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 a 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 ever 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 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 has 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 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, to have greater storage capacity, higher computing performance, and several parallel processes far more than that of IOE. That’s why Hadoop is highly anticipated.
However, IOE still has an advantage over Hadoop for its great data computing capability. 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 complex business logic, 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 fact, 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 it 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 middleware of Hadoop. esProc is just the choice to meet this demand.
esProc is a parallel computing framework software that 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, you can find an alternative to IOE to perform a range of data computing of whatsoever complexity. It is especially good at the computation requiring complex business logic and stored procedures.
esProc supports the professional data scripting languages, offering the true set data type, easy for algorithm design from the business client’s perspective, and effortless to implement the complex business logic of clients. Also, esProc supports the ordered set for arbitrary access to the member of the set and performs serial-number-related computation. The set of a set can be used to represent the complex grouping style easily, for example, the equal grouping, align grouping, and enum grouping. Users can operate on the single record in the as same way of operating on an object. esProc scripts are written and presented in a grid. In this way, the intermediate result can be referenced without definition. To add convenience, the complete code editing and debugging functions are provided. esProc can be regarded as a dynamic set-lized language that has something in common with the 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 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.
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.