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Apache Flink vs Google Cloud Data Fusion: What are the differences?
Introduction: Apache Flink and Google Cloud Data Fusion are two popular tools used for big data processing and analytics. While both serve similar purposes, they have key differences that make them unique in their own ways.
Deployment Environment: Apache Flink is an open-source stream processing framework that can be deployed on various platforms, including on-premises or on cloud providers such as AWS, Azure, and Google Cloud Platform. On the other hand, Google Cloud Data Fusion is a fully-managed ETL and data integration service that runs on Google Cloud Platform, offering a more streamlined and integrated deployment experience.
Programming Model: Apache Flink provides a flexible and powerful API for building complex data processing pipelines in Java, Scala, or Python. It supports both batch and stream processing with its unified programming model. In contrast, Google Cloud Data Fusion offers a visual interface for building data pipelines using a drag-and-drop approach without the need for writing code, making it more user-friendly for non-technical users.
Integration with Ecosystem: Apache Flink integrates well with other big data tools and frameworks such as Apache Kafka, Apache Hadoop, and Apache Cassandra, providing a wide range of options for data ingestion and processing. Google Cloud Data Fusion, being a Google Cloud service, seamlessly integrates with other GCP services like BigQuery, Cloud Storage, and Pub/Sub, offering a more cohesive environment for data processing workflows.
Scalability and Performance: Apache Flink is known for its high throughput, low latency, and fault-tolerance features, making it suitable for processing large volumes of data in real-time. It can scale horizontally to handle massive workloads effectively. On the other hand, Google Cloud Data Fusion offers scalability benefits by automatically scaling resources based on workload demands, providing a more managed approach to scalability without the need for manual tuning.
Pricing Model: Apache Flink is an open-source framework, making it free to use without any licensing costs. However, users need to manage infrastructure and deployment costs when running Flink applications on cloud platforms. Google Cloud Data Fusion follows a usage-based pricing model, where users pay for the resources consumed and the services used, providing a more predictable cost structure for data integration and processing tasks.
Community Support and Updates: Apache Flink has a large and active community of contributors and users, regularly releasing updates and new features to enhance the framework's capabilities. On the other hand, Google Cloud Data Fusion is a managed service with updates and maintenance handled by Google Cloud Platform, offering a more hands-off approach for users who prefer seamless updates and support from the service provider.
In Summary, Apache Flink and Google Cloud Data Fusion differ in deployment environment, programming model, integration with ecosystem, scalability and performance, pricing model, and community support and updates, catering to different use cases and preferences in the big data analytics space.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Google Cloud Data Fusion
- Lower total cost of pipeline ownership1