
logging-patterns
Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/decebals/claude-code-java --skill logging-patterns- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads logging-patterns automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
# 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
{
"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 requeststep- track progress through flowduration_ms- identify slow operationslevel- quick filter for errors
Reading Logs with AI/Claude Code
When asking AI to analyze logs:
# 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!
# application.yml
logging:
structured:
format:
console: logstash # or "ecs" for Elastic Common Schema
# Supported formats: logstash, ecs, gelf
Profile-Based Switching
# 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:
# 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:
<dependency>
<groupId>net.logstash.logback</groupId>
<artifactId>logstash-logback-encoder</artifactId>
<version>7.4</version>
</dependency>
logback-spring.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)
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
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
// ✅ 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) |
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
@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
// 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
// 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)
// 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)
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)
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
// ❌ 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
// ❌ 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
// ❌ 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
// === 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)
# 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 configurationjpa-patterns- Database logging (SQL queries)- Future:
observability-patterns- Metrics, tracing, full observability
FAQ
What does the logging-patterns skill do?
Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.
How do I install logging-patterns?
Run npx skills add https://github.com/decebals/claude-code-java --skill logging-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/logging-patterns/ (personal) or .claude/skills/logging-patterns/ (project).
Which AI agents can use logging-patterns?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is logging-patterns safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains logging-patterns?
It is published by decebals in decebals/claude-code-java. It has 275 recorded installs.
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