3.1 Spark Architecture and RDDs
3 min read•july 23, 2024
Spark's architecture powers distributed data processing with a , , , and . This design enables efficient parallel computation across clusters, making it ideal for big data analytics.
At the heart of Spark are Resilient Distributed Datasets (RDDs), and fault-tolerant data structures. RDDs support in-memory processing, , and , allowing for faster and more flexible data manipulation compared to traditional disk-based systems.
Spark Architecture
Components of Spark architecture
Top images from around the web for Components of Spark architecture
Apache Spark View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
spark学习-42-Spark的driver理解和executor理解 - 灰信网（软件开发博客聚合） View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
1 of 3
Top images from around the web for Components of Spark architecture
Apache Spark View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
spark学习-42-Spark的driver理解和executor理解 - 灰信网（软件开发博客聚合） View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
Apache Spark View original
Is this image relevant?
1 of 3
- Driver Program
- Coordinates execution of Spark application
- Maintains information about application
- Analyzes, distributes, schedules work across executors
- Cluster Manager
- Manages resources across cluster
- Allocates resources to each Spark application
- Supports different cluster managers (Standalone, YARN, )
- Worker Nodes
- Run Spark executors
- Execute tasks assigned by driver program
- Can be physical machines or virtual machines
- Executors
- Processes running on worker nodes
- Perform actual computation and data processing
- Store computation results in memory, persist data to disk, return results to driver
- Communicate with driver program to report status and results
Resilient Distributed Datasets (RDDs)
Characteristics of RDDs
- Immutable distributed collection of objects
- Partitioned across nodes of cluster enabling parallel processing
- Created through deterministic operations on stable storage or other RDDs
- Once created, cannot be modified, ensuring data consistency
- Fault-tolerant
- Automatically rebuilt on failure using lineage information
- Tracks transformations used to build dataset
- Enables quick recovery from failures without expensive replication
-
- Allows for faster processing compared to disk-based systems (Hadoop)
- Keeps data in memory across operations and nodes
- Lazy evaluation
- Transformations not immediately computed, deferred until action called
- Enables optimization by combining transformations before execution
- Cacheable
- Persist intermediate results in memory or disk for future reuse
- Improves performance by reducing redundant computations
- Especially beneficial for iterative algorithms (machine learning)
Operations on RDDs
- Creating RDDs
distributes local collection to form RDDparallelize()
reads data from external storage (HDFS) to create RDDtextFile()
- Transformations
applies function to each RDD element, returns new RDDmap()- Example:
multiplies each element by 2rdd.map(x => x * 2)
- Example:
selects elements from RDD based on predicate functionfilter()- Example:
keeps elements greater than 10rdd.filter(x => x > 10)
- Example:
generates multiple output elements for each input elementflatMap()- Useful for splitting lines of text into words
combines multiple RDDs into single RDDunion()- Concatenates elements from input RDDs
- Actions
retrieves all RDD elements to driver program[collect](https://www.fiveableKeyTerm:collect)()- Returns results as array, use with caution on large datasets
returns number of elements in RDD[count](https://www.fiveableKeyTerm:count)()
aggregates RDD elements using specified function[reduce](https://www.fiveableKeyTerm:Reduce)()- Example:
sums all elementsrdd.reduce((x, y) => x + y)
- Example:
writes RDD elements to external storage (HDFS)[saveAsTextFile](https://www.fiveableKeyTerm:saveAsTextFile)()
Spark vs Hadoop evaluation models
- Lazy Evaluation (Spark)
- Transformations not immediately computed, deferred until action called
- Optimizes execution plan by combining transformations
- Minimizes data movement and improves performance
- Avoids unnecessary intermediate computations
- Eager Evaluation (Hadoop)
- Computation performed immediately after each transformation
- Intermediate results materialized and stored to disk
- Increases data movement and disk I/O
- Less efficient compared to Spark's lazy evaluation model
- Suitable for linear data processing pipelines
Key Terms to Review (22)
Caching: Caching is a performance optimization technique that stores copies of frequently accessed data in a temporary storage layer, allowing for quicker retrieval when needed. By minimizing the need to fetch data from slower storage systems or perform redundant calculations, caching significantly enhances the efficiency of data processing and retrieval operations. It plays a crucial role in handling large volumes of data and improving overall system performance across various technologies.
Cluster Manager: A cluster manager is a system that oversees and manages a group of interconnected computers, or nodes, working together to perform tasks in parallel. In the context of Spark, the cluster manager allocates resources across the cluster, facilitates task scheduling, and helps maintain the overall health of the computing environment. This ensures efficient resource usage and enables Spark to execute computations across multiple nodes seamlessly.
Collect: In the context of data processing and analytics, 'collect' refers to the operation that retrieves and gathers data from an RDD (Resilient Distributed Dataset) into a local collection on the driver node. This action is essential because it allows users to bring distributed data back into a single structure for easier manipulation and analysis, making it a fundamental part of working with Spark's architecture.
Count: In the context of big data processing with Spark, 'count' refers to a transformation operation that computes the total number of elements in a distributed dataset, typically represented as a Resilient Distributed Dataset (RDD). This operation is crucial for analyzing data as it allows users to quickly ascertain the size of datasets, which can inform decisions about data processing strategies and resource allocation. The 'count' function is often one of the first steps in data exploration, providing essential insight into the dataset’s structure.
Driver Program: A driver program is the main application in a Spark environment that coordinates the execution of tasks across a cluster. It acts as the central controller, managing resources, distributing work to worker nodes, and gathering results. The driver program also handles user interactions and defines how data is processed, making it an essential part of Spark’s architecture and its ability to handle Resilient Distributed Datasets (RDDs).
Executors: Executors are components in Apache Spark responsible for executing tasks across the cluster. They are essential in the Spark architecture, as they manage the execution of jobs and are responsible for processing data stored in resilient distributed datasets (RDDs). Executors run on worker nodes and handle the data processing workload by running computations, returning results, and storing data in memory or on disk.
Fault Tolerance: Fault tolerance is the ability of a system to continue functioning correctly even when one or more of its components fail. This characteristic is crucial for maintaining data integrity and availability, especially in distributed computing environments where failures can occur at any time due to hardware issues, network problems, or software bugs.
Filter Transformation: Filter transformation is an operation in data processing that allows users to extract a subset of data by applying specific conditions or criteria. This technique is crucial for managing large datasets, as it helps to focus on relevant information while discarding unnecessary data. In the context of Spark, filter transformation is applied to resilient distributed datasets (RDDs) and plays a significant role in data processing pipelines, enabling efficient data manipulation and real-time stream filtering.
FlatMap transformation: The flatMap transformation is a fundamental operation in data processing frameworks like Spark that enables the conversion of each element in a dataset into a collection of elements, effectively flattening the structure of the data. This transformation is particularly useful for handling nested data or when a single input element can correspond to multiple output elements, resulting in a more streamlined dataset that can be easily processed or analyzed further.
Immutable: Immutable means that once an object is created, it cannot be changed or modified in any way. This concept is important in data processing because it helps ensure consistency and reliability in the way data is handled, especially in distributed computing environments. By making data immutable, systems can avoid unexpected side effects that can arise from changes made to shared data, promoting safer operations and simplifying debugging.
In-memory computation: In-memory computation refers to the processing of data in the main memory (RAM) of a computing system rather than on traditional disk storage. This method significantly speeds up data processing and analysis by reducing the time taken to read and write data, making it a key feature in frameworks that handle big data, such as Spark, which relies heavily on this technique for its operations on Resilient Distributed Datasets (RDDs). In-memory computation enables faster data access and iterative algorithms, allowing for real-time analytics and improved performance.
Lazy Evaluation: Lazy evaluation is a programming strategy that postpones the evaluation of an expression until its value is actually needed. This approach helps optimize performance by avoiding unnecessary calculations, particularly in large datasets. In the context of data processing frameworks, lazy evaluation allows for more efficient use of resources and enhances execution speed, as operations can be optimized before they are executed.
Map transformation: Map transformation is an operation that applies a specified function to each element of a dataset, producing a new dataset with the transformed values. This process is crucial for data processing and analysis, allowing users to reshape data, perform calculations, and prepare it for further analysis. It plays a significant role in efficient data handling within distributed computing frameworks, enabling operations like filtering and mapping across large datasets seamlessly.
Mesos: Mesos is an open-source cluster manager designed to facilitate the management and scheduling of resources across a distributed system. It plays a critical role in optimizing resource allocation and utilization for applications running on large-scale data processing frameworks like Apache Spark, allowing multiple frameworks to share the same cluster resources efficiently.
Partitioning: Partitioning refers to the process of dividing data into smaller, manageable subsets, or partitions, to enhance performance and efficiency in distributed computing systems. This technique is crucial for optimizing resource usage, improving parallel processing, and ensuring fault tolerance within data processing frameworks.
Reduce: In the context of data processing, 'reduce' refers to a function that aggregates or combines data elements to produce a summary result. This operation is often used to process large datasets, enabling efficient calculations and transformations by minimizing the amount of data that needs to be handled, which is essential for optimizing performance in distributed computing environments like Spark.
Resilient Distributed Dataset (RDD): A Resilient Distributed Dataset (RDD) is a fundamental data structure in Apache Spark that represents a collection of objects partitioned across a cluster, allowing for distributed processing. RDDs are fault-tolerant, meaning they can recover from failures, and they support parallel operations, enabling efficient computation over large datasets. This design makes RDDs an essential part of Spark’s architecture, optimizing both memory usage and processing speed while providing a high-level abstraction for handling distributed data.
SaveAsTextFile: The `saveAsTextFile` method is used in Apache Spark to write the contents of a Resilient Distributed Dataset (RDD) to a text file in a specified directory. This method is crucial for persisting data processed within Spark, allowing users to store the output of their transformations and actions for further analysis or use in other applications. It simplifies the process of exporting RDDs and is essential for managing data flow within Spark's distributed computing environment.
Spark SQL: Spark SQL is a component of Apache Spark that enables users to run SQL queries on large datasets. It provides a programming interface for working with structured and semi-structured data, and allows for integration with various data sources, making it easier to analyze big data using familiar SQL syntax. This powerful feature enhances the capabilities of Spark's architecture and its underlying Resilient Distributed Datasets (RDDs), while also allowing seamless transitions between DataFrames and traditional SQL queries.
Spark Streaming: Spark Streaming is an extension of Apache Spark that enables processing of real-time data streams. It allows users to build scalable and fault-tolerant streaming applications, leveraging Spark’s fast processing capabilities with the concept of micro-batching, where incoming data is processed in small batches rather than as individual events. This feature seamlessly integrates with Spark’s core architecture, enhancing its capabilities for handling both batch and streaming data, which is essential for creating interactive and responsive data applications.
Union Transformation: Union transformation is a method used in big data processing frameworks like Spark that allows the combination of multiple datasets into a single dataset. This operation merges two or more Resilient Distributed Datasets (RDDs) into one, helping to streamline data processing and analysis by ensuring that related data is grouped together. It's particularly useful for situations where data needs to be consolidated for further computations or transformations.
Worker nodes: Worker nodes are the computing machines in a distributed system responsible for executing tasks and processing data. They play a crucial role in frameworks like Apache Spark by carrying out the computations on the data stored in Resilient Distributed Datasets (RDDs), while also managing memory and resource allocation efficiently across the cluster. The collaboration of worker nodes allows Spark to achieve high scalability and performance for big data processing tasks.