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Apache Spark vs Hue: What are the differences?
Apache Spark vs Hue
Introduction:
Apache Spark and Hue are both popular tools used in big data processing tasks. However, they have significant differences in terms of their functionality and purpose. Below are the key differences between Apache Spark and Hue:
Data Processing Paradigm: Apache Spark is a distributed computing system that focuses on data processing tasks, such as data manipulation, analytics, and machine learning. It provides a programming interface that allows users to write complex data processing workflows. In contrast, Hue is a web-based interface that simplifies the use and management of Apache Hadoop and related big data technologies. It provides a graphical user interface for various Hadoop ecosystem applications, including Spark, Hive, and Impala.
Ease of Use: Despite its powerful capabilities, Apache Spark requires users to have programming knowledge and skills to write Spark applications. It provides APIs in various programming languages, such as Scala, Java, and Python. On the other hand, Hue offers a user-friendly web-based interface that allows users to perform various big data tasks without writing code. It provides a point-and-click interface for data exploration, query execution, and job scheduling.
Scope of Tasks: Apache Spark is designed for processing large-scale datasets and performing complex analytics tasks. It can handle a wide range of data processing tasks, including batch processing, real-time streaming, and iterative algorithms. In contrast, Hue is primarily focused on providing an easy-to-use interface for querying and analyzing data stored in Hadoop. It allows users to write SQL queries, create visualizations, and manage workflows within the Hadoop ecosystem.
Integration with Hadoop Ecosystem: Apache Spark is a part of the Hadoop ecosystem and can seamlessly integrate with other Hadoop components, such as HDFS, YARN, and Hive. It can leverage the distributed storage and processing capabilities provided by Hadoop. Hue, on the other hand, serves as a comprehensive web-based interface for managing and accessing various Hadoop ecosystem components. It provides integration with popular technologies like Spark, Hive, Impala, and HBase.
Cluster Management: Apache Spark includes built-in cluster management capabilities through its standalone mode, YARN, or Apache Mesos. It allows users to easily scale their Spark applications to run on a cluster of machines. Hue, on the other hand, focuses on providing a centralized interface for managing and monitoring Hadoop clusters. It allows users to view and manage cluster resources, monitor job progress, and configure cluster settings.
Use Case Scenarios: Apache Spark is commonly used in scenarios where there is a need for large-scale data processing, advanced analytics, and machine learning tasks. It is suitable for industries such as finance, healthcare, and e-commerce, which deal with vast amounts of data. On the other hand, Hue is often used in scenarios where the focus is on data exploration, ad hoc querying, and data visualization. It is popular in data analysis teams and organizations that require user-friendly tools for interacting with Hadoop.
In summary, Apache Spark is a distributed computing system that focuses on large-scale data processing, analytics, and machine learning, while Hue provides a user-friendly interface for managing and accessing various Hadoop ecosystem components, including Spark.
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 Hue
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 Hue
Cons of Apache Spark
- Speed4
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What is Hue?
What is Apache Spark?
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What companies use Apache Spark?
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