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Apache Spark vs Superhuman: What are the differences?
Introduction
Apache Spark and Superhuman are two different technologies used in different domains. Apache Spark is an open-source distributed computing system used for big data processing and analytics, while Superhuman is an email client designed for enhanced productivity and workflow management. Here are the key differences between Apache Spark and Superhuman:
Purpose: Apache Spark is mainly used for distributed data processing and analytics tasks, such as data cleansing, data transformation, and machine learning. On the other hand, Superhuman is a specialized email client that focuses on providing a seamless email experience with features like lightning-fast search, advanced keyboard shortcuts, and email tracking.
Domain: Apache Spark is widely used in the big data industry for processing large-scale datasets and real-time streaming data. It caters to the needs of developers, data scientists, and analysts working on big data projects. Superhuman, on the other hand, is primarily used by individuals who deal with a high volume of emails, such as executives, salespeople, and entrepreneurs.
Architecture: Apache Spark follows a distributed computing model and utilizes a cluster of machines to process data in parallel. It provides fault-tolerance and scalability, making it suitable for handling large datasets. Superhuman, on the other hand, is a desktop application that focuses on optimizing the user interface and user experience for email management. It leverages local resources and network connectivity to deliver a smooth email workflow.
Features: Apache Spark offers a rich set of features for distributed data processing, including data streaming, SQL queries, machine learning libraries, and graph processing. It enables users to perform complex data operations and build sophisticated analytics pipelines. Superhuman, on the other hand, offers features like snooze, email scheduling, read receipts, and contact insights. It aims to enhance the productivity and efficiency of email management.
Community and Ecosystem: Apache Spark has a large and vibrant open-source community, providing support, updates, and contributions to the platform. It has a wide range of integrations with various big data tools and frameworks. Superhuman, on the other hand, is a proprietary software with a smaller community base. It offers limited integrations with other applications and focuses more on providing a curated email experience.
Deployment and Scalability: Apache Spark can be deployed on various platforms, including standalone clusters, Hadoop clusters, and cloud environments. It can scale horizontally by adding more nodes to the cluster to handle larger datasets and higher workloads. Superhuman, being a desktop application, relies on the resources of the local machine. It may not have the same level of scalability as Apache Spark in terms of data processing capabilities.
In summary, Apache Spark is a distributed computing platform for big data processing and analytics, while Superhuman is an optimized email client aiming to enhance productivity and workflow management. They differ in terms of purpose, domain, architecture, features, community support, and scalability capabilities.
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 Superhuman
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Cons of Apache Spark
- Speed4
Cons of Superhuman
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What is Apache Spark?
What is Superhuman?
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