3 (More) Tips for Optimizing Apache Flink Applications

Earlier this year, we shared our tips for optimizing large stateful Apache Flink applications. Below we’ll walk you through 3 more best practices.

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Shopify
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Kevin Lam
•8 min read•intermediate•
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ApacheApache KafkaScala

Overview

This article presents three additional tips for optimizing Apache Flink applications, focusing on enhancing performance through proper parallelism, avoiding sink bottlenecks, and utilizing HybridSource for combining heterogeneous data sources. The insights are drawn from practical experiences at Shopify, aimed at improving stateful streaming applications.

What You'll Learn

1

How to set the right parallelism for Apache Flink applications

2

Why avoiding sink bottlenecks is crucial for performance

3

How to use HybridSource to combine data from multiple sources

Prerequisites & Requirements

  • Understanding of Apache Flink and its architecture
  • Experience with data streaming concepts(optional)

Key Questions Answered

How can I optimize parallelism in my Flink application?
To optimize parallelism in your Flink application, configure it at the execution environment, operator, client, or system level. Start with a single execution environment level value and adjust based on bottlenecks, ensuring task managers and slots are appropriately matched to the highest parallelism value.
What strategies can I use to avoid sink bottlenecks in Flink?
To avoid sink bottlenecks, consider batch writing to sinks to improve throughput and reduce CPU load. Additionally, ensure that data skew is minimized by properly distributing keys across partitions, which can prevent certain nodes from becoming overloaded.
What is HybridSource in Apache Flink and how is it used?
HybridSource in Apache Flink allows you to combine data from heterogeneous sources, such as real-time Kafka topics and archived cloud storage, into a single logical source. This simplifies data access and improves throughput by leveraging better partitioning strategies.

Key Statistics & Figures

Task managers required for parallelism
25
For a parallelism of 100 with each task manager having four slots, you need 25 task managers.

Technologies & Tools

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Stream Processing
Apache Flink
Used as the stateful streaming engine for various applications at Shopify.
Message Broker
Kafka
Used for real-time data streaming in Flink applications.
Database
Bigtable
Serves as a data sink for Flink applications.

Key Actionable Insights

1
Start with a single execution environment level parallelism value and increase it only if necessary to optimize resource utilization.
This approach allows for better task slot sharing, which can enhance performance, especially when I/O intensive tasks block non-I/O tasks.
2
Implement batch writing to your sinks to improve throughput and reduce the impact of high CPU utilization on your Flink application.
Batch writing collects multiple events into a single request, which can lead to better compression and lower network usage, thus alleviating bottlenecks.
3
Utilize the bucketing technique to distribute workload evenly when using keys that may cause data skew.
By appending a randomly generated value to your key, you can improve the distribution of processing across task managers, preventing out-of-memory exceptions.

Common Pitfalls

1
Choosing a poorly distributed key can lead to significant performance issues and out-of-memory exceptions.
If keys are not well distributed, some task managers may become overloaded while others remain idle, causing inefficiencies in processing.

Related Concepts

Data Streaming
Stateful Processing
Performance Optimization Techniques