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Airflow vs Apache Flink: What are the differences?
Scalability: One key difference between Airflow and Apache Flink is their scalability. Airflow primarily focuses on task scheduling and orchestration, while Apache Flink is designed to handle large-scale data processing with real-time streaming capabilities. Apache Flink is built to scale horizontally and vertically, making it suitable for handling massive amounts of data and supporting high-throughput workloads.
Data Processing Model: Another significant difference is the data processing model used by Airflow and Apache Flink. Airflow uses a batch processing model, where tasks are executed at scheduled intervals. On the other hand, Apache Flink follows a stream processing model, meaning it can process data in real-time as it arrives, enabling near-instantaneous analysis and response to streaming data.
Fault Tolerance: When it comes to fault tolerance, Apache Flink provides strong guarantees for exactly-once processing semantics. It ensures data integrity by transparently handling failures and providing mechanisms to recover from failures, ensuring each event is processed exactly once. In contrast, Airflow focuses on fault recovery but does not offer the same level of support for exactly-once processing semantics.
State Management: Apache Flink includes a built-in state management feature that allows for storing and managing both key-value and stream state. This makes it possible to maintain the application's state across failures, ensuring continuity even in the event of unexpected incidents. Airflow, on the other hand, does not provide built-in state management capabilities, as it primarily focuses on task scheduling and does not require state persistence.
Use Cases: While Airflow is well-suited for workflow management and task scheduling, Apache Flink is often preferred for data-intensive and real-time streaming applications. Apache Flink is frequently used in scenarios where data processing needs to be done in near real-time, such as fraud detection, real-time analytics, and continuous data processing. Airflow, on the other hand, is commonly used for ETL (Extract, Transform, Load) workflows and data pipeline orchestration.
Community Size and Maturity: Airflow has been around for a longer time and has a larger community compared to Apache Flink. Airflow has an extensive ecosystem, including numerous plugins and integrations, and a mature community that actively contributes to its development. However, Apache Flink has gained significant traction in recent years and has a rapidly growing community that continues to enhance its capabilities.
In Summary, Airflow primarily focuses on task scheduling and workflow management, while Apache Flink is a powerful data processing system with real-time streaming capabilities. Apache Flink offers scalability, a stream processing model, strong fault tolerance, built-in state management, and is often used for real-time data-intensive applications. Airflow, on the other hand, is well-suited for workflow management, ETL, and data pipeline orchestration.
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"
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
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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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Apache Flink
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