Features:esProc vs. Hadoop

esProc does not belong to Hadoop ecological system, and its parallel computing has nothing to do with MapReduce.

esProc aims at improving complex (semi)structured data computing, while Hadoop aims at mathematical algorithm of huge volume non-structured data.

esProc only focus on the computing jobs in (semi) structured data process, while hadoop has complete ecosystem and product line.

esProc can take Hadoop as data source to access HDFS and call Hive to control complex procedure, and assist Hadoop to accomplish complex procedure based computing.

esProc has more flexible and simpler parallel mechanism, and performance is close to or even better than database under small or mediate clusters. esProc can also achieve light-weight parallel commuting based on Hadoop.

Several Methods for Structured Big Data Computation

All data can only have the existence value by getting involved in the computation to create value. The big data makes no exception. The computational capability on structural big data determines the range of practical applications of big data. In this article, I'd like to introduce several commonest computation methods: API, Script, SQL, and SQL-like languages.

API: The "API" here refers to a self-contained API access method without using JDBC or ODBC. Let's take MapReduce as an example. MapReduce is designed to handle the parallel computation cost-effectively from the very bottom layer. So, MapReduce offers superior scale-out, hot-swap, and cost efficiency. MapReduce is one of the Hadoop components with open-source code and abundant resources.

Sample code:

                public void reduce(Text key, Iterator<Text> value,

                OutputCollector<Text, Text> output, Reporter arg3)

                throws IOException {

            double avgX=0;

            double avgY=0;

            double sumX=0;

            double sumY=0;

            int count=0;

            String [] strValue = null;

            while(value.hasNext()){

                count++;

                strValue = value.next().toString().split("\t");

                sumX = sumX + Integer.parseInt(strValue[1]);

                sumY = sumY + Integer.parseInt(strValue[1]);

            }

            avgX = sumX/count;

            avgY = sumY/count;

            tKey.set("K"+key.toString().substring(1,2));

            tValue.set(avgX + "\t" + avgY);

            output.collect(tKey, tValue);

        }

Since the universal programming language adopted is unsuitable for the specialized data computing, MapReduce is less capable than SQL and other specialized computation languages in computing. Plus, it is inefficient in developing. No wonder that the programmers generally complain it is "painful". In addition, the rigid framework of MapReduce results in the relatively poorer performance.

There are several products using API, and MapReduce is the most typical one among them.

Script: The "Script" here refers to the specialized script for computing. Take esProc as an example. esProc is designed to improve the computational capability of Hadoop. So, in addition to the inexpensive scale-out, it also offers the high performance, great computational capability, and convenient computation between heterogeneous data sources, especially ideal for achieving the complex computational goal. In addition, it is the grid-stylescript characterized with the high development efficiency and complete debug functions.

Sample code:

	A	B
1	=file(“hdfs://192.168.1.200/data/sales.txt”).size()	//file size
2	=10	//number of tasks
3	=to(A2)	//1 ~ 10, 10 tasks
4	=A3.(~*int(A1/A2))	//parameter list for start pos
5	=A3.((~-1)*int(A1/A2)+1)	//parameter list for end pos
6	=callx(“groupSub.dfx”,A5,A4;[“192.168.1.107:8281”,   “192.168.1.108:8281”])	//sub-program calling, 10 tasks to 2 parallel nodes
7	=A6.merge(empID)	//mergingtaskresult
8	=A7.group@i(empID;~.sum(totalAmount):orderAmount ,~.max(max):maxAmount,~.min(min) :minAmount,~.max(max)/~.sum(quantity):avgAmount)	//summarizing is completed

Java users can invoke the result from esProc via JDBC, but they are only allowed to invoke the result in the form of stored procedure instead of any SQL statement. Plus,esProc is not open source. These are two disadvantages of esProc.

The Script is widespread used in MongoDB, Redis, and many other big data solutions, but they are not specialized enough in computing. For another example,the multi-table joining operation for MongoDB is not only inefficient, but also involves the coding of one order of magnitude more complex than that of SQL or esProc.

SQL: The "SQL" here refers to the complete and whole SQL/SP, i.e. ANSI 2000 and its superset. Take Greenplum as an example, the major advantages of Greenplum SQL are the powerful computing, highly efficient developing, and great performance. Other advantages include the widespread use of its language, low learning cost, simple maintenance, and migration possibility -not to mention its trump-card of offering support for stored procedure to handle the complex computation. By this way, business value can be exploited from the big data conveniently.

Sample code:

CREATE OR REPLACE function view.merge_emp()

returns voidas$$

BEGIN

                  truncate view.updated_record;

                  insert into view.updated_recordselect y.* from view.emp_edw x right outer join        emp_src y       on x.empid=y.empid where x.empid is not null;

                  update view.emp_edwset deptno=y.deptno,sal=y.salfrom view.updated_record y       where          view.emp_edw.empid=y.empid;

                  insert into emp_edwselect y.* from emp_edw x right outer join emp_src y on    x.empid=y.empid where  x.empid is null;

end;

$$ language 'plpgsql';

The other databases with the similar structure to MPP include Teradata, Vertical, Oracle, and IBM. Their syntax characteristics are mostly alike. The disadvantages are similar. Theacquisition cost and the ongoing maintenance expenses are extremely high. Charging its users by data scale, the so-called inexpensive Greenplum is actually not a bargain at all - it is way more like making big money under cover of big data. Other disadvantages include awkward debugging, incompatible syntax, lengthy down-time if expansion, and awkward multi-data-source computation.

SQL-likelanguage: It refers to the output interfaces like JDBC/ODBC and only limited to those scripting languages that are the subset of standard SQL. Take Hive QL as an example. The greatest advantage of Hive QL is its ability to scale out cost-effectively while still a convenient tool for users to develop. The SQL syntax feature is kept in Hive QL, so that the learning cost is low, development efficient, and maintenance simple. In addition, Hive is a component of Hadoop. The open-source is another advantage.

Sample code:

SELECT e.* FROM (

         SELECT name, salary, deductions["Federal Taxes"] as ded,

                  salary * (1 – deductions["Federal Taxes"]) as salary_minus_fed_taxes

         FROM employees

         ) e

WHERE round(e.salary_minus_fed_taxes) > 70000;

The weak point of Hive QL is its non-support for stored procedure. Due to this, it is difficult for HiveQL to undertake the complex computation, and thus difficult to provide the truly valuable result. The slightly more complex computation will rely on MapReduce. Needless to say, the development efficiency is low. The poor performance and the threshold time can be regarded as a bane, especially in task allocation, multi-table joining, inter-row computation, multi-level query, and ordered grouping, as well as implementing other algorithm alike. So, it is quite difficult for HiveQL to implement the real-time Hadoop application for big data.

There are also some other products with SQL-like languages - MongoDB as an example - they are still worse than Hive yet.

The big data computation methods currently available are no more than these 4 types of API, Script, SQL, and SQL-like languages. Wish they would develop steadfastly and there would be more and more cost-effective, powerful, practical and usable products for data computing.

About esProc: http://www.raqsoft.com/product-esproc

esProc Helps Database realize Real-time Big Data computing

The Big Data Real-time Application is a scenario to return the computation and analysis results in real time even if there are huge amount of data. This is an emerging demand on database applications in recent years.

In the past, because there are not so many data, the computation is simple, and few parallelisms, the pressure on the database is not great. A high-end or middle-range database server or cluster can allocate enough resource to meet the demand. Moreover, in order to rapidly and parallelly access to the current business data and the historic data, users also tend to arrange a same database server for both the query analysis system and the production system. By this way, the database cost can be lowered, the data management streamlined, and the parallelism ensured to some extent. We are in the prime time of database real time application development.

In recent years, due to the data explosion, and the more and more diversified and complex application, new changes occur to the database system. The obvious change is that the data is growing at an accelerated pace with ever higher and higher volume. Applications are increasingly complex, and the number of concurrent accesses makes no exception. In this time of big data, the database is under increasing pressure, posing a serious challenge to the real-time application.

The first challenge is the real-time. With the heavy workload on the database, the database performance drops dramatically, the response is sluggish, and user experience is going from bad to worse quickly. The normal operation of the critical business system has been affected seriously. The real-time application has actually become the half real-time.

The second challenge is the cost. In order to alleviate the performance pressure, users have to upgrade the database. The database server is expensive, so are the storage media and user license agreement. Most databases require additional charges on the number of CPUs, cluster nodes, and size of storage space. Due to the constant increase of data volume and pressure on database, such upgrade will be done at intervals.

The third challenge is the database application. The increasing pressure on database can seriously affect the core business application. Users would have to off-load the historic data from the database. Two groups of database servers thus come into being: one group for storing the historical data, and the other group for storing the core business data. As we know, the native cross-database query ability of databases are quite weak, and the performance is very low. To deliver the latest and promptest analysis result on time, applications must perform the cross-database query on the data from both groups of databases. The application programing would be getting ever more complex.

The forth challenge is the database management. In order to deliver the latest and promptest analysis result on time, and avoid the complex and inefficient cross-database programming, most users choose to accept the management cost and difficulty increase - timely update the historic library with the latest data from the business library. The advanced edition of database will usually provide the similar subscription & distribution or data duplicate functions.

The real-time big data application is hard to progress when beset with these four challenges.

How to guarantee the parallelism of the big data application? How to reduce the database cost while ensuring the real-time? How to implement the cross-database query easily? How to reduce the management cost and difficulty? This is the one of hottest topics being discussed among the CIOs or CTOs.

esProc is a good remedy to this stubborn headache. It is the database middleware with the complete computational capability, offering the support for the computing no matter in external storage, across databases, or parallelly. The combination of database and esProc can deliver enough capability to solve the four challenges to big data applications.

esProc supports for the computation over files from external storage and the HDFS. This is to say, you can store a great volume of historical data in several cheap hard disks of average PCs, and leave them to esProc to handle. By comparison, database alone can only store and manage the current core business data. The goal of cutting cost and diverting computational load is thus achieved.

esProc supports the parallel cluster computing, so that the computational pressure can be averted to several cheap node machines when there are heavy workload and a great many of parallel and sudden access requests. Its real-time is equal or even superior to that of the high-end database.

esProc offers the complete computational capability especially for the complex data computing. Even it alone can handle those applications involving the complex business logics. What's even better, esProc can do a better job when working with the database. It supports the computations over data from multiple data sources, including various structural data, non-structural data, database data, local files, the big data files in the HDFS, and the distributed databases. esProc can provide a unified JDBC interface to the application at upper level. Thus the coupling difficulty between big data and traditional databases is reduced, the limitation on the single-source report removed, and the difficulty of the big data application reduced.

With the seamless support for the combined computation over files stored in external storage and the database data, users no longer need the complex and expensive data synchronization technology. The database only focus on the current data and core business applications, while esProc enable users to access both the historic data in external storage and the current business data in database. By doing so, the latest and promptest analysis result can be delivered on time.

The cross-database computation and external storage computation capability of esProc can ensure the real-time query while alleviating the pressure on database. Under the assistance of esProc, the big data real-time application can be implemented efficiently at relatively low cost.

About esProc: http://www.raqsoft.com/product-esproc

An Alternative of IOE in the Big Data World

What is IOE? I=IBM, O=Oracle, and E=EMC. They represent the typical high-end database and data warehouse architecture. The high-end servers include HP, IBM, and Fujitsu, the high-end database software includes Teradata, Oracle, Greenplum; the high-end storages include EMC, Violin, and Fusion-io.

In the past, such typical high performance database architecture is the preference of large and middle sized organizations. They can run stably with superior performance, and became popular when the informatization degree was not so high and the enterprise application was simple. With the explosive data growth and the nowadays diversified and complex enterprise applications, most enterprises have gradually realized that they should replacing IOE, and quite a few of them have successfully implemented their road map to cancel the high-end database totally, including Intel, Alibaba, Amazon, eBay, Yahoo, and Facebook.

The data explosion has brought about sharp increase in the storage capacity demand, and the diversified and complex applications pose the challenge to meet the fast-growing computation pressure and parallel access requests. The only solution is to upgrade even more frequently. More and more enterprise managements get to feel the pressure of the great cost to upgrade IOE. More often than not, enterprises still suffer from the slow response and high workloads even if they've invested heavily. That is why these enterprises are determined to replace IOE.

Hadoop is one of the IOE solutions on which the enterprise management have pinned great hope.

It supports the cheap desktop hard disk as a replacement to high-end storage media of IOE.
Its HDFS file system can replace the disk cabinet of IOE, ensuring the secure data redundancy.
It supports the cheap PC to replace the high-end database server.
It is the open source software, not incurring any cost on additional CPUs, storage capacities, and user licenses.

With the support for parallel computing, the inexpensive scale-out can be implemented, and the storage pressure can be averted to multiple inexpensive PCs at less acquisition and management cost, so as to have greater storage capacity, higher computing performance, and a number of paralleling processes far more than that of IOE. That's why Hadoop is highly anticipated.

However, Hadoop’s structured data computation still cannot reach that level as IOE did, especially in relational database led by Oracle. The data computing is the most important software function for the modern enterprise data center. Nowadays, it is normal to find some data computing involving the complex business logics, in particular the applications of enterprise decision-making, procedure optimizing, performance benchmarking, time control, and cost management. However, Hadoop alone cannot replace IOE. As a matter of facts, those enterprises of high-profile champions for replacing IOE have to partly keep the IOE. With the drawback of insufficient computing capability, Hadoop can only be used to compute the simple ETL, data storage and locating, and is awkward to handle the truly massive business data computation.

To replace IOE, we need to have the computational capability no weaker than the enterprise-level database and seamlessly incorporating this capability to Hadoop to give full play to the advantageous computing solution of Hadoop. esProc can meet this demand.

esProc is a parallel computing middleware which is built with pure Java and focused on powering Hadoop. It can access Hive via JDBC or directly read and write to HDFS. With the complete data computing system, esProc can replace the most data computing ability of IOE in a simpler way. It is especially good at the computation requiring complex business logics and stored procedures.

esProc supports the professional data scripting languages, offering the true set data type, easy for algorithm design from business client's perspective, and effortless to implement the complex business logics. In addition, esProc supports the ordered set for arbitrary access to the member of set and perform the serial-number-related computation. The set of set can be used to represent the complex grouping style easily, for example, the equal grouping, align grouping, and enum grouping. esProc also provides the complete code editing and debugging functions. It can be regarded as a dynamic set-lized language which has something in common with R language, and offers native support for distributed parallel computation from the core. Programmers can surely be benefited from the efficient parallel computation of esProc while still having the simple syntax of R. It is built for the data computing, and optimized for data processing. For the complex analysis business, both its development efficiency and computing performance are beyond the existing solution of Hadoop in structured data computing.

The combined use of Hadoop + esProc can fully remedy the drawback to Hadoop, empowering Hadoop to replace the very most of IOE features and improving its computing capability dramatically.

About esProc: http://www.raqsoft.com/product-esproc

Computing Data Outside Database to Alleviate Data Warehouse Expansion Pressure

The data warehouse is essential to enterprise business intelligence, which accounts for a great part of the total enterprise cost.  With the global data explosion in recent years, the business data volume grow significantly, posing a serious challenge for enterprise data warehouse to meet the diverse and complex business demands. More data, more data warehouse applications, more concurrent accesses, higher performance, and faster I/O – all these demands give more pressure on data warehouse. Every IT manager nowadays has concern over expanding the data warehouse capacity at lower cost.

Here is an example. A data warehouse is originally provisioned, as shown below:

Server: One cluster with two high performance database servers.

Storage space: 5TB high performance disk array.

CPU: 8 high performance CPUs.

User license agreement: 100

To meet the storage capacity expansion need for the recent 12 months:

Computational performance: Double

Storage space: Quadruple

Concurrency: Double

How can an IT manager achieve his storage expansion goal? The common practice is to upgrade the database hardware and software: replace with more advanced data warehouse servers, replenish two data warehouse servers of the same class, add a 15T data-warehouse-specific disk or change to a 20T hard disk cabinet, and add 8 CPUs. In addition, they have to pay for the additional user license agreement, CPU, and disk storage space with expensive software licensing fees.

No matter which way you choose to upgrade, the data warehouse vendor will ultimately bind you with their products and charge you for the expansive upgrades.

The computation outside database is an alternative to expand storage capacity. As we all know, of the 20T data warehouse data (including 30% real data, and 70% buffer), the core data is usually less than 1/10, i.e. taking up 1T space. The remaining 19T spaces are all for the redundant data. For example, after a new application is deployed, for the sake of core data security protection, the data warehouse usually requires a copy of the used data, not allowing for the direct access to core data from application. Quite often, the new application needs the access to the records with summarized and processed core data. For which, a core-data-based intermediate table is fabricated to speed the access. Such redundant data are growing with the development of existing and emerging business. The total amount of core data will always keep low.

These redundant data is not the core data, not requiring the high level of security protection. To move these redundant data to the average PC, and use the tools other than database for reading/writing and computing, the cost of database capacity expansion will be reduced dramatically. So, we can say the computation outside database in combination with the database computing is the best choice to achieve the database capacity expansion. The benefits include:

Computational performance: Implement parallel computation across multiple nodes using the inexpensive PCs and desktop CPUs. Compared with the high performance of databases, the same or even greater computational performance can be achieved at the relatively lower cost.

Storage space: With the cost-effective desktop level disk, users can get a storage space far greater than data-warehouse-specific disk at a extremely low cost. HDFS also facilitates the data security protection, access consistency, and non-stop disk capacity expansion.

Concurrency: With the concurrent access from multi-nodes, the centralized concurrent access can be allocated to multiple node machines for more accesses than just the centralized access from data warehouse. In addition, users do not have to pay for the access license agreement, additional CPUs, and disk storage spaces.

It seems that the computation outside database is pretty good. Hadoop and other alike software are available in the market to meet all above demands. But why few people take Hadoop as an option to alleviate the pressure on expanding the data warehouse capacity? This is because they are not as powerful as database in computing, in particular the computation involving complex logics.

What about there is the software meeting the above-mentioned demands on computational performance, storage space, and concurrency, while is still equal or even more powerful than database in computing? With this software, it’s evident that the storage capacity expansion pressure on database will be relieved greatly, so does the database capacity expansion cost.

esProc is built to meet these demands. It is the middleware specially designed to undertake the computation jobs between database and application. For the application layer, esProc has the easy-to-use JDBC interface; For the database layer, esProc is powerful in parallel computation. By implementing the computation outside database or in external storage, esProc alleviates the computational pressure on the database & storage, and concurrency. Owing to this, organizations can cut the cost of database software and hardware effectively while still optimizing the database administration.

esProc is built with a comprehensive and well-defined computing architecture, which is fully capable of sharing the workload on databases, and undertaking various computations of whatsoever complexity for applications. In addition, esProc supports the parallel computations across multiple nodes. The massive or intensive data computation workload can be shared by multiple average servers or inexpensive PCs balancedly.

With the supports for parallel computation, esProc can balancedly decompose and allocate the computation jobs used to solve centrally to multiple average PCs. Each node only needs to undertakes a few data computations.

With esProc, the core data can be stored in the database, while the intermediate table and script deprived from the core data can now be stored outside the database. By leveraging resources reasonably, the workload pressure on database will be alleviated effectively, database cost will be kept under control, management problems will be solved effectively, and various data warehouse applications will be handled with ease. These applications include the real-time high performance application, non-real-time big data application, desktop BI, report application, and ETL.

About esProc: http://www.raqsoft.com/product-esproc

To write Hadoop code using agile program language

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 computing codes 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:

     

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.

Official web: http://www.raqsoft.com/