Overview

Scala

Stacks11.1K

Followers7.8K

Votes1.5K

GitHub Stars14.4K

Forks3.1K

Apache Spark

Stacks3.0K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

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.

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Advice on Scala, Apache Spark

Govind

Aug 5, 2020

Needs adviceon

Apache Spark

Scala

Java

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.

2.95M views2.95M

Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

576k views576k

Comments

Jakub

Jan 2, 2020

Decided

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.

290k views290k

Comments

Detailed Comparison

Scala	Apache Spark
Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.	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.
-	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 14.4K	GitHub Stars 42.2K
GitHub Forks 3.1K	GitHub Forks 28.9K
Stacks 11.1K	Stacks 3.0K
Followers 7.8K	Followers 3.5K
Votes 1.5K	Votes 140
Pros & Cons
Pros 188 Static typing 178 Pattern-matching 175 Jvm 172 Scala is fun 138 Types Cons 11 Slow compilation time 7 Multiple ropes and styles to hang your self 6 Too few developers available 4 Complicated subtyping 2 My coworkers using scala are racist against other stuff	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed
Integrations
Java	No integrations available

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