Welcome!

Java IoT Authors: Elizabeth White, Carmen Gonzalez, Jyoti Bansal, Liz McMillan, Pat Romanski

Related Topics: Java IoT, Microservices Expo, Containers Expo Blog

Java IoT: Article

Dataflow Programming: A Scalable Data-Centric Approach to Parallelism

Dataflow allows developers to easily take advantage of today’s multicore processors

There are two major drivers behind the need to embrace parallelism: the dramatic shift to commodity multicore CPUs, and the striking increase in the amount of data being processed by the applications that run our enterprises. These two factors must be addressed by any approach to parallelism or we will find ourselves falling short of resolving the crisis that is upon us. While there are data-centric approaches that have generated interest, including Map-Reduce, dataflow programming is arguably the easiest parallel strategy to adopt for the millions of developers trained in serial programming.

The blog gives a nice summary of why parallel processing is important.

Hardware Support for Parallelism
Let's start with an overview of the supported parallelism available today in modern processors. First there is processor-level parallelism involving instruction pipelining and other techniques handled by the processor. These are all optimized by compilers and runtime environments such as the Java Virtual Machine. This goodness is available to all developers without much effort on our part.

Recently commodity multicore processors have brought parallelism into the mainstream. As we move into many-core systems, we now have available essentially a "cluster in a box." But, software has lagged behind hardware in the area of parallelism. As a result, many of today's multicore systems are woefully under-utilized. We need a paradigm shift to a new programming model that embraces this high level of parallelism from the start, making it easy for developers to create highly scalable applications. However, focusing only on cores doesn't take into account the whole system. Data-intensive applications by definition have significant amounts of I/O operations. A parallel programming model must take into account parallelizing I/O operations with compute. Otherwise we'll be unable to build applications that can keep the multicore monster fed and happy.

Virtualization is a popular way to divvy up multicore machines. This is essentially treating a single machine as multiple, separate machines. Each virtual slice has its function to provide and each operates somewhat independently. This works well for splitting up IT types of functions such as email servers, and web servers. But it doesn't help with the problem of crunching big data. For big data types of problems, taking advantage of the whole machine, the "cluster in a box," is imperative.

Scale-out, using multiple machines to execute big data jobs, is another way to implement parallelism. This technique has been around for ages and is seeing new instantiations in systems such as Hadoop, built on the Map-Reduce design pattern. Scaling out to large cluster systems certainly has its advantages and is absolutely required for the Internet-scale data problem. It does however introduce inefficiencies that can be critical barriers to full utilization in smaller cluster configurations (less than 100-node size clusters).

The Next Step for Hadoop
In a talk on Hadoop, Jeff Hammerbacher stated, "More programmer-friendly parallel dataflow languages await discovery, I think. MapReduce is one (small) step in that direction." His talk is summarized in this blog. As Jeff points out, Map-Reduce is a great first step, but is lacking as a programming model. Integrating dataflow with the scale-out capabilities available in frameworks such as Hadoop offers the next big step in handling big data.

Dataflow Programming
Dataflow architecture is based on the concept of using a dataflow graph for program execution. A dataflow graph consists of nodes that are computational elements. The edges in a dataflow graph provide data paths between nodes. A dataflow graph is directed and acyclic (DAG). Figure 1 provides a snapshot of an executing dataflow application. Note how all of the nodes are executing in parallel, flowing data in a pipeline fashion.

Figure 1

Nodes in the graph do work by reading data from their input flow(s), transforming the data and pushing the results to their outputs. Nodes that provide connectivity may have only input or output flows. A graph is constructed by creating nodes and linking their data flows together. Once a graph is constructed and executed, the connectivity nodes begin reading data and pushing it downstream. Downstream consumers read the data, process it and send their results downstream. This results in pipeline parallelism, allowing each node in the graph to run in parallel as the pipeline begins to fill.

Dataflow provides a computational model. A dataflow graph must first be constructed before it can be executed. This leads to a very nice modularity: creating building blocks (nodes) that can be plugged together in an endless number of ways to create complex applications. This model is analogous to the UNIX shell model. With the UNIX shell, you can string together multiple commands that are pipelined for execution. Each command reads its input, does something with the data and writes to its output. The commands operate independently in the sense that they don't care what is upstream or downstream from them. It is up to the pipeline composer (the end user) to create the pipeline correctly to process the data as wanted. Dataflow is very similar to this model, but provides more capabilities.

The dataflow architecture provides flow control. Flow control prevents fast producers from overrunning slower consumers. Flow control is inherent in the way dataflow works and puts no burden on the programmer to deal with issues such as deadlock or race conditions.

Dataflow is focused on data parallelism. As such, it is not a great fit for all computational problems. But as has become evident over the past few years, there are many domains of parallel problems and one solution or architecture will not solve all problems for all domains. Dataflow provides a different programming paradigm for most developers, so it requires a bit of a shift in thinking to a more data-centric way of designing solutions. But once that shift takes place, dataflow programming is a natural way to express data-centric solutions.

Dataflow Programming and Actors
Dataflow programming and the Actor model available in languages such as Scala and Erlang share many similarities. The Actor model provides for independent actors to communicate using message passing. Within an actor, pattern matching is used to allow an actor determine how to handle a message. Messages are generally asynchronous, but synchronous behavior with flow control can be built on top of the Actor model with some effort.

 

In general, the Actor model is best used for task parallelism. For example, Erlang was originally developed within the telecom industry for building non-stop control systems. Dataflow is data centric and therefore well suited for big data processing tasks.

Dataflow Goodness
As just mentioned, dataflow programming is a different paradigm and so it does require somewhat of a shift in design thinking. This is not a critical issue as the concepts around dataflow are easy to grasp, which is a very important point. A parallel framework that provides great multicore utilization but takes months if not years to master is not all that helpful. Dataflow programming makes the simple things easy and the hard tasks possible.

Dataflow applications are simple to express. Dataflow uses a composition programming model based on a building blocks approach. This leads to very modular designs that provide a high amount of reuse.

Dataflow does a good job of abstracting the details of parallel development. This is important as all of the lower level tools for parallel application development are available today in frameworks such as the java.util.concurrent library available in the JDK. However, these libraries are low-level and require a high degree of expertise to use them correctly. They rely on shared state that must be protected using synchronization techniques that can lead to race conditions, deadlocks and extremely hard-to-debug problems.

Being based on a shared-nothing, immutable message passing architecture makes dataflow a simplified programming model. The nodes within a dataflow graph don't have to worry about using synchronization techniques to produce shared memory. They are lock-free so deadlock and race conditions are not a worry either. The dataflow architecture inherently handles these conditions, allowing the developer to focus on their job at hand. Since the data streams are immutable, this allows multiple readers to attach to the output node. This feature provides more flexibility and reuse in the programming model.

The immutability of the data flows also limits the side effects of nodes within a dataflow program. Nodes within a dataflow graph can only communicate over dataflow channels. By following this model, you are assured that no global state or state of other nodes can be affected by a node. Again, this helps to simplify the programming model. Developing new nodes is free of most of the worries normally involved with parallel programming.

The dataflow programming model is functional in style. Each node within a graph provides a very specific, continuous function on its input data. Programs are built by stitching these functions together in various ways to create complex applications.

Dataflow-based architecture elegantly takes advantage of multicore processors on a single machine (scale up). It's also a good architecture for scaling out to multiple machines. Nodes that run across machine boundaries can communicate over data channels using network sockets. This provides the same simple, flexible dataflow programming model in a distributed configuration.

Dataflow and Big Data
The inherent pipeline parallelism built into dataflow programming makes dataflow great for datasets ranging from thousands to billions of records. Applications written using dataflow techniques can scale easily to extremely large data sizes, generally without much strain on the memory system as a dataflow application will eventually enter into a steady state of memory consumption. The overall amount of data pumped through the application doesn't affect that steady state memory size.

Not all dataflow operators are friendly when it comes to memory consumption. Many are designed specifically to load data into memory. For example a hash join operator may load one of its data sources into an in-memory index. This is the nature of the operator and must be taken into account when using it.

Being pipelined in nature also allows for great overlap of I/O and computational tasks. As mentioned earlier, this is an important "whole" application approach that is highly critical to success in building big data applications.

Dataflow systems are easily embeddable in the current commonly used systems. For instance, a dataflow-based application can easily be executed within the context of a Map-Reduce application. Experimentation with a dataflow-based platform named Pervasive DataRush has shown that the Hadoop system can be used to scale out an application using DataRush within each map step to help parallelize the mapper to take advantage of multicore efficiencies. Allowing each mapper to handle larger chunks of data allows the overall Map-Reduce application to run faster since each mapper is itself parallelized.

Summary
Dataflow is a software architecture that is based on the idea of continuous functions executing in parallel on data streams. It's focused on data-intensive applications, lending itself to today's big data challenges. Dataflow is easy to grasp and simple to express, and this design-time scalability can be as important as its run-time scalability.

Dataflow allows developers to easily take advantage of today's multicore processors and also fits well into a distributed environment. Tackling big data problems with dataflow is straightforward and ensures your applications will be able to scale in the future to meet the growing demands of your organization.

More Stories By Jim Falgout

Jim Falgout has 20+ years of large-scale software development experience and is active in the Java development community. As Chief Technologist for Pervasive DataRush, he’s responsible for setting innovative design principles that guide the company’s engineering teams as they develop new releases and products for partners and customers. He applied dataflow principles to help architect Pervasive DataRush.

Prior to Pervasive, Jim held senior positions with NexQL, Voyence Net Perceptions/KD1 Convex Computer, Sequel Systems and E-Systems. Jim has a B.Sc. (Cum Laude) in Computer Science from Nicholls State University. He can be reached at [email protected]

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@ThingsExpo Stories
The Internet of Things can drive efficiency for airlines and airports. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Sudip Majumder, senior director of development at Oracle, discussed the technical details of the connected airline baggage and related social media solutions. These IoT applications will enhance travelers' journey experience and drive efficiency for the airlines and the airports.
In 2014, Amazon announced a new form of compute called Lambda. We didn't know it at the time, but this represented a fundamental shift in what we expect from cloud computing. Now, all of the major cloud computing vendors want to take part in this disruptive technology. In his session at 20th Cloud Expo, John Jelinek IV, a web developer at Linux Academy, will discuss why major players like AWS, Microsoft Azure, IBM Bluemix, and Google Cloud Platform are all trying to sidestep VMs and containers...
SYS-CON Events announced today that Catchpoint, a leading digital experience intelligence company, has been named “Silver Sponsor” of SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Catchpoint Systems is a leading Digital Performance Analytics company that provides unparalleled insight into your customer-critical services to help you consistently deliver an amazing customer experience. Designed for digital business, C...
With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo 2016 in New York. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be! Internet of @ThingsExpo, taking place June 6-8, 2017, at the Javits Center in New York City, New York, is co-located with 20th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry p...
In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
Things are changing so quickly in IoT that it would take a wizard to predict which ecosystem will gain the most traction. In order for IoT to reach its potential, smart devices must be able to work together. Today, there are a slew of interoperability standards being promoted by big names to make this happen: HomeKit, Brillo and Alljoyn. In his session at @ThingsExpo, Adam Justice, vice president and general manager of Grid Connect, will review what happens when smart devices don’t work togethe...
"Tintri was started in 2008 with the express purpose of building a storage appliance that is ideal for virtualized environments. We support a lot of different hypervisor platforms from VMware to OpenStack to Hyper-V," explained Dan Florea, Director of Product Management at Tintri, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
"There's a growing demand from users for things to be faster. When you think about all the transactions or interactions users will have with your product and everything that is between those transactions and interactions - what drives us at Catchpoint Systems is the idea to measure that and to analyze it," explained Leo Vasiliou, Director of Web Performance Engineering at Catchpoint Systems, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York Ci...
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 ...
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.
SYS-CON Events announced today that Super Micro Computer, Inc., a global leader in Embedded and IoT solutions, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 7-9, 2017, at the Javits Center in New York City, NY. Supermicro (NASDAQ: SMCI), the leading innovator in high-performance, high-efficiency server technology, is a premier provider of advanced server Building Block Solutions® for Data Center, Cloud Computing, Enterprise IT, Hadoop/Big Data, HPC and E...
SYS-CON Events announced today that Linux Academy, the foremost online Linux and cloud training platform and community, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Linux Academy was founded on the belief that providing high-quality, in-depth training should be available at an affordable price. Industry leaders in quality training, provided services, and student certification passes, its goal is to c...
IoT is at the core or many Digital Transformation initiatives with the goal of re-inventing a company's business model. We all agree that collecting relevant IoT data will result in massive amounts of data needing to be stored. However, with the rapid development of IoT devices and ongoing business model transformation, we are not able to predict the volume and growth of IoT data. And with the lack of IoT history, traditional methods of IT and infrastructure planning based on the past do not app...
In the next five to ten years, millions, if not billions of things will become smarter. This smartness goes beyond connected things in our homes like the fridge, thermostat and fancy lighting, and into heavily regulated industries including aerospace, pharmaceutical/medical devices and energy. “Smartness” will embed itself within individual products that are part of our daily lives. We will engage with smart products - learning from them, informing them, and communicating with them. Smart produc...
Fact is, enterprises have significant legacy voice infrastructure that’s costly to replace with pure IP solutions. How can we bring this analog infrastructure into our shiny new cloud applications? There are proven methods to bind both legacy voice applications and traditional PSTN audio into cloud-based applications and services at a carrier scale. Some of the most successful implementations leverage WebRTC, WebSockets, SIP and other open source technologies. In his session at @ThingsExpo, Da...
Why do your mobile transformations need to happen today? Mobile is the strategy that enterprise transformation centers on to drive customer engagement. In his general session at @ThingsExpo, Roger Woods, Director, Mobile Product & Strategy – Adobe Marketing Cloud, covered key IoT and mobile trends that are forcing mobile transformation, key components of a solid mobile strategy and explored how brands are effectively driving mobile change throughout the enterprise.
Smart Cities are here to stay, but for their promise to be delivered, the data they produce must not be put in new siloes. In his session at @ThingsExpo, Mathias Herberts, Co-founder and CTO of Cityzen Data, discussed the best practices that will ensure a successful smart city journey.
WebRTC sits at the intersection between VoIP and the Web. As such, it poses some interesting challenges for those developing services on top of it, but also for those who need to test and monitor these services. In his session at WebRTC Summit, Tsahi Levent-Levi, co-founder of testRTC, reviewed the various challenges posed by WebRTC when it comes to testing and monitoring and on ways to overcome them.
For basic one-to-one voice or video calling solutions, WebRTC has proven to be a very powerful technology. Although WebRTC’s core functionality is to provide secure, real-time p2p media streaming, leveraging native platform features and server-side components brings up new communication capabilities for web and native mobile applications, allowing for advanced multi-user use cases such as video broadcasting, conferencing, and media recording.
The best-practices for building IoT applications with Go Code that attendees can use to build their own IoT applications. In his session at @ThingsExpo, Indraneel Mitra, Senior Solutions Architect & Technology Evangelist at Cognizant, provided valuable information and resources for both novice and experienced developers on how to get started with IoT and Golang in a day. He also provided information on how to use Intel Arduino Kit, Go Robotics API and AWS IoT stack to build an application tha...