Welcome!

Open Source Cloud Authors: Elizabeth White, William Schmarzo, Rishi Bhargava, Stefan Bernbo, Liz McMillan

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
The Internet of Things 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 and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and sh...
Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before. They have a question on their mind like “How is my application doing” but no id...
@GonzalezCarmen has been ranked the Number One Influencer and @ThingsExpo has been named the Number One Brand in the “M2M 2016: Top 100 Influencers and Brands” by Onalytica. Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR “Machine to Machine.” They then identified the top 100 most influential brands and individuals leading the discussion on Twitter.
DevOps is being widely accepted (if not fully adopted) as essential in enterprise IT. But as Enterprise DevOps gains maturity, expands scope, and increases velocity, the need for data-driven decisions across teams becomes more acute. DevOps teams in any modern business must wrangle the ‘digital exhaust’ from the delivery toolchain, "pervasive" and "cognitive" computing, APIs and services, mobile devices and applications, the Internet of Things, and now even blockchain. In this power panel at @...
IoT is rapidly changing the way enterprises are using data to improve business decision-making. In order to derive business value, organizations must unlock insights from the data gathered and then act on these. In their session at @ThingsExpo, Eric Hoffman, Vice President at EastBanc Technologies, and Peter Shashkin, Head of Development Department at EastBanc Technologies, discussed how one organization leveraged IoT, cloud technology and data analysis to improve customer experiences and effici...
As data explodes in quantity, importance and from new sources, the need for managing and protecting data residing across physical, virtual, and cloud environments grow with it. Managing data includes protecting it, indexing and classifying it for true, long-term management, compliance and E-Discovery. Commvault can ensure this with a single pane of glass solution – whether in a private cloud, a Service Provider delivered public cloud or a hybrid cloud environment – across the heterogeneous enter...
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
Successful digital transformation requires new organizational competencies and capabilities. Research tells us that the biggest impediment to successful transformation is human; consequently, the biggest enabler is a properly skilled and empowered workforce. In the digital age, new individual and collective competencies are required. In his session at 19th Cloud Expo, Bob Newhouse, CEO and founder of Agilitiv, drew together recent research and lessons learned from emerging and established compa...
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...
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Information technology is an industry that has always experienced change, and the dramatic change sweeping across the industry today could not be truthfully described as the first time we've seen such widespread change impacting customer investments. However, the rate of the change, and the potential outcomes from today's digital transformation has the distinct potential to separate the industry into two camps: Organizations that see the change coming, embrace it, and successful leverage it; and...
Everyone knows that truly innovative companies learn as they go along, pushing boundaries in response to market changes and demands. What's more of a mystery is how to balance innovation on a fresh platform built from scratch with the legacy tech stack, product suite and customers that continue to serve as the business' foundation. In his General Session at 19th Cloud Expo, Michael Chambliss, Head of Engineering at ReadyTalk, discussed why and how ReadyTalk diverted from healthy revenue and mor...
Extracting business value from Internet of Things (IoT) data doesn’t happen overnight. There are several requirements that must be satisfied, including IoT device enablement, data analysis, real-time detection of complex events and automated orchestration of actions. Unfortunately, too many companies fall short in achieving their business goals by implementing incomplete solutions or not focusing on tangible use cases. In his general session at @ThingsExpo, Dave McCarthy, Director of Products...
20th Cloud Expo, taking place June 6-8, 2017, at the Javits Center in New York City, NY, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy.
You have great SaaS business app ideas. You want to turn your idea quickly into a functional and engaging proof of concept. You need to be able to modify it to meet customers' needs, and you need to deliver a complete and secure SaaS application. How could you achieve all the above and yet avoid unforeseen IT requirements that add unnecessary cost and complexity? You also want your app to be responsive in any device at any time. In his session at 19th Cloud Expo, Mark Allen, General Manager of...
The 20th International Cloud Expo has announced that its Call for Papers is open. Cloud Expo, to be held June 6-8, 2017, at the Javits Center in New York City, brings together Cloud Computing, Big Data, Internet of Things, DevOps, Containers, Microservices and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding business opportunity. Submit your speaking proposal ...
Major trends and emerging technologies – from virtual reality and IoT, to Big Data and algorithms – are helping organizations innovate in the digital era. However, to create real business value, IT must think beyond the ‘what’ of digital transformation to the ‘how’ to harness emerging trends, innovation and disruption. Architecture is the key that underpins and ties all these efforts together. In the digital age, it’s important to invest in architecture, extend the enterprise footprint to the cl...
Bert Loomis was a visionary. This general session will highlight how Bert Loomis and people like him inspire us to build great things with small inventions. In their general session at 19th Cloud Expo, Harold Hannon, Architect at IBM Bluemix, and Michael O'Neill, Strategic Business Development at Nvidia, discussed the accelerating pace of AI development and how IBM Cloud and NVIDIA are partnering to bring AI capabilities to "every day," on-demand. They also reviewed two "free infrastructure" pr...
Businesses and business units of all sizes can benefit from cloud computing, but many don't want the cost, performance and security concerns of public cloud nor the complexity of building their own private clouds. Today, some cloud vendors are using artificial intelligence (AI) to simplify cloud deployment and management. In his session at 20th Cloud Expo, Ajay Gulati, Co-founder and CEO of ZeroStack, will discuss how AI can simplify cloud operations. He will cover the following topics: why clou...
"Dice has been around for the last 20 years. We have been helping tech professionals find new jobs and career opportunities," explained Manish Dixit, VP of Product and Engineering at Dice, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.