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Apache Spark vs PySpark: What are the differences?
Apache Spark and PySpark are two popular choices for big data processing and analytics. While Apache Spark is a powerful open-source distributed computing system, PySpark is the Python API for Apache Spark. Here are the key differences between the two:
Language: The most significant difference between Apache Spark and PySpark is the programming language. Apache Spark is primarily written in Scala, while PySpark is the Python API for Spark, allowing developers to use Python for Spark applications.
Development Environment: Apache Spark provides its own development environment, which includes a Scala-based interactive shell and a built-in web-based UI. On the other hand, PySpark leverages the existing Python development environment, making it easier for Python developers to work with Spark.
Integration with Python Libraries: PySpark seamlessly integrates with popular Python libraries and frameworks such as NumPy and pandas, allowing data scientists to leverage their existing Python ecosystem for data analysis and machine learning tasks. Apache Spark, being primarily built for Scala, may require additional effort to integrate with Python libraries.
Data Processing: While both Apache Spark and PySpark provide efficient data processing capabilities, PySpark may have some performance overhead compared to Spark's native Scala API. However, this overhead is often minimal and does not significantly impact the overall performance.
Community and Documentation: Apache Spark has a larger and more mature community compared to PySpark. Consequently, Apache Spark has more extensive documentation, online resources, and community support. PySpark, being a Python API, also benefits from the broader Python community.
Implementation Flexibility: Apache Spark provides more implementation flexibility as it supports multiple programming languages such as Scala, Java, Python, and R. PySpark is limited to Python, which may not be suitable for projects that require integration with languages other than Python.
In summary, Apache Spark and PySpark differ primarily in terms of programming language, development environment, integration with Python libraries, and community support. However, both frameworks offer powerful big data processing capabilities, and the choice between them depends on the specific requirements and preferences of the development team.
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 PySpark
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 PySpark
Cons of Apache Spark
- Speed4
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What is PySpark?
What is Apache Spark?
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