Background: Databricks and Apache Spark

Apache Spark, created in 2009 at U.C. Berkeley, is an open-source distributed processing framework for big data analytics and machine learning. It is well-known for its capabilities to process big data volumes in a fault-tolerant, scalable and flexible fashion and achieves this by distributing data processing tasks across multiple computers. These features make Apache Spark the number one distributed computing system for data analytics and processing used by data science groups in companies, universities, research laboratories and data engineers worldwide. Databricks, founded in 2013 by the original creators of Apache Spark, is a cloud-based platform designed to provide an enhanced environment for big data analytics and machine learning. While Apache Spark itself is an open-source distributed processing engine, Databricks offers a unified platform that simplifies the use of Spark and extends its capabilities with additional tools and services tailored for large-scale data processing.

Streamlining Big Data Analytics

Although Databricks and Apache Spark are linked, they are inherently different products, and each serves different needs within the big data and analytics space. Spark is a cluster computing framework that provides speed, ease of use and breadth of use benefits with fast in-memory caching and other modern tools that can handle complex analytical queries within:

• Data integration and Extract, Transform, Load (ETL) tasks
• Interactive analytics
• Machine learning and advanced analytics
• Real-time data processing

It should also be noted that deploying and managing a Spark application requires manual setup and management of clusters. Users must handle infrastructure, scaling, and configuration.

On the other hand, Databricks is a company and provides a commercial product built on top of Spark, which adds additional service features. At the heart of Databricks is an enhanced version of Spark, called the “Databricks Runtime”, which delivers superior performance compared to a standard Spark cluster. The platform also offers a comprehensive set of data management and developer tools, built-in visualization features, and several convenience enhancements for users. In contrast to Apache Spark, it provides a fully managed service, abstracting away the complexities of infrastructure management. It is a cloud-based service. Thus, Databricks can be deployed across multiple cloud providers (Azure, AWS or Google Cloud), and its interface allows for quick and easy cluster creation and scaling.

Understanding Common Bottlenecks

Apache Spark, the engine behind Databricks, supports large-scale distributed data processing with built-in parallelism and fault tolerance. However, as workflows and data volumes scale up, the project structure around multiple components can introduce performance bottlenecks. These are often attributed to various aspects of Spark’s architecture and the way it interacts with the underlying infrastructure in Databricks.

Spark Architecture Basic Overview

• Driver program: The driver program is the central controller that coordinates the execution of Spark jobs. This program calls the main program of an application and is the code written by the user. This is where the SparkContext that provides access to the functionalities within Spark is created. Together with the cluster manager, the driver program converts user-defined transformations and actions into tasks and schedules them for execution on the worker nodes.
• Cluster manager: This component manages resources across the Spark cluster and assigns the tasks to the executors. Databricks typically uses its own managed clusters, but other Spark clusters can be managed using Hadoop YARN, Apache Mesos, or Kubernetes.
• Executors: These are the worker processes that run on cluster nodes and execute the tasks assigned to them. Each executor is responsible for managing its own memory and disk space to process the data. Typically, an executor’s lifetime is the same as that of the Spark application and the executor is available to accept and process tasks while the spark application is running. In case cluster autoscaling features are used in Databricks, adding and removing worker nodes works dynamically and an executor could be terminated or started on demand. This autoscaling process is handled and coordinated via the Databricks Runtime engine.
• Tasks: A task is the smallest unit of work in Spark, which is executed by executors. Tasks are derived from Spark’s transformations on Resilient Distributed Datasets (RDDs) or DataFrames.
• Stages: A job is broken down into stages, each representing a group of tasks that can be executed in parallel. The boundaries of stages are determined by shuffling (e.g., between operations like joins or groupBys).
• RDDs/DataFrames: RDDs are immutable, distributed collections of objects. Once an RDD is created in a SparkContext, it can be allocated across the different worker nodes and features local caching. DataFrames are built on RDDs but offer optimizations and abstractions for tabular data. They are the primary API for data processing in Spark and demonstrate better performance and scalability than RDDs.

Let’s look at Spark’s performace features next.