Big data is expected to reach 79 zettabytes in 2021, and 150 zettabytes in 2025. As a result, big data is constantly expanding, and businesses are using it to outperform their competitors, seize new opportunities, drive innovation, gain market insights, and much more than you might imagine. There are different forms of big data: structured, unstructured, and semi-strictured.
A competent framework is required by businesses to extract only useful information from raw data. Frameworks that not only remove unwanted information but also help them to make well-informed decisions as per their business needs are essential. That’s how Apache Spark comes into the picture.
Apache Spark is a large-scale data processing open-source unified analytics engine. Apache Spark is a programming interface for clusters that includes implicit data parallelism and fault tolerance. The Apache Spark codebase was originally developed at the University of California, Berkeley’s AMPLab and later donated to the Apache Software Foundation, which has since maintained it.
This article talks about Spark Fault Tolerance and how it is achieved in detail. In addition to that, it also describes Apache Spark and its key features.
Table Of Contents
- What is Apache Spark?
- Understanding Spark Fault Tolerance Aspects
What is Apache Spark?
Matei Zaharia was the original author of Spark, who designed this framework on 26 May 2014 and wrote Spark framework in Scala. Apache initiated the concept of Spark in 2009 as a study project while collaborating with students, co-workers, staff, and researchers concentrated on data-intensive computing fields.
Apache Spark began as a research project at UC Berkley’s AMPLab, a collaboration of students, researchers, and faculty focusing on data-intensive application domains, in 2009. Apache Spark’s goal was to create a new framework that was optimized for fast iterative processing like Machine Learning and interactive Data Analysis while retaining Hadoop MapReduce’s scalability and fault tolerance.
Apache Spark was open-sourced under a BSD license after the first paper, “Spark: Cluster Computing with Working Sets,” was published in June 2010. In June 2013, Apache Spark was accepted into the Apache Software Foundation’s (ASF) incubation program, and in February 2014, it was named an Apache Top-Level Project. Apache Spark can run standalone, on Apache Mesos, or on Apache Hadoop, which is the most common configuration.
Apache Spark is a distributed processing system for big data workloads that is open-source and free to use. For quick analytic queries against any size of data, it uses in-memory caching and optimized query execution. It supports code reuse across multiple workloads, including Batch Processing, Interactive Queries, Real-Time Analytics, Machine Learning, and Graph Processing, and it provides development APIs in Java, Scala, Python, and R. FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike are just a few examples of companies that use it. With 365,000 meetup members in 2017, Apache Spark is one of the most popular big data distributed processing frameworks.
Apache Spark is now one of the most popular projects in the Hadoop ecosystem, with many companies using it in conjunction with Hadoop to process large amounts of data. Apache Spark had 365,000 meetup members in 2017, a 5x increase in just two years. Since 2009, it has benefited from the contributions of over 1,000 developers from over 200 organizations.
At its most basic level, an Apache Spark application is made up of two parts: a driver that converts user code into multiple tasks that can be distributed across worker nodes, and executors that run on those nodes and carry out the tasks assigned to them. To mediate between the two, some sort of cluster manager is required.
Without having to worry about work distribution or fault tolerance, developers can write massively parallelized operators. However, MapReduce struggles with the sequential multi-step process required to run a job. MapReduce reads data from the cluster, runs operations on it, and writes the results back to HDFS at the end of each step. MapReduce jobs are slower due to the latency of disc I/O because each step necessitates a disc read and write.
By performing processing in memory, reducing the number of steps in a job, and reusing data across multiple parallel operations, Apache Spark was created to address the limitations of MapReduce. With Spark, data is read into memory in a single step, operations are performed, and the results are written back, resulting in significantly faster execution.
Apache Spark also reuses data by using an in-memory cache to greatly accelerate machine learning algorithms that call the same function on the same dataset multiple times. The creation of DataFrames, an abstraction over the Resilient Distributed Dataset (RDD), which is a collection of objects cached in memory and reused in multiple Apache Spark operations, allows for data reuse. Apache Spark is now many times faster than MapReduce, especially when performing machine learning and interactive analytics.
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Key Features of Apache Spark
Spark is regarded as a lightning-fast cluster computing platform in comparison to Hadoop because it provides faster and more widely used data processing methods, allowing users to program applications 100 times faster in memory and 10 times faster on disc. On Spark, it is easy to write code more quickly as programmers can access 80 high-level operators to lessen the code from 50 lines of code to 5 lines.
It has REPL (Read-Evaluate-Print loop), also called Spark shell or Spark CLI, letting you test the result of code without coding and executing the complete program of an application. With Spark shell, programmers can create a DataFrame and play with code to sidestep powering up remote servers, which is a costly method.
Additional features of Spark are:
- Cutting-Edge Analytics: The job of Apache Spark is to facilitate its users about SQL queries, Graph algorithms, Graph processing, Machine learning (ML), and Streaming data. Apache Spark is well-known in a variety of industries, including banking, government, gaming, and telecommunications, due to this feature. Spark is widely used to perform BigData operations by large corporations such as Apple, Amazon, Alibaba Taobao, eBay, IBM, Shopify, Microsoft, and others.
- Multi-lingual Functionality: Spark is designed to work with a variety of languages, making it easy to write applications in Scala, Java, and Python. Once written, you can run these applications 10x times faster on disk and 100x faster in memory in contrast to MapReduce applications.
- Real-time Processing: Data processing can be done in real-time with Spark. Clients can easily process data and generate results in real-time as a result of this.
- Reusability: With Apache Spark, you can reuse programming code for batch processing, merge streams with historical data, and run ad-hoc queries in stream mode.
- Flexibility: Spark is a flexible framework that works well when migrating Hadoop applications. It can run on the top of Hadoop Yarn, Apache Mesos, and Kubernetes because it works independently in cluster mode and can share multiple sources, such as Cassandra, Hive, HDFS, and HBase.
Understanding Spark Fault Tolerance Aspects
- Spark Fault Tolerance Aspects: What is Fault Tolerance?
- Spark Fault Tolerance Aspects: Types of Failures
- Spark Fault Tolerance Aspects: How to Achieve Fault Tolerance?
Spark Fault Tolerance Aspects: What is Fault Tolerance?
Spark Fault Tolerance is the ability of a system to continue to function properly even if some of its components fail (or have one or more faults within them). When compared to a naively designed system, where even a minor failure can cause a total breakdown, the decrease in operating quality is proportional to the severity of the failure. In high-availability or life-critical systems, spark fault tolerance is especially important. Graceful degradation refers to the ability of a system to maintain functionality when parts of it fail.
Intrinsically, Apache Spark possesses strong attributes to support Spark fault tolerance. In the programming world, faults and application failures at the production scale are extremely common. To tackle such incidents, Spark recovers loss once the failure occurs.
This self-recovery feature of Spark is due to the presence of a Resilient Distributed Dataset (RDD). Besides, Spark fault tolerance through DAG, Directed Acyclic Graph, that controls the background of all the modifications or activities mandated to finish a task. It works like a backbone of this software because it can recover the loss if the application faces any error or failure.
Spark’s fault-tolerant semantics are as follows:
- Because the Apache Spark RDD is immutable, each Spark RDD retains the lineage of the deterministic operation that was used to create it on a fault-tolerant input dataset.
- If any partition of an RDD is lost due to a worker node failure, that partition can be re-computed using the lineage of operations from the original fault-tolerant dataset.
- If all of the RDD transformations are deterministic, regardless of Spark cluster failures, the data in the final transformed RDD will always be the same.
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Spark Fault Tolerance Aspects: Types of Failures
Generally, there are two types of failures:
Worker Node Failure
The worker node (or slave node) executes the application code on the Spark cluster. The application will bear the loss of in-memory in case of any worker nodes operating stop functioning. The loss of buffer data will occur if a receiver is used over crashed nodes.
Driver Node Failure
The driver node’s purpose is to run the Spark Streaming application; however, if it fails, the SparkContent will be lost, and the executors will be unable to access any in-memory data.
Spark Fault Tolerance Aspects: How to Achieve Fault Tolerance?
To achieve Spark fault tolerance, you use SparkRDDs. SparkRDDs are created to keep an eye on the failure of worker nodes in the cluster to certify that the chances of losing data must be zero. In Apache Spark, there are several ways to create RDDs:
- Parallelized collection
- Loading or referencing a dataset from the external storage system
- Forming a new RDD from the existing RDD
A redundant component is required to restore the lost data. Spark’s self-recovery feature works to recover lost data while redundant data is available. Different transformation principles can be applied to RDDs that help to determine the execution plan for all missions conducted, known as lineage graph.
In this case, there are chances to relinquish any RDD if the machine encounters any crash. However, by performing an exact analysis on that endpoint, the same dataset can be recovered.
In a nutshell, RDD is programmatically divided, and each node is running on a partition basis at any given time. Execution of codes that are being conducted is primarily a cycle of Scala functions and enforced on the RDD partition. The cycle of these operations is aligned together to initiate a DAG to keep a track of all the operations conducted.
Now, consider a scenario in which any node fails to execute during the operation of code. In this outbreak, the cluster manager will identify that node and choose a different node to continue processing an operation on the same RDD partition, resulting in no data loss.
To achieve Spark fault tolerance for all the RDDs, the entire data is copied across multiple nodes in the cluster. Types of data that need recovery in the incident of failure are as follows:
Data Received and Replicated
In this case, data clones are created on the other node so that they can be retrieved in the event of a failure or crash during the operation.
Data Received but Buffered for Replication
Compared to the previous case, it’s not possible to retrieve data from any node. Hence, use the data source to gain access to the received data. To accomplish this, Apache Spark uses Apache Mesos, open-source software that sits between the application layer and the operating system to manage the application in a large clustered environment.
Apache Spark is a large-scale data analytics engine that is unified and multilingual. Thousands of companies use the Spark framework to execute data engineering, data science, and machine learning tasks on single-node engines or clusters. It’s a fast, scalable, and easy-to-use framework for running distributed ANSI SQL queries for dashboarding and ad-hoc reporting that’s 100 times faster than most of its competitors.
In this article, you explored the features of Spark and explained how this open-source framework achieves Spark fault tolerance via RDDs and DAG to handle all operations executed and retrieve data in case of any failure or crash. In conclusion, Apache Spark is a powerful Big Data platform with features that are designed to ease the life of big companies.visit our website to explore hevo
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If you’re interested in learning more about Apache Spark, check out Hevo Data’s previous blog posts.