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Apache Spark vs Scala: What are the differences?
Introduction:
Apache Spark and Scala are both popular technologies used in big data processing and analytics. While Apache Spark is a distributed computing framework, Scala is a programming language that runs on the Java Virtual Machine. Despite their differences, they can be used together to build efficient and scalable data processing applications.
Performance and Scalability: Apache Spark is known for its superior performance and scalability compared to Scala. In Spark, data processing tasks are divided into smaller chunks and processed in parallel on a cluster of machines. This distributed computing approach allows Spark to handle large datasets and perform computations faster. On the other hand, Scala is a general-purpose programming language that can run on a single machine, limiting its scalability for big data processing.
Data Processing Capabilities: Apache Spark provides a wide range of built-in libraries and APIs for various data processing tasks such as batch processing, real-time streaming, machine learning, and graph processing. These libraries and APIs make it easier for developers to perform complex data processing tasks without the need for additional tools or frameworks. In contrast, Scala provides a rich set of programming language features but lacks the specific data processing capabilities offered by Spark.
Ease of Use: Scala is a programming language that follows a functional programming paradigm, which can be challenging for developers who are more familiar with object-oriented programming. On the other hand, Spark provides a high-level API that abstracts the underlying complexities of distributed computing, making it easier for developers to write and manage big data applications.
Integration with other Technologies: Apache Spark integrates well with other big data technologies such as Hadoop, Hive, and HBase. This seamless integration allows Spark to leverage the existing infrastructure and data storage systems, making it a popular choice for big data processing. Scala, on the other hand, can be used with various libraries and frameworks, but it may require more effort to integrate with specific big data technologies.
Job Execution Model: Apache Spark follows a Resilient Distributed Dataset (RDD) model, where data is stored in memory and can be processed multiple times. This in-memory processing model enables Spark to achieve faster execution times compared to traditional disk-based processing. Scala, on the other hand, follows a traditional execution model where data is read from disk and processed sequentially, which can result in slower execution times for large datasets.
Community and Ecosystem: Apache Spark has a large and vibrant community with extensive documentation, tutorials, and support resources. The Spark ecosystem also includes various third-party libraries and tools that extend its functionality. This community-driven ecosystem makes it easier for developers to get help, find solutions, and leverage additional features. Scala also has a supportive community, but its ecosystem may not be as extensive as Spark's.
In summary, Apache Spark and Scala are both powerful technologies for big data processing, but they have distinct differences. Apache Spark excels in performance, scalability, and built-in data processing capabilities, while Scala offers a more general-purpose programming language with a rich set of features. Their integration with other technologies, job execution models, ease of use, and community support also differ.
I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.
I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.
Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.
I may be a little biased, but if you need some good introductions to Scala have a look at the free books from https://underscore.io/training/ - click through to each course and there is a free book.
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"
Finding the best server-side tool for building a personal information organizer that focuses on performance, simplicity, and scalability.
performance and scalability get a prototype going fast by keeping codebase simple find hosting that is affordable and scales well (Java/Scala-based ones might not be affordable)
I've picked Node.js here but honestly it's a toss up between that and Go around this. It really depends on your background and skillset around "get something going fast" for one of these languages. Based on not knowing that I've suggested Node because it can be easier to prototype quickly and built right is performant enough. The scaffolding provided around Node.js services (Koa, Restify, NestJS) means you can get up and running pretty easily. It's important to note that the tooling surrounding this is good also, such as tracing, metrics et al (important when you're building production ready services).
You'll get more scalability and perf from go, but balancing them out I would say that you'll get pretty far with a well built Node.JS service (our entire site with over 1.5k requests/m scales easily and holds it's own with 4 pods in production.
Without knowing the scale you are building for and the systems you are using around it it's hard to say for certain this is the right route.
We needed to incorporate Big Data Framework for data stream analysis, specifically Apache Spark / Apache Storm. The three options of languages were most suitable for the job - Python, Java, Scala.
The winner was Python for the top of the class, high-performance data analysis libraries (NumPy, Pandas) written in C, quick learning curve, quick prototyping allowance, and a great connection with other future tools for machine learning as Tensorflow.
The whole code was shorter & more readable which made it easier to develop and maintain.
Pros of Scala
- Static typing187
- Pattern-matching178
- Jvm177
- Scala is fun172
- Types138
- Concurrency95
- Actor library88
- Solve functional problems86
- Open source81
- Solve concurrency in a safer way80
- Functional44
- Fast24
- Generics23
- It makes me a better engineer18
- Syntactic sugar17
- Scalable13
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Case classes6
- Implicit parameters6
- Rapid and Safe Development using Functional Programming4
- JVM, OOP and Functional programming, and static typing4
- Object-oriented4
- Used by Twitter4
- Functional Proframming3
- Spark2
- Beautiful Code2
- Safety2
- Growing Community2
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Akka1
- Reactive Streams1
- Easy embedded DSLs1
- Mill build tool1
- Freedom to choose the right tools for a job0
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 Scala
- Slow compilation time11
- Multiple ropes and styles to hang your self7
- Too few developers available6
- Complicated subtyping4
- My coworkers using scala are racist against other stuff2
Cons of Apache Spark
- Speed4
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