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. groups@o(${gruopField};${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. groups@o(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. groups@o(${gruopField};${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
Information technology (IT) advances are transforming the way we innovate in business, thereby disrupting the old guard and their predictable status-quo. It’s creating global market turbulence. Industries are converging, and new opportunities and threats are emerging, like never before. So, how are savvy chief information officers (CIOs) leading this transition? Back in 2015, the IBM Institute for Business Value conducted a market study that included the findings from over 1,800 CIO interviews ...
With the proliferation of both SQL and NoSQL databases, organizations can now target specific fit-for-purpose database tools for their different application needs regarding scalability, ease of use, ACID support, etc. Platform as a Service offerings make this even easier now, enabling developers to roll out their own database infrastructure in minutes with minimal management overhead. However, this same amount of flexibility also comes with the challenges of picking the right tool, on the right ...
“We're a global managed hosting provider. Our core customer set is a U.S.-based customer that is looking to go global,” explained Adam Rogers, Managing Director at ANEXIA, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
In today's uber-connected, consumer-centric, cloud-enabled, insights-driven, multi-device, global world, the focus of solutions has shifted from the product that is sold to the person who is buying the product or service. Enterprises have rebranded their business around the consumers of their products. The buyer is the person and the focus is not on the offering. The person is connected through multiple devices, wearables, at home, on the road, and in multiple locations, sometimes simultaneously...
Security, data privacy, reliability and regulatory compliance are critical factors when evaluating whether to move business applications from in-house client hosted environments to a cloud platform. In her session at 18th Cloud Expo, Vandana Viswanathan, Associate Director at Cognizant, In this session, will provide an orientation to the five stages required to implement a cloud hosted solution validation strategy.
"We host and fully manage cloud data services, whether we store, the data, move the data, or run analytics on the data," stated Kamal Shannak, Senior Development Manager, Cloud Data Services, IBM, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
China Unicom exhibit at the 19th International Cloud Expo, which took place at the Santa Clara Convention Center in Santa Clara, CA, in November 2016. China United Network Communications Group Co. Ltd ("China Unicom") was officially established in 2009 on the basis of the merger of former China Netcom and former China Unicom. China Unicom mainly operates a full range of telecommunications services including mobile broadband (GSM, WCDMA, LTE FDD, TD-LTE), fixed-line broadband, ICT, data communica...
All organizations that did not originate this moment have a pre-existing culture as well as legacy technology and processes that can be more or less amenable to DevOps implementation. That organizational culture is influenced by the personalities and management styles of Executive Management, the wider culture in which the organization is situated, and the personalities of key team members at all levels of the organization. This culture and entrenched interests usually throw a wrench in the work...
Zerto exhibited at SYS-CON's 18th International Cloud Expo®, which took place at the Javits Center in New York City, NY, in June 2016. Zerto is committed to keeping enterprise and cloud IT running 24/7 by providing innovative, simple, reliable and scalable business continuity software solutions. Through the Zerto Cloud Continuity Platform™, organizations can seamlessly move and protect virtualized workloads between public, private and hybrid clouds. The company’s flagship product, Zerto Virtual...
As businesses adopt functionalities in cloud computing, it’s imperative that IT operations consistently ensure cloud systems work correctly – all of the time, and to their best capabilities. In his session at @BigDataExpo, Bernd Harzog, CEO and founder of OpsDataStore, will present an industry answer to the common question, “Are you running IT operations as efficiently and as cost effectively as you need to?” He will expound on the industry issues he frequently came up against as an analyst, and...
WebRTC is about the data channel as much as about video and audio conferencing. However, basically all commercial WebRTC applications have been built with a focus on audio and video. The handling of “data” has been limited to text chat and file download – all other data sharing seems to end with screensharing. What is holding back a more intensive use of peer-to-peer data? In her session at @ThingsExpo, Dr Silvia Pfeiffer, WebRTC Applications Team Lead at National ICT Australia, looked at differ...
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...
IoT offers a value of almost $4 trillion to the manufacturing industry through platforms that can improve margins, optimize operations & drive high performance work teams. By using IoT technologies as a foundation, manufacturing customers are integrating worker safety with manufacturing systems, driving deep collaboration and utilizing analytics to exponentially increased per-unit margins. However, as Benoit Lheureux, the VP for Research at Gartner points out, “IoT project implementers often un...
SYS-CON Events announced today that Technologic Systems Inc., an embedded systems solutions company, will exhibit at SYS-CON's @ThingsExpo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Technologic Systems is an embedded systems company with headquarters in Fountain Hills, Arizona. They have been in business for 32 years, helping more than 8,000 OEM customers and building over a hundred COTS products that have never been discontinued. Technologic Systems’ pr...
SYS-CON Events announced today that IoT Now has been named “Media 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. IoT Now explores the evolving opportunities and challenges facing CSPs, and it passes on some lessons learned from those who have taken the first steps in next-gen IoT services.