Welcome!

Apache Authors: Pat Romanski, Liz McMillan, Elizabeth White, Christopher Harrold, Janakiram MSV

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Open Source Cloud, Apache, @BigDataExpo

@CloudExpo: Blog Post

Hadoop – 100x Faster By @GridGain | @CloudExpo [#BigData]

How we did it...

If you know anything about Hadoop architecture - the task seemed daunting to us and it proved to be one of the most challenging engineering feat that we have accomplished so far.

After almost 24 months of development, tens of thousands of lines of Java, Scala and C++ code, multiple design iterations, several releases and dozens of benchmarks later we have the product that can deliver real-time performance to Hadoop with only minimal integration and no ETL required. Backed-up by customer deployments that prove our performance claims and validate our architecture.

Here's how we did it.

The Idea - In-Memory Hadoop Accelerator
Hadoop is based on two key technologies: HDFS for storing data, and MapReduce for processing that data in parallel. Everything else in Hadoop itself and the entire ecosystem coalesce around these two technologies.

Both - HDFS and MapReduce - were not necessarily designed with real-time performance in mind and in order to deliver real-time processing without moving data out of Hadoop into an alternative technology, we had to improve the performance of each of these sub-systems directly.

in_memory_hadoop2_white

We decided to develop a high performance in-memory file system that provides 100% compatibility with HDFS and an optimized MapReduce implementation that would take advantage of this real-time file system. By doing so, we could offer all of the advantages of our in-memory platform while minimizing the disruption of our customers' existing Hadoop investments.

There are many projects and products that aim to improve Hadoop performance. Projects like HDFS2, Apache Tez, Cloudera Impala, HortonWorks Stinger, ScaleOut hServer and Apache Spark to name but a few, all aim to solve Hadoop performance issues in various ways.

From a technology stand point GridGain's In-Memory Hadoop Accelerator has some similarity to the architecture of Spark (optimized MapReduce), ScaleOut and HDFS2 (in-memory caching without ETL) and some features of Apache Tez (in-process execution), however, GridGain's In-Memory Accelerator is the only product for Hadoop available today that combines the both the high performance HDFS-compatible file system and optimized in-memory MapReduce along with many other features in one fully integrated product.

In-Memory File System
First, we implemented GridGain's In-Memory File System (GGFS) to accelerate I/O in the Hadoop stack. The original idea was that GGFS alone will be enough to gain significant performance increase. However, while we saw significant performance gains using GGFS, when working with our customers we quickly found that there were some not so obvious performance limitations to the way in which Hadoop performs MapReduce. It quickly became clear to us that GGFS alone won't be enough but it was a critical piece that we needed to build first.

Note that you shouldn't confuse GGFS with much slower alternatives like RAM disk. GGFS is based on our Memory-First architecture and addresses more than just the seek time of the "device".

From the get go we designed GGFS to support both Hadoop v1 and YARN Hadoop v2. Further, we designed GGFS to work in two modes:

  • Primary (standalone), and
  • Secondary (caching HDFS).

In primary standalone mode GGFS acts as a bona-fide Hadoop file system that is PnP compatible with the standard HDFS interface. Our customers use it to deploy a high-performance in-memory Hadoop cluster and use it as any other Hadoop file system - albeit one that trades capacity for maximum performance.

One of the great added benefits of the primary mode is that it does away with NamedNode in the Hadoop deployment. Unlike a standard Hadoop deployment that requires shared storage for primary and secondary NameNodes which is usually implemented with a complex NFS setup mounted on each NameNode machine, GGFS seamlessly utilizes GridGain's In-Memory Database under the hood to provide completely automatic scaling and failover without any need for additional shared storage or risky Single Point Of Failure (SPOF) architectures.

Furthermore, unlike Hadoop's master-slave design for NamedNodes that prevents it from linear runtime scaling when adding new nodes, GGFS is built on a highly scalable, natively distributed partitioned data store that provides linear scalability and auto-discovery of new nodes. Removing NamedNode form the picture and all its chattiness enabled dramatically better performance for IO operations.

GGFS primary mode provides maximum performance for IO operations but will require moving data from disk-based HDFS to in-memory based GGFS (i.e. from one file system to another). While data movement may be appropriate for some use cases, we have a second mode, in which absolutely no ETL is required.

In the second mode, GGFS works as an intelligent secondary in-memory distributed cache over the primary disk-based HDFS file system. In this mode GGFS supports bothsynchronous and asynchronous read-through and write-through to and from HDFS providing either strong consistency or better performance in exchange for relaxed consistency with absolute transparency to the user and applications running on top of it. In this mode users can manually select which set of files and/or directories should be stored in GGFS and what mode - synchronous or asynchronous - should be used for each one of them for read-through and write-through to and from HDFS.

Another interesting feature of GGFS is its smart usage of block-level or file-level caching and eviction design. When working in primary mode GGFS utilizes file level caching to ensure corruption free storage (the file is either fully in GGFS or not at all). When in secondary mode, GridGain will automatically switch to block-level caching and eviction. What we discovered when working with our customers on real-world Hadoop payloads is that files on HDFS are often accessed not uniformly, i.e. they have significant "locality" in how portions of the file is being accessed. Put another way, certain blocks of a file are accessed more frequently than others. That observation led to our block-level caching implementation for the secondary mode that enables dramatically better memory utilization since GGFS can store only the most frequently used file blocks in memory - and not entire files which can easily measure in 100GBs in Hadoop.

No good caching can work effectively without equally sophisticated eviction management to make sure that memory is optimally utilized - and we've built a very neat one too. Apart from obvious eviction features you can configure certain files to never be evicted preserving them in memory in all cases for maximum performance.

To ensure seamless and continuous performance during MapReduce file scanning, we've implemented smart data prefetching via streaming data that is expected to be read in the nearest future to the MapReduce task ahead of time. By doing so, GGFS ensures that whenever a MapReduce task finishes reading a file block, the next file block is already available in memory. A significant performance boost was achieved here due to ourproprietary Inter-Process Communication (IPC) implementation which allows GGFS to achieve throughput of up to 30Gbit/s between two processes.

The table below shows GGFS vs. HDFS (on Flash-based SSDs) benchmark results for raw IO operations:

BenchmarkGGFS, ms.HDFS, ms.Boost, %
File Scan 27 667 2470%
File Create 96 961 1001%
File Random Access 413 2931 710%
File Delete 185 1234 667%

The above tests were performed on a 10-node cluster of Dell R610 blades with Dual 8-core CPUs, running Ubuntu 12.4 OS, 10GBE network fabric and stock unmodified Apache Hadoop 2.x distribution.

As you can see from these results the IO performance difference is quite significant. However, HDFS performance is only part of the total Hadoop overhead. Another part is MapReduce overhead and that's what we address with In-Memory MapReduce.

In-Memory MapReduce
Once we had our high performance in-memory file system built and tested, we turned our attention to a MapReduce implementation that would take advantage of in-memory technology.

Hadoop's MapReduce design is one of the weakest points in Hadoop. It's basically a inefficiently designed system when it comes to distributed processing. GridGain In-Memory MapReduce implementation relies heavily on 7 years of experience developing our widely deployed In-Memory HPC product. GridGain's In-Memory MapReduce is designed on record-based approach vs. key-value approach of traditional MapReduce, and it enables much more streamlined parallel execution path on data stored in in-memory file system.

Furthermore, In-Memory MapReduce eliminates the standard overhead associated with the typical Hadoop job tracker polling, task tracker process creation, deployment and provisioning. All in all - GridGain's In-Memory MapReduce is a highly optimized HPC-based implementation of the MapReduce concept enabling true low-latency data processing of data stored in GGFS.

The diagram below demonstrates the difference between a standard Hadoop MapReduce execution path and GridGain's In-Memory MapReduce execution path:

gg_hadoop_mapred_800

As seen in this diagram our MapReduce implementation supports direct execution path from client to data node. Moreover, all execution in GridGain happens in-process with deployment handled automatically and transparently by GridGain.

In-Memory MapReduce also provides integration capability for MapReduce code written in any Hadoop supported language and not only in native Java or Scala. Developers can easily reuse existing C/C++/Python or any other existing MapReduce code with our In-Memory Accelerator for Hadoop to gain significant performance boost.

So finally - now that we can remove the task and job tracker polling, out of process execution, and the often unnecessary shuffling and sorting from MapReduce and couple it with high-performance in-memory file system we started seeing anywhere between 10x and 100x performance increases on typical MapReduce payloads in our tests.

Below are the results for one of the internal tests that utilizes both In-Memory File System and In-Memory MapReduce. This test was specifically designed to show maximum GridGain's Accelerator performance vs. stock Hadoop distribution for heavy I/O MapReduce jobs:

NodesHadoop, ms.Hadoop + GridGain Accelerator, ms.Boost, %
5 298,000 11,622 2,564%
10 201,350 5,537 3,636%
15 158,997 2,385 6,667%
20 122,008 1,647 7,407%
30 97,833 1,174 8,333%
40 82,771 780 10,612%

hadoop_chart

Tests were performed on a cluster of Dell R610 blades with Dual 8-core CPUs, running Ubuntu 12.4 OS, 10GBE network fabric and stock unmodified Apache Hadoop 2.x distribution and GridGain 5.2 release.

Management and Monitoring
No serious distributed system can be used without comprehensive DevOps support and In-Memory Accelerator for Hadoop comes with a comprehensive unified GUI-based management and monitoring tool called GridGain Visor. Over the last 12 months we've added significant support in Visor for Hadoop Accelerator.

Visor provides deep DevOps capabilities including an operations & telemetry dashboard, database and compute grid management, as well as GGFS management that provides GGFS monitoring and file management between HDFS, local and GGFS file systems.

visor_fm2

visor_ggfs

As part of GridGain Visor, In-Memory Accelerator For Hadoop also comes with a GUI-based file system profiler, which allows you to keep track of all operations your GGFS or HDFS file systems make and identifies potential hot spots.

GGFS profiler tracks speed and throughput of reads, writes, various directory operations, for all files and displays these metrics in a convenient view which allows you to sort based on any profiled criteria, e.g. from slowest write to fastest. Profiler also makes suggestions whenever it is possible to gain performance by loading file data into in-memory GGFS.

visor_profiler

Conclusion
After almost 2 years of development we have a well rounded product that can help you accelerate Hadoop MapReduce up to 100x times with minimal integration and effort. It's based on our innovative high-performance in-memory file system and in-memory MapReduce implementation coupled with one of the best management and monitoring tools.

If you want to be able to say words "milliseconds" and "Hadoop" in one sentence - you need to take a serious look at GridGain's In-Memory Hadoop Accelerator.

hadoop_acc_logo

More Stories By Nikita Ivanov

Nikita Ivanov is founder and CEO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita has led GridGain to develop advanced and distributed in-memory data processing technologies – the top Java in-memory computing platform starting every 10 seconds around the world today.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996.

He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from Baltic State Technical University, Saint Petersburg, Russia.

@ThingsExpo Stories
SYS-CON Events announced today that DXWorldExpo has been named “Global Sponsor” of SYS-CON's 21st International Cloud Expo, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Digital Transformation is the key issue driving the global enterprise IT business. Digital Transformation is most prominent among Global 2000 enterprises and government institutions.
SYS-CON Events announced today that NetApp has been named “Bronze Sponsor” of SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. NetApp is the data authority for hybrid cloud. NetApp provides a full range of hybrid cloud data services that simplify management of applications and data across cloud and on-premises environments to accelerate digital transformation. Together with their partners, NetApp em...
SYS-CON Events announced today that SIGMA Corporation will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. uLaser flow inspection device from the Japanese top share to Global Standard! Then, make the best use of data to flip to next page. For more information, visit http://www.sigma-k.co.jp/en/.
SYS-CON Events announced today that N3N will exhibit at SYS-CON's @ThingsExpo, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. N3N’s solutions increase the effectiveness of operations and control centers, increase the value of IoT investments, and facilitate real-time operational decision making. N3N enables operations teams with a four dimensional digital “big board” that consolidates real-time live video feeds alongside IoT sensor data a...
Real IoT production deployments running at scale are collecting sensor data from hundreds / thousands / millions of devices. The goal is to take business-critical actions on the real-time data and find insights from stored datasets. In his session at @ThingsExpo, John Walicki, Watson IoT Developer Advocate at IBM Cloud, will provide a fast-paced developer journey that follows the IoT sensor data from generation, to edge gateway, to edge analytics, to encryption, to the IBM Bluemix cloud, to Wa...
There is huge complexity in implementing a successful digital business that requires efficient on-premise and cloud back-end infrastructure, IT and Internet of Things (IoT) data, analytics, Machine Learning, Artificial Intelligence (AI) and Digital Applications. In the data center alone, there are physical and virtual infrastructures, multiple operating systems, multiple applications and new and emerging business and technological paradigms such as cloud computing and XaaS. And then there are pe...
DevOps at Cloud Expo – being held October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA – announces that its Call for Papers is open. Born out of proven success in agile development, cloud computing, and process automation, DevOps is a macro trend you cannot afford to miss. From showcase success stories from early adopters and web-scale businesses, DevOps is expanding to organizations of all sizes, including the world's largest enterprises – and delivering real r...
SYS-CON Events announced today that B2Cloud will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. B2Cloud specializes in IoT devices for preventive and predictive maintenance in any kind of equipment retrieving data like Energy consumption, working time, temperature, humidity, pressure, etc.
SYS-CON Events announced today that Massive Networks, that helps your business operate seamlessly with fast, reliable, and secure internet and network solutions, has been named "Exhibitor" of SYS-CON's 21st International Cloud Expo ®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. As a premier telecommunications provider, Massive Networks is headquartered out of Louisville, Colorado. With years of experience under their belt, their team of...
SYS-CON Events announced today that Suzuki Inc. will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Suzuki Inc. is a semiconductor-related business, including sales of consuming parts, parts repair, and maintenance for semiconductor manufacturing machines, etc. It is also a health care business providing experimental research for...
SYS-CON Events announced today that Fusic will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Fusic Co. provides mocks as virtual IoT devices. You can customize mocks, and get any amount of data at any time in your test. For more information, visit https://fusic.co.jp/english/.
SYS-CON Events announced today that Ryobi Systems will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Ryobi Systems Co., Ltd., as an information service company, specialized in business support for local governments and medical industry. We are challenging to achive the precision farming with AI. For more information, visit http:...
SYS-CON Events announced today that Keisoku Research Consultant Co. will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Keisoku Research Consultant, Co. offers research and consulting in a wide range of civil engineering-related fields from information construction to preservation of cultural properties. For more information, vi...
SYS-CON Events announced today that Daiya Industry will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Daiya Industry specializes in orthotic support systems and assistive devices with pneumatic artificial muscles in order to contribute to an extended healthy life expectancy. For more information, please visit https://www.daiyak...
SYS-CON Events announced today that Interface Corporation will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Interface Corporation is a company developing, manufacturing and marketing high quality and wide variety of industrial computers and interface modules such as PCIs and PCI express. For more information, visit http://www.i...
SYS-CON Events announced today that Mobile Create USA will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Mobile Create USA Inc. is an MVNO-based business model that uses portable communication devices and cellular-based infrastructure in the development, sales, operation and mobile communications systems incorporating GPS capabi...
In his session at @ThingsExpo, Greg Gorman is the Director, IoT Developer Ecosystem, Watson IoT, will provide a short tutorial on Node-RED, a Node.js-based programming tool for wiring together hardware devices, APIs and online services in new and interesting ways. It provides a browser-based editor that makes it easy to wire together flows using a wide range of nodes in the palette that can be deployed to its runtime in a single-click. There is a large library of contributed nodes that help so...
Elon Musk is among the notable industry figures who worries about the power of AI to destroy rather than help society. Mark Zuckerberg, on the other hand, embraces all that is going on. AI is most powerful when deployed across the vast networks being built for Internets of Things in the manufacturing, transportation and logistics, retail, healthcare, government and other sectors. Is AI transforming IoT for the good or the bad? Do we need to worry about its potential destructive power? Or will we...
SYS-CON Events announced today that mruby Forum will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. mruby is the lightweight implementation of the Ruby language. We introduce mruby and the mruby IoT framework that enhances development productivity. For more information, visit http://forum.mruby.org/.
SYS-CON Events announced today that Nihon Micron will exhibit at the Japan External Trade Organization (JETRO) Pavilion at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Nihon Micron Co., Ltd. strives for technological innovation to establish high-density, high-precision processing technology for providing printed circuit board and metal mount RFID tags used for communication devices. For more inf...