Monthly Archives: July 2013

NIO-based Reactor

Over the past few years, event-based architecture with non-blocking operations has been the norm for high-concurrency server architecture. The per-connection threading (process-based) architecture is no longer favored as an efficient design, especially for handling high volume of concurrent connections. The increasing popularity of Nginx and the relative decline of Apache httpd these days demonstrated the trend.

Java New I/O

Java’s NIO (New I/O, a.k.a. Non-blocking I/O) provides a set of APIs to efficiently handle I/O operations. The key ingredients of NIO include Buffer, Channel and Selector. A NIO Buffer virtually provides direct access to the operating system’s physical memory along with a rich set of methods for alignment and paging of the selected memory that stores any primitive-type data of interest. A NIO Channel then serves as the conduit for bulk data transfers between the Buffer and the associated entity (e.g. a socket).

A socket channel can be configured in non-blocking mode and events such as reading data from the associated socket no longer block the invoking thread for more time than necessary. Together with the NIO Selector responsible for selecting those of the concurrent events that are ready to be processed, NIO APIs are well equipped to handle event-based operations in an efficient fashion. The NIO buffer-channel-selector relationship is best explained by an interesting analogy to train-platform-bell at

Non-blocking vs Asynchronous

Note that non-blocking mode is different from asynchronous mode. In non-blocking mode, a requested operation always returns the result immediately regardless of success or failure, thus freeing the invoking thread from being blocked. In asynchronous mode, a separate thread is used to carry out the requested operation in parallel with the invoking thread. Java 7 enhanced NIO to include support for asynchronous file and socket channels.

Reactor pattern

The Reactor pattern is a popular event-based architecture. Using NIO, implementing a basic event-based server on top of Reactor pattern is pretty straight forward. Appended is a bare minimal Reactor-pattern server consisting of a Reactor class and a Handler class.

The single-threaded Reactor class houses the main dispatcher loop responsible for selecting registered events that are ready for socket read/write operations. Registered with the dispatcher during initialization, the also single-threaded Acceptor is responsible for accepting socket connections requested by clients. Finally, the Handler class takes care of the actual events (read from socket, process data, write to socket) in accordance with its operational state.

Each Handler is associated with a SocketChannel and the Selector maintained by the Reactor class. Both variables are declared immutable for performance as well as allowing access by the inner Runnable class. The handler registers with the dispatcher indicating its interested operation (read or write) and gets dispatched when the associated socket is ready for the operation. The Runnable class forms the worker thread pool and is responsible for data processing (in this simple case, echoing), leaving the Handler thread responsible for just socket read/write.

To test the server, just launch it on a host (e.g. and run a few telnet instances connecting to the server port (e.g. telnet 9090).

Programming Exercise – Binary Tree

Like sorting algorithms, binary tree implementation is another good programming exercise. In particular, methods for traversing a tree, searching nodes in a binary search tree and many other binary tree operations form a great recipe for refreshing one’s programming skills. Appended is an implementation of a pretty comprehensive set of binary tree (and binary search tree) operations in Java.

Iterative and recursive methods for each of the operations are developed and grouped in two separate classes, BinTreeI and BinTreeR. In general, most operations are easier to be implemented using recursive methods. For instance, calculating tree height using iterative method is a rather non-trivial exercise whereas it’s almost an one-liner using recursion. Some of the operations such as searching and inserting a tree node are only applicable to binary search tree (BST), for which in-order tree traversal should be used. For generality, pre-order and post-order traversal methods are also included in the code.

Similar to the implementation of sorting algorithms in a previous blog, Java Generics and Comparable interface are used. If wanted, the underlying tree node could be further expanded to contain more complex node data such as map entries (e.g. with class type <Key extends Comparable<Key>, Value>, and Map.Entry<K,V> data). – binary tree node – base binary tree class – binary tree class using recursive methods – binary tree class using iterative methods – test application

The War For Software Talent

Having built software engineering teams for startups throughout my recent career, I must say that it’s not the easiest thing to do. It has gotten even tougher in the past few years as competition for software veterans has been more fierce than ever.

A mundane supply-and-demand issue

Prominent technology companies such as Google, Twitter, Amazon, have evolved over the past decade to become powerful titans by all standards. They have been vacuuming the talent space like black holes. Their continual sky-high stock value has led to spin-offs of child startups by their departed alumni who are often times some of the best talents themselves. These spun-off entrepreneurs were once key contributors but are no longer so, thus depleting the talent pool for hire.

The spin-offs leave the mother companies no choice but to step up their vacuum machine power. Meanwhile, these spun-offs create their own vacuum machines and, with some elite company names in their profiles, instantly become additional black holes to scoop up talents. That’s all good in helping to prosper the technology industry. But it inconveniently starves the average startups of desperately needed veterans, and running the code below is not going to help.

Because of the imbalance in the supply-and-demand of top talents, the compensation package for them has skyrocketed to the point that the average startups are having a hard time to justify it. Unlike established and well-funded companies, these startups must watch where every single dime goes and a salary at the level of a small startup CEO’s range for a veteran engineer can be a deal breaker.

Product Managers, QA, DevOps, …

For any given software engineering team, focus is often put on the engineers responsible for the very coding task. But any company who has ever built real-world products realizes how tough it would be to deliver them without an able product manager. Star product managers with adequate technical background and product management skills are equally hard to find. In all my previous startup ventures, product managers have always been some of the hardest roles to fill with the right people.

Good QA engineers are more difficult to find than R&D engineers. Many engineers simply lack the attributes, including patience and a detective mind, required to perform well in quality assurance. In addition, most people with strong programming skills prefer seeking R&D jobs to QA jobs. The end result is that many QA engineers come from a background of less stringent training in programming. In a technology demanding engineering team, that can be a barrier to carrying out quality QA tasks.

Experienced operations staff who handle systems and database administration have always been hard to find. The fact that I’ve been forced to play those roles myself on and off since my first startup venture in the 90′s tells a lot. With more and more cloud-based services arising and blurring the line between the software development and network operations worlds, DevOps were born. Instead of supporting non-technical users, they support highly technical software engineers. So, now we’re talking about a hard-to-find sys op with some software engineering background. These DevOps are like network engineers in the old days. They are endangered species.

What about quants?

The recent Big Data analytics movement has spiked a sudden demand of data scientists, a.k.a. quants. Quants possess domain expertise in quantitative, statistical analysis and machine learning algorithms that are crucial to businesses that need to digest their ever growing Big Data. Often times, an advanced degree in a natural science discipline such as physics or mathematics is required to qualify for such jobs, although other disciplines like mechanical engineering, operations research also prove highly applicable.

Despite the demanding requirement, I was having less trouble finding qualified quants than veteran software engineers. Perhaps demand for quants is still relatively fresh and not many companies know how best to tackle it yet. Coming from a natural science academic background myself, it was also a bit troubling to find out that a quant with a PhD in Physics from MIT and 5 years of post-Doctoral work costs less than a software engineer with a BS in Computer Science from an average college and 5 years of programming experience.

Where can I find them?

To build or grow your engineering team, if you do not already have at least a couple of trusted lieutenants and engineers as part of the team’s backbone, you will surely be up against a pretty big challenge. Unfortunately, that’s not an uncommon situation. As you’re advancing your career throughout the years, those who were once your star team members might’ve grown to playing similar role to yours or starting their own ventures (and competing with you for talents). So, chances are that there are some critical roles you need to fill from time to time.

Conventional wisdom suggests that hiring through internal referrals is always preferred. That is still true, as ever. It also makes sense to expand it further to connect with your friends, alumni, excolleagues, advisory board and board of directors for more leads. While it’s hard to guarantee success, posting jobs on job boards and professional social networks such as Monster and LinkedIn remains logical steps to advertise your hiring needs. One can also try local community networks like Craigslist especially if you only want local candidates.

For projects with well-defined specs and clear metric for measuring success, using less expensive off-shore resources may make sense. Cost for a near-shore full-time engineer is between one-third and half of that of a local engineer, but one should factor in the extra management cost incurred. Near-shore has an advantage of more synchronous time zones. Trusted references about their service quality are essential in your evaluation process.

As for quants, there might not be many candidates available for hiring through the above channels. Because academic specialty weighs a lot in a quant job’s requirement, it makes sense to try acquire those talents directly from the academia. NACELink ( is a great starting point for advertising your need through their extensive school network.


If you foresee an on-going hiring need, you should get help from technical recruiters. They work on a contingency or retained basis. The latter is often preferred when you have a relatively large number of job openings to be filled in the short term. It’s recommended that a goal with a timeline be set upfront to measure success so as to keep precious time and budget in control. On the other hand, there is no reason for you to limit your hiring channel to only one type of recruiters.

Finding competent technical recruiters is tricky, as you wouldn’t know whether a recruiter is good or not till you have the chance to at least go through a couple of leads from him/her. For each job opening, it’s recommended that you always provide your recruiters a carefully thought-out set of technical questions for them to go over as the initial filter. A couple of reasons for that:

1. Nobody knows better than yourself what exactly the expertise you want from the candidate
2. Many technical recruiters don’t necessarily have strong technical background, despite their technical title

Conclusion: No magic pills

Finding top talents to join your engineering team can be an exhausting effort, fruitless at times. You have to invest a huge amount of your own time and effort, even if you use recruiters. The fact is that there are great techies who aren’t good at marketing themselves and mediocre ones with excellent profiles on paper. People talk about the 80-20 rule, but you’ll be considered very lucky if 20% of your team are truly top talents.

A significant part in the process of acquiring talents involves some selling effort. While selling the company’s prospect and evangelizing the adopted technologies help, many engineering veterans nowadays are sophisticated enough to have done their homework. So, the key is not to oversell them and be consistent among the “sellers”. It’s kind of analogous to reporting to your investors in a board meeting – just highlight the key achievements that count and back them by unambiguous metrics. They already did their homework. Even if not, you should assume they did.