Businesses utilise Apache Spark to manage their high-volume data loads and to perform various operations on petabytes of data in one go. This open-source tool promises high-speed data processing & query analysis, making it the first choice of enterprises. The functionality of Apache Spark revolves around its unique Resilient Distributed Datasets (RDDs). These datasets provide users with a fault-tolerant and robust collection of elements to perform parallel computations.
This article will introduce you to Apache Spark along with its unique features. It will also introduce the concept of Resilient Distributed Datasets and explain their importance & features. The article also lists the various operations you can perform on RDDs and provides 2 methods to set up these datasets for your own business. Read along to learn more about Resilient Distributed Datasets and try out their features today!
Table of Contents
- What is Apache Spark?
- What are Resilient Distributed Datasets (RDDs) in Apache Spark?
- Key Features & Importance of RDDs in Apache Spark
- RDD vs Data Set vs Data Frame: Key Differences
- Transformations & Actions on Resilient Distributed Datasets in Apache Spark
- Methods to Create Efficient RDDs in Apache Spark
- Limitations of RDDs in Apache Spark
What is Apache Spark?
Apache Spark is a popular, open-source, high-speed, Distributed Data Processing System that finds widespread applications in tasks related to Big Data and Machine Learning. Since its inception in 2014, Apache Spark has been in huge demand and is used by business giants such as Netflix, Yahoo, eBay, etc. This tool was designed to overcome the shortcomings of existing computational engines like MapReduce. Apache Spark operates by retaining all the working data in the memory until the tsk is over. This way, it is able to save cost on all the expensive intermediate computational steps.
Apache Spark leverages its 8000+ clusters to operate on Petabytes of data daily. Moreover, with its use of Memory Caching tools and Rapid Query Execution, Spark performs multiple workloads simultaneously. This is why companies prefer it to execute heavy tasks, including Batch Processing, Real-Time Analytics, Graph Processing and much more. Furthermore, developers are drawn toward Spark because of its ability to accept codes in various languages like Python, Java, R, Scala, etc.
Key Features of Apache Spark
Apache Spark provides the following rich features to ensure a hassle-free Data Analytics experience:
- High Processing Capabilities: Spark leverages Resilient Distributed Datasets (RDDs) to minimise the I/O operations as compared to its peer MapReduce. Moreover, it offers 100 times faster memory performance, and on disk, it operates with 10 times faster speed.
- Easy Usage: Spark allows you to work with numerous programming languages. Moreover, it offers 80 operators to simplify your development tasks. Spark’s user interface is simple to understand and even allows you to reuse the code for critical tasks like manipulating historical data, running ad-hoc queries, etc.
- Fault Tolerance: RDDs allow Spark to manage situations of node failure and safeguard your cluster from data loss. Moreover, it regularly stores the transformations and actions, empowering you to restart from the last checkpoint.
- Real-Time Processing: Traditional tools like MapReduce allow for processing data only if available in Hadoop Clusters. Spark, on the other hand, uses multiple language-integrated robust APIs to support data processing in real-time.
To learn more about Apache Spark, visit here.
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What are Resilient Distributed Datasets (RDDs) in Apache Spark?
The primary Spark abstraction is the Resilient Distributed Dataset (RDD) concept which is responsible for Spark’s high-speed and efficient MapReduce operations. The RDD is the key data structure available in Spark and consists of distributed collections of multiple objects. The popularity of this Resilient Distributed Dataset comes from its fault-tolerant nature, which allows them to store your petabytes of records secure. Nodes.
Each Dataset in Spark’s Resilient Distributed Datasets is segregated into several logical partitions. These partitions are spread across a cluster and are easy to work in parallel on different nodes of that cluster. You can even create such RDDs by performing deterministic operations on your stable storage data or by modifying existing RDDs using a Scala collection. Users prefer RDDs as it allows them to reuse the same code multiple times. Furthermore, Resilient Distributed Datasets have an unmatched capability to recover from faulty situations automatically.
Key Features & Importance of RDDs in Apache Spark
Resilient Distributed Datasets are relevant to the high processing functionality of Apache Spark because of the following features:
- In-memory Computation: The RDDs are capable of performing in-memory computation. Moreover, it stores the intermediate results temporarily in the RAM (distributed memory) itself instead of sending them to a disk (stable storage). This allows faster access, and RDDs are able to provide you with real-time results.
- Lazy Evaluations: Resilient Distributed Datasets rely on lazy transformations. This implies that the RDDs do not perform the in-between calculations immediately. Instead, they simply store the modifications and compute them together at the final stage.
- Fault Tolerance: The main feature of these RDDs is their fault-tolerant structure. In case of failure, the RDDs track data’s lineage and regenerate the lost data. This process is automatic, and RDDs use the stored transformations to rebuild the dataset.
- Security: Resilient Distributed Data is secure, and you can share it across different processes. Furthermore, you can easily retrieve the required data anytime and perform operations like caching and replication seamlessly. This way RDDs ensure consistency and security for your computations.
- Partitioning: RDDs use partitioning to facilitate parallelism in Spark Apache. Every single partition is a logical segment of data and is mutable. This implies you can generate a partition even by performing certain modifications on the existing partitions.
Apart from the above features, Resilient Distributed Datasets offer other advantages as well. They have the capability to define and manage your data partitioning. Moreover, RDDs operate on both disk and RAM memories, unlike traditional systems. Since Apache Spark utilises the functions of RDDs lazily, it calls RDDs when necessary. This way, it is able to save your time and provide you with high efficiency. Furthermore, you can anytime call a method and decide the state of RDD that you wish to use in near future.
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RDD vs Data Set vs Data Frame: Key Differences
The structure and workings of RDD, Dataset and Data Frames, put them in a similar light. However, these 3 structures are different from each other in the following way:
- RDD: An Resilient Distributed Dataset is the original data Structure provided by Apache Spark. It is an immutable collection of various types of objects which operate on separate Nodes in a given Spark Cluster. RDDs are responsible for facilitating the functionality to carry out computations inside the memory. This way you can process data stored in large clusters without worrying about crashes and faults. Furthermore, all RDDs in Spark are partitioned across multiple servers. This way, they work efficiently even on different nodes in a parallel manner.
- DataFrame: An Apache Spark DataFrame is a form of dataset organised using named columns. DataFrames operate similar to relational database tables. Their basic design idea is to support the fast processing of huge Structured Datasets. All DataFrames are made up of rows with a schema that illustrates the required format of your data. Moreover, DataFrames provide optimal memory management and well-developed execution plans.
- DataSet: Apache Spark consists of a Dataset which is considered a type of data structure in the Spark SQL functionalities. Unlike RDD, Dataset is strongly typed and maps to a relational schema. This Object-oriented structure contains an API that works as an extension to the Dataframe APIs. You can easily perform queries using Datasets, but unlike RDDs, normal Datasets are not functional in a parallel manner. Furthermore, it offers only a single interface for your Scala and Java languages and therefore, reduces the excess overhead.
Transformations & Actions on Resilient Distributed Datasets in Apache Spark
Depending on the type of output that you require, Apache Spark allows you to modify its RDDs using the following 2 processes:
1) Transformations on Resilient Distributed Datasets
Resilient Distributed Datasets Transformations are simply functions that consume an RDD as input and supply one or more RDDs as output. Since RDDs are immutable, the transformations do not alter the contents of the input RDD. Instead, the transformations apply computational functions to generate new RDDs. The following transformations are most popular among RDD users:
- map(func): This function passes each source element as input through a given function and outputs a new distributed dataset.
- filter(func): This function returns a newly formed dataset as output. The dataset consists of those elements for which the input function returns true as a result. This way, it filters the data and removes all inuts that generate a false value from the input expression.
- sample(withReplacement, fraction, seed): This function produces a database containing a fraction of the input data. The fraction is calculated with the help of a random seed that generates numbers. The whole process can be done using both with replacement & without replacement approaches.
- union(otherDataset): This function combines the values present in the source dataset and input argument. IT finally returns a dataset containing more data than the input dataset.
- intersection(otherDataset): This function works as opposed to the Union function as it returns a dataset that contains only the common data values between the input argument and dataset.
- repartition(numPartitions): Tis functions, takes in an RDD and reshuffles its data. This way, it creates more or fewer partitions than the input data. This function creates a balance in the data and shuffles your input data over a network.
2) Actions on Resilient Distributed Datasets
Actions in Resilient Distributed Datasets are operations that are capable of producing non-RDD data as output. These Actions work to materialise a new value using your Spark program. Moreover, An RDD Action is a simple way to transfer results between executors and the driver program. The following Actions are popular among RDD users:
- reduce(func): It aggregates the input dataset’s elements with the help of the input function. Reduce works in parallel only if the input function follows the commutative and associative properties.
- collect(): It takes in an RDD as input and returns an array containing all the elements of that RDD to the driver program. This action is useful when performed after a filter or other transformations that provide you with a smaller subset of data.
- count(): It is used to get the total number of elements present in your input RDD.
- first(): This action returns the very first element of your input RDD.
- take(n): It is responsible for returning an array containing the first n elements of your input RDD.
Methods to Create Efficient RDDs in Apache Spark
You can create Resilient Distributed Datasets by using the following 2 ways:
- Method 1: Setting Up Resilient Distributed Datasets Using Parallelised Collections
- Method 2: Setting Up Resilient Distributed DatasetsUsing External Datasets
Method 1: Setting up Resilient Distributed Datasets using Parallelised Collections
This method requires you to parallelise an existing Collection in your driver program.
You can call the SparkContext to generate Parallell Collections or leverage an existing iterable present in your driver program. The process generally copies the Collection elements from a Distributed Dataset. You can create a parallel collection consisting of the numbers from 1 to 5 using the python programming language as follows:
data = [1, 2, 3, 4, 5] distData = sc.parallelize(data)
Once created, you can use this distributed dataset for parallel operations. For instance, you can use the following function to get a sum from the given numbers:
distData.reduce(lambda a, b: a + b)
Method 2: Setting Up Resilient Distributed Datasets Using External Datasets
You can also create Resilient Distributed Datasets using an external dataset such as a shared filesystem, HBase, etc., which can work with a Hadoop InputFormat. You can utilise the PySpark tool to generate Resilient Distributed Datasets by transforming the external datasets into the required form. For instance, you can build the Text file RDDs via SparkContext’s textFile function.
This method inputs a URL ( a local path on your system, or a hdfs://, s3a://, etc URI) and treats it as a single collection of lines as shown below:
distFile = sc.textFile("data.txt")
Once the required RDD is ready, you can use the distFile to perform operations as shown below:
distFile.map(lambda s: len(s)).reduce(lambda a, b: a + b).
Limitations of RDDs in Apache Spark
RDDs are a key aspect of Apache Spark. It has multiple utilities, but it also comes along with the following limitations:
- Apache Spark does not support the automatic input optimisation feature for its RDDs. This implies RDDs are not able to use certain features such as the Spark Catalyst optimiser and Spark Tungsten execution engine. Therefore, you will need to perform data optimisations in RDDs manually.
- The security measures of RDDs do not offer Static or Runtime Safety. You can utilise the RDD compile-time safety check to work with complex data workflows. However, it does not allow you to check for errors during runtime.
- The RDD functionality degrades if there is a memory shortage. In such situations, the RDD partitions will overflow from RAM, and your performance will degrade. To overcome this issue, you need to spend money and increase the physical memory size.
- RDDs can not manage structured data easily. This is due to the lack of schema view provision in Sparks. This implies that, unlike Dataset and DataFrames, RDDs suffer when it comes to managing data organised according to named columns.
This article introduced you to Apache Spark and discussed its key features. It also explained the concept of Resilient Distributed Datasets and why they are important. The article further discussed the transformations & actions related to RDDs and the various methods you can use to set up the required RDD. Furthermore, the article explained certain limitations associated with the RDD feature of Apache Spark.Visit our Website to Explore Hevo
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