Apache Kafka® Issues in Production – How to Diagnose and Prevent Failures

Many developers discover Kafka issues in production only after their applications face real-world workloads, when staging scenarios simply don’t reflect the scale, complexity, or variability of live data streams. These issues in Kafka typically include:

• High latency: Messages take longer to reach consumers.

• Data loss or duplication: Events aren’t delivered once — or at all.

• Retry storms: Misconfigured producers or consumers endlessly retry.

• Consumer lag: Consumers can't keep up with the pace of ingestion.

• Unavailable brokers or partitions: Infrastructure failures affecting availability.

These failures can lead to serious business consequences — delayed analytics, broken applications, or missed SLAs—especially when real-time data powers critical systems.

What You’ll Learn About Deploying Kafka in Production

In this article, we’ll walk through the most common Kafka issues in production and how to troubleshoot them effectively. You’ll learn:

• Why these problems occur under real-world conditions

• How to recognize symptoms before they escalate

• Practical steps to prevent issues in future deployments

By the end, you’ll have a clearer understanding of how to operate Kafka more reliably in production and reduce the risk of outages, data loss, or unexpected costs — even at scale.

Common Kafka Production Issues

Running Kafka in production presents unique challenges that rarely surface in development environments. Unlike traditional databases or message queues, Kafka's distributed architecture means that issues can cascade across multiple brokers, topics, and consumer groups simultaneously. The stateful nature of Kafka brokers, combined with complex replication protocols and JVM garbage collection patterns, creates failure modes that only become apparent under sustained production load.

Here are the eight most common issues teams face when running Kafka in production environments:

1. What Causes Consumer Lag in Production?
   
   Consumer Lag – The delay between when a message is produced to Kafka and when it is read by a consumer.
   
   Symptoms:

• lag metrics increasing on consumer dashboards

• Messages delayed in downstream processing

• Alerts like: kafka.consumer: fetch.max.wait.ms exceeded

Root Causes:

• Consumers are slower than producers (I/O bottlenecks, processing logic delays)

• Imbalanced partition assignments

• Inefficient Kafka consumer configuration (e.g., small fetch.min.bytes)

Quick Fix:

• Enable lag monitoring per topic/partition

• Scale out consumer group instances

• Rebalance using KafkaRebalanceListener

Related Resources:

Blog Post: How to survive a Kafka outage

Customer Story: Learn how DKV Mobility adopted Confluent to process billions of real-time transactions with reduced lag and lower downtime.

2. Why Do Under-Provisioned Partitions Cause Bottlenecks?

Under-Provisioned Partitions – A condition where a Kafka topic has too few partitions to handle parallelism or throughput demands.

Symptoms:

• Few partitions, many consumers → idle workers

• Throughput drops during spikes

• Log: Not enough partitions for assignment

Root Causes:

• Improper Kafka partitioning strategy

• Lack of key-based distribution

• Static partition count that doesn’t scale with load

Quick Fix:

• Reassess partition count based on throughput and parallelism needs

• Use partitioning keys for better load distribution

Tip:
Tools like Confluent Auto Data Balancer (available in Confluent Platform) help redistribute partitions without downtime.

3. Why Does Broker Disk or Memory Exhaustion Happen?
   
   Broker Disk or Memory Exhaustion – A situation where a Kafka broker runs out of storage space or memory resources, affecting stability and message durability.

Symptoms:

• Alerts like: Kafka disk usage > 90%

• Broker crashes or GC pauses

• Errors: Failed to append to log segment

Root Causes:

• Large backlogs from slow consumers

• Infinite retention settings or runaway topics

• JVM heap mismanagement

Quick Fix:

• Monitor broker disk and heap usage

• Set retention limits on topics

• Use tiered storage in Confluent Cloud for long-term data

Storyblocks improved stability by offloading large backlogs using Confluent tiered storage.

4. What Causes ISR Churn from Replication Problems?
   
   ISR Churn from Replication Problems – Frequent changes in the set of in-sync replicas, disrupting consistency and availability guarantees.

Symptoms:

• Frequent ISR (In-Sync Replica) reassignments

• Alert: Under-replicated partitions > 0

• High network usage

Root Causes:

• Network latency or overloaded follower brokers

• Large messages or high replication factor

• Improper Kafka replication settings

Quick Fix:

• Monitor ISR size and replication lag

• Right-size replicas based on throughput

• Enable rack-aware replication in production
  
  

5. What Happens When ZooKeeper Gets Saturated?
   
   ZooKeeper Saturation – Overloading of ZooKeeper (used for Kafka metadata and coordination) that slows down cluster operations and leader elections.

Symptoms:

• Delays in leader elections

• Alert: Session expired due to heartbeat timeout

• New topics or brokers fail to register

Root Causes:

• Excessive metadata changes (too many topics)

• ZooKeeper node misconfiguration

• Too many concurrent client connections

Quick Fix:

• Avoid unnecessary metadata churn

• Upgrade to Confluent Platform with KRaft mode (ZooKeeper-free Kafka)

• Monitor ZooKeeper latency and throughput
  
  

6. Why Does Topic Sprawl Break Kafka?
   
   Topic Sprawl – The uncontrolled growth of topics and partitions in a Kafka cluster, leading to metadata bloat and operational inefficiency.

Symptoms:

• Thousands of topics with low traffic

• Controller log warnings: Failed to propagate metadata

• Increased cluster start-up or failover times

Root Causes:

• Teams self-creating topics without governance

• Lack of topic lifecycle policies

Quick Fix:

• Enforce naming conventions

• Use topic auto-deletion (where safe)

• Monitor topic count via JMX metrics

Tip:
Use Confluent's centralized topic governance tools to control sprawl and simplify audits.

7. What Causes Producer Retries and Message Duplication?
   
   Producer Retries and Message Duplication – The occurrence of repeated message sends from producers, resulting in duplicate events in downstream systems.

Symptoms:

• Log: org.apache.kafka.common.errors.TimeoutException

• Duplicate entries in downstream systems

• Increased broker load

Root Causes:

• Slow or misconfigured Kafka producer

• Idempotence disabled

• Network-level packet loss

Quick Fix:

• Enable idempotent producer (enable.idempotence=true)

• Use appropriate acks and retries values

• Monitor retry metrics on producer side
  
  

8. Why Do Large GC Pauses Happen in Kafka Brokers?
   
   Large GC Pauses in Brokers – Long garbage collection pauses inside Kafka brokers that temporarily freeze processing and degrade performance.

Symptoms:

• High broker CPU

• GC logs show: Pause time > 10s

• Consumer lag spikes during GC

Root Causes:

• Improper JVM settings

• Large message sizes

• Memory leaks from custom interceptors or serializers

Quick Fix:

• Tune heap size and GC algorithm (G1GC recommended)

• Keep message sizes small

• Use metrics to track GC frequency and duration

Kafka production challenges are not just about “higher load” — they’re tied to its unique architecture and distributed nature:

• Distributed and stateful: Brokers must stay consistent via replication, making failures more complex than in stateless systems.

• Partition-driven scalability: Throughput depends on Kafka partitioning, which requires careful planning up front.

• JVM-based: Kafka brokers run on the JVM, so garbage collection problems emerge under high heap pressure.

• Interconnected ecosystem: Producers, brokers, ZooKeeper/KRaft controllers, and consumers must all align, meaning a single bottleneck cascades quickly.
  
  

Why Certain Issues Surface in Production but Not in Development?

Kafka behaves differently under production workloads because the scale, traffic patterns, and operational complexity are fundamentally different. What feels reliable in a lab environment can quickly break down when exposed to real business data.

In development or test environments:

• Traffic is minimal and predictable - message rates are much lower than production, masking bottlenecks.

• Cluster topologies are simple - often one or two brokers, no cross-cluster replication.

• Few consumer groups - minimal coordination overhead, no rebalancing storms.

• Short retention windows - data is purged quickly, avoiding disk/memory pressure.

• Limited monitoring/alerting - inefficiencies go unnoticed without dashboards or lag alerts.

In production environments:

• Traffic is spiky and unpredictable - sustained high throughput exposes partitioning, I/O, and consumer bottlenecks.

• Longer retention requirements - weeks/months of data accumulate, stressing disks and JVM heaps.

• Multi-tenant clusters are the norm - many teams create topics, leading to topic sprawl.

• Dozens of consumer groups - uneven lag, consumer rebalancing storms, and ISR churn become common.

• Monitoring gaps are exposed - without watching Kafka metrics, issues like lag or ISR flapping can silently escalate.

• Scaling assumptions break down - partition counts that worked in tests are insufficient once traffic surges.

Moreover, many dev teams overlook Kafka-specific observability until after issues arise. Key Kafka metrics to watch — like under-replicated partitions, consumer lag, or disk usage — may not be visible without robust monitoring.

What to Monitor to Diagnose Kafka Issues

When something feels “off” in your Kafka environment, knowing what to monitor can make the difference between a quick fix and prolonged downtime.
These recommendations come directly from Confluent’s millions of hours of Kafka engineering and production support experience—battle-tested in some of the largest and most complex streaming environments in the world.

Pro Tip: For a complete proactive monitoring solution, check out the Health+ from Confluent service.

Kafka Diagnostic Checklist

Below is a quick-reference checklist for common Kafka health signals, the metrics to watch, and what those metrics might mean when they go out of bounds.

Key areas to watch:

• Broker Health: CPU, heap, disk utilization

• Partition Distribution: Avoid skew to prevent broker hotspots

• Consumer Performance: Monitor lag to detect processing delays

• Replication Status: Keep UnderReplicatedPartitions at zero

• ZooKeeper Latency: Watch for spikes that may slow controller operations

• Producer Connectivity: Spot retries or disconnects early

How to Prevent Kafka Issues Before They Happen

Proactive planning and the right architectural decisions can prevent costly incidents in your Kafka environment. By implementing the following strategies, you can ensure stability, performance, and security from day one.

Configuration Best Practices

• Right-size partitions – Allocate the correct number of partitions for your throughput needs to avoid underutilization or broker overload.

• Use compression – Enable message compression (e.g., lz4 or snappy) to reduce network bandwidth and storage costs while improving throughput.

• Enable TLS and authentication – Secure your cluster with TLS encryption and SASL authentication to protect data in transit and prevent unauthorized access.

• Avoid over-replication – Maintain replication factors that meet durability needs without causing unnecessary storage and network strain.

Infrastructure Planning

• Use tiered storage for long-term retention – Store older data in cost-effective cloud or object storage while keeping recent data on fast local disks for performance.

• Plan for scaling – Choose cluster sizes, broker specs, and storage types that can handle peak workloads and future growth without constant rebalancing.

Observability and Continuous Validation

• Monitor critical metrics – Track broker health, disk usage, partition lag, and controller events to detect problems early.

• Automate validation through CI/CD – Integrate Kafka configuration checks into your CI/CD pipelines to ensure safe, consistent changes across environments.

By treating Kafka architecture as a living system—monitored, validated, and tuned continuously—you can prevent most operational issues before they ever reach production.

How Confluent Helps Avoid Production Issues

Managing Kafka in production requires specialized expertise and continuous monitoring that many organizations struggle to maintain in-house. Confluent Cloud addresses these challenges through automated infrastructure management and built-in safeguards that prevent common production issues before they occur.

Automated Scaling and Self-Healing Infrastructure

Automated scaling in Confluent Cloud eliminates the guesswork involved in capacity planning. The platform monitors cluster utilization in real-time and automatically adjusts broker capacity, partition distribution, and storage allocation based on actual workload demands. This prevents the under-provisioning issues that commonly cause production bottlenecks.

Self-healing capabilities detect and remediate infrastructure failures automatically, including:

• Automatic broker replacement during hardware failures

• Dynamic partition reassignment when brokers become unavailable

• Intelligent load balancing to prevent resource exhaustion

• Proactive disk space management with automatic storage expansion

Built-In Observability and Monitoring

Confluent Cloud provides comprehensive observability dashboards that surface critical metrics without requiring custom monitoring infrastructure:

• Real-time lag detection with automatic alerting when consumer groups fall behind

• Broker health monitoring with JVM garbage collection and memory utilization tracking

• Producer and consumer performance metrics with latency percentile tracking

• Topic-level insights including partition distribution and replication status

These dashboards eliminate the observability gap that causes production issues to go undetected in traditional Kafka deployments. Teams receive actionable alerts with specific remediation guidance rather than generic threshold warnings.

Production Guardrails and Governance

RBAC for Kafka in Confluent Cloud prevents unauthorized topic creation and configuration changes that lead to cluster instability:

• Role-based access controls limit topic creation to authorized personnel

• Schema validation prevents incompatible message formats from causing consumer failures

• Quota management prevents individual applications from consuming excessive cluster resources

• Automated retention policy enforcement prevents uncontrolled log growth

Enterprise-Grade Reliability

Confluent Cloud delivers 99.99% uptime SLAs backed by multi-zone redundancy and automated disaster recovery. This reliability eliminates the operational overhead of managing ZooKeeper clusters, broker failover procedures, and cross-region replication strategies that often cause production outages in self-managed deployments.

24/7 expert support provides immediate assistance during critical issues, with access to Kafka committers and platform engineers who understand the nuances of production streaming workloads.

Value Delivered to Development Teams

The automation and guardrails in Confluent Cloud translate to measurable business benefits:

• Time Savings: Development teams can focus on building applications rather than managing infrastructure, reducing time-to-market for streaming data projects by 60-80%.

• Risk Reduction: Automated scaling and self-healing capabilities prevent the cascading failures that cause extended outages and data loss scenarios.

• Cost Optimization: Right-sized infrastructure scaling eliminates over-provisioning while preventing performance degradation, typically reducing total streaming infrastructure costs by 30-40%.

• Business Focus: Teams can concentrate on solving problems that directly impact business outcomes rather than troubleshooting broker configurations and partition rebalancing strategies.

Ready to eliminate Kafka production issues from your streaming infrastructure? Get Started with Confluent Cloud today.

Summary: Kafka Production Issues and Solutions:

Get Started With Production-Ready Kafka on Confluent

Kafka is a powerful streaming platform, but production stability depends on proactive monitoring, capacity planning, and governance. The troubleshooting techniques and prevention strategies covered in this guide—along with automated scaling, observability, and governance tools like those in Confluent Cloud—can help teams minimize downtime, avoid costly issues, and keep streaming workloads healthy at any scale.

Get Started for Free

FAQs: Kafka Production Troubleshooting

Why is Kafka slow in production?

Kafka performance degrades in production due to resource contention, inadequate partition distribution, and JVM garbage collection pauses. Common causes include under-provisioned brokers handling larger message volumes than tested, consumer groups experiencing rebalancing events during peak traffic, and network latency between distributed components. Monitor broker CPU, memory usage, and consumer lag metrics to identify bottlenecks.

What causes high consumer lag in Kafka?

High consumer lag occurs when consumers process messages slower than producers publish them. Primary causes include downstream service dependencies causing processing delays, inefficient message deserialization logic, consumer group rebalancing during deployments, and inadequate consumer parallelism relative to partition count. Check processing time per message and increase consumer instances or optimize processing logic.

How do I detect Kafka replication issues?

Monitor the under-replicated-partitions metric, which indicates partitions with fewer in-sync replicas than configured. Check broker logs for ISR shrink/expand events and replica lag warnings. Use kafka-topics --describe --under-replicated-partitions to identify affected partitions. Network issues between brokers and resource exhaustion are common root causes.

Can Kafka handle production workloads at scale?

Yes, Kafka scales horizontally across multiple brokers and handles millions of messages per second when properly configured. Production scalability depends on adequate partition distribution, sufficient broker resources, and proper replication factor settings. Many organizations process terabytes daily through Kafka clusters, but scaling requires careful capacity planning and monitoring.

What are signs that my Kafka cluster is unhealthy?

Key indicators include increasing consumer lag across multiple groups, frequent broker disconnections, growing disk usage without retention cleanup, and ZooKeeper connection timeouts. Monitor under-replicated-partitions, offline-partitions-count, and JVM garbage collection frequency. Healthy clusters maintain consistent replication, low consumer lag, and stable broker connectivity.

How do I troubleshoot Kafka producer failures?

Producer failures typically manifest as timeout exceptions, retry storms, or message delivery failures. Check network connectivity to brokers, verify topic existence and permissions, and monitor record-error-rate metrics. Enable producer logging and review retry configuration settings. Common issues include broker unavailability, authentication failures, and insufficient timeout values.

What causes Kafka broker memory issues?

Broker memory problems stem from inadequate JVM heap sizing, memory leaks in custom components, or excessive page cache usage from large messages. Monitor JVM heap utilization and garbage collection patterns. Large message batches, insufficient heap size relative to workload, and memory-intensive serializers contribute to OutOfMemory errors.

How do I monitor Kafka partition health?

Track partition metrics including leader election rate, replica lag, and size distribution across brokers. Use kafka-topics --describe to check replication status and leader assignment. Monitor preferred-replica-imbalance-count to detect uneven partition distribution. Healthy partitions maintain consistent leadership and balanced replica placement.

What should I do when Kafka consumers stop processing messages?

First, check consumer group status with kafka-consumer-groups --describe --group [group-name]. Look for consumer instances that haven't committed offsets recently, rebalancing loops, or processing exceptions. Verify downstream service availability, check consumer application logs, and monitor session timeout settings. Restart stuck consumers if processing logic is functioning correctly.

How do I prevent Kafka data loss in production?

Configure acks=all for producers to ensure leader and replica acknowledgment, set min.insync.replicas to at least 2, and use replication factor 3 or higher. Enable producer idempotence to prevent duplicates during retries. Monitor ISR health and implement proper retention policies. Regular backups and cross-region replication provide additional data protection.