Welcome!

@CloudExpo Authors: Igor Drobiazko, Elizabeth White, Jayaram Krishnaswamy, Pat Romanski, Dana Gardner

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.

@CloudExpo Stories
Peak 10, Inc., has announced the implementation of IT service management, a business process alignment initiative based on the widely adopted Information Technology Infrastructure Library (ITIL) framework. The implementation of IT service management enhances Peak 10’s current service-minded approach to IT delivery by propelling the company to deliver higher levels of personalized and prompt service. The majority of Peak 10’s operations employees have been trained and certified in the ITIL frame...
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...
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...
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...
SYS-CON Events announced today that DatacenterDynamics has been named “Media Sponsor” of 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. DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Between the mockups and specs produced by analysts, and resulting applications built by developers, there exists a gulf where projects fail, costs spiral, and applications disappoint. Methodologies like Agile attempt to address this with intensified communication, with partial success but many limitations. In his session at 18th Cloud Expo, Charles Kendrick, CTO & Chief Architect at Isomorphic Software, will present a revolutionary model enabled by new technologies. Learn how business and devel...
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 ...
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...
If there is anything we have learned by now, is that every business paves their own unique path for releasing software- every pipeline, implementation and practices are a bit different, and DevOps comes in all shapes and sizes. Software delivery practices are often comprised of set of several complementing (or even competing) methodologies – such as leveraging Agile, DevOps and even a mix of ITIL, to create the combination that’s most suitable for your organization and that maximize your busines...
Struggling to keep up with increasing application demand? Learn how Platform as a Service (PaaS) can streamline application development processes and make resource management easy.
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...
See storage differently! Storage performance problems have only gotten worse and harder to solve as applications have become largely virtualized and moved to a cloud-based infrastructure. Storage performance in a virtualized environment is not just about IOPS, it is about how well that potential performance is guaranteed to individual VMs for these apps as the number of VMs keep going up real time. In his session at 18th Cloud Expo, Dhiraj Sehgal, in product and marketing at Tintri, will discu...
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, will discuss how research has demonstrated the value of Machine Learning in delivering next generation analytics to im...
Up until last year, enterprises that were looking into cloud services usually undertook a long-term pilot with one of the large cloud providers, running test and dev workloads in the cloud. With cloud’s transition to mainstream adoption in 2015, and with enterprises migrating more and more workloads into the cloud and in between public and private environments, the single-provider approach must be revisited. In his session at 18th Cloud Expo, Yoav Mor, multi-cloud solution evangelist at Cloudy...
This is not a small hotel event. It is also not a big vendor party where politicians and entertainers are more important than real content. This is Cloud Expo, the world's longest-running conference and exhibition focused on Cloud Computing and all that it entails. If you want serious presentations and valuable insight about Cloud Computing for three straight days, then register now for Cloud Expo.
Redis is not only the fastest database, but it has become the most popular among the new wave of applications running in containers. Redis speeds up just about every data interaction between your users or operational systems. In his session at 18th Cloud Expo, Dave Nielsen, Developer Relations at Redis Labs, will shares the functions and data structures used to solve everyday use cases that are driving Redis' popularity.
SYS-CON Events announced today that Stratoscale, the software company developing the next generation data center operating system, 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. Stratoscale is revolutionizing the data center with a zero-to-cloud-in-minutes solution. With Stratoscale’s hardware-agnostic, Software Defined Data Center (SDDC) solution to store everything, run anything and scale everywhere...
Many private cloud projects were built to deliver self-service access to development and test resources. While those clouds delivered faster access to resources, they lacked visibility, control and security needed for production deployments. In their session at 18th Cloud Expo, Steve Anderson, Product Manager at BMC Software, and Rick Lefort, Principal Technical Marketing Consultant at BMC Software, will discuss how a cloud designed for production operations not only helps accelerate developer...
Angular 2 is a complete re-write of the popular framework AngularJS. Programming in Angular 2 is greatly simplified – now it's a component-based well-performing framework. This immersive one-day workshop at 18th Cloud Expo, led by Yakov Fain, a Java Champion and a co-founder of the IT consultancy Farata Systems and the product company SuranceBay, will provide you with everything you wanted to know about Angular 2.
SYS-CON Events announced today that Men & Mice, the leading global provider of DNS, DHCP and IP address management overlay solutions, 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. The Men & Mice Suite overlay solution is already known for its powerful application in heterogeneous operating environments, enabling enterprises to scale without fuss. Building on a solid range of diverse platform support,...