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Apache Spark vs Mule: What are the differences?
- Data Processing: Apache Spark is a distributed processing framework that allows for parallel processing of large datasets, while Mule is an integration platform that focuses on connecting different systems and integrating data from various sources.
- Technology Stack: Apache Spark is built using Scala, a programming language that runs on the Java Virtual Machine (JVM), and it provides high-level APIs for programming in Scala, Java, Python, and R. On the other hand, Mule is built using Java and provides a Java-based API for integration purposes.
- Parallelism: One of the key differences between Apache Spark and Mule is their approach to parallelism. Apache Spark utilizes a distributed computing model called Resilient Distributed Datasets (RDDs) to achieve parallel processing, while Mule leverages parallel flows and message processing to handle multiple tasks simultaneously.
- Data Transformation: Apache Spark provides a wide range of libraries and functions for data transformation and manipulation, such as Spark SQL, DataFrame API, and Spark Streaming. Mule, on the other hand, focuses more on data transformation using its Anypoint Data Mapper and DataWeave languages.
- Real-time Processing: While both Apache Spark and Mule can handle real-time data processing, Apache Spark is specifically designed for large-scale real-time analytics and streaming processing with its Spark Streaming and Structured Streaming capabilities. Mule, on the other hand, focuses more on real-time integration and event-driven architectures.
- Scalability: Apache Spark can easily scale horizontally by adding more nodes to the cluster, allowing it to handle large volumes of data and support high-concurrency workloads. Mule also offers scalability but focuses more on vertical scalability, allowing organizations to scale up their integration infrastructure by deploying more powerful hardware resources.
In Summary, Apache Spark is a distributed processing framework with a focus on data processing and analytics, leveraging parallelism and real-time capabilities, while Mule is an integration platform that emphasizes connecting systems, data transformation, and real-time integration.
Advice on Mule runtime engine and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 518.1K views
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.
Replies (2)
lvhuyen
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.
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 362.5K views
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"
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Learn MorePros of Mule runtime engine
Pros of Apache Spark
Pros of Mule runtime engine
- Open Source4
- Integration2
- Microservices2
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 Mule runtime engine
Cons of Apache Spark
Cons of Mule runtime engine
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Cons of Apache Spark
- Speed4
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What is Mule runtime engine?
Its mission is to connect the world’s applications, data and devices. It makes connecting anything easy with Anypoint Platform™, the only complete integration platform for SaaS, SOA and APIs. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use it to innovate faster and gain competitive advantage.
What is Apache Spark?
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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What are some alternatives to Mule runtime engine and Apache Spark?
Apache Camel
An open source Java framework that focuses on making integration easier and more accessible to developers.
Splunk
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Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Apache Hive
Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.