Logging Patterns Skill

Effective logging for Java applications with focus on structured, AI-parsable formats.

When to Use

User says "add logging" / "improve logs" / "debug this"
Analyzing application flow from logs
Setting up structured logging (JSON)
Request tracing with correlation IDs
AI/Claude Code needs to analyze application behavior

AI-Friendly Logging

Key insight: JSON logs are better for AI analysis - faster parsing, fewer tokens, direct field access.

Why JSON for AI/Claude Code?

# Text format - AI must "interpret" the string
2026-01-29 10:15:30 INFO OrderService - Order 12345 created for user-789, total: 99.99

# JSON format - AI extracts fields directly
{"timestamp":"2026-01-29T10:15:30Z","level":"INFO","orderId":12345,"userId":"user-789","total":99.99}

Aspect	Text	JSON
Parsing	Regex/interpretation	Direct field access
Token usage	Higher (repeated patterns)	Lower (structured)
Error extraction	Parse stack trace text	`exception` field
Filtering	grep patterns	`jq` queries

Recommended Setup for AI-Assisted Development

yaml

# application.yml - JSON by default
logging:
  structured:
    format:
      console: logstash  # Spring Boot 3.4+

# When YOU need to read logs manually:
# Option 1: Use jq
# tail -f app.log | jq .

# Option 2: Switch profile temporarily
# java -jar app.jar --spring.profiles.active=human-logs

Log Format Optimized for AI Analysis

json

{
  "timestamp": "2026-01-29T10:15:30.123Z",
  "level": "INFO",
  "logger": "com.example.OrderService",
  "message": "Order created",
  "requestId": "req-abc123",
  "traceId": "trace-xyz",
  "orderId": 12345,
  "userId": "user-789",
  "duration_ms": 45,
  "step": "payment_completed"
}

Key fields for AI debugging:

requestId - group all logs from same request
step - track progress through flow
duration_ms - identify slow operations
level - quick filter for errors

Reading Logs with AI/Claude Code

When asking AI to analyze logs:

bash

# Get recent errors
cat app.log | jq 'select(.level == "ERROR")' | tail -20

# Follow specific request
cat app.log | jq 'select(.requestId == "req-abc123")'

# Find slow operations
cat app.log | jq 'select(.duration_ms > 1000)'

AI can then:

Parse JSON directly (no guessing)
Follow request flow via requestId
Identify exactly where errors occurred
Measure timing between steps

Quick Setup (Spring Boot 3.4+)

Native Structured Logging

Spring Boot 3.4+ has built-in support - no extra dependencies!

yaml

# application.yml
logging:
  structured:
    format:
      console: logstash    # or "ecs" for Elastic Common Schema

# Supported formats: logstash, ecs, gelf

Profile-Based Switching

yaml

# application.yml (default - JSON for AI/prod)
spring:
  profiles:
    default: json-logs

---
spring:
  config:
    activate:
      on-profile: json-logs
logging:
  structured:
    format:
      console: logstash

---
spring:
  config:
    activate:
      on-profile: human-logs
# No structured format = human-readable default
logging:
  pattern:
    console: "%d{HH:mm:ss.SSS} %-5level [%thread] %logger{36} - %msg%n"

Usage:

bash

# Default: JSON (for AI, CI/CD, production)
./mvnw spring-boot:run

# Human-readable when needed
./mvnw spring-boot:run -Dspring.profiles.active=human-logs

Setup for Spring Boot < 3.4

Logstash Logback Encoder

pom.xml:

xml

<dependency>
    <groupId>net.logstash.logback</groupId>
    <artifactId>logstash-logback-encoder</artifactId>
    <version>7.4</version>
</dependency>

logback-spring.xml:

xml

<?xml version="1.0" encoding="UTF-8"?>
<configuration>

    <!-- JSON (default) -->
    <springProfile name="!human-logs">
        <appender name="JSON" class="ch.qos.logback.core.ConsoleAppender">
            <encoder class="net.logstash.logback.encoder.LogstashEncoder">
                <includeMdcKeyName>requestId</includeMdcKeyName>
                <includeMdcKeyName>userId</includeMdcKeyName>
            </encoder>
        </appender>
        <root level="INFO">
            <appender-ref ref="JSON"/>
        </root>
    </springProfile>

    <!-- Human-readable (optional) -->
    <springProfile name="human-logs">
        <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
            <encoder>
                <pattern>%d{HH:mm:ss.SSS} %-5level [%thread] %logger{36} - %msg%n</pattern>
            </encoder>
        </appender>
        <root level="INFO">
            <appender-ref ref="CONSOLE"/>
        </root>
    </springProfile>

</configuration>

Adding Custom Fields (Logstash Encoder)

java

import static net.logstash.logback.argument.StructuredArguments.kv;

// Fields appear as separate JSON keys
log.info("Order created",
    kv("orderId", order.getId()),
    kv("userId", user.getId()),
    kv("total", order.getTotal()),
    kv("step", "order_created")
);

// Output:
// {"message":"Order created","orderId":123,"userId":"u-456","total":99.99,"step":"order_created"}

SLF4J Basics

Logger Declaration

java

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

@Service
public class OrderService {
    private static final Logger log = LoggerFactory.getLogger(OrderService.class);
}

// Or with Lombok
@Slf4j
@Service
public class OrderService {
    // use `log` directly
}

Parameterized Logging

java

// ✅ GOOD: Evaluated only if level enabled
log.debug("Processing order {} for user {}", orderId, userId);

// ❌ BAD: Always concatenates
log.debug("Processing order " + orderId + " for user " + userId);

// ✅ For expensive operations
if (log.isDebugEnabled()) {
    log.debug("Full order details: {}", order.toJson());
}

Log Levels

Level	When	Example
ERROR	Failures needing attention	Unhandled exception, service down
WARN	Unexpected but handled	Retry succeeded, deprecated API used
INFO	Business events	Order created, payment processed
DEBUG	Technical details	Method params, SQL queries
TRACE	Very detailed	Loop iterations (rarely used)

java

log.error("Payment failed", kv("orderId", id), kv("reason", reason), exception);
log.warn("Retry succeeded", kv("attempt", 3), kv("orderId", id));
log.info("Order shipped", kv("orderId", id), kv("trackingNumber", tracking));
log.debug("Fetching from DB", kv("query", "findById"), kv("id", id));

MDC (Mapped Diagnostic Context)

MDC adds context to every log entry in a request - essential for tracing.

Request ID Filter

java

@Component
@Order(Ordered.HIGHEST_PRECEDENCE)
public class RequestContextFilter extends OncePerRequestFilter {

    @Override
    protected void doFilterInternal(HttpServletRequest request,
                                    HttpServletResponse response,
                                    FilterChain chain) throws ServletException, IOException {
        try {
            String requestId = Optional.ofNullable(request.getHeader("X-Request-ID"))
                .filter(s -> !s.isBlank())
                .orElse(UUID.randomUUID().toString().substring(0, 8));

            MDC.put("requestId", requestId);
            response.setHeader("X-Request-ID", requestId);

            chain.doFilter(request, response);
        } finally {
            MDC.clear();
        }
    }
}

Add User Context

java

// After authentication
MDC.put("userId", authentication.getName());

// All subsequent logs include userId automatically
log.info("User action performed");  // {"userId":"john123","message":"User action performed"}

MDC in Async Operations

java

// MDC doesn't auto-propagate to new threads!

// ✅ Copy MDC context
Map<String, String> context = MDC.getCopyOfContextMap();

CompletableFuture.runAsync(() -> {
    try {
        if (context != null) MDC.setContextMap(context);
        log.info("Async task running");  // Has requestId, userId
    } finally {
        MDC.clear();
    }
});

What to Log

Business Events (INFO)

java

// Include key identifiers and state
log.info("Order created",
    kv("orderId", id),
    kv("userId", userId),
    kv("total", total),
    kv("itemCount", items.size()),
    kv("step", "order_created"));

log.info("Payment processed",
    kv("orderId", id),
    kv("amount", amount),
    kv("method", "card"),
    kv("step", "payment_completed"));

External Calls (with timing)

java

long start = System.currentTimeMillis();
try {
    Result result = externalService.call(params);
    log.info("External call succeeded",
        kv("service", "PaymentGateway"),
        kv("operation", "charge"),
        kv("duration_ms", System.currentTimeMillis() - start));
    return result;
} catch (Exception e) {
    log.error("External call failed",
        kv("service", "PaymentGateway"),
        kv("operation", "charge"),
        kv("duration_ms", System.currentTimeMillis() - start),
        e);
    throw e;
}

Flow Steps (for AI tracing)

java

public Order processOrder(CreateOrderRequest request) {
    log.info("Processing started", kv("step", "start"), kv("requestData", request.summary()));

    Order order = createOrder(request);
    log.info("Order created", kv("step", "order_created"), kv("orderId", order.getId()));

    validateInventory(order);
    log.info("Inventory validated", kv("step", "inventory_ok"), kv("orderId", order.getId()));

    processPayment(order);
    log.info("Payment processed", kv("step", "payment_done"), kv("orderId", order.getId()));

    log.info("Processing completed", kv("step", "complete"), kv("orderId", order.getId()));
    return order;
}

What NOT to Log

java

// ❌ NEVER log sensitive data
log.info("Login", kv("password", password));           // Passwords
log.info("Payment", kv("cardNumber", card));           // Full card numbers
log.info("Request", kv("token", jwtToken));            // Tokens
log.info("User", kv("ssn", socialSecurity));           // PII

// ✅ Safe alternatives
log.info("Login attempted", kv("userId", userId));
log.info("Payment", kv("cardLast4", last4));
log.info("Token validated", kv("subject", sub), kv("exp", expiry));

Exception Logging

Log Once at Boundary

java

// ❌ BAD: Logs same exception multiple times
void methodA() {
    try { methodB(); }
    catch (Exception e) { log.error("Error", e); throw e; }  // Log #1
}
void methodB() {
    try { methodC(); }
    catch (Exception e) { log.error("Error", e); throw e; }  // Log #2
}

// ✅ GOOD: Log at service boundary only
@RestControllerAdvice
public class GlobalExceptionHandler {

    @ExceptionHandler(Exception.class)
    public ResponseEntity<?> handle(Exception e, HttpServletRequest request) {
        log.error("Request failed",
            kv("path", request.getRequestURI()),
            kv("method", request.getMethod()),
            kv("errorType", e.getClass().getSimpleName()),
            e);  // Full stack trace
        return ResponseEntity.status(500).body(errorResponse);
    }
}

Include Context

java

// ❌ Useless
log.error("Error occurred", e);

// ✅ Useful for debugging
log.error("Order processing failed",
    kv("orderId", orderId),
    kv("step", "payment"),
    kv("userId", userId),
    kv("attemptNumber", attempt),
    e);

Quick Reference

java

// === Setup ===
private static final Logger log = LoggerFactory.getLogger(MyClass.class);

// === Logging with structured fields ===
import static net.logstash.logback.argument.StructuredArguments.kv;

log.info("Event", kv("key1", value1), kv("key2", value2));
log.error("Failed", kv("context", ctx), exception);

// === MDC ===
MDC.put("requestId", requestId);
MDC.put("userId", userId);
// ... all logs now include these
MDC.clear();  // cleanup

// === Levels ===
log.error()  // Failures
log.warn()   // Handled issues
log.info()   // Business events
log.debug()  // Technical details

Analyzing Logs (AI/Human)

bash

# Pretty print JSON logs
tail -f app.log | jq .

# Filter errors
cat app.log | jq 'select(.level == "ERROR")'

# Follow request flow
cat app.log | jq 'select(.requestId == "abc123")'

# Find slow operations (>1s)
cat app.log | jq 'select(.duration_ms > 1000)'

# Get timeline of steps
cat app.log | jq 'select(.requestId == "abc123") | {time: .timestamp, step: .step, message: .message}'

Related Skills

spring-boot-patterns - Spring Boot configuration
jpa-patterns - Database logging (SQL queries)
Future: observability-patterns - Metrics, tracing, full observability

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SKILL.md

Logging Patterns Skill

When to Use

AI-Friendly Logging

Why JSON for AI/Claude Code?

Recommended Setup for AI-Assisted Development

Log Format Optimized for AI Analysis

Reading Logs with AI/Claude Code

Quick Setup (Spring Boot 3.4+)

Native Structured Logging

Profile-Based Switching

Setup for Spring Boot < 3.4

Logstash Logback Encoder

Adding Custom Fields (Logstash Encoder)

SLF4J Basics

Logger Declaration

Parameterized Logging

Log Levels

MDC (Mapped Diagnostic Context)

Request ID Filter

Add User Context

MDC in Async Operations

What to Log

Business Events (INFO)

External Calls (with timing)

Flow Steps (for AI tracing)

What NOT to Log

Exception Logging

Log Once at Boundary

Include Context

Quick Reference

Analyzing Logs (AI/Human)

Related Skills