China biggest threat to Indian Software in 5 years: Indian Tech CEO

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An interview with a noted Indian Software CEO, mentions China the possible biggest threat in next 5 years at


China could be the biggest threat to India in next five years, positioning itself as the lowest-cost manpower supplier in the IT sector by 2015, according to Mr Vineet Nayar, CEO, HCL Technologies.

“I believe it (China) is the biggest threat in the next five years that we are going to face…So India will have to up its game,” he told reporters on sidelines of ‘Directions’, the company’s annual town hall.

Terming China, as both “threat and opportunity”, Mr Nayar said that India will have to find alternate “differentiators” than the ones it currently has. Despite issues of language and the purported inability to scale-up, China has sharpened its technological and innovation edge, he added.

“Look at the technology companies from China…how does that fit in with the assumption that they (China) do not understand English or technology. They are producing cutting edge technology at a price which is lower than everyone else,” he said.


By 2015, Mr Nayar said, China will be the lowest cost manpower supplier in IT sector to the world


I wonder how he did his forecast. Did he do a time series analysis using a software, did he peer into his crystal ball, or did he spend a lot of time brainstorming with his strategic macro economic team on Chinese threat.

China has various advantages over India (and in fact the US)-

1) Big pool of reliable scientific manpower

2) State funded education in higher studies and STEM

3) Increasing exposure with the West-English speaking is no longer an issue. Almost 50 % of Grad Students in the US in STEM and certain sectors are Chinese and they not only retain fraternal ties with the motherland- they often remain un-assimilated with American Culture mainstream. or they have a separate interaction with fellow American Chinese and seperate with American Americans.

Chinese suffer from some disadvantages in software-

1) Communism Perception- Just because the Govt is communist and likes to confront US once a year (and India twice a month)- is no excuse for the hapless Chinese startup guy to lose out on software outsourcing contracts. unfortunately there have been reported cases where sneak codes have been inserted in code deliverables for American partners, just like American companies are forced to work with DoD (especially in software, embedded chips and telecom)

If you have 10000 lines of code delivered by your Chinese partner, how sure are you of going through each line of code for each sub routine or call procedure.

2) English- Chinese accent is like Chinese cooking. Unique- many Chinese are unable to master the different style of English even after years (derived from Latin and Indo European class of languages)

Sales jobs tend to go to American trained Chinese or to Westerners.

In Indian software companies, accent is a lesser problem.


The biggest threat to Indian software in 5 years is actually Indian software itself- Can it evolve and mature to a product based model from a service only model.

Can Indian software partner with Chinese companies and maybe teach the Indian government why friendship is more profitable than envy and suspicion. If the US and China can trade enormously despite annual tensions, why cant Indian services do the same- if they lose this opportunity, US companies will likely bypass them and create the same GE/McKinsey style backoffices that started the Indian offshoring phenomenon.

3) Lastly- what did the poor American grad student do to deserve that even if devotes years to study STEM (and being called a Geek and Nerd) his job will get outsourced to India or China (if not now- in his 30s or worse in his 40s). Talk to any middle aged IT chap in the US who is middle class- and India and China would figure in why he still worries about his overpriced mortgage.

Unless the US wants only Twitter and Facebook as dominant technologies in the 21 st century.





Sector/ Sphere – Faster than Hadoop/Mapreduce at Terasort

Here is a preview of a relatively young software Sector and Sphere- which are claimed to be better than Hadoop /MapReduce at TeraSort Benchmark among others.

System Overview

The Sector/Sphere stack consists of the Sector distributed file system and the Sphere parallel data processing framework. The objective is to support highly effective and efficient large data storage and processing over commodity computer clusters.

Sector/Sphere Architecture

Sector consists of 4 parts, as shown in the above diagram. The Security server maintains the system security configurations such as user accounts, data IO permissions, and IP access control lists. The master servers maintain file system metadata, schedule jobs, and respond users’ requests. Sector supports multiple active masters that can join and leave at run time and they all actively respond users’ requests. The slave nodes are racks of computers that store and process data. The slaves nodes can be located within a single data center to across multiple data centers with high speed network connections. Finally, the client includes tools and programming APIs to access and process Sector data.

Sphere: Parallel Data Processing Framework

Sphere allows developers to write parallel data processing applications with a very simple set of API. It applies user-defined functions (UDF) on all input data segments in parallel. In a Sphere application, both inputs and outputs are Sector files. Multiple Sphere processing can be combined to support more complicated applications, with inputs/outputs exchanged/shared via the Sector file system.

Data segments are processed at their storage locations whenever possible (data locality). Failed data segments may be restarted on other nodes to achieve fault tolerance.

The Sphere framework can be compared to MapReduce as they both enforce data locality and provide simplified programming interfaces. In fact, Sphere can simulate any MapReduce operations, but Sphere is more efficient and flexible. Sphere can provide better data locality for applications that process files or multiple files as minimum input units and for applications that involve with iterative/combinative processing, which requires coordination of multiple UDFs to obtain the final result.

A Sphere application includes two parts: the client program that organizes inputs (including certain parameters), outputs, and UDFs; and the UDFs that process data segments. Data segmentation, load balancing, and fault tolerance are transparent to developers.

Space: Column-based Distbuted Data Table

Space stores data tables in Sector and uses Sphere for parallel query processing. Space is similar to BigTable. Table is stored by columns and is segmented on to multiple slave nodes. Tables are independent and no relationship between tables are supported. A reduced set of SQL operations is supported, including but not limited to table creation and modification, key-value update and lookup, and select operations based on UDF.

Supported by the Sector data placement mechanism and the Sphere parallel processing framework, Space can support efficient key-value lookup and certain SQL queries on very large data tables.

Space is currently still in development.

and just when you thought Hadoop was the only way to be on the cloud.

The Terasort Benchmark

The table below lists the performance (total processing time in seconds) of the Terasort benchmark of both Sphere and Hadoop. (Terasort benchmark: suppose there are N nodes in the system, the benchmark generates a 10GB file on each node and sorts the total N*10GB data. Data generation time is excluded.) Note that it is normal to see a longer processing time for more nodes because the total amount of data also increases proportionally.

The performance value listed in this page was achieved using the Open Cloud Testbed. Currently the testbed consists of 4 racks. Each rack has 32 nodes, including 1 NFS server, 1 head node, and 30 compute/slave nodes. The head node is a Dell 1950, dual dual-core Xeon 3.0GHz, 16GB RAM. The compute nodes are Dell 1435s, single dual core AMD Opteron 2.0GHz, 4GB RAM, and 1TB single disk. The 4 racks are located in JHU (Baltimore), StarLight (Chicago), UIC (Chicago), and Calit2(San Diego). The inter-rack bandwidth is 10GE, supported by CiscoWave deployed over National Lambda Rail.

Hadoop (3 replicas)
Hadoop (1 replica)
1265 2889 2252
UIC + StarLight
1361 2896 2617
UIC + StarLight + Calit2
1430 4341 3069
UIC + StarLight + Calit2 + JHU
1526 6675 3702

The benchmark uses the testfs/testdc examples of Sphere and randomwriter/sort examples of Hadoop. Hadoop parameters were tuned to reach good results.

Updated on Sep. 22, 2009: We have benchmarked the most recent versions of Sector/Sphere (1.24a) and Hadoop (0.20.1) on a new set of servers. Each server node costs $2,200 and consits of a single Intel Xeon E5410 2.4GHz CPU, 16GB RAM, 4*1TB RAID0 disk, and 1Gb/s NIC. The 120 nodes are hosted on 4 racks within the same data center and the inter-rack bandwidth is 20Gb/s.

The table below lists the performance of sorting 1TB data using Sector/Sphere version 1.24a and Hadoop 0.20.1. Related Hadoop parameters have been tuned for better performance (e.g., big block size), while Sector/Sphere does not require tuning. In addition, to achieve the highest performance, replication is disabled in both systems (note that replication does not afftect the performance of Sphere but will significantly decrease the performance of Hadoop).

Number of Racks
28m 25s 85m 49s
15m 20s 37m 0s
10m 19s 25m 14s
7m 56s 17m 45s
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