Logging Spring Boot di 2026: log terstruktur produksi dengan Logback dan JSON
Panduan lengkap logging terstruktur di Spring Boot. Konfigurasi Logback JSON, MDC untuk tracing, praktik terbaik di produksi, dan integrasi ELK Stack.

Log teks tradisional dengan cepat menjadi sulit dikelola di produksi. Dengan ratusan instance yang menghasilkan ribuan baris per detik, mencari satu error spesifik berubah menjadi mimpi buruk. Log terstruktur dalam format JSON mengubah situasi ini sepenuhnya karena setiap event menjadi dapat dikueri dan dianalisis secara otomatis.
Spring Boot 3.4+ mendukung secara native logging terstruktur JSON tanpa dependensi eksternal. Untuk versi yang lebih lama, Logback Logstash Encoder tetap menjadi solusi rujukan.
Mengapa mengadopsi log terstruktur
Keterbatasan log teks klasik
Log teks tipikal terlihat seperti ini:
2026-03-27 10:15:32.456 INFO [order-service,abc123] c.e.s.OrderService - Order created for user john@example.com, amount: 150.00€, items: 3Format ini menimbulkan beberapa masalah di produksi. Mengekstrak informasi tertentu memerlukan ekspresi reguler yang kompleks dan rapuh. Korelasi antar layanan menuntut konvensi ketat yang ditafsirkan berbeda oleh setiap tim. Alat analisis seperti Elasticsearch kesulitan mengindeks string tidak terstruktur ini secara efisien.
Keuntungan format JSON
Event yang sama dalam JSON langsung dapat dimanfaatkan:
{
"@timestamp": "2026-03-27T10:15:32.456Z",
"level": "INFO",
"logger": "com.example.service.OrderService",
"message": "Order created",
"service": "order-service",
"traceId": "abc123",
"userId": "john@example.com",
"orderId": "ORD-789456",
"amount": 150.00,
"currency": "EUR",
"itemCount": 3
}Setiap field menjadi dapat difilter dan diagregasi. Sebuah kueri Elasticsearch dapat menemukan seketika semua pesanan di atas 100 € dalam lima belas menit terakhir. Dashboard Kibana memvisualisasikan tren tanpa parsing manual.
Konfigurasi native Spring Boot 3.4+
Mengaktifkan log JSON terstruktur
Spring Boot 3.4 memperkenalkan dukungan native untuk logging terstruktur melalui properti logging.structured. Pendekatan ini tidak memerlukan dependensi tambahan.
# application.yml
# Native structured logging configuration for Spring Boot 3.4+
logging:
structured:
# Output format: ecs (Elastic), logstash, gelf
format:
console: ecs
file: ecs
file:
name: /var/log/app/application.log
level:
root: INFO
com.example: DEBUGFormat ECS (Elastic Common Schema) menjamin kompatibilitas langsung dengan Elasticsearch dan Kibana tanpa konfigurasi tambahan.
Menyesuaikan field JSON
Untuk menambahkan field bisnis pada setiap log, Spring Boot memungkinkan konfigurasi atribut tambahan.
# application.yml
# Custom fields in structured logs
logging:
structured:
format:
console: ecs
ecs:
# Service information added to every log
service:
name: ${spring.application.name}
version: ${app.version:1.0.0}
environment: ${spring.profiles.active:default}
node-name: ${HOSTNAME:unknown}// Programmatic configuration for additional fields
package com.example.logging.config;
import org.springframework.boot.logging.structured.StructuredLogFormatterCustomizer;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class LoggingConfig {
@Bean
StructuredLogFormatterCustomizer<EcsStructuredLogFormatter> ecsCustomizer() {
return formatter -> formatter
// Adds static fields to all logs
.addStaticField("team", "backend")
.addStaticField("region", System.getenv("AWS_REGION"))
// Customizes exception formatting
.setIncludeStacktrace(true)
.setStacktraceMaxLength(5000);
}
}Field-field tersebut muncul pada setiap baris log dan memudahkan pemfilteran berdasarkan tim atau wilayah pada dashboard.
Konfigurasi Logback klasik dengan encoder JSON
Dependensi Logstash Encoder
Untuk versi Spring Boot sebelum 3.4 atau kebutuhan kustomisasi lanjutan, Logstash Logback Encoder tetap menjadi solusi rujukan.
<!-- pom.xml -->
<!-- Dependency for JSON logging with Logback -->
<dependency>
<groupId>net.logstash.logback</groupId>
<artifactId>logstash-logback-encoder</artifactId>
<version>7.4</version>
</dependency>Konfigurasi Logback lengkap
File logback-spring.xml memberikan kendali penuh atas format keluaran.
<!-- src/main/resources/logback-spring.xml -->
<!-- Logback configuration for structured JSON logs -->
<?xml version="1.0" encoding="UTF-8"?>
<configuration>
<!-- Spring Boot properties -->
<springProperty scope="context" name="appName" source="spring.application.name" defaultValue="app"/>
<springProperty scope="context" name="appVersion" source="app.version" defaultValue="1.0.0"/>
<!-- JSON console appender for production -->
<appender name="JSON_CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
<encoder class="net.logstash.logback.encoder.LogstashEncoder">
<!-- Custom fields added to every log -->
<customFields>{"service":"${appName}","version":"${appVersion}"}</customFields>
<!-- Includes MDC (tracing context) -->
<includeMdcKeyName>traceId</includeMdcKeyName>
<includeMdcKeyName>spanId</includeMdcKeyName>
<includeMdcKeyName>userId</includeMdcKeyName>
<includeMdcKeyName>requestId</includeMdcKeyName>
<!-- ISO8601 timestamp format -->
<timestampPattern>yyyy-MM-dd'T'HH:mm:ss.SSSZ</timestampPattern>
<!-- Complete stack traces -->
<throwableConverter class="net.logstash.logback.stacktrace.ShortenedThrowableConverter">
<maxDepthPerThrowable>30</maxDepthPerThrowable>
<maxLength>4096</maxLength>
<shortenedClassNameLength>36</shortenedClassNameLength>
<rootCauseFirst>true</rootCauseFirst>
</throwableConverter>
</encoder>
</appender>
<!-- Rolling JSON file appender -->
<appender name="JSON_FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">
<file>/var/log/${appName}/application.json</file>
<rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
<fileNamePattern>/var/log/${appName}/application.%d{yyyy-MM-dd}.%i.json.gz</fileNamePattern>
<maxHistory>30</maxHistory>
<maxFileSize>100MB</maxFileSize>
<totalSizeCap>3GB</totalSizeCap>
</rollingPolicy>
<encoder class="net.logstash.logback.encoder.LogstashEncoder">
<customFields>{"service":"${appName}","version":"${appVersion}"}</customFields>
</encoder>
</appender>
<!-- Text appender for development -->
<appender name="TEXT_CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
<encoder>
<pattern>%d{HH:mm:ss.SSS} %highlight(%-5level) [%thread] %cyan(%logger{36}) - %msg%n</pattern>
</encoder>
</appender>
<!-- Activation by Spring profile -->
<springProfile name="prod,staging">
<root level="INFO">
<appender-ref ref="JSON_CONSOLE"/>
<appender-ref ref="JSON_FILE"/>
</root>
</springProfile>
<springProfile name="dev,local">
<root level="DEBUG">
<appender-ref ref="TEXT_CONSOLE"/>
</root>
</springProfile>
</configuration>Konfigurasi ini hanya mengaktifkan log JSON di produksi dan mempertahankan log yang mudah dibaca saat pengembangan.
Penggunaan <springProfile> memungkinkan peralihan otomatis antara format teks dan JSON sesuai lingkungan tanpa mengubah konfigurasi.
MDC untuk distributed tracing
Propagasi konteks trace
MDC (Mapped Diagnostic Context) memperkaya setiap log dengan informasi konteks seperti identifier request atau trace.
// Filter for automatic trace context injection
package com.example.logging.filter;
import jakarta.servlet.FilterChain;
import jakarta.servlet.ServletException;
import jakarta.servlet.http.HttpServletRequest;
import jakarta.servlet.http.HttpServletResponse;
import org.slf4j.MDC;
import org.springframework.core.Ordered;
import org.springframework.core.annotation.Order;
import org.springframework.stereotype.Component;
import org.springframework.web.filter.OncePerRequestFilter;
import java.io.IOException;
import java.util.UUID;
@Component
@Order(Ordered.HIGHEST_PRECEDENCE)
public class TracingFilter extends OncePerRequestFilter {
// Standard MDC keys for tracing
private static final String TRACE_ID_KEY = "traceId";
private static final String SPAN_ID_KEY = "spanId";
private static final String REQUEST_ID_KEY = "requestId";
private static final String USER_ID_KEY = "userId";
@Override
protected void doFilterInternal(
HttpServletRequest request,
HttpServletResponse response,
FilterChain filterChain) throws ServletException, IOException {
try {
// Retrieve or generate trace identifiers
String traceId = extractOrGenerate(request, "X-Trace-Id", TRACE_ID_KEY);
String spanId = generateSpanId();
String requestId = extractOrGenerate(request, "X-Request-Id", REQUEST_ID_KEY);
String userId = request.getHeader("X-User-Id");
// Inject into MDC to appear in all logs
MDC.put(TRACE_ID_KEY, traceId);
MDC.put(SPAN_ID_KEY, spanId);
MDC.put(REQUEST_ID_KEY, requestId);
if (userId != null) {
MDC.put(USER_ID_KEY, userId);
}
// Propagate to responses for inter-service chaining
response.setHeader("X-Trace-Id", traceId);
response.setHeader("X-Request-Id", requestId);
filterChain.doFilter(request, response);
} finally {
// Clean MDC after each request
MDC.clear();
}
}
private String extractOrGenerate(HttpServletRequest request, String header, String key) {
String value = request.getHeader(header);
return value != null ? value : UUID.randomUUID().toString().replace("-", "").substring(0, 16);
}
private String generateSpanId() {
return UUID.randomUUID().toString().replace("-", "").substring(0, 8);
}
}Setiap log yang diterbitkan saat pemrosesan request akan otomatis berisi identifier tersebut.
Penggunaan MDC dalam kode bisnis
// Business service with enriched contextual logging
package com.example.service;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.slf4j.MDC;
import org.springframework.stereotype.Service;
@Service
public class OrderService {
private static final Logger log = LoggerFactory.getLogger(OrderService.class);
public Order createOrder(CreateOrderRequest request) {
// Add business information to MDC context
MDC.put("orderId", request.getOrderId());
MDC.put("customerId", request.getCustomerId());
try {
log.info("Creating order with {} items", request.getItems().size());
// Business logic...
Order order = processOrder(request);
log.info("Order created successfully, total: {} {}",
order.getTotal(), order.getCurrency());
return order;
} catch (Exception e) {
// Exception appears with full MDC context
log.error("Failed to create order", e);
throw e;
} finally {
// Clean business keys added
MDC.remove("orderId");
MDC.remove("customerId");
}
}
}Log JSON yang dihasilkan berisi seluruh informasi yang diperlukan untuk debugging:
{
"@timestamp": "2026-03-27T10:15:32.456Z",
"level": "INFO",
"logger": "com.example.service.OrderService",
"message": "Order created successfully, total: 150.00 EUR",
"traceId": "a1b2c3d4e5f67890",
"spanId": "12345678",
"requestId": "req-abc-123",
"userId": "user-456",
"orderId": "ORD-789",
"customerId": "CUST-321"
}Siap menguasai wawancara Spring Boot Anda?
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Logging asinkron untuk performa
Konfigurasi thread pool
Di produksi, penulisan log secara sinkron memengaruhi latensi request. Appender asinkron memisahkan logging dari thread utama.
<!-- logback-spring.xml -->
<!-- High-performance asynchronous appender configuration -->
<appender name="ASYNC_JSON" class="ch.qos.logback.classic.AsyncAppender">
<!-- Pending log buffer size -->
<queueSize>1024</queueSize>
<!-- Never block the calling thread -->
<neverBlock>true</neverBlock>
<!-- Threshold before dropping DEBUG/TRACE logs -->
<discardingThreshold>20</discardingThreshold>
<!-- Include caller information (expensive) -->
<includeCallerData>false</includeCallerData>
<!-- Actual appender for writing -->
<appender-ref ref="JSON_FILE"/>
</appender>
<springProfile name="prod">
<root level="INFO">
<appender-ref ref="ASYNC_JSON"/>
</root>
</springProfile>Metrik sistem logging
Memantau sistem logging itu sendiri mencegah hilangnya log secara diam-diam.
// Exposing Logback metrics via Micrometer
package com.example.logging.metrics;
import ch.qos.logback.classic.Logger;
import ch.qos.logback.classic.LoggerContext;
import ch.qos.logback.classic.spi.ILoggingEvent;
import ch.qos.logback.core.Appender;
import ch.qos.logback.classic.AsyncAppender;
import io.micrometer.core.instrument.Gauge;
import io.micrometer.core.instrument.MeterRegistry;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Component;
import jakarta.annotation.PostConstruct;
import java.util.Iterator;
@Component
public class LoggingMetrics {
private final MeterRegistry registry;
public LoggingMetrics(MeterRegistry registry) {
this.registry = registry;
}
@PostConstruct
void registerMetrics() {
LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
Logger rootLogger = context.getLogger(Logger.ROOT_LOGGER_NAME);
// Iterate through appenders to find AsyncAppenders
Iterator<Appender<ILoggingEvent>> it = rootLogger.iteratorForAppenders();
while (it.hasNext()) {
Appender<ILoggingEvent> appender = it.next();
if (appender instanceof AsyncAppender asyncAppender) {
registerAsyncMetrics(asyncAppender);
}
}
}
private void registerAsyncMetrics(AsyncAppender appender) {
String appenderName = appender.getName();
// Current queue size
Gauge.builder("logback.async.queue.size", appender, AsyncAppender::getQueueSize)
.tag("appender", appenderName)
.description("Current async appender queue size")
.register(registry);
// Remaining capacity
Gauge.builder("logback.async.queue.remaining", appender, AsyncAppender::getRemainingCapacity)
.tag("appender", appenderName)
.description("Remaining capacity in async queue")
.register(registry);
// Number of dropped logs
Gauge.builder("logback.async.discarded", appender, AsyncAppender::getNumberOfElementsInQueue)
.tag("appender", appenderName)
.description("Number of discarded log events")
.register(registry);
}
}Alert Prometheus pada logback.async.queue.remaining < 100 memperingatkan adanya risiko hilangnya log.
Integrasi dengan ELK Stack
Konfigurasi Filebeat
Filebeat mengumpulkan file JSON dan mengirimkannya ke Elasticsearch tanpa transformasi.
# filebeat.yml
# Filebeat configuration for Spring Boot JSON logs
filebeat.inputs:
- type: log
enabled: true
paths:
- /var/log/*/application.json
# Automatic JSON parsing
json:
keys_under_root: true
overwrite_keys: true
add_error_key: true
message_key: message
processors:
# Add Kubernetes metadata if available
- add_kubernetes_metadata:
host: ${NODE_NAME}
matchers:
- logs_path:
logs_path: "/var/log/containers/"
# Parse timestamp
- timestamp:
field: "@timestamp"
layouts:
- '2006-01-02T15:04:05.000Z'
- '2006-01-02T15:04:05.000-07:00'
test:
- '2026-03-27T10:15:32.456Z'
output.elasticsearch:
hosts: ["elasticsearch:9200"]
index: "logs-%{[service]}-%{+yyyy.MM.dd}"
pipeline: "spring-boot-logs"
setup.template:
name: "logs"
pattern: "logs-*"Pipeline Elasticsearch untuk pengayaan
// PUT _ingest/pipeline/spring-boot-logs
{
"description": "Spring Boot logs enrichment",
"processors": [
{
"geoip": {
"field": "client.ip",
"target_field": "client.geo",
"ignore_missing": true
}
},
{
"user_agent": {
"field": "user_agent.original",
"target_field": "user_agent",
"ignore_missing": true
}
},
{
"set": {
"field": "event.ingested",
"value": "{{_ingest.timestamp}}"
}
},
{
"script": {
"description": "Classify log level severity",
"source": """
def level = ctx.level;
if (level == 'ERROR') ctx.severity = 4;
else if (level == 'WARN') ctx.severity = 3;
else if (level == 'INFO') ctx.severity = 2;
else ctx.severity = 1;
"""
}
}
]
}Praktik terbaik di produksi
Informasi yang harus disertakan secara sistematis
Setiap log harus memuat informasi minimum yang diperlukan untuk debugging dan korelasi.
// Helper for consistent structured logs
package com.example.logging;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.slf4j.MDC;
import java.util.Map;
import java.util.function.Supplier;
public final class StructuredLogger {
private final Logger delegate;
private StructuredLogger(Class<?> clazz) {
this.delegate = LoggerFactory.getLogger(clazz);
}
public static StructuredLogger getLogger(Class<?> clazz) {
return new StructuredLogger(clazz);
}
// Log with temporary business context
public void info(String message, Map<String, String> context) {
try {
context.forEach(MDC::put);
delegate.info(message);
} finally {
context.keySet().forEach(MDC::remove);
}
}
// Log with supplier for lazy evaluation
public void debug(Supplier<String> messageSupplier, Map<String, String> context) {
if (delegate.isDebugEnabled()) {
try {
context.forEach(MDC::put);
delegate.debug(messageSupplier.get());
} finally {
context.keySet().forEach(MDC::remove);
}
}
}
// Error log with full context
public void error(String message, Throwable t, Map<String, String> context) {
try {
context.forEach(MDC::put);
delegate.error(message, t);
} finally {
context.keySet().forEach(MDC::remove);
}
}
}// Usage in business code
private static final StructuredLogger log = StructuredLogger.getLogger(PaymentService.class);
public void processPayment(Payment payment) {
log.info("Processing payment", Map.of(
"paymentId", payment.getId(),
"amount", String.valueOf(payment.getAmount()),
"currency", payment.getCurrency(),
"method", payment.getMethod().name()
));
}Informasi sensitif yang harus dikecualikan
Log tidak boleh memuat data pribadi atau sensitif.
// Sensitive data masking filter
package com.example.logging.filter;
import ch.qos.logback.classic.spi.ILoggingEvent;
import ch.qos.logback.core.filter.Filter;
import ch.qos.logback.core.spi.FilterReply;
import java.util.regex.Pattern;
public class SensitiveDataFilter extends Filter<ILoggingEvent> {
// Sensitive data patterns to mask
private static final Pattern EMAIL_PATTERN =
Pattern.compile("[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}");
private static final Pattern CREDIT_CARD_PATTERN =
Pattern.compile("\\b\\d{4}[- ]?\\d{4}[- ]?\\d{4}[- ]?\\d{4}\\b");
private static final Pattern PASSWORD_PATTERN =
Pattern.compile("(?i)(password|pwd|secret|token)[\"']?\\s*[:=]\\s*[\"']?[^\\s,}\"']+");
private static final Pattern PHONE_PATTERN =
Pattern.compile("\\+?\\d{1,3}[- ]?\\d{6,14}");
@Override
public FilterReply decide(ILoggingEvent event) {
// Accept all logs but modify the message
// Note: for real masking, use a custom converter
return FilterReply.NEUTRAL;
}
// Utility method to mask data
public static String maskSensitiveData(String input) {
if (input == null) return null;
String result = input;
result = EMAIL_PATTERN.matcher(result).replaceAll("[EMAIL_MASKED]");
result = CREDIT_CARD_PATTERN.matcher(result).replaceAll("[CARD_MASKED]");
result = PASSWORD_PATTERN.matcher(result).replaceAll("$1=[REDACTED]");
result = PHONE_PATTERN.matcher(result).replaceAll("[PHONE_MASKED]");
return result;
}
}Log yang berisi data pribadi tunduk pada GDPR. Alamat IP, email, dan identifier pengguna memerlukan kebijakan retensi serta, bila perlu, persetujuan.
Level log yang sesuai
// Appropriate log level guidelines
package com.example.logging;
public class LogLevelGuidelines {
// ERROR: Failure requiring intervention
// - Unrecoverable exceptions
// - Critical transaction failures
// - External service unavailability
log.error("Payment gateway unreachable after 3 retries", exception);
// WARN: Abnormal but handled situation
// - Retry in progress
// - Performance degradation
// - Resources near limits
log.warn("Database connection pool at 85% capacity");
// INFO: Significant business events
// - Transaction start/end
// - Important state changes
// - Key user actions
log.info("Order {} shipped to customer {}", orderId, customerId);
// DEBUG: Diagnostic information
// - Execution details
// - Important variable values
// - Branching decisions
log.debug("Cache miss for key {}, fetching from database", cacheKey);
// TRACE: Very fine details
// - Method entry/exit
// - Complete object contents
// - Loops and iterations
log.trace("Processing item {} of {}", index, total);
}Pengujian dan validasi log
Unit test struktur JSON
// Structured log validation tests
package com.example.logging;
import ch.qos.logback.classic.Logger;
import ch.qos.logback.classic.spi.ILoggingEvent;
import ch.qos.logback.core.read.ListAppender;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.slf4j.LoggerFactory;
import org.slf4j.MDC;
import static org.assertj.core.api.Assertions.assertThat;
class StructuredLoggingTest {
private ListAppender<ILoggingEvent> listAppender;
private Logger logger;
private ObjectMapper objectMapper;
@BeforeEach
void setUp() {
logger = (Logger) LoggerFactory.getLogger(StructuredLoggingTest.class);
listAppender = new ListAppender<>();
listAppender.start();
logger.addAppender(listAppender);
objectMapper = new ObjectMapper();
}
@Test
void shouldIncludeMdcFieldsInLog() {
// Given
MDC.put("traceId", "test-trace-123");
MDC.put("userId", "user-456");
// When
logger.info("Test message with MDC context");
// Then
ILoggingEvent event = listAppender.list.get(0);
assertThat(event.getMDCPropertyMap())
.containsEntry("traceId", "test-trace-123")
.containsEntry("userId", "user-456");
MDC.clear();
}
@Test
void shouldLogExceptionWithStackTrace() {
// Given
Exception testException = new RuntimeException("Test error");
// When
logger.error("Operation failed", testException);
// Then
ILoggingEvent event = listAppender.list.get(0);
assertThat(event.getThrowableProxy()).isNotNull();
assertThat(event.getThrowableProxy().getMessage()).isEqualTo("Test error");
}
}Kesimpulan
Log terstruktur JSON mengubah observability aplikasi Spring Boot:
✅ Dapat dikueri: setiap field bisa difilter di Elasticsearch atau CloudWatch
✅ Dapat dikorelasikan: MDC menyebarkan identifier trace antar layanan
✅ Berkinerja tinggi: appender asinkron memisahkan logging dari proses utama
✅ Aman: masking data sensitif menjamin kepatuhan GDPR
✅ Terintegrasi: kompatibilitas native dengan ELK Stack, Datadog, Splunk
✅ Dapat di-alert: field terstruktur memungkinkan aturan alert yang presisi
✅ Mudah dirawat: format JSON menghapus regex parsing yang rapuh
Pendekatan ini menjadi fondasi observability modern bersama metrik (Micrometer) dan distributed tracing (OpenTelemetry).
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