We’ve spent a lot of time on performance tuning our Apache Flink application. We’ll walk you through key lessons for optimizing large stateful Apache Flink applications.
Overview
This article provides seven actionable tips for optimizing Apache Flink applications, focusing on performance and resiliency. The authors share their experiences at Shopify, detailing tools, configurations, and best practices that enhance the efficiency of stateful streaming applications.
What You'll Learn
1
How to select and use profiling tools for debugging Flink applications
2
Why avoiding Kryo serialization can improve application throughput
3
How to tune Flink configurations based on workload profiles
4
When to use SSDs for RocksDB storage to enhance performance
5
How to prevent memory leaks by avoiding dynamic classloading
Prerequisites & Requirements
- Understanding of Apache Flink and stateful stream processing
- Familiarity with JVM profiling tools like Async-profiler and VisualVM(optional)
Key Questions Answered
What profiling tools are recommended for optimizing Flink applications?
The article recommends several profiling tools for optimizing Flink applications, including Async-profiler for tracing events, VisualVM for live heap analysis, and Eclipse Memory Analyzer for inspecting heap dumps. These tools help identify performance bottlenecks and memory issues.
How can tuning configurations improve Flink application performance?
Tuning configurations based on workload profiles, such as backfill and steady state, can significantly enhance performance. For example, prioritizing throughput during backfills and minimizing latency during steady states ensures efficient resource utilization and faster processing.
What are the consequences of using Kryo serialization in Flink?
Using Kryo serialization can lead to significant performance degradation, as it is slower than other serializers. The article highlights that disabling Kryo fallback and addressing serialization issues can lead to a 20% increase in throughput.
Why is SSD recommended for RocksDB storage in Flink applications?
SSDs are recommended for RocksDB storage because they significantly improve disk I/O rates, leading to processing speed improvements. The article notes a tenfold increase in processing rates when switching from NFS to local SSDs for large state applications.
Key Statistics & Figures
Throughput increase after fixing serialization issues
20%
This increase was observed after disabling Kryo serialization fallback and addressing serialization failures.
Processing speed improvement with SSDs
10 times
This improvement was noted when switching from NFS to local SSDs for applications with over 8 terabytes of state.
Reduction in active buckets per Task Manager
90%
This reduction was achieved by keying records by partition string before writing to the sink.
Technologies & Tools
Some links below are affiliate links. We may earn a commission if you make a purchase.
Stream Processing Engine
Apache Flink
Used as the primary framework for stateful streaming applications at Shopify.
State Backend
Rocksdb
Utilized for managing state in Flink applications, particularly for large stateful workloads.
Cloud Storage
Google Cloud Storage
Used for saving checkpoints and state in Flink applications.
Container Orchestration
Kubernetes
Employed for deploying Flink applications in a high availability configuration.
Memory Allocator
Jemalloc
Used as the default memory allocator in Flink starting from version 1.12.
Profiling Tool
Visualvm
Used for live monitoring of JVM instances to analyze memory usage and CPU performance.
Heap Analysis Tool
Eclipse Memory Analyzer
Utilized for inspecting JVM heap dumps to identify memory leaks and utilization issues.
Key Actionable Insights
1Use profiling tools like Async-profiler and VisualVM to identify performance bottlenecks in your Flink applications.These tools can provide insights into CPU cycles, memory allocations, and help debug issues related to serialization, ultimately leading to optimized application performance.
2Avoid Kryo serialization by ensuring that your data structures are compatible with Flink's built-in serializers.This can lead to a significant increase in throughput, as Kryo serialization has been shown to be much slower, impacting overall application performance.
3Tune your Flink application configurations based on the specific workload profile to maximize efficiency.Adjusting settings for backfill versus steady state can help manage resource utilization effectively, ensuring that your application performs optimally under varying loads.
4Implement SSDs for your RocksDB state backend to enhance performance.Using SSDs can drastically improve disk I/O rates, which is crucial for applications handling large states, as demonstrated by the tenfold increase in processing rates.
5Disable dynamic classloading to prevent memory leaks in Flink applications.By placing your application code on the Java common classpath, you can avoid issues related to class unloading and memory consumption during job restarts.
Common Pitfalls
1
Failing to configure the File Sink properly can lead to excessive memory usage and application crashes.
This occurs when too many buckets are held in memory without proper data partitioning, resulting in inefficient flushing and memory overflow.
2
Using Kryo serialization without addressing its performance implications can degrade application throughput.
Many developers may not realize the impact of Kryo serialization until profiling tools reveal its memory footprint and performance costs.
Related Concepts
Stateful Stream Processing
Performance Tuning In Distributed Systems
Memory Management In Java Applications