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Apache Flink vs Apache Storm: What are the differences?
Apache Flink and Apache Storm are both popular distributed stream processing systems used for real-time analytics. While they have some similarities, there are several key differences that distinguish them from each other.
Processing Model: Apache Flink uses a dataflow-based processing model, where the computation is represented as a directed acyclic graph (DAG) of operators. This allows for complex processing pipelines and supports both batch and stream processing. On the other hand, Apache Storm follows a micro-batching model, where data is processed in small batches and the processing logic is expressed as a series of spouts and bolts. This makes it more suitable for high-throughput stream processing.
Fault-tolerance: Apache Flink provides exactly-once processing semantics out of the box, ensuring that every event is processed exactly once, even in the presence of failures. It achieves this by using checkpoints to save the state of the computation and guarantees consistency. Apache Storm, on the other hand, provides at-least-once processing semantics by default and requires additional configuration and coordination to achieve exactly-once processing. This makes Flink better suited for applications where data accuracy is critical.
Latency: Apache Storm has lower latency compared to Apache Flink. Storm's micro-batching model can achieve sub-millisecond latencies, making it suitable for low-latency use cases such as real-time monitoring and alerting. Flink, on the other hand, has slightly higher latencies due to its dataflow model and the need for buffering and coordination between operators. However, Flink's latency is still in the order of milliseconds, making it suitable for most real-time applications.
Ease of Use: Apache Storm has a simpler programming model compared to Apache Flink. The spout-bolt paradigm in Storm makes it easier to understand and write stream processing code, especially for developers who are new to distributed stream processing. Flink, on the other hand, provides a more expressive API and has a steeper learning curve. It offers advanced features like event time processing and stateful stream processing, but these features require additional understanding and setup.
Memory Management: Apache Flink has a more efficient memory management system compared to Apache Storm. Flink manages memory explicitly and provides fine-grained control over memory usage, allowing users to optimize their applications for better performance. Storm, on the other hand, relies on the underlying JVM's garbage collection for memory management, which can lead to higher memory usage and overhead.
Data Processing Scale: Apache Flink is designed for both small-scale and large-scale data processing. It provides efficient and scalable fault-tolerant state management and can handle massive streams of data. On the other hand, Apache Storm is better suited for processing large volumes of data, especially in scenarios where low-latency processing is required.
In Summary, Apache Flink and Apache Storm differ in their processing models, fault-tolerance mechanisms, latency, ease of use, memory management, and scalability. Flink offers a more expressive programming model, exactly-once processing semantics, and efficient memory management, while Storm provides lower latency, simplicity, and high throughput for large-scale data processing.
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 Storm
- Flexible10
- Easy setup6
- Event Processing4
- Clojure3
- Real Time2
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
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