Welcome!

Open Source Cloud Authors: Derek Weeks, Jonathan Fries, Kevin Benedict, Anders Wallgren, Kareen Kircher

Related Topics: Apache, Java IoT, Open Source Cloud, IoT User Interface, @CloudExpo

Apache: Blog Feed Post

GridGain and Hadoop: Differences and Synergies

Now data can be analyzed and processed at any point of its lifecycle

GridGain is Java-based middleware for in-memory processing of big data in a distributed environment. It is based on high performance in-memory data platform that integrates fast In-Memory MapReduce implementation with In-Memory Data Grid technology delivering easy to use and easy to scale software. Using GridGain you can process terabytes of data, on 1000s of nodes in under a second.

GridGain typically resides between business, analytics, transactional or BI applications and long term data storage such as RDBMS, ERP or Hadoop HDFS, and provides in-memory data platform for high performance, low latency data storage and processing.

Both, GridGain and Hadoop, are designed for parallel processing of distributed data. However, both products serve very different goals and in most cases are very complementary to each other. Hadoop is mostly geared towards batch-oriented offline processing of historical and analytics payloads where latencies and transactions don’t really matter, while GridGain is meant for real-time in-memory processing of both transactional and non-transactional live data with very low latencies. To better understand where each product really fits, let us compare some main concepts of each product.

GridGain In-Memory Compute Grid vs Hadoop MapReduce
MapReduce
is a programming model developed by Google for processing large data sets of data stored on disks. Hadoop MapReduce is an implementation of such model. The model is based on the fact that data in a single file can be distributed across multiple nodes and hence the processing of those files has to be co-located on the same nodes to avoid moving data around. The processing is based on scanning files record by record in parallel on multiple nodes and then reducing the results in parallel on multiple nodes as well. Because of that, standard disk-based MapReduce is good for problem sets which require analyzing every single record in a file and does not fit for cases when direct access to a certain data record is required. Furthermore, due to offline batch orientation of Hadoop it is not suited for low-latency applications.

GridGain In-Memory Compute Grid (IMCG) on the other hand is geared towards in-memory computations and very low latencies. GridGain IMCG has its own implementation of MapReduce which is designed specifically for real-time in-memory processing use cases and is very different from Hadoop one. Its main goal is to split a task into multiple sub-tasks, load balance those sub-tasks among available cluster nodes, execute them in parallel, then aggregate the results from those sub-tasks and return them to user.



Splitting tasks into multiple sub-tasks and assigning them to nodes is the *mapping* step and aggregating of results is *reducing* step. However, there is no concept of mandatory data built in into this design and it can work in the absence of any data at all which makes it a good fit for both, stateless and state-full computations, like traditional HPC. In cases when data is present, GridGain IMCG will also automatically colocate computations with the nodes where the data is to avoid redundant data movement.

It is also worth mentioning, that unlike Hadoop, GridGain IMCG is very well suited for processing of computations which are very short-lived in nature, e.g. below 100 milliseconds and may not require any mapping or reducing.

Here is a simple Java coding example of GridGain IMCG which counts number of letters in a phrase by splitting it into multiple words, assigning each word to a sub-task for parallel remote execution in the map step, and then adding all lengths receives from remote jobs in reduce step.

    int letterCount = g.reduce(
        BALANCE,
        // Mapper
        new GridClosure<String, Integer>() {
            @Override public Integer apply(String s) {
                return s.length();
            }
        },
        Arrays.asList("GridGain Letter Count".split(" ")),
        // Reducer
        F.sumIntReducer()
    ));

GridGain In-Memory Data Grid vs Hadoop Distributed File System
Hadoop Distributed File System (HDFS) is designed for storing large amounts of data in files on disk. Just like any file system, the data is mostly stored in textual or binary formats. To find a single record inside an HDFS file requires a file scan. Also, being distributed in nature, to update a single record within a file in HDFS requires copying of a whole file (file in HDFS can only be appended). This makes HDFS well-suited for cases when data is appended at the end of a file, but not well suited for cases when data needs to be located and/or updated in the middle of a file. With indexing technologies, like HBase or Impala, data access becomes somewhat easier because keys can be indexed, but not being able to index into values (secondary indexes) only allow for primitive query execution.

GridGain In-Memory Data Grid (IMDG) on the other hand is an in-memory key-value data store. The roots of IMDGs came from distributed caching, however GridGain IMDG also adds transactions, data partitioning, and SQL querying to cached data. The main difference with HDFS (or Hadoop ecosystem overall) is the ability to transact and update any data directly in real time. This makes GridGain IMDG well suited for working on operational data sets, the data sets that are currently being updated and queried, while HDFS is suited for working on historical data which is constant and will never change.

Unlike a file system, GridGain IMDG works with user domain model by directly caching user application objects. Objects are accessed and updated by key which allows IMDG to work with volatile data which requires direct key-based access.



GridGain IMDG allows for indexing into keys and values (i.e. primary and secondary indices) and supports native SQL for data querying & processing. One of unique features of GridGain IMDG is support for distributed joins which allow to execute complex SQL queries on the data in-memory without limitations.

GridGain and Hadoop Working Together
To summarize:

Hadoop essentially is a Big Data warehouse which is good for batch processing of historic data that never changes, while GridGain, on the other hand, is an In-Memory Data Platform which works with your current operational data set in transactional fashion with very low latencies. Focusing on very different use cases make GridGain and Hadoop very complementary with each other.



Up-Stream Integration
The diagram above shows integration between GridGain and Hadoop. Here we have GridGain In-Memory Compute Grid and Data Grid working directly in real-time with user application by partitioning and caching data within data grid, and executing in-memory computations and SQL queries on it. Every so often, when data becomes historic, it is snapshotted into HDFS where it can be analyzed using Hadoop MapReduce and analytical tools from Hadoop eco-system.

Down-Stream Integration
Another possible way to integrate would be for cases when data is already stored in HDFS but needs to be loaded into IMDG for faster in-memory processing. For cases like that GridGain provides fast loading mechanisms from HDFS into GridGain IMDG where it can be further analyzed using GridGain in-memory Map Reduce and indexed SQL queries.

Conclusion
Integration between an in-memory data platform like GridGain and disk based data platform like Hadoop allows businesses to get valuable insights into the whole data set at once, including volatile operational data set cached in memory, as well as historic data set stored in Hadoop. This essentially eliminates any gaps in processing time caused by Extract-Transfer-Load (ETL) process of copying data from operational system of records, like standard databases, into historic data warehouses like Hadoop. Now data can be analyzed and processed at any point of its lifecycle, from the moment when it gets into the system up until it gets put away into a warehouse.

Read the original blog entry...

More Stories By Thomas Krafft

Over 15 years of experience in marketing and demand creation, with strategies driving over $500 million in revenue for a variety of companies in several high-growth and competitive markets, including consumer software and web services, ecommerce, demand creation through web and search, big data, and now healthcare.

@ThingsExpo Stories
We're entering the post-smartphone era, where wearable gadgets from watches and fitness bands to glasses and health aids will power the next technological revolution. With mass adoption of wearable devices comes a new data ecosystem that must be protected. Wearables open new pathways that facilitate the tracking, sharing and storing of consumers’ personal health, location and daily activity data. Consumers have some idea of the data these devices capture, but most don’t realize how revealing and...
When it comes to IoT in the enterprise, namely the commercial building and hospitality markets, a benefit not getting the attention it deserves is energy efficiency, and IoT's direct impact on a cleaner, greener environment when installed in smart buildings. Until now clean technology was offered piecemeal and led with point solutions that require significant systems integration to orchestrate and deploy. There didn't exist a 'top down' approach that can manage and monitor the way a Smart Buildi...
There is an ever-growing explosion of new devices that are connected to the Internet using “cloud” solutions. This rapid growth is creating a massive new demand for efficient access to data. And it’s not just about connecting to that data anymore. This new demand is bringing new issues and challenges and it is important for companies to scale for the coming growth. And with that scaling comes the need for greater security, gathering and data analysis, storage, connectivity and, of course, the...
The IETF draft standard for M2M certificates is a security solution specifically designed for the demanding needs of IoT/M2M applications. In his session at @ThingsExpo, Brian Romansky, VP of Strategic Technology at TrustPoint Innovation, will explain how M2M certificates can efficiently enable confidentiality, integrity, and authenticity on highly constrained devices.
trust and privacy in their ecosystem. Assurance and protection of device identity, secure data encryption and authentication are the key security challenges organizations are trying to address when integrating IoT devices. This holds true for IoT applications in a wide range of industries, for example, healthcare, consumer devices, and manufacturing. In his session at @ThingsExpo, Lancen LaChance, vice president of product management, IoT solutions at GlobalSign, will teach IoT developers how t...
So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine lear...
SYS-CON Events announced today that Peak 10, Inc., a national IT infrastructure and cloud services provider, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Peak 10 provides reliable, tailored data center and network services, cloud and managed services. Its solutions are designed to scale and adapt to customers’ changing business needs, enabling them to lower costs, improve performance and focus inter...
Digital payments using wearable devices such as smart watches, fitness trackers, and payment wristbands are an increasing area of focus for industry participants, and consumer acceptance from early trials and deployments has encouraged some of the biggest names in technology and banking to continue their push to drive growth in this nascent market. Wearable payment systems may utilize near field communication (NFC), radio frequency identification (RFID), or quick response (QR) codes and barcodes...
You think you know what’s in your data. But do you? Most organizations are now aware of the business intelligence represented by their data. Data science stands to take this to a level you never thought of – literally. The techniques of data science, when used with the capabilities of Big Data technologies, can make connections you had not yet imagined, helping you discover new insights and ask new questions of your data. In his session at @ThingsExpo, Sarbjit Sarkaria, data science team lead ...
SYS-CON Events announced today that Ericsson has been named “Gold Sponsor” of SYS-CON's @ThingsExpo, which will take place on June 7-9, 2016, at the Javits Center in New York, New York. Ericsson is a world leader in the rapidly changing environment of communications technology – providing equipment, software and services to enable transformation through mobility. Some 40 percent of global mobile traffic runs through networks we have supplied. More than 1 billion subscribers around the world re...
The demand for organizations to expand their infrastructure to multiple IT environments like the cloud, on-premise, mobile, bring your own device (BYOD) and the Internet of Things (IoT) continues to grow. As this hybrid infrastructure increases, the challenge to monitor the security of these systems increases in volume and complexity. In his session at 18th Cloud Expo, Stephen Coty, Chief Security Evangelist at Alert Logic, will show how properly configured and managed security architecture can...
The IoTs will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, will demonstrate how to move beyond today's coding paradigm and share the must-have mindsets for removing complexity from the development proc...
Artificial Intelligence has the potential to massively disrupt IoT. In his session at 18th Cloud Expo, AJ Abdallat, CEO of Beyond AI, will discuss what the five main drivers are in Artificial Intelligence that could shape the future of the Internet of Things. AJ Abdallat is CEO of Beyond AI. He has over 20 years of management experience in the fields of artificial intelligence, sensors, instruments, devices and software for telecommunications, life sciences, environmental monitoring, process...
In his session at @ThingsExpo, Chris Klein, CEO and Co-founder of Rachio, will discuss next generation communities that are using IoT to create more sustainable, intelligent communities. One example is Sterling Ranch, a 10,000 home development that – with the help of Siemens – will integrate IoT technology into the community to provide residents with energy and water savings as well as intelligent security. Everything from stop lights to sprinkler systems to building infrastructures will run ef...
We’ve worked with dozens of early adopters across numerous industries and will debunk common misperceptions, which starts with understanding that many of the connected products we’ll use over the next 5 years are already products, they’re just not yet connected. With an IoT product, time-in-market provides much more essential feedback than ever before. Innovation comes from what you do with the data that the connected product provides in order to enhance the customer experience and optimize busi...
Manufacturers are embracing the Industrial Internet the same way consumers are leveraging Fitbits – to improve overall health and wellness. Both can provide consistent measurement, visibility, and suggest performance improvements customized to help reach goals. Fitbit users can view real-time data and make adjustments to increase their activity. In his session at @ThingsExpo, Mark Bernardo Professional Services Leader, Americas, at GE Digital, will discuss how leveraging the Industrial Interne...
The increasing popularity of the Internet of Things necessitates that our physical and cognitive relationship with wearable technology will change rapidly in the near future. This advent means logging has become a thing of the past. Before, it was on us to track our own data, but now that data is automatically available. What does this mean for mHealth and the "connected" body? In her session at @ThingsExpo, Lisa Calkins, CEO and co-founder of Amadeus Consulting, will discuss the impact of wea...
Increasing IoT connectivity is forcing enterprises to find elegant solutions to organize and visualize all incoming data from these connected devices with re-configurable dashboard widgets to effectively allow rapid decision-making for everything from immediate actions in tactical situations to strategic analysis and reporting. In his session at 18th Cloud Expo, Shikhir Singh, Senior Developer Relations Manager at Sencha, will discuss how to create HTML5 dashboards that interact with IoT devic...
Whether your IoT service is connecting cars, homes, appliances, wearable, cameras or other devices, one question hangs in the balance – how do you actually make money from this service? The ability to turn your IoT service into profit requires the ability to create a monetization strategy that is flexible, scalable and working for you in real-time. It must be a transparent, smoothly implemented strategy that all stakeholders – from customers to the board – will be able to understand and comprehe...
A critical component of any IoT project is the back-end systems that capture data from remote IoT devices and structure it in a way to answer useful questions. Traditional data warehouse and analytical systems are mature technologies that can be used to handle large data sets, but they are not well suited to many IoT-scale products and the need for real-time insights. At Fuze, we have developed a backend platform as part of our mobility-oriented cloud service that uses Big Data-based approache...