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Airflow vs Apache Spark: What are the differences?
Apache Airflow and Apache Spark are both powerful tools used in the data engineering and data analysis domains. While they have some similarities, there are key differences between the two.
Distributed Computing Framework vs Workflow Management: Apache Spark is primarily a fast and general-purpose distributed computing system that provides in-memory processing capabilities. It is designed for big data processing and analytics, enabling processing of large datasets across multiple machines. On the other hand, Apache Airflow is a workflow management platform that allows users to coordinate and schedule tasks in a declarative manner, making it easier to manage complex data pipelines.
Data Processing vs Workflow Orchestration: Apache Spark focuses on data processing and provides a powerful processing engine with support for various data manipulation operations such as transformations, aggregations, and machine learning algorithms. It excels in parallel and distributed data processing using its resilient distributed dataset (RDD) and DataFrame APIs. In contrast, Apache Airflow focuses on workflow orchestration and provides a way to define and manage complex workflows using Directed Acyclic Graphs (DAGs). It allows for task dependency management, scheduling, and monitoring.
Real-time vs Batch Processing: Apache Spark offers both real-time and batch processing capabilities, making it suitable for a variety of use cases. It provides options for stream processing with its Structured Streaming API, allowing for real-time data analysis and processing. Apache Airflow, on the other hand, is more suited for batch processing as it focuses on orchestrating and scheduling tasks at a defined time or interval.
Language Support: Apache Spark has support for multiple programming languages, including Scala, Java, Python, and R. This allows users to develop applications and perform data analysis using their preferred language. In contrast, Apache Airflow is predominantly Python-based, making it a popular choice for Python developers and data engineers.
Built-in Libraries and Ecosystem: Apache Spark comes with a rich ecosystem of libraries and integrations that enhance its capabilities. It provides built-in support for various data formats, machine learning algorithms, graph processing, and more. Apache Airflow, while it has a smaller ecosystem compared to Spark, offers a wide range of operators and integrations for executing different types of tasks and interacting with various systems.
Data Storage and Execution Model: Apache Spark relies on distributed file systems, such as Hadoop Distributed File System (HDFS) or cloud storage systems like Amazon S3 or Azure Blob Storage, to store and process data. It utilizes a distributed computing model where data is partitioned and processed in parallel across a cluster of machines. Apache Airflow does not provide native data storage capabilities and relies on external systems such as databases or cloud storage to store and retrieve data.
In summary, Apache Spark is primarily focused on distributed data processing and provides fast, in-memory data analytics capabilities. It supports real-time and batch processing and has extensive language support and a rich ecosystem of libraries. Apache Airflow, on the other hand, is a workflow management platform that allows for task scheduling, monitoring, and dependency management. It is more suited for orchestrating complex data pipelines and batch processing workflows.
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 Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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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 Spark
- Speed4
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