"Cloudera's leadership on Spark has delivered real innovations that our customers depend on for speed and sophistication in large-scale machine learning. From everything from improving health outcomes to predicting network outages, Spark is emerging as the "must have" layer in the Hadoop stack" - said Steven Hillion, Chief Product Officer at Alpine Data Labs
“Spark is what you might call a Swiss Army knife of Big Data analytics tools”- said Reynold Xin, Berkeley AmpLab Shark Development Lead
Storm official documentation states- “Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing."
Apache Spark and Storm are creating hype and have become the open-source choices for organizations to support streaming analytics in the Hadoop stack.
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Traditional data warehousing environments were expensive and had high latency towards batch operations. As a result of this, organizations were not able to embrace the power of real time business intelligence and big data analytics in real time. There are several powerful open-source tools that have emerged to overcome this challenge- Hadoop, Spark and Storm are some of the popular open source platforms for real time data processing. Each of these tools has some intersecting functionalities. However, they have different role to play.
Apache Hadoop is definitely the choice from the open source frameworks available for computing large data sets and analysing them. Apache Foundation has endowed the big data market with two other robust open source tools- Spark and Storm. Spark and Storm comply with the batch processing nature of Hadoop by offering distribution computation functionalities and even processing features through directed acyclic graphs (DAG).Spark and Storm are the bright new toys in the big data playground, however there are still several use cases for the tiny elephant in the big data room. Hadoop needs to be run side by side with Spark and Storm for a complete Big Data Analytics package.
Hadoop is an open source distributed processing framework. Hadoop is used for storing large data sets and running distributed analytics processes on various clusters. Hadoop is the choice for many organizations for storing large data sets quickly when they are constricted by budget and time constraints.
Hadoop is efficient because it does not require big data applications to send massive amounts of data across the network and is robust in nature because big data applications continue to run even if the clusters or individual servers fail. Hadoop MapReduce is limited to batch processing of one job at a time. This is the reason why these days Hadoop is being used extensively as a data warehousing tool and not as a data analysis tool.
Read More - What is Hadoop?
Spark is a data parallel open source processing framework. Spark workflows are designed in Hadoop MapReduce but are comparatively more efficient than Hadoop MapReduce. The best feature of Apache Spark is that it does not use Hadoop YARN for functioning but has its own streaming API and independent processes for continuous batch processing across varying short time intervals. Spark runs 100 times faster than Hadoop in certain situations, however doesn’t have its own distributed storage system. This is the reason why most of the big data projects install Apache Spark on Hadoop so that the advanced big data applications can be run on Spark by using the data stored in Hadoop Distributed File System.
Read More – Spark vs. Hadoop
Storm is a task parallel, open source distributed computing system. Storm has its independent workflows in topologies i.e. Directed Acyclic Graphs. The topologies in Storm execute until there is some kind of a disturbance or if the system shuts down completely. Storm does not run on Hadoop clusters but uses Zookeeper and its own minion worker to manage its processes. Storm can read and write files to HDFS.
The purpose is not to cast decision about which one is better than the other, but rather understand the differences and similarities of the three- Hadoop, Spark and Storm. Apache Hadoop is hot in the big data market but its cousins Spark and Storm are hotter.
Spark vs. Hadoop vs. Storm
Understanding the Similarities-
1) Hadoop, Spark and Storm are open source processing frameworks.
2) Hadoop, Spark and Storm can be used for real time BI and big data analytics.
3) Hadoop, Spark and Storm provide fault tolerance and scalability.
4) Hadoop, Spark and Storm are preferred choice of frameworks amongst developers for big data applications (based on the requirements) because of their simple implementation methodology.
5) Hadoop, Spark and Storm are implemented in JVM based programming languages- Java, Scala and Clojure respectively.
Understanding the Differences –
1) Data Processing Models
Hadoop MapReduce is best suited for batch processing. For big data applications that require real time options, organizations must use other open source platform like Impala or Storm. Apache Spark is designed to do more than plain data processing as it can make use of existing machine learning libraries and process graphs. Thanks to the high performance of Apache Spark, it can be used for both batch processing and real time processing. Spark provides an opportunity to use a single platform for everything rather than splitting the tasks on different open source platforms-avoiding the overhead of learning and maintaining different platforms.
Micro-batching is a special kind of batch processing wherein the batch size is orders smaller. Windowing becomes easy with micro-batching as it offer stateful computation of data. Storm is a complete stream processing engine that supports micro-batching whereas Spark is a batch processing engine that micro-batches but does not render support for streaming in the strictest sense.
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Spark processes in-memory data whereas Hadoop MapReduce persists back to the disk after a map action or a reduce action thereby Hadoop MapReduce lags behind when compared to Spark in this aspect. Spark requires huge memory just like any other database - as it loads the process into the memory and stores it for caching. However, if Spark runs on top of YARN with various other resources demanding services, then there is a possibility of performance deprivation for Spark. In the case of Hadoop MapReduce, the process is killed as soon as the job is completed - making it possible to run along with other resource demanding services with just a slight difference in performance.
Similarly, comparing Spark and Storm both provide fault tolerance and scalability but differ in the processing model. Spark streams events in small batches that come in short time window before it processes them whereas Storm processes the events one at a time. Thus, Spark has a latency of few seconds whereas Storm processes an event with just millisecond latency.
Spark has good performance on dedicated clusters when the entire data can fit in the memory whereas Hadoop can perform well along other services when data does not fit in memory. Storm is a good option when an application needs sub second latency without data loss whereas Spark can be used in stateful computations to ensure that the event is just processed once.
3) Ease of Development
Developing for Hadoop
Hadoop MapReduce is written in Java. Apache Pig makes it easier to develop in Hadoop, although some time needs to be spent on understand and learning the Syntax of Apache Pig. To add the SQL compatibility to Hadoop, developers can use Hive on top of Hadoop. In fact, there are several data integration services and tools that allow developers to run MapReduce jobs without any programming. Hadoop MapReduce lacks the interactive mode but tools like Impala provide a complete package of querying to Hadoop.
Developing for Spark
Spark uses Scala tuples and they can only be intensified by nesting the generic types because Scala tuples are difficult to be implemented in Java. However, this does not require compromising on the compile time type safety checks.
Developing for Storm
Storm uses DAG’s which are natural to the processing model. Every node in the directed acyclic graph transforms the data in some way and continues the process. The data transfer between the nodes in directed acyclic graphs has a natural interface and this happens through Storm tuples. However, this can be achieved by compromising at the expense of compile time type safety checks.
Spark is easier to program as it has interactive mode which is not possible directly with Hadoop. However many tools are coming up to make programming with Hadoop easier. Also, if the project requires an interactive mode for data exploration through API calls - then it is not supported by Storm. Spark has to be used.
Hadoop, Spark and Storm have their own benefits, however there are certain aspects like Cost of Development, Performance, and Data Processing models, Message Delivery Guarantees, Latency, Fault Tolerance and Scalability which play a vital role in deciding which one is better for a particular big data application.
Hadoop, Spark or Storm can each be a great choice for big data analytics stack and choosing the ideal solution is merely a matter of considering the above mentioned similarities and differences. The beauty of open source tools is that - based on the application requirements, workloads and infrastructure, the ideal choice could be a combination of Spark and Storm together with other open source tools like Apache Hadoop, Apache Kafka, Apache Flume, etc.
Regardless of what open source tools an organization chooses-either it is Hadoop, Spark , Storm or a combination of either of the three-these tools have changed real time business intelligence, as all midsize to large organizations are embracing their advantages.