Apache Spark vs PySpark

Overview

Apache Spark

Stacks3.0K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

PySpark

Stacks269

Followers295

Votes0

Apache Spark vs PySpark: What are the differences?

Apache Spark and PySpark are two popular choices for big data processing and analytics. While Apache Spark is a powerful open-source distributed computing system, PySpark is the Python API for Apache Spark. Here are the key differences between the two:

Language: The most significant difference between Apache Spark and PySpark is the programming language. Apache Spark is primarily written in Scala, while PySpark is the Python API for Spark, allowing developers to use Python for Spark applications.
Development Environment: Apache Spark provides its own development environment, which includes a Scala-based interactive shell and a built-in web-based UI. On the other hand, PySpark leverages the existing Python development environment, making it easier for Python developers to work with Spark.
Integration with Python Libraries: PySpark seamlessly integrates with popular Python libraries and frameworks such as NumPy and pandas, allowing data scientists to leverage their existing Python ecosystem for data analysis and machine learning tasks. Apache Spark, being primarily built for Scala, may require additional effort to integrate with Python libraries.
Data Processing: While both Apache Spark and PySpark provide efficient data processing capabilities, PySpark may have some performance overhead compared to Spark's native Scala API. However, this overhead is often minimal and does not significantly impact the overall performance.
Community and Documentation: Apache Spark has a larger and more mature community compared to PySpark. Consequently, Apache Spark has more extensive documentation, online resources, and community support. PySpark, being a Python API, also benefits from the broader Python community.
Implementation Flexibility: Apache Spark provides more implementation flexibility as it supports multiple programming languages such as Scala, Java, Python, and R. PySpark is limited to Python, which may not be suitable for projects that require integration with languages other than Python.

In summary, Apache Spark and PySpark differ primarily in terms of programming language, development environment, integration with Python libraries, and community support. However, both frameworks offer powerful big data processing capabilities, and the choice between them depends on the specific requirements and preferences of the development team.

Advice on Apache Spark, PySpark

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.

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Comments

Detailed Comparison

Apache Spark	PySpark
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.	It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.0K	Stacks 269
Followers 3.5K	Followers 295
Votes 140	Votes 0
Pros & Cons
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	No community feedback yet

What are some alternatives to Apache Spark, PySpark?

Presto

Distributed SQL Query Engine for Big Data

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 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.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Apache Spark vs PySpark: What are the differences?

Language: The most significant difference between Apache Spark and PySpark is the programming language. Apache Spark is primarily written in Scala, while PySpark is the Python API for Spark, allowing developers to use Python for Spark applications.
Development Environment: Apache Spark provides its own development environment, which includes a Scala-based interactive shell and a built-in web-based UI. On the other hand, PySpark leverages the existing Python development environment, making it easier for Python developers to work with Spark.
Integration with Python Libraries: PySpark seamlessly integrates with popular Python libraries and frameworks such as NumPy and pandas, allowing data scientists to leverage their existing Python ecosystem for data analysis and machine learning tasks. Apache Spark, being primarily built for Scala, may require additional effort to integrate with Python libraries.
Data Processing: While both Apache Spark and PySpark provide efficient data processing capabilities, PySpark may have some performance overhead compared to Spark's native Scala API. However, this overhead is often minimal and does not significantly impact the overall performance.
Community and Documentation: Apache Spark has a larger and more mature community compared to PySpark. Consequently, Apache Spark has more extensive documentation, online resources, and community support. PySpark, being a Python API, also benefits from the broader Python community.
Implementation Flexibility: Apache Spark provides more implementation flexibility as it supports multiple programming languages such as Scala, Java, Python, and R. PySpark is limited to Python, which may not be suitable for projects that require integration with languages other than Python.

Apache Spark vs PySpark

Overview

Apache Spark vs PySpark: What are the differences?

Advice on Apache Spark, PySpark

Detailed Comparison