Welcome!

Article

Agile Program Language to Deal with Complex Procedures

Parallel computing with agile program language will be the future

Hadoop is an outstanding parallel computing system whose default parallel computing mode is MapReduce. However, such parallel computing is not specially designed for parallel data computing. Plus, it is not an agile parallel computing program language, the coding efficiency for data computing is relatively low, and this parallel computing is even more difficult to compose the universal algorithm.

Regarding the agile program language and parallel computing, esProc and MapReduce are very similar in function.

Here is an example illustrating how to develop parallel computing in Hadoop with an agile program language. Take the common Group algorithm in MapReduce for example: According to the order data on HDFS, sum up the sales amount of sales person, and seek the top N salesman. In the example code of agile program language, the big data file fileName, fields-to-group groupField, fileds-to-summarizing sumField, syntax-for-summarizing method, and the top-N-list topN are all parameters. In esProc, the corresponding agile program language codes are shown below:

Agile program language code for summary machine:

Agile program language code for node machine:

How to perform the parallel data computing over big data? The most intuitive idea occurs to you would be: Decompose a task into several parallel segments to conduct parallel computing; distribute them to the unit machine to summarize initially; and then further summarize the summary machine for the second time.

From the above codes, we can see that esProc has parallel data computing into two categories: The respective codes for summary machine and node machine. The summary machine is responsible for task scheduling, distributing the task to every parallel computing node in the form of parameter to conduct parallel computing, and ultimately consolidating and summarizing the parallel computing results from parallel computing node machines. The node machines are used to get a segment of the whole data piece as specified by parameters, and then group and summarize the data of this segment.

Then, let's discuss the above-mentioned parallel data computingcodes in details.

Variable definition in parallel computing

As can be seen from the above parallel computing codes, esProc is the codes written in the cells. Each cell is represented with a unique combination of row ID and column ID. The variable is the cell name requiring no definition, for example, in the summary machine code:

n  A2: =40

n  A6: = ["192. 168. 1. 200: 8281","192. 168. 1. 201: 8281","192. 168. 1. 202: 8281","192. 168. 1. 203: 8281"]

A2 and A6 are just two variables representing the number of parallel computing tasks and the list of node machines respectively. The other agile program language codes can reference the variables with the cell name directly. For example, the A3, A4, and A5 all reference A2, and A7 references A6.

Since the variable is itself the cell name, the reference between cells is intuitive and convenient. Obviously, this parallel computing method allows for decomposing a great goal into several simple parallel computing steps, and achieving the ultimate goal by invoking progressively between steps. In the above codes: A8 makes references to A7, A9 references the A8, and A9 references A10. Each step is aimed to solve a small problem in parallel computing. Step by step, the parallel computing goal of this example is ultimately solved.

 

External parameter in parallel computing

 

In esProc, a parameter can be used as the normal parameter or macro. For example, in the agile program language code of summary machine, the fileName, groupField, sumField, and method are all external parameters:

n  A1: =file(fileName). size()

n  A7: =callx("groupSub. dfx",A5,A4,fileName,groupField,sumField,method;A6)

They respectively have the below meanings:

n  filename, the name of big data file, for example, " hdfs: //192. 168. 1. 10/sales. txt"

n  groupField, fields to group, for example: empID

n  sumField, fields to summarize, for example: amount

n  parallel computing method, method for summarizing, for example: sum, min, max, and etc.

If enclosing parameter with ${}, then this enclosed parameter can be used as macro, for example, the piece of agile program language code from summary machine

n  A8: =A7. merge(${gruopField})

n  A9: =A8. [email protected](${gruopField};${method}(Amount): sumAmount)

In this case, the macro will be interpreted as code by esProc to execute, instead of the normal parameters. The translated parallel computing codes can be:

n  A8: =A7. merge(empID)

n  A9: =A8. [email protected](empID;sum(Amount): sumAmount)

 

Macro is one of the dynamic agile program languages. Compared with parameters, macro can be used directly in data computing as codes in a much more flexible way, and reused very easily.

 

Two-dimensional table in A10

Why A10 deserves special discussion? It is because A10 is a two-dimensional table. This type of tables is frequently used in our parallel data computing. There are two columns, representing the character string type and float type respectively. Its structure is like this:

empID

sumAmount

C010010

456734. 12

C010211

443123. 15

C120038

421348. 41

...

...

In this parallel computing solution, the application of two-dimensional table itself indicates that esProc supports the dynamic data type. In other words, we can organize various types of data to one variable, not having to make any extra effort to specify it. The dynamic data type not only saves the effort of defining the data type, but is also convenient for its strong ability in expressing. In using the above two-dimensional table, you may find that using the dynamic data type for big data parallel computing would be more convenient.

Besides the two-dimensional table, the dynamic data type can also be array, for example, A3: =to(A2), A3 is an array whose value is [1,2,3.... . 40]. Needless to say, the simple values are more acceptable. I've verified the data of date, string, and integer types.

The dynamic data type must support the nested data structure. For example, the first member of array is a member, the second member is an array, and the third member is a two-dimensional table. This makes the dynamic data type ever more flexible.

Parallel computing functions for big data

In esProc, there are many functions that are aimed for the big data parallel computing, for example, the A3 in the above-mentioned codes: =to(A2), then it generates an array [1,2,3.... . 40].

Regarding this array, you can directly compute over each of its members without the loop statements, for example, A4: =A3. (long(~*A1/A2)). In this formula, the current member of A3 (represented with "~") will be multiplied with A1, and then divided by A2. Suppose A1=20000000, then the computing result of A4 would be like this: [50000, 100000, 1500000, 2000000... 20000000]

The official name of such function is loop function, which is designed to make the agile program language more agile by reducing the loop statements.

The loop functions can be used to handle whatsoever big data parallel computing; even the two-dimensional tables from the database are also acceptable. For example, A8, A9, A10 - they are loop functions acting on the two dimensional table:

n  A8: =A7. merge(${gruopField})

n  A9: =A8. [email protected](${gruopField};${method}(Amount): sumAmount)

n  A10: =A9. sort(sumAmount: -1). select(#<=10)

Parameters in the loop function

Check out the codes in A10: =A9. sort(sumAmount: -1). select(#<=10)

sort(sumAmount: -1) indicates to sort in reverse order by the sumAmount field of the two-dimensional table of A9. select(#<=10) indicates to filter the previous result of sorting, and filter out the records whose serial numbers (represented with #) are not greater than 10.

The parameters of these two parallel computing functions are not the fixed parameter value but parallel computing method. They can be formulas or functions. The usage of such parallel computing parameter is the parameter formula.

As can be seen here, the parameter formula is also more agile syntax program language. It makes the usage of parameters more flexible. The function calling is more convenient, and the workload of coding can be greatly reduced because of its parallel computing mechanism.

From the above example, we can see that esProc can be used to write Hadoop with an agile program language with parallel computing. By doing so, the code maintenance cost is greatly reduced, and the code reuse and data migration would be ever more convenient and better performance with parallel computing mechanism.

Personal blog: http://datakeyword.blogspot.com/

Web: http://www.raqsoft.com/

More Stories By Jessica Qiu

Jessica Qiu is the editor of Raqsoft. She provides press releases for data computation and data analytics.

Latest Stories
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 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 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 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...
WebRTC is great technology to build your own communication tools. It will be even more exciting experience it with advanced devices, such as a 360 Camera, 360 microphone, and a depth sensor camera. In his session at @ThingsExpo, Masashi Ganeko, a manager at INFOCOM Corporation, will introduce two experimental projects from his team and what they learned from them. "Shotoku Tamago" uses the robot audition software HARK to track speakers in 360 video of a remote party. "Virtual Teleport" uses a mu...
Internet of @ThingsExpo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devic...
Mobile device usage has increased exponentially during the past several years, as consumers rely on handhelds for everything from news and weather to banking and purchases. What can we expect in the next few years? The way in which we interact with our devices will fundamentally change, as businesses leverage Artificial Intelligence. We already see this taking shape as businesses leverage AI for cost savings and customer responsiveness. This trend will continue, as AI is used for more sophistica...
SYS-CON Events announced today that SourceForge has been named “Media 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. SourceForge is the largest, most trusted destination for Open Source Software development, collaboration, discovery and download on the web serving over 32 million viewers, 150 million downloads and over 460,000 active development projects each and every month.
"NetApp's vision is how we help organizations manage data - delivering the right data in the right place, in the right time, to the people who need it, and doing it agnostic to what the platform is," explained Josh Atwell, Developer Advocate for NetApp, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
What You Need to Know You know you need the cloud, but you’re hesitant to simply dump everything at Amazon since you know that not all workloads are suitable for cloud. You know that you want the kind of ease of use and scalability that you get with public cloud, but your applications are architected in a way that makes the public cloud a non-starter. You’re looking at private cloud solutions based on hyperconverged infrastructure, but you’re concerned with the limits inherent in those technolog...
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.
One of the biggest challenges with adopting a DevOps mentality is: new applications are easily adapted to cloud-native, microservice-based, or containerized architectures - they can be built for them - but old applications need complex refactoring. On the other hand, these new technologies can require relearning or adapting new, oftentimes more complex, methodologies and tools to be ready for production. In his general session at @DevOpsSummit at 20th Cloud Expo, Chris Brown, Solutions Marketi...
Most of the time there is a lot of work involved to move to the cloud, and most of that isn't really related to AWS or Azure or Google Cloud. Before we talk about public cloud vendors and DevOps tools, there are usually several technical and non-technical challenges that are connected to it and that every company needs to solve to move to the cloud. In his session at 21st Cloud Expo, Stefano Bellasio, CEO and founder of Cloud Academy Inc., will discuss what the tools, disciplines, and cultural...
Why Federal cloud? What is in Federal Clouds and integrations? This session will identify the process and the FedRAMP initiative. But is it sufficient? What is the remedy for keeping abreast of cutting-edge technology? In his session at 21st Cloud Expo, Rasananda Behera will examine the proposed solutions: Private or public or hybrid cloud Responsible governing bodies How can we accomplish?
DevOps at Cloud Expo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to w...