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Apache Spark vs TensorFlow: What are the differences?
Introduction
In the realm of big data and machine learning, Apache Spark and TensorFlow are two popular tools that serve distinct purposes. Here, we highlight key differences between the two technologies.
Frameworks: Apache Spark is a distributed computing framework that is primarily known for processing large-scale data sets, while TensorFlow is an open-source machine learning library developed by Google for creating and training artificial intelligence models.
Use Cases: Apache Spark is commonly used for data processing, ETL (Extract, Transform, Load) workflows, and data analytics tasks, such as data transformations and aggregations. On the other hand, TensorFlow is favored for machine learning applications like building neural networks, deep learning models, and training complex algorithms.
Programming Languages: Apache Spark supports multiple programming languages such as Scala, Java, Python, and SQL, providing flexibility for developers to choose their preferred language. TensorFlow, however, is specifically designed for Python programming, with support for other languages through wrappers and APIs.
Execution Engine: Apache Spark utilizes in-memory processing and lazy evaluation to optimize performance, making it efficient for iterative machine learning algorithms and interactive data analysis. TensorFlow, on the other hand, focuses on computational graphs and uses GPUs for acceleration, enabling rapid execution of deep learning tasks.
Community Support and Ecosystem: Both Apache Spark and TensorFlow have vibrant communities backing them, but Apache Spark has a broader ecosystem with extensions like MLlib for machine learning and GraphX for graph processing. TensorFlow, being specialized in deep learning, offers extensive support for building and training neural networks.
Learning Curve: While Apache Spark is relatively easier to learn and implement for data processing tasks due to its SQL-like syntax and high-level APIs, TensorFlow has a steeper learning curve that requires understanding concepts like tensors, graphs, and neural network architectures for effective use in complex machine learning projects.
In Summary, Apache Spark is ideal for processing big data and executing data analytics tasks, whereas TensorFlow excels in building and training machine learning models, particularly focusing on deep learning applications.
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 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
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of Apache Spark
- Speed4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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