Stream Processing Monitoring
Monitor and optimize your Kafka Streams applications and real-time data processing with comprehensive topology visualization, performance tracking, and intelligent error handling.
Stream Processing Monitoring Challenges
Why traditional monitoring falls short for real-time stream processing
Complex Topologies
Stream processing applications have complex data flow topologies with multiple processors, state stores, and sub-topologies that are difficult to visualize and debug.
50+ processors in a single topology
Stateful Processing
Managing state stores, monitoring state size, and tracking state restoration requires specialized monitoring that standard tools don't provide.
State restoration can take hours
Performance Bottlenecks
Identifying performance bottlenecks in stream processing requires understanding task distribution, threading, and inter-processor communication patterns.
One slow processor affects entire topology
Complete Stream Processing Visibility
Monitor every aspect of your Kafka Streams applications
Topology Visualization
Interactive Topology Maps
Visualize data flow through processors, state stores, and sub-topologies
Real-Time Data Flow
Monitor throughput and latency at each processing step
Bottleneck Identification
Automatically highlight slow processors and processing delays
State Store Monitoring
State Size & Growth Tracking
Monitor state store size, growth patterns, and capacity planning
Restoration Performance
Track state restoration time and progress during rebalancing
Operation Performance
Monitor put/get rates and latency for state store operations
Supported Processing Patterns
Monitor all types of stream processing applications and patterns
Stateless Processing
Monitor filter, map, and flatMap operations with throughput and latency tracking.
Aggregations
Track windowed and sessionized aggregations with state size and update frequency.
Stream Joins
Monitor stream-stream and stream-table joins with join rate and buffer metrics.
Advanced Stream Monitoring Features
Sophisticated monitoring capabilities for complex stream processing scenarios
Task Distribution Analysis
Monitor how processing tasks are distributed across instances and threads for optimal load balancing and performance.
Exactly-Once Semantics
Monitor exactly-once processing guarantees with transaction success rates and idempotent producer performance tracking.
Error Handling & Recovery
Comprehensive error tracking with deserialization errors, processing exceptions, and automatic recovery monitoring.
Performance Optimization
AI-powered optimization recommendations based on topology analysis, resource utilization patterns, and performance bottlenecks.
Stream Processing Use Cases
How organizations monitor their Kafka Streams applications
Real-Time Analytics Pipeline
Complex stream processing topology with multiple aggregations, joins, and windowing operations for real-time business intelligence.
Event-Driven Microservices
Distributed stream processing across multiple microservices with event sourcing, CQRS patterns, and complex business logic.
Measurable Benefits
Real improvements from comprehensive stream processing monitoring
Frequently Asked Questions
Kafka Streams is a client library for building real-time stream processing applications. Monitoring is crucial because stream processing has complex topologies, stateful operations, and performance requirements that need specialized visibility beyond standard Kafka monitoring.
KLogic provides interactive topology maps showing processors, state stores, sub-topologies, and data flow. You can see real-time throughput at each processing step, identify bottlenecks, and drill down into any component for detailed metrics.
KLogic monitors state store size, growth rate, put/get operation rates, latency percentiles, and restoration progress. We alert on capacity issues, slow operations, and long restoration times that could impact processing.
Yes, KLogic tracks transaction success rates, idempotent producer metrics, and duplicate detection. We provide visibility into EOS (exactly-once semantics) performance and alert on transaction failures that could affect data consistency.
KLogic highlights slow processors, shows processing time at each step, and identifies tasks with uneven load. Our AI provides optimization recommendations for parallelism, partition assignment, and configuration tuning.
Yes, KLogic can monitor multiple Kafka Streams applications across different clusters. You can compare topology performance, track inter-application dependencies, and get a unified view of your entire stream processing infrastructure.
Optimize Your Stream Processing
Get complete visibility into your Kafka Streams applications. Monitor topology performance, track state stores, and optimize real-time data processing with intelligent insights.
Free 14-day trial • Topology visualization • State store monitoring • Performance optimization