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Apache Flink vs Hue: What are the differences?
Introduction: Apache Flink and Hue are two popular tools in the big data processing and analytics landscape. While both tools offer capabilities for data processing and management, there are key differences between them that cater to different use cases and requirements.
1. Programming Paradigm: One key difference between Apache Flink and Hue is the programming paradigm they support. Apache Flink is designed for stream processing and supports complex event processing with support for high-throughput and low-latency data processing. On the other hand, Hue is more focused on providing a user-friendly interface for managing Hadoop clusters and executing queries in Hive, Impala, and other Hadoop ecosystem tools.
2. Processing Model: Apache Flink employs a dataflow processing model, which enables efficient parallel processing of data streams with fault tolerance and high throughput. Conversely, Hue facilitates batch processing primarily and provides an interface for running queries and managing large datasets on Hadoop clusters.
3. Real-time Processing: Apache Flink excels in real-time processing scenarios by offering low-latency data processing capabilities and support for event time processing. In contrast, Hue is more suited for batch processing tasks where the focus is on executing queries or jobs on Hadoop clusters.
4. Data Visualization: While both Apache Flink and Hue offer some level of data visualization, Hue provides a more interactive and user-friendly interface for visualizing data through charts and graphs. Apache Flink, on the other hand, is more focused on data processing and analysis rather than visualization capabilities.
5. Job Monitoring and Management: Apache Flink provides robust job monitoring and management features for tracking the progress of data processing tasks, managing checkpoints, and handling failures effectively. In comparison, Hue offers a centralized platform for managing Hadoop clusters, executing queries, and accessing various data sources through a single interface.
6. Integration with Ecosystem: Apache Flink integrates well with various data sources and sinks, supporting connectors for popular systems like Kafka, HDFS, and Elasticsearch. Meanwhile, Hue is tightly integrated with the Hadoop ecosystem, providing seamless access to HDFS, Hive, Impala, and other components within the Hadoop ecosystem.
Summary: In summary, Apache Flink excels in real-time stream processing with a dataflow model and low-latency capabilities, while Hue focuses on providing a user-friendly interface for managing Hadoop clusters and executing queries primarily in batch processing scenarios.
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 Hue
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