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Apache Spark vs Laravel Spark: What are the differences?
Apache Spark and Laravel Spark are popular frameworks used in the field of big data processing and web development, respectively. Below are the key differences between Apache Spark and Laravel Spark:
Data Processing vs. Web Development: The main difference between Apache Spark and Laravel Spark lies in their primary use cases. Apache Spark is a distributed computing framework used primarily for processing large volumes of data in parallel, while Laravel Spark is a web application framework specifically designed for building web applications and APIs.
Technology Stack: Apache Spark is built on top of the Spark Core engine, which provides in-memory computing capabilities and supports various data processing tasks, whereas Laravel Spark is built on top of the Laravel PHP framework, which offers a comprehensive set of tools and libraries for web development.
Scalability: Apache Spark is known for its ability to scale horizontally across multiple nodes in a cluster to process large datasets efficiently, whereas Laravel Spark is more focused on providing a streamlined development experience for building web applications without the need for complex scalability requirements.
Language Support: Apache Spark primarily supports Scala, Java, and Python programming languages for writing data processing tasks, while Laravel Spark is specifically tailored for PHP developers who prefer using the Laravel framework for building web applications.
Community and Ecosystem: Apache Spark has a large and active open-source community with a wide range of third-party integrations and libraries available for data processing tasks, whereas Laravel Spark has a dedicated community of Laravel developers and a set of official packages and extensions for enhancing web application development within the Laravel ecosystem.
Complexity and Learning Curve: Apache Spark is known to have a steeper learning curve due to its distributed computing architecture and advanced data processing capabilities, whereas Laravel Spark offers a more beginner-friendly experience with its well-documented API and user-friendly features for web development tasks.
In Summary, Apache Spark is designed for data processing tasks in a distributed environment, while Laravel Spark is focused on simplifying web application development within the Laravel PHP framework.
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 Laravel Spark
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 Laravel Spark
Cons of Apache Spark
- Speed4
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What is Laravel Spark?
What is Apache Spark?
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