With numerous big data frameworks available, picking the right one can be a perplexing task. With each passing year, new frameworks hit the market that promise stronger features, more speed, and better scaling capability. Among big data frameworks out there, Hadoop and Spark are the two that keep on getting the most traction.
However, choosing one above another requires an individual to have detailed information about their features, and most importantly, their differences. But before finding out about them in detail, we should first get clear with the rudiments.
What is Big Data?
It is a large data set that requires a robust and pocket-friendly way for processing and enhancing insights for carving out better results and decisions. The ever-increasing pace at which data sets are generated nowadays is very dynamic and is composed of structured, semi-structured, and unstructured data. Their processing, if done through traditional ways, can be time-consuming, complex, and would require lots of hardware resources, proper tools, and techniques.
The large size and dynamic behavior of big data demand new and innovative frameworks to collect, store, and process data efficiently. Therefore, platforms like Apache Spark and Apache Hadoop are used for simplifying the tasks via parallel processing and distributed processing.
What is Parallel Processing and Distributed Processing
Knowing about parallel processing and distributed processing is crucial for understanding how Apache Hadoop and Apache Spark are utilized for analyzing big data. Parallel processing is the procedure of using multiple processors simultaneously for solving problems. Whereas, distributed processing is the procedure of using multiple computers simultaneously for solving particular problems.
The key difference between these two is their contrasting memory architectures. However, their primary work is to divide the computing into many smaller parts. In parallel processing, the platform makes use of the same memory, but in distributed processing, computing is done on the basis of disks.
What is Hadoop?
Apache Hadoop is a powerful platform that is used for handling enormous datasets in a distributed structure. The system utilizes MapReduce to part the information into blocks and allocates them to nodes of the clusters. MapReduce then processes all the information equally on every node for delivering desired output.
All the machines deployed within the clusters are then responsible for storing and processing the data. For storing them on disks, Hadoop utilizes HDFS (Hadoop Distributed File System) and therefore, offers lots of scalability options for making systems future-proof. Administrators can start with as few as one machine and further increase it to hundreds or even thousands by adding more hardware and resources.
Having a high fault-tolerance rate, Hadoop is capable of managing resources and making the most out of them; therefore, it is not completely dependent on hardwares for availability. At the center, Hadoop is developed for looking out for the faults in the application layer. If particular hardware dies, Hadoop will instantly replicate the complete data from all the clusters to enable the infrastructure to develop the missing part. Following are the four main modules of Hadoop:
HDFS: It is the acronym for Hadoop Distributed File System which is used for managing the storage of data through various clusters. It is capable of handling both structured and unstructured data and can be ordinary HDDs or enterprise drives.
MapReduce: It is the main component responsible for processing data in Hadoop infrastructure. It allocates all the data segments directly from HDFS to map tasks available within the clusters. It is responsible for running the processes simultaneously and also adds them up with one other if required.
YARN: The full form of YARN is “Yet Another Resource Negotiator” whose job is to manage computing resources and schedule them to perform actions.
Hadoop Common: It is the collection of important libraries on which modules are based. It is also referred to as Hadoop core because it offers every Hadoop component in one centralized place.
Key Features of Hadoop
1. Linear processing
Hadoop MapReduce enables massive amounts of data to be processed in parallel. It divides a huge data set into smaller chunks, which are processed separately on multiple nodes, and then gathers data from multiple nodes to generate outcomes. Hadoop MapReduce handles more effectively than Spark when the final output is larger than the RAM available.
2. Fault tolerance
Hadoop ensures that hardware failures do not affect data or applications to work simultaneously. The job of the faulty node is automatically routed to other nodes, ensuring that distributed computing doesn’t come to halt. All data gets automatically duplicated and stored in multiple locations.
3. Scalability
By simply including more nodes, you may easily expand your system for handling larger data. There is very little administrative work to be done.
4. Agility
Hadoop is built for processing large amounts of data. That’s an important concern because data volume is something that grows constantly, especially when it comes to social media platforms and IoT devices.
What is Spark?
Spark is a free and open-source platform that can be installed on a desktop, in a cloud, onto a cluster, and run on a variety of platforms. It caches and processes data in RAM and ensures faster processing of the data.
Spark can handle a variety of large data efficiently. This comprises batch processing, as well as real-time data process, machine learning, graph computation, etc. Spark can connect with a variety of libraries, including PyTorch and TensorFlow, thanks to its simple high-level APIs.
The Spark engine was intended to enhance MapReduce’s efficiency while maintaining its benefits. Spark does not have its own file system, but it can access data from a variety of storage options. Spark’s data structure is known as RDD (Resilient Distributed Dataset). Have a look at 5 integral components of Spark:
Apache Spark Core: Apache Spark Core is the foundation for the entire platform. This component takes care of tasks like scheduling, managing the input/output, recovering faults, and so on. Other features are layered on top of it.
Spark Streaming: It allows live data streams to be processed. Data can come from a variety of places, including Kafka, Kinesis, Flume, and others.
Spark SQL: This element is used by Spark to collect details regarding structured data and ways for processing the same.
Machine Learning Library (MLlib): It is a library composed of a large number of machine learning algorithms. The purpose of MLlib is to make ML more scalable and accessible.
GraphX: It is a collection of APIs utilized to facilitate tasks related to graph analytics.
Key Features of Spark
1. Fast data process
Spark is more powerful when it comes to the speed of processing data. It outperforms Hadoop MapReduce with around a hundred times more efficiency for data in RAM and 10 times for data in storage because of in-memory processing.
2. Iterative processing
Spark leads Hadoop MapReduce when it comes to processing data repeatedly. Spark’s Resilient Distributed Datasets (RDDs) allow numerous processes to be performed within memory, whereas Hadoop MapReduce requires interim results to be sent to disc.
3. Faster outcomes
If a company requires quick outcomes, Spark and its in-memory computation are the best options one should go with.
4. Graph processing
The computational paradigm of Spark is well suited to iterative computation, which is common in graph processing. GraphX is an interface for graph processing in Spark.
5. Machine learning
Spark comes with MLlib, which is a machine learning library. MLlib has robust algorithms that also execute in memory as well. However, if necessary, an individual can make use of Spark experts for modifying and altering them to meet specific requirements.
6. Joining datasets
Since Spark offers faster speed, it makes all the combinations quickly, yet Hadoop may be superior if you need to work on huge data sets that need lots of shuffling and sorting.
Hadoop vs Spark: Differences
1. Architecture
Every file transferred by Hadoop into HDFS (Hadoop Distributed File System) is divided into certain blocks. Based on configured block sizes and replication factors, each block is duplicated at certain times on clusters. The NameNode receives this information and keeps track of everything in the cluster. The NameNode assigns the files to one or more data nodes, which are then written to.
Spark works similarly to Hadoop, with the exception that calculations are performed in memory and held there until the user explicitly saves them. Spark begins by reading data from a file on HDFS, S3, or another filesystem into the SparkContext, a well-established method. Spark creates an RDD (Resilient Distributed Dataset) that represents an irreversible collection of items that run in parallel.
2. Performance
Spark is way faster than Hadoop. Spark is very quick in machine learning applications as well. However, if Spark is operating on YARN with various distributed services, then its performance may suffer, and the chances of memory leaks increase. As a result, if an individual has a batch processing use case, Hadoop can be a more efficient solution.
3. Costs
Spark and Hadoop are both open-source Apache programs, which means you may theoretically operate them with no installation fees. However, the entire cost of operation must be considered, which includes maintenance, hardware/software expenditures, and hiring cluster management staff.
On-premises, it is a well-known fact that Hadoop requires more disc capacity and Spark requires more RAM, which means that putting up Spark clusters would be more costly. Furthermore, because Spark is a newer technology, professionals that can work with it are limited and expensive.
4. Fault Tolerance
Since it was intended to duplicate data across multiple nodes, Hadoop is way more capable when it comes to fault tolerance. Every file is divided into blocks and copied multiple times over multiple machines, guaranteeing that the file can be reconstructed from other blocks if one system fails.
RDD (Resilient Distributed Datasets) operations are primarily responsible for Spark’s fault tolerance. As an RDD is created, a lineage is created as well, which recalls how the dataset was created and because it is immutable, users may have to rebuild them from scratch if necessary. The DAG (Directed Acyclic Graph) may also be used to rebuild data across Spark partitions between master nodes. Data is copied among worker nodes and can be lost if a node or communication between master and worker nodes goes down.
5. Security
When it comes to security, Hadoop wins hands down. Spark’s security is turned off by default. Therefore, if an individual doesn’t turn the security on, their setup will be revealed to others. Authentication through shared secret or event recording can be used to increase Spark’s security. However, for production workloads, this is insufficient.
Hadoop, on the other hand, uses a variety of authentication and authorization control techniques. Kerberos authentication is the most complex to establish. However, Hadoop also supports Ranger, LDAP, ACLs, inter-node encryption, and normal file permissions on HDFS as well for simple installation and high security.
6. Scheduling and Resource Management
Hadoop doesn’t come with a built-in scheduler. For resource management and scheduling, it relies on third-party software. YARN takes care of resource management in Hadoop clusters, thanks to ResourceManager and NodeManager (agents responsible for monitoring their resource usage of Containers).
Spark, on the other hand, includes these features. Its DAG scheduler is in charge of breaking down operators into phases. DAG scheduling is important for directing Spark to perform various jobs at each stage. Job and task scheduling, monitoring, and resource distribution are all handled by Spark Scheduler and Block Manager within clusters.
7. Data Processing
Both paradigms approach data in very different ways. Despite the fact that both Hadoop and Spark use MapReduce for processing the data in a distributed setting, Hadoop is better suited to batch computing. Spark, on the other hand, excels in real-time computing.
The objective of Hadoop is to save information on discs and then analyze it in groups within a distributed pattern. To process massive amounts of data, MapReduce doesn’t really require lots of Memory. Hadoop stores data on common hardware and therefore is suitable for linear information processing.
Apache Spark interacts with robust distributed datasets (RDDs). An RDD is a distributed set of items kept on nodes throughout the clusters. An RDD is generally too big for a single node to manage. As a result, Spark divides the RDDs and conducts the tasks in parallel on the nearest node.
Hadoop vs Spark: Head-to-Head Comparison table
| Hadoop | Spark | |
| Performance | Relatively slow performance because it relies on disc writing and reading speeds for storage. | Fast in-memory performance with reduced disk reading and writing operations. |
| Cost | It is an open-source platform with lower operating costs. It makes use of low-cost consumer hardware and it’ll be easier for organizations to find Hadoop experts. | An open-source platform, but relies on memory for computation, which considerably increases running costs. |
| Data Processing | It is the best option for batch processing. Hadoop splits a big dataset over clusters for parallel processing using MapReduce. | Suitable for iterative and live-stream data analysis. Works with RDDs and DAGs to run operations. |
| Fault Tolerance | Hadoop offers a high level of fault tolerance. Data is replicated between nodes and used in the event of a problem. | Tracks the RDD block creation process, and then it can rebuild a dataset when a partition fails. Spark can also use a DAG to rebuild data across nodes. |
| Scalability | Offers high scalability options by the simple addition of nodes and storage drives. There is no known limit to the number of nodes that can be supported. | A bit more challenging to scale because it relies on RAM for computations. Supports thousands of nodes in a cluster. |
| Security | Exceptionally safe. Covers LDAP, ACLs, Kerberos, and SLAs, and various tools for ensuring end-to-end security. | Not secure. By default, the security is turned off. Relies on integration with Hadoop to achieve the necessary security level. |
| Language Support | Hadoop supports very few languages that make it difficult to use. MapReduce programs are written only in Java or Python. | More user-friendly. Allows interactive shell mode. APIs can be written in Java, Scala, R, Python, Spark SQL. |
| Machine Learning | It is slower as compared to Spark. Bottlenecks can occur when data pieces are too big. Its major library is Mahout. | In-memory computing is much quicker. For calculations, MLlib is used. |
| Scheduling and Resource Management | External solutions are used. The most popular resource management solution is YARN and it schedules workflows with Oozie. | Includes resource allocation, scheduling, and monitoring features. |
Conclusion
Big data with high volume and dynamic behavior is increasing rapidly and so does the demand for platforms that can collect, store, and process such enormous amounts of data. Platforms like Apache Spark and Apache Hadoop are two widely used platforms for processing big data. However, choosing any one of them won’t be a good idea without understanding everything that they have to offer.
Hadoop is a powerful platform that is used for handling enormous datasets in a distributed structure. It makes use of MapReduce for dividing data into blocks and allocating them to nodes of the clusters. It comes with lots of benefits such as fault-tolerance, scalability, managing resources, making the environment run seamlessly, etc.
Spark is mainly used for handling large data efficiently. It is composed of batch processing and offers real-time data processing, machine learning, graph computation, etc. The main intent of Spark is to enhance MapReduce’s efficiency while maintaining its benefits. This blog provided detailed information about both Apache Spark and Apache Hadoop, and also highlighted all the main differences between them.