interfaces

Create an interface called VectorDataRepository, inherited from JpaRepository, to manage the storage and query of vector data:

import ;

public interface VectorDataRepository extends JpaRepository&lt;VectorData, Long&gt; {
    // Custom query methods can be defined here}

Add, delete, modify and check vector data

Insert vector data

existVectorDataServiceIn the class, write methods to insert vector data:

import ;
import ;

@Service
public class VectorDataService {

    @Autowired
    private VectorDataRepository vectorDataRepository;

    public VectorData saveVectorData(String name, double[] vector) {
        VectorData vectorData = new VectorData();
        (name);
        (vector);
        return (vectorData);
    }

    // Other methods of adding, deleting, modifying and checking}

Query vector data

existVectorDataServiceIn the class, write methods to query vector data:

public List<VectorData> getAllVectorData() {
    return ();
}

public Optional<VectorData> getVectorDataById(Long id) {
    return (id);
}

Update and delete vector data

existVectorDataServiceIn the class, write methods to update and delete vector data:

public VectorData updateVectorData(Long id, String name, double[] vector) {
    Optional<VectorData> optionalVectorData = (id);
    if (()) {
        VectorData vectorData = ();
        (name);
        (vector);
        return (vectorData);
    }
    return null;
}

public void deleteVectorData(Long id) {
    (id);
}

Efficient query vector data

Vector similarity calculation

In order to efficiently query similar vectors in PostgreSQL, you can use PostgreSQL's functions and indexing capabilities. For example, the similarity between two vectors can be calculated using the Euclidean distance.

Create a custom query

existVectorDataRepositoryAdd a custom query method to calculate vector similarity:

import ;
import ;

import ;

public interface VectorDataRepository extends JpaRepository<VectorData, Long> {

    @Query("SELECT v FROM VectorData v WHERE sqrt(power([1] - :vector1, 2) + power([2] - :vector2, 2) + power([3] - :vector3, 2)) < :threshold")
    List<VectorData> findSimilarVectors(@Param("vector1") double vector1,
                                        @Param("vector2") double vector2,
                                        @Param("vector3") double vector3,
                                        @Param("threshold") double threshold);
}

existVectorDataServiceCall the custom query method:

public List<VectorData> findSimilarVectors(double[] vector, double threshold) {
    return (vector[0], vector[1], vector[2], threshold);
}

Performance optimization

Using GIN and GiST indexes

PostgreSQL supports GIN (Generalized Inverted Index) and GiST (Generalized Search Tree) indexes, which are very useful for multidimensional data and full-text searches. GIN or GiST indexes can be created on vector fields to improve query performance.

Partition table

For large-scale datasets, partitioned tables can be used to spread the data across multiple tables, thereby improving query performance.

Practical case: Image similarity search

Background introduction

Suppose we have an image library where each image is converted into a feature vector. We want to implement a function that can enter an image, search and return the image that is most similar to it.

Implementation steps

  • Image feature extraction: Use deep learning models (such as ResNet) to extract the feature vectors of images.
  • Vector storage: Store the feature vector of the image into a PostgreSQL database.
  • Similarity query: Use vector similarity calculation to search for similar images from the database.

Image feature extraction example

Suppose we use TensorFlow to extract image features:

import tensorflow as tf
import numpy as np

# Load the pretrained modelmodel = .ResNet50(weights='imagenet', include_top=False, pooling='avg')

# Load the image and preprocess itimg_path = 'path_to_your_image.jpg'
img = .load_img(img_path, target_size=(224, 224))
img_array = .img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = .resnet50.preprocess_input(img_array)

# Extract eigenvectorsfeatures = (img_array)

Store feature vectors to the database

double[] features = ...; // Eigenvectors obtained from feature extraction modelString imageName = "";
(imageName, features);

Query similar images

double[] queryVector = ...; //Enter image feature vectordouble threshold = 0.5;
List&lt;VectorData&gt; similarImages = (queryVector, threshold);

// Output similar images(image -&gt; (()));

in conclusion

This article details how to store and manage vector data using Java and PostgreSQL, covering project settings, data model creation, addition, deletion, modification and query operations, and efficient query methods. By combining actual cases, the application of vector data in image similarity search is demonstrated. I hope this article can help readers understand and master the storage and management technology of vector data, and improve data processing capabilities and application level.

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