25 Spring Boot Mistakes That Cause Production Problems

Spring Boot makes it easy to build production-ready applications.

But Spring Boot does not automatically make an application production-ready.

Many serious production incidents happen because developers use Spring Boot features correctly from a coding perspective, but without understanding their behavior under high traffic, failures, concurrency, distributed systems, database load, or Kubernetes resource constraints.

A connection pool that works perfectly in development can become a bottleneck in production. A simple JPA repository call can generate hundreds of SQL queries. A retry mechanism can turn a temporary downstream failure into a cascading outage. A scheduled job can execute multiple times because several application instances are running.

This article covers 25 common Spring Boot mistakes that can cause real production problems, along with why they happen, how to identify them, and what a better production approach looks like.

1. Using Default Database Connection Pool Settings

One of the most common mistakes is deploying a Spring Boot application with the default database connection pool configuration without understanding the production workload.

Spring Boot commonly uses HikariCP for JDBC connection pooling. The application does not create a new database connection for every request. Instead, connections are maintained in a pool and borrowed by application threads when needed.

The problem appears when the production traffic and database workload are significantly larger than the assumptions made during development.

What can happen?

• Requests wait for an available connection.
• Connection acquisition time increases.
• Thread pools become blocked waiting for database connections.
• API latency increases even though CPU usage is low.
• Database connections can become the bottleneck.

For example, imagine a service with a small connection pool receiving hundreds of concurrent requests. If most requests require database access and connections remain occupied for a long time, new requests may wait for connections.

Request
   |
   v
Application Thread
   |
   v
Wait for DB Connection
   |
   v
HikariCP
   |
   v
Database

Increasing the pool size blindly is not necessarily the solution. The database itself has a maximum capacity. Giving every application instance a huge connection pool can overwhelm the database.

What should you monitor?

• Active connections
• Idle connections
• Pending connection requests
• Connection acquisition time
• Connection usage time
• Database CPU and connection count

Production lesson: Tune the connection pool based on application concurrency, query duration, database capacity, and the number of application instances.

2. Returning JPA Entities Directly From REST APIs

Returning JPA entities directly from REST controllers looks convenient:

@GetMapping("/users/{id}")
public User getUser(@PathVariable Long id) {
    return userRepository.findById(id).orElseThrow();
}

But this tightly couples your API contract to your persistence model.

Your database entity is designed to represent persistence. Your API response is designed to represent what clients should receive. These are different responsibilities.

Problems this can cause

• Internal fields can accidentally become exposed.
• Changing the database model can break API consumers.
• Lazy-loaded relationships can trigger unexpected queries.
• Bidirectional relationships can cause serialization problems.
• Large object graphs can accidentally be serialized.
• Security-sensitive fields may become visible.

A better approach is to use DTOs.

public record UserResponse(
    Long id,
    String name,
    String email
) {}

Then explicitly map the entity to the response model.

This gives the API an independent contract and prevents persistence implementation details from leaking into the API.

Production lesson: Entities belong to the persistence layer. DTOs define the API contract.

3. Ignoring the N+1 Query Problem

One of the most expensive Spring Data JPA mistakes is allowing one database query to silently become hundreds or thousands.

Consider loading a list of orders and then accessing each order's customer:

List<Order> orders = orderRepository.findAll();

for (Order order : orders) {
    System.out.println(order.getCustomer().getName());
}

The first query might load all orders:

SELECT * FROM orders;

Then accessing the customer for each order can trigger additional queries:

SELECT * FROM customer WHERE id = 1;
SELECT * FROM customer WHERE id = 2;
SELECT * FROM customer WHERE id = 3;
...

One request can therefore produce hundreds of SQL queries.

How do you detect it?

• Enable SQL logging carefully in non-production environments.
• Use database monitoring.
• Use APM traces.
• Check query count per request.
• Look for repeated SELECT statements.

Depending on the use case, solutions include JOIN FETCH, entity graphs, DTO projections, carefully designed queries, or batch fetching.

Production lesson: A JPA repository method that looks simple in Java can generate a surprisingly expensive database workload.

4. Using @Transactional Without Understanding Transaction Boundaries

@Transactional is powerful, but many developers treat it as a magic annotation.

Consider:

@Transactional
public void createOrder() {
    saveOrder();
    reduceInventory();
    createPaymentRecord();
}

The important question is not simply whether the annotation exists. You need to understand what code executes inside the transaction, what database resources participate in it, and what happens when an exception occurs.

Transactions can affect:

• Database consistency
• Lock duration
• Connection usage
• Rollback behavior
• Concurrency
• Performance

A transaction that remains open while performing slow external HTTP calls can hold a database connection and potentially database locks for much longer than necessary.

BEGIN TRANSACTION

Update database

Call external API  <-- slow

Another database operation

COMMIT

This can increase connection pool pressure and lock contention.

Production lesson: Keep transaction boundaries intentional and as small as practical while preserving business consistency.

5. Calling @Transactional From the Same Class

This is a classic Spring interview question and a common production mistake.

public void methodA() {
    methodB();
}

@Transactional
public void methodB() {
    // database operation
}

Developers may assume that calling methodB() automatically activates the transaction.

But Spring's declarative transaction management commonly relies on proxies. A call made internally through this.methodB() does not pass through the Spring proxy.

External caller
      |
      v
Spring Proxy
      |
      v
@Transactional method

But self-invocation looks more like:

methodA()
   |
   +---- this.methodB()
             |
             X
        proxy bypassed

Possible solutions include moving the transactional method to another Spring bean or restructuring the service boundary.

Production lesson: Understanding Spring proxy behavior is essential when using annotations such as @Transactional, @Async, and caching annotations.

6. Using Unlimited Retries

Retries can help recover from temporary failures.

But unlimited retries can turn a small outage into a much larger one.

Imagine Service A calls Service B.

Service A
   |
   v
Service B unavailable
   |
   v
Retry
   |
   v
Retry
   |
   v
Retry
   |
   v
More traffic to an already unhealthy service

This creates a retry storm.

Retries should normally have:

• A maximum attempt count
• Timeouts
• Exponential backoff
• Jitter where appropriate
• Failure classification
• Circuit-breaking or bulkheading where appropriate

Not every error should be retried. Retrying a validation error or authentication failure usually makes no sense.

Production lesson: A retry is additional traffic. Always ask whether the dependency can handle that additional traffic during failure.

7. Calling External Services Without Timeouts

One of the most dangerous mistakes in a microservice is making an HTTP call without properly configured connection and read timeouts.

Consider:

Spring Boot API
      |
      v
External API
      |
      X
No response

If there is no effective timeout, application threads can remain occupied waiting for the remote service.

Eventually:

External service slow
       |
       v
Requests wait
       |
       v
Threads become occupied
       |
       v
Thread pool exhaustion
       |
       v
New requests cannot execute
       |
       v
Service becomes unhealthy

Configure appropriate connection and response/read timeouts for every remote dependency.

Also consider connection pool limits, retry policies, circuit breakers, and fallback behavior.

Production lesson: Every remote call should have a clearly defined failure boundary.

8. Creating Large Thread Pools

A common reaction to slow requests is:

"Let's increase the thread pool."

This can make the problem worse.

Threads consume memory and CPU. More threads do not automatically mean more throughput.

For an I/O-heavy workload, increasing concurrency may help up to a point. Beyond that point, you may create more contention and resource pressure.

Too much concurrency
        |
        +-- CPU contention
        +-- Memory pressure
        +-- Database connection pressure
        +-- Context switching
        +-- Downstream overload

Thread pool sizing should consider the workload, CPU count, blocking behavior, downstream capacity, database pool size, and request characteristics.

Production lesson: Concurrency is a resource. More threads are not free.

9. Using parallelStream() Blindly

parallelStream() can make code look faster:

items.parallelStream()
     .map(this::process)
     .toList();

But it uses shared JVM resources, including the common ForkJoinPool in typical usage.

Problems can occur when:

• The operation is blocking.
• Each task performs database access.
• Each task calls an external service.
• The workload is small.
• Multiple requests use parallelStream simultaneously.
• The common pool becomes saturated.

For example, if every element performs an HTTP request, parallelStream may generate a large amount of concurrent network traffic.

For production workloads, explicitly designed executors or asynchronous architectures are often more predictable.

Production lesson: Parallelism should be an intentional architecture decision, not a shortcut for performance.

10. Loading Large Datasets Into Memory

This is another common cause of production memory problems.

Code such as:

List<Customer> customers = repository.findAll();

may work perfectly with 10,000 records in development.

What happens when the table contains 10 million records?

The application may attempt to load a massive amount of data into memory.

Potential consequences include:

• Large heap usage
• Long garbage collection pauses
• OutOfMemoryError
• Slow response times
• Application crashes

Use pagination, streaming, batch processing, or database-side operations depending on the use case.

Page<Customer> page =
    repository.findAll(PageRequest.of(0, 500));

Production lesson: Never assume production datasets will remain the size of development datasets.

11. Using @Async Without Configuring the Executor

@Async can move work away from the calling thread, but simply adding the annotation does not create a complete asynchronous architecture.

@Async
public void sendNotification() {
    // work
}

You need to understand which executor processes the work, how many threads it can create, what happens when the queue fills, and how failures are handled.

Without proper configuration and monitoring, asynchronous workloads can create:

• Unbounded queues
• Thread exhaustion
• Memory pressure
• Lost visibility into failures
• Unexpected application shutdown behavior

Production executors should have explicit capacity and monitoring.

Production lesson: Asynchronous execution moves work somewhere else; it does not eliminate resource consumption.

12. Storing Secrets in application.properties

Never treat configuration files inside source control as secure secret storage.

A configuration such as:

spring.datasource.password=mySecretPassword
payment.api-key=abc123

can accidentally expose credentials through Git history, build artifacts, logs, or developer machines.

Production applications should use appropriate secret-management mechanisms and inject secrets securely at runtime.

Also remember that simply hiding a value from the main configuration file does not make it secure if the secret is subsequently printed into logs.

Production lesson: Configuration and secrets are different concerns. Treat credentials as secrets throughout their lifecycle.

13. Using the Same Configuration for Development and Production

Development and production have different characteristics.

Examples include:

• Database pool sizes
• Logging levels
• External service endpoints
• Security settings
• Cache configuration
• Thread pools
• Timeouts
• Feature flags
• Observability settings

Using the same configuration everywhere often leads to either over-provisioning development environments or under-configuring production.

Use environment-specific configuration while keeping the configuration structure consistent.

Production lesson: The application should behave predictably across environments, but the resource and operational configuration should reflect the environment.

14. Relying Only on Logs

Logs are important, but logs alone are not enough for modern production systems.

Imagine an API becomes slow.

Your logs may tell you that requests are arriving, but they may not tell you exactly where the 4 seconds are being spent.

You need multiple observability signals:

Logs
  +
Metrics
  +
Distributed Tracing
  +
Health Checks
  +
JVM Monitoring
  +
Infrastructure Metrics

Useful metrics include:

• Request rate
• Error rate
• p50/p95/p99 latency
• Database query latency
• Connection pool usage
• JVM heap usage
• Garbage collection
• Thread pool usage
• CPU and memory
• Cache hit ratio

Production lesson: Logs explain events. Metrics show trends. Traces show where time is spent.

15. Exposing Sensitive Information Through Exceptions

Returning internal exception details directly to clients can expose implementation information.

For example:

{
  "error": "SQL error: password authentication failed for user admin"
}

This is inappropriate for an external API.

Clients generally need a controlled error response rather than internal stack traces, SQL statements, filesystem paths, database details, or internal service information.

Use centralized exception handling with controlled response models.

{
  "code": "INTERNAL_ERROR",
  "message": "Something went wrong",
  "traceId": "abc-123"
}

The detailed technical information can remain in secure server-side logs and observability systems.

Production lesson: Error responses are part of your security boundary.

16. Using catch (Exception e) Everywhere

This pattern is dangerous:

try {
    processOrder();
} catch (Exception e) {
    log.error("Something went wrong", e);
}

What happens after the exception?

If the application simply logs the exception and continues, the caller may receive a misleading success response.

Broad exception handling can also hide programming bugs and make operational diagnosis difficult.

Prefer handling exceptions according to business meaning.

catch (PaymentTimeoutException e) {
    // specific recovery
}

catch (InsufficientBalanceException e) {
    // business response
}

Unexpected exceptions should generally be allowed to reach a centralized error-handling layer rather than being silently swallowed.

Production lesson: Exception handling should recover from known failures, not hide unknown failures.

17. Ignoring Graceful Shutdown

During deployments, application instances are often terminated and replaced.

If shutdown is not handled correctly, requests currently being processed can be interrupted.

Load Balancer
      |
      v
Spring Boot Instance
      |
      X
Instance terminated
      |
      X
Request interrupted

A graceful shutdown strategy allows the instance to stop receiving new work while completing existing requests within an appropriate termination window.

This becomes particularly important for:

• Kubernetes deployments
• Rolling deployments
• Long-running HTTP requests
• Message consumers
• Scheduled jobs

Production lesson: Deployment is not just starting a new version. It is also safely shutting down the old version.

18. Running Scheduled Jobs on Every Instance

Consider:

@Scheduled(cron = "0 0 * * * *")
public void generateReport() {
    // job
}

If you have five application instances, the scheduled task may execute on every instance.

Instance 1 ----+
Instance 2 ----+
Instance 3 ----+----> Same scheduled job
Instance 4 ----+
Instance 5 ----+

If the job sends emails, generates reports, processes payments, or modifies records, duplicate execution can cause serious problems.

Solutions include distributed locking, dedicated job workers, external schedulers, queue-based processing, or another architecture that guarantees appropriate execution semantics.

Production lesson: In a distributed system, "run once" requires coordination.

19. Adding Caching Without Planning Invalidation

Caching can dramatically improve performance.

But stale data can create correctness problems.

Database
   |
   v
Cache contains old value

Database updated
   |
   X
Cache still contains old value

The difficult question is not usually "How do I add a cache?"

The difficult question is:

"When is this cached value no longer valid?"

Before implementing caching, define:

• TTL
• Invalidation strategy
• Consistency requirements
• Cache ownership
• Failure behavior
• Eviction policy
• Cold-cache behavior

Also consider cache stampedes when many requests attempt to rebuild the same missing cache entry simultaneously.

Production lesson: Caching is not simply a performance feature. It changes data-consistency behavior.

20. Deploying Incompatible Database Schema Changes

This becomes particularly dangerous during rolling deployments.

Imagine the old application expects:

customer_name

while the new application expects:

full_name

If both versions temporarily run during deployment, one version may fail against the database schema required by the other.

Old Application
       |
       +----------------+
                        |
                    Database
                        |
       +----------------+
       |
New Application

Production deployments often require backward-compatible schema changes.

A common approach is:

1. Add the new database structure.
2. Deploy application code that can work with both versions.
3. Migrate or backfill data.
4. Switch traffic or behavior.
5. Remove the old structure later.

This is commonly described as an expand-and-contract migration strategy.

Production lesson: Database changes must consider application versions that coexist during deployment.

21. Using Synchronous Calls for Every Microservice Interaction

Synchronous REST calls are simple and often appropriate.

But not every operation needs to block the user's request while waiting for another service.

Consider an order workflow:

Order API
   |
   +--> Payment
   |
   +--> Inventory
   |
   +--> Notification
   |
   +--> Analytics

If notification and analytics do not need to complete before the response is returned, synchronously waiting for them increases latency and coupling.

Asynchronous messaging can be more appropriate for independent, eventually consistent operations.

Order Service
     |
     v
Message Broker
   |       |
   v       v
Notification   Analytics

This does not mean "use Kafka everywhere." Synchronous communication is still appropriate when an immediate response is required.

Production lesson: Choose synchronous or asynchronous communication based on business requirements, consistency needs, latency, and failure behavior.

22. Ignoring Idempotency for Critical APIs

This is especially dangerous for payments, orders, inventory, and financial operations.

Imagine a client sends:

POST /payments

The server successfully processes the payment, but the response is lost because of a network problem.

The client retries the request.

Request 1
   |
   v
Payment succeeds
   |
   X
Response lost

Request 2
   |
   v
Payment processed again

Without idempotency, the customer may be charged twice.

A common approach is to accept an idempotency key:

Idempotency-Key: payment-12345

The server stores the result associated with that key and ensures repeated requests do not execute the business operation multiple times.

Production lesson: Network retries mean the same logical request can arrive more than once. Critical operations must be designed accordingly.

23. Setting Kubernetes CPU and Memory Limits Without Understanding the JVM

Java applications need memory not only for the Java heap.

A simplified view is:

Container Memory
|
+-- Java Heap
+-- Metaspace
+-- Thread Stacks
+-- Direct Buffers
+-- Native Memory
+-- JVM Overhead

If Kubernetes memory limits are set too aggressively, the container can be killed even when the configured Java heap appears to have available memory.

CPU limits can also affect application latency. CPU throttling may cause an application to become slow even when the code itself has not changed.

Monitor:

• Container CPU usage
• CPU throttling
• Container memory usage
• OOMKilled events
• JVM heap usage
• GC activity
• Pod restarts

Production lesson: JVM tuning and Kubernetes resource configuration must be considered together.

24. Using Health Checks That Only Verify the Process Is Running

A process being alive does not necessarily mean the application is healthy.

For example:

Spring Boot process
       |
       v
Running

Database
       |
       X
Unavailable

The process may still respond to a basic health check even though it cannot serve important requests.

However, blindly making every dependency mandatory for liveness can also create problems. A temporary dependency outage should not necessarily cause every application instance to restart.

This is why readiness and liveness need to have different purposes.

• Liveness: Is the application process fundamentally alive?
• Readiness: Should this instance receive traffic?

Dependency checks should be designed carefully based on the role of the dependency and the desired recovery behavior.

Production lesson: A health check should help the platform make the right operational decision, not simply return "UP."

25. Optimizing Code Before Measuring the Bottleneck

This is perhaps the most important mistake on the list.

An API becomes slow.

The developer immediately starts optimizing Java code.

But the actual problem could be:

• A slow SQL query
• N+1 queries
• Database connection pool exhaustion
• A slow downstream service
• Network latency
• Thread pool saturation
• Garbage collection
• Cache misses
• CPU throttling
• External API latency

Consider an API whose response time increased from 300 milliseconds to 4 seconds.

Before modifying application code, look at the distributed trace:

Total request: 4,000 ms

Spring Boot processing:      150 ms
Database:                  2,700 ms
Downstream API:              800 ms
Connection wait:             250 ms
Other:                       100 ms

Optimizing 150 milliseconds of Java processing will barely change the user experience.

The correct question is:

"Where is the request actually spending its time?"

Measure first. Then optimize.

Production lesson: Performance engineering should be evidence-driven, not assumption-driven.

How These Mistakes Connect in Real Production Incidents

The most interesting production incidents usually involve several mistakes at the same time.

For example:

Traffic increases
      |
      v
N+1 queries
      |
      v
Database queries become slower
      |
      v
Connections remain occupied longer
      |
      v
HikariCP pool becomes exhausted
      |
      v
Application threads wait
      |
      v
API latency increases
      |
      v
Clients retry
      |
      v
More requests arrive
      |
      v
Database gets more traffic
      |
      v
System-wide degradation

Notice that the original problem was not necessarily a single Java coding mistake.

It was a chain reaction.

A Practical Production Troubleshooting Framework

When a Spring Boot service suddenly becomes slow or unhealthy, avoid immediately changing configuration.

Use a structured investigation.

Step 1: Confirm the symptom

• Is latency actually increasing?
• Is p95 affected?
• Is p99 affected?
• Did traffic increase?
• Did error rate increase?

Step 2: Check recent changes

• New deployment
• Configuration change
• Database migration
• Infrastructure change
• Feature flag
• Dependency change

Step 3: Check distributed traces

Find where the request spends its time.

Request
 |
 +-- Controller
 |
 +-- Database
 |
 +-- Redis
 |
 +-- Payment Service
 |
 +-- External API

Step 4: Check the database

• Slow queries
• Query volume
• Connection count
• Lock contention
• CPU
• Indexes

Step 5: Check JVM health

• Heap
• GC
• CPU
• Threads
• Class loading
• Native memory where relevant

Step 6: Check infrastructure

• Pod CPU
• CPU throttling
• Memory pressure
• Restarts
• HPA behavior
• Node health

Step 7: Check downstream dependencies

Look at latency, errors, timeouts, retries, and saturation.

Step 8: Change one thing at a time

A production investigation becomes difficult when multiple configuration changes are made simultaneously.

Make a controlled change, measure the result, and continue from evidence.

Production Mistakes vs Production Symptoms

Production Symptom	Possible Mistakes to Investigate
High API latency	N+1, slow DB, downstream latency, thread pool, connection pool
Low CPU but slow requests	Database waits, connection pool, locks, network I/O
High CPU	Excessive computation, GC, thread contention, traffic spike
Memory continuously increasing	Large datasets, cache growth, object retention, memory leak
Duplicate operations	Retries, scheduled jobs, missing idempotency
Service unavailable during deployment	No graceful shutdown, incompatible schema, readiness problems
Database overloaded	N+1, oversized connection pools, excessive retries, inefficient queries
Stale data	Incorrect cache invalidation or TTL strategy
Pod restarts	Memory limits, JVM configuration, health checks, application crashes
Intermittent downstream failures	No timeout, retry storm, connection exhaustion, dependency saturation

Common Spring Boot Production Anti-Patterns

• "It works on my machine."
• "Let's increase the thread pool."
• "Let's increase HikariCP."
• "Let's add more retries."
• "Let's add Redis."
• "Let's increase the JVM heap."
• "Let's increase Kubernetes CPU."
• "Let's catch Exception so the API doesn't fail."
• "The process is running, so the service is healthy."
• "The average response time is fine."
• "The database query is fast when I run it manually."
• "The code is fast locally, so production must have a network problem."

Each of these statements can sometimes be true, but none should be treated as a diagnosis.

What Senior Engineers Look For

A senior engineer does not simply ask whether the code works.

They ask:

• What happens under 10x traffic?
• What happens when the database becomes slow?
• What happens when the downstream service is unavailable?
• What happens when the request is retried?
• What happens when two instances execute the same job?
• What happens during deployment?
• What happens when the cache is empty?
• What happens when the pod runs out of CPU?
• What happens when a request arrives twice?
• How will we know where the failure occurred?

This is the difference between writing application code and engineering a production system.

Key Takeaways

• Understand your database connection pool before production traffic arrives.
• Do not expose persistence entities as API contracts without considering the consequences.
• Always watch for N+1 queries in JPA.
• Understand Spring proxy behavior when using annotations.
• Keep transaction boundaries intentional.
• Never use unlimited retries.
• Configure timeouts for external calls.
• Treat thread pools as finite resources.
• Do not use parallelStream() without understanding its execution model.
• Use pagination and streaming for large datasets.
• Configure and monitor asynchronous executors.
• Keep secrets out of source-controlled configuration.
• Use environment-appropriate configuration.
• Combine logs, metrics, traces, and health checks.
• Never expose internal exception details unnecessarily.
• Handle known exceptions explicitly.
• Design deployments for graceful shutdown.
• Coordinate scheduled jobs across multiple instances.
• Plan cache invalidation before adding caching.
• Make database migrations compatible with rolling deployments.
• Use asynchronous communication when business requirements allow it.
• Design critical operations for idempotency.
• Understand JVM memory alongside Kubernetes memory limits.
• Design liveness and readiness checks for their actual purposes.
• Measure before optimizing.

Frequently Asked Questions

1. What is the most common Spring Boot production mistake?

There is no single mistake responsible for every incident, but database-related issues such as inefficient queries, N+1 queries, connection pool exhaustion, and poor transaction boundaries are extremely common production bottlenecks.

2. Is using DTOs mandatory in Spring Boot?

Not technically, but DTOs are strongly useful when the API contract should be independent of the persistence model. They also help control the response shape and prevent accidental exposure of internal fields and relationships.

3. Why can a Spring Boot API be slow when CPU usage is low?

Low CPU does not mean the application is idle. Threads may be waiting for database connections, database queries, locks, network responses, external APIs, or other I/O operations.

4. Should I increase HikariCP pool size when the API is slow?

Not automatically. First determine whether requests are waiting for connections and whether the database has enough capacity. Increasing the pool without understanding the database workload can make the problem worse.

5. Why are unlimited retries dangerous?

Retries generate additional traffic. When a downstream service is already unhealthy, unlimited retries can overload it further and create a cascading failure.

6. Why are timeouts important for microservices?

Without timeouts, application threads can remain blocked while waiting for an unhealthy dependency. Enough blocked requests can eventually exhaust the application's available concurrency.

7. Why is self-invocation a problem with @Transactional?

Spring's declarative transaction support commonly relies on proxies. An internal method call within the same object can bypass the proxy, meaning the transactional interceptor may not be invoked.

8. Why can caching create production problems?

Caching introduces another copy of data. If invalidation, TTL, consistency, and failure behavior are not planned, applications can serve stale or incorrect data.

9. Why is idempotency important for payment APIs?

Networks are unreliable and clients may retry requests. Without idempotency, the same logical payment request can potentially execute more than once.

10. What should I check first when a Spring Boot API becomes slow?

Start with evidence: compare p50/p95/p99 latency, request volume, errors, recent deployments, distributed traces, database latency, connection pool usage, downstream latency, JVM metrics, and Kubernetes resource metrics.

Final Thought

Spring Boot is rarely the real problem in a production incident.

The bigger problem is usually an incorrect assumption about how the application behaves under real-world conditions.

A connection pool that was sufficient during development may fail under production concurrency.

A JPA query that looked harmless may generate thousands of SQL statements.

A retry that helped during testing may become a retry storm during an outage.

A scheduled task that worked perfectly on one machine may execute five times when five application instances are deployed.

A Java application that appears healthy may actually be waiting on a database, downstream service, lock, network call, or Kubernetes resource.

The most important production mindset is simple:

Don't ask only whether the code works.

Ask how the system behaves when everything doesn't go as planned.

That is where production engineering begins.

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