Durchsetzung von Constraints

Datenbereinigung in Java

Dennis Lee

Software Engineer

Qualitätskontrolle für Daten

  • Validierungsanmerkungen: Qualitätskontrolle für Daten
  • Fehler: fehlende Länder, ungültige Beträge, negative Boxen versendet
  • Tippfehler: 1000 Boxen statt 100 Boxen

 

 

Salesperson Country Product Date Amount Boxes Shipped
James Rudeforth UK Mint Chip Choco 4-Jan-22 $5320 100
Van Tuxwell India 85% Dark Bars 1-Aug-22 $7896 94
Gigi Bohling US Peanut Butter Cubes 7-Jul-22 $4501 91
Datenbereinigung in Java

Grundlegende Validierungsanmerkungen

import jakarta.validation.constraints.NotNull;     // Import rules for empty fields
import jakarta.validation.constraints.Size;        // Import rules for string length

public class ChocolateSale {
    @NotNull(message = "Salesperson cannot be empty")
    private String salesperson;


@NotNull(message = "Country cannot be empty") // Multiple rules can be stacked @Size(min = 2, max = 50) // Enforces country name length private String country;
@NotNull(message = "Product cannot be empty") private String product; }
Datenbereinigung in Java

Numerische Constraints

import jakarta.validation.constraints.Max;
import jakarta.validation.constraints.Min;
// We will show message outputs later
public class ChocolateSale {
    @Min(value = 0, message = "Sales amount must be positive")
    private Double amount;


@Min(value = 1, message = "Must ship at least 1 box") @Max(value = 1000, message = "Cannot ship more than 1000 boxes") private Integer boxesShipped; }
Datenbereinigung in Java

Imports für Constraint-Durchsetzung

// Stores unique validation errors (no duplicates)
import java.util.Set;
// Holds details about a single validation error (field, message, etc.)
import jakarta.validation.ConstraintViolation;
// Entry point for creating validators
import jakarta.validation.Validation;
// Checks data against rules
import jakarta.validation.Validator;
// Creates configured validators
import jakarta.validation.ValidatorFactory;
// Thrown when validation fails
import jakarta.validation.ConstraintViolationException;
Datenbereinigung in Java

Validator implementieren

class SalesValidator {
    // Create tools for checking our data
    private static final ValidatorFactory factory = Validation.buildDefaultValidatorFactory();

// Get a validator to check sales records private static final Validator validator = factory.getValidator();
public static Set<ConstraintViolation<ChocolateSale>> validateSale(ChocolateSale sale) { // Check sale record and return any problems found return validator.validate(sale); } }
Datenbereinigung in Java

Validierungsergebnisse verarbeiten

public class Main {
    public static void main(String[] args) {
        // Create sale with some invalid data (null country, negative amount)
        ChocolateSale sale = new ChocolateSale("James Rudeforth", null, "Mint Chip Choco",
                LocalDate.parse("2022-01-04"), -5320.0, 1500);


// Check sale for validation violations Set<ConstraintViolation<ChocolateSale>> violations = SalesValidator.validateSale(sale); // Print each validation error message violations.forEach(violation -> System.out.println(violation.getMessage()));
// If any violations found, throw exception if (!violations.isEmpty()) throw new ConstraintViolationException(violations); } }
Datenbereinigung in Java

Validierungsausgabe

Country cannot be empty
Sales amount must be positive
Cannot ship more than 1000 boxes

Exception in thread "main" jakarta.validation.ConstraintViolationException
Datenbereinigung in Java

Validierung verhindert teure Fehler

  • @NotNull, @Size, @Min und @Max fangen Fehler früh ab
  • Wende Validierung auf allgemeine Geschäftsprobleme an
  • Erste Verteidigungslinie gegen teure Datenfehler

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Datenbereinigung in Java

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Datenbereinigung in Java

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