PySpark Concepts and Key Features
What Is PySpark?
PySpark is the Python API for Apache Spark, an open source distributed computing framework and set of libraries for real-time large-scale data processing. If you are already familiar with Python and libraries such as Pandas, PySpark is a good language for learning how to build more scalable analytics and pipelines.
Apache Spark is basically a compute engine that works with huge datasets by processing them in parallel and batch systems. Spark is written in Scala, and PySpark was released to support collaboration between Spark and Python. In addition to providing APIs for Spark, PySpark uses the Py4J library to support interfaces with RDDs (Resilient Distributed Datasets).
The main data type used in PySpark is the Spark DataFrame. This object can be thought of as a table distributed across a cluster, and it has functionality similar to data frames in R and Pandas. To perform distributed computation with PySpark, you must run operations on Spark DataFrames rather than other Python data types.
One of the main differences between Pandas and Spark DataFrames is eager execution versus lazy execution. In PySpark, operations are delayed until results are actually requested in the pipeline. For example, you can specify a job that loads a dataset from Amazon S3 and applies multiple transformations to a DataFrame, but those operations are not applied immediately. Instead, the transformation graph is recorded, and when the data is actually needed, such as when writing results back to S3, the transformations are applied as a single pipeline job. This approach is used to avoid bringing the entire DataFrame into memory and to enable more effective processing across a cluster of systems. With Pandas DataFrames, everything is brought into memory and all Pandas operations are applied immediately.
PySpark Features and Libraries
Py4J is a widely used library integrated into PySpark that enables Python to dynamically interface with JVM (Java Virtual Machine) objects. PySpark provides many libraries for writing efficient programs. It is also compatible with various external libraries, including the following.
PySparkSQL
PySparkSQL is a PySpark library that applies SQL-like analysis to large volumes of structured or semi-structured data. SQL queries can also be used with PySparkSQL.
MLlib
MLlib is the wrapper machine learning (ML) library for PySpark and Spark. MLlib supports many machine learning algorithms for classification, regression, clustering, collaborative filtering, dimensionality reduction, and basic optimization primitives.
GraphFrames
GraphFrames is a graph processing library that provides a set of APIs for efficiently performing graph analysis using PySpark Core and PySparkSQL. It is optimized for fast distributed computing.
Conclusion
For data engineers who know Python but not Scala, PySpark is much easier to use than pure Spark, but it also has drawbacks. PySpark errors show both Java stack trace errors and references to Python code, so debugging PySpark applications can be very difficult.
Spark includes more processing overhead and more complex setup than other data processing options. Ray and Dask have emerged recently. Because Dask is a pure Python framework, most data engineers can use Dask immediately.