Is Algorithm Test Required To Hire Software Engineer?

I remembered my first interview to apply web developer job back in 2000 during Indonesia first Web bubble. I didn’t know about SQL Database and HTML, even had never heard about IDE to write code before. As theoretical physic graduate, I knew Matlab, Fortran and C compilers, but never in touch with latest trend. I brought my printed thesis; CV and some published scientific papers (on optical properties of amorphous semiconductor), with intention to show what I’ve done before. I honestly answered all technical questions with only two sentences – “I don’t know and I never heard”. Done! He lost his words and I also silent. Final question was, “if you got the job, how you can convince me that you will perform well?”. Then I showed all of my works and told him, “If the job you offer is more complicated than these, I will need time to learn. If not, then give me the job because I do need it”. Done!. I got hired. But before I went home he gave me bunch of Microsoft Press books to read at home before my first join date. I had a mixed feeling that day.

Now, 15 years have passed. I did hundred of interviews to hire fresh graduates, for various marketing, sales, technical evangelist and software engineer roles. I realized that the world has changed (at least in Indonesia). What I mostly found was Dunning-Kruger effect – a famous cognitive bias wherein unskilled individuals overestimate their superiority and highly skilled individuals tend to underestimate their relative competence. Forget all of those papers (or patents) or thesis as proof of competence, they didn’t even write a proper intro email. With the booming of Tech Companies in Indonesia, dramatic stories are around about behaviors of what we called millennial or Gen Y. I always have mixed feelings after interview session as I expect to see humility, curiosity and problem solving skills. Confidence during interview is no longer best barometer for attitude.

Now back to the question. With the high growth of demand for good Software Engineers, is it necessary to assess their atomic problem solving skills with algorithm, or their practical skills with coding test? Why do Google, Microsoft, Facebook or Samsung required that process? Why others don’t? I believe you’ll find a lot of debates, as it will relate to velocity of hiring process, or revenue size of headhunter companies, or something else. What about if you ended up hiring Google-bot, lazy-pseudo-hacker, taichi-master or ninja-turtle? Statistic showed – it cost the company for 2 years cash lost (~25k-50k in Indonesia), as need one year to let them learn and another one to fire. But costs are beyond cash, what about productivity, time loss, morale, impact to your customers? It’s a serious business problem instead of geeky interviewer brainteaser habit.

Sometime I think we need a “deep learning algorithm” to classify candidates for hire, no hire or hire for other roles. Human assessment is very weak for sure. What about if the interviewer is also “a not so good engineer”? How can he assess technical skills with Q&A approach? Or even worst, how he can assess humility and curiosity? I can floor so many questions to convince anyone that we need a proper assessment mechanism, but does algorithm test is an answer?

Let see. How many times do engineers really use algorithms when they code for common apps projects (Mobile, Web, Wearable, API, Database)? Even if they need for sorting, searching, for example, they can easily find by simple keyword search on Google, or Wikipedia. And for more strange things like BFS and DFS for Graph traversal, its also can easily found good tutorial on Youtube. Most of programming languages already provide common combinatorial data structures that have facilities to perform algorithms. Or if not enough, tons of reusable open source libraries (with documentations and sample codes) are available and huge number of technical papers proof better than their own memories or analytical skills. Why on earth they have to learn or memorize about it today, if Google/Wikipedia can help them to find right information quicker? Let Google memorizes, just copy paste, don’t think! What about correctness and efficiency of the codes? No one cares as long as it works, computer has big memory nowadays and compilers are smarter – no more premature optimization required, no more pointers in Python. The best engineers don’t waste time committing to memorize things in Wikipedia (is that possible?). And as long as no bugs reported and customer happy, just move on. Now is the era where software has to move quickly to eat the world.

Is algorithm test still an effective determination of technical/coding proficiency? It is the candidate’s fundamental technical strength and understanding, creative thinking, and problem solving that are critical to evaluate? Let the future PhD candidates answer that question with a proper deep learning based research. But, I’ve heard that a computer scientist is a mathematician who only knows how to prove things by induction. Curious to try?

Let assume that codes that engineer will write is a collection of Classes, Functions in those Classes, and Relations between Classes and Functions. That’s common in modern programming languages, so the whole codes are a Set of {Class, Function, Relation} with size N, M and L. But the relation between classes is “Gang of Four” art with less impact to correctness and efficiency (again, its my two cents bias from functional programming). So let L = 0 and N = M to oversimplify the problem. We end up with Set {Class, Function} with size N, in which one Class has only one Function in my model. The Class and Function can represent any feature in the program like display a list of texts, or show a dialog form, or anything relates to program context. With mathematical induction, we can easily proof that if for basic case like N = 1 or 2 it was true that it doesn’t require algorithm, then assumed it was true all the way to N1 before proving it was true for general N using the assumption. With that simple model and induction technique, you can easily proof that for common apps project like building mobile, wearable, web, API and database, where we reuse existing libraries and frameworks, algorithm is NOT really required to make it works correctly and efficiently. Only in small specific function you may need to use algorithm, but minor. Maybe when the library developer wrote the codes they used algorithms, but I believe it is also small portion. So, is it mathematically true that algorithm is not really required to write common apps?.

If you give me more time, I will confuse you more and more. That’s my bad habit if I can’t convince you in single sentence. But hold on. I agreed that a solid foundation in computer science is a great asset especially for software product based company or internet scale complexities to serve hundred million users like big boys, Google, Facebook, Microsoft, Apple and Samsung. Not for those tiny ambitious Startups who want to conquer the world from Indonesia. I heard argument like this. We need velocity to release our products and meet the Silicon Valley VC!. If we got their money, then hire Google and Microsoft engineers who graduated from MIT, Stanford, CMU or Princeton to rewrite the codes or lead Indonesian local engineers to write better codes. Let be apologetic without algorithms or quality codes at first, but boldly consistent with entrepreneurial business goals. Sounds a great plan, right?

Are you convinced now that algorithm is not required? Great if you are not. Now let me tell you the truth. Small things matter in a big room. Engineers are human and sneaky bugs are still able to creep in even for simple code. The context of algorithm is very broad as it cover many types of computing purposes – such as numerical, scientific and non-numerical methods in general and specific purpose algorithms like Graphic, Crypto, Parallel & Distributed System etc. But even if we scale down the context to a simple computation logic, which require basic recursive and incremental logic in Turing Machine, it is still a valid problem as the coder is still human.  Engineer mixes other unrelated problems to the codes, like his girl friend affair or credit card bill. Bugs can cost million dollars even worst can kill people in Spacecraft mission due to missed algorithmic or numerical calculation. Common applications can failed as its buggy. It cost a lot, not only cash, but other bigger things including company reputation and morale. Manual human assessment is limited and can’t no longer help, even Google implement a machine-learning algorithm to predict buggy codes.

The care of algorithm basic skill on software engineering is a reflection of quality culture and mindset. Quality is pride of engineering and craftsmanship, bread and butter of the profession. It is not means that an engineer has to self-implement algorithms in daily basis, but they need strong acumen which is a product of continuous practice of basic things. If you are a Samurai, do you practice the whole advance techniques in daily basis? No, only some basic moves. No warlord will assign samurai who can’t perform basic cuts to a dead fight, as it’s a suicide. The basic move of software engineer is algorithm or analytical thinking, to keep technical acumen alive, to keep logical consciousness and to have unconscious-reflect in project sprint. It’s a knowledge and skill to be kept by practicing few basics, not memorizing advance concepts. Its brain exercises to form more  System 2 neuron connections. Like a samurai, engineers can’t memorize the whole techniques (let Wikipedia does), but they have to really master few basics to have reflects on a fight, you need sixth sense. That’s can only happen if you are familiar and keep practice the basic moves in daily basis. And in interview – that is what we are looking for, A sign of intelligent and acumen on basic concepts.

I am a strong believer that algorithm test is required in interview to assess problem solving and analytical skills of software engineer candidate. Its not the best but that is an option. Its kind of risk management till we have a better approach like deep-learning software to help. That software will ask candidate to write some codes and make better decision regarding his technical/coding proficiency. What available usually white board in the interview room, or well-prepared test code system and basic problem sets, or maybe principal / mature engineer who can ask questions and assess basic intelligent and acumen.

Its not the end of story by the way, a lot of things need to be assessed on soft-skill side such as deep-humility and deep-curiosity on small things, and it can compensate each other. Also, you can’t really measure human capability precisely, no one can. If I got non-numerical algorithm test during my first interview, I might not in this industry, many others from physics might fail too as they are not well trained or even never heard. So in summary, that is required, but not the only one to assess. On top of algorithm test, strong evidences like code blogs, papers/patents, OSS contribution on Github, project portfolios, recommendation from trusted top engineers (rarely head hunters for Indonesia case), will also help. Thanks for reading this >1600 words post. If you regret, please read disclaimer!

Good Software Engineer?

Usually it takes sometime for me to answer such question or I just give my best two cents smile because the context is not well defined. We hear a lot of myth of being good software engineers and mostly more familiar with the other side of the equation – the bad habits of software engineers, such as lazy-pseudo-hacker, dictator, brute-force superman, careless tai-chi master, overcautious, Google-bot, documentation-hater, not-a-tester, dirty coders, ninja-turtle, or maybe to be more business-centric, we call them short-term-investor. You don’t need to consult the experts in Quora to get to know their bad behaviors, as obviously you are familiar.

Let me make my own definition on the question – if we care about bad, good or great – or any level of judges, it means we talk about employment role inside company as software engineer, where people have to work with others. My opinion in this post will not work for free-mind individuals who are doing it only for fun to achieve masterpiece, they don’t have KPI or job evaluation by the way.

First of all, most long answers in Quora on “How to Become Good Software Engineer” dominated by Googlers (less Microsofties nowadays but may change later) who proudly explained importance of fundamental or analytical part to perform the role, like math, compiler, algorithm/data structure, crypto, parallel-distributed algorithm, artificial intelligence, or practical skills like coding, programming language, test, debugging, framework, tools, domain expertise, SDLC etc. It looks like you need Master degree or maybe PhD from top CS school before taking a role at Google (or later MBA to ladder up once you get in). Uppss – as I mentioned AI – that requires numerical/scientific computing, statistics, probabilistic, and many more. I’m lazy to write all but to give it a name I pick hard skills. (I am free to give any name to anything, as this is my blog).

OK – before you think you are not fit to become good Software Engineer because of above paragraph, you may have to look Life and Time of Anders Hejlsberg sometime, not only Martin OderskyKen Thompson or Terence Tao who have proper genius level academic background. Many other stories like that we can found – even though I’m not really recommending it for my kids. It may helps to know all of those academic hard skills, but no guarantee at all.

In any profession that requires people to work with other people, it takes time to become good at, not only in engineering. Why? The problem domain is much bigger. Software engineering is no longer single person craftsmanship to write codes and the physics of it becomes physics of people and its business focus. If I list down the competencies to deal with people and business, you’ll get longer list for sure. Just to name a few, communication, writing, presentation, listening, business-awareness, social-awareness, time management, daily discipline, product planning, estimation, and the scariest one – leadership. There is no fundamental law to deal with people and business till now. All of us have to experience, read, learn, think, discuss and practice a lot of things to get better on it. Lets give it name, soft skills.

Assuming you agreed on my naming convention – hard and soft skills are not something people can build in short time period. To be a good software engineer, you have to balance both, gradually become good at both. Yes, it takes time but if you really want do it, it is not impossible. How long? Most of young fresh graduate will NOT like my consistent statistical answer, 10 years to be good at something. Lucky if one started early by any reason, for example he started to love math and code since high school because his father bought him a computer and he got to love it. As I used love to state a more than casual interest, don’t be confuse. You can pick any other profession – I believe even Clown Balls needs 10 years to be good at balancing practical and entertaining skills.

People who are not accepting the statistical rule of 10 years usually choose to under estimate the process or even worst the contents of learning. They will say – fundamental/analytical knowledge and skills is perfectionism, they are biased to weight more on the practical/pragmatic skills. In fact – balancing is key and it takes time.  Hard vs Soft, Perfectionism vs Pragmatism, Sprint vs Marathon, Business vs Technical, all needed to be balance and it takes time.

Be humblebold to accept the 10 years rule will give you steady state to focus on building competencies. No matter where you started – from high school math level or top CS school level. People who started earlier will secure more time, but if you were not, don’t worry. Average working period now is around 30 years, you can still spend your first 10 years to be good software engineer competency, push yourself hard (usually only hard at the beginning), and stay passionate about it. The problem is when you are not accepting the rule and live with full of biases for later ending up wasting time never be good at anything. Assuming you do, then you can read on all of Googlers formula in Quora and start building your hard+soft skills with deep humility. Enjoy the process and find the beauty of small things you do. As Feynman said – There’s plenty of room at the bottom where you can find beauty of small things.

Once you decided, then you have to pay the price. Software engineering works requires you to have durability to focus (without distractions) on specific analytic, craftsmanship, people and business related problems. Your System 2 have to work 4-8 hours a day consistently and your System 1 perhaps same or lesser. System 2 is part of your brain that is slower, more deliberative, effortful, infrequent, logical, calculating, conscious and more logical. System 1 is fast, automatic, frequent, emotional, stereotypic, subconscious. Yes, it is hard for average people to balance between System 1 and 2, not easy.

Look back. You started with math in school, then learn other fundamentals gradually. Be good at algorithm and data structure first, try to translate the computation concept to programming languages, get to learn not-only-one language (but be really good at one first), get a job at good-culture company, write lot of codes, invest your time to think before code, read other people codes, read papers to help you solve complex technical problems, care about quality of code you write, communicate, documenting, testing, and all of those engineering, people and business stuffs. Yes, never forget the balance between hard and soft skills. With availability of MOOC now – like Coursera, Udacity and Edx, you can learn from the best even for free. You can get best Professors teach you on things you want to learn. Enjoy the journey, as it is really worth to pursue nowadays. Don’t get distracted by Startups dream if you are not really ready (be honest on your assessment). If you think you have the basics and already decided your commitment to work – then you can send me your CV.

Hope this helps!.

A Quick Intro to Spark

Fast computers and cheaper memory have stimulated the rapid growth of a new way of doing data computation. During this time, parallel computation infrastructures have evolved from experimental in a lab to become everyday tools of data scientists who need to analyze and get insights from data. However, barriers to the widespread use of parallelism are still at least one of three common large subdivision of computing – hardware, algorithms and software.

Imagine old days when we should deal with all of those three at same time, from high speed intercommunication network switches, parallelizing sequential algorithms and various software stacks from compilers, libraries, frameworks and middleware. Many parallelism models have been introduced for decades, like data partitioning in old day FORTRAN or other SIMD machines, shared memory parallelisms and message passing (remember C/C++ MS-MPI cluster in old days). I am part of generation who faced the “Dark Age” period of distributed numerical computing. Now is much better, I hope.

Apache Spark – is fast and general-purpose cluster computing system. Spark promises to make our life easier in writing distributed programs like other normal programs by abstracting away the “nitty-gritty” details of distributed systems – like my previous experiences with message passing (MPI). I know it is too early to make prediction on the success of Spark, but I’m biased with my previous distributed system experiences and liked to continue that in this post ☺.

We all need speed in data computation. Imagine if your forecasting analytic on large business datasets requires a day to complete while your business people expected it to produce results in hours or minutes – nowcasting vs forecasting. On the speed side, Spark extended the MapReduce model to supports more types of computations like batch, iterative/recursive algorithms, interactive queries and micro-batch streaming processing. Spark makes it easy and inexpensive (as price of CPU and GPU become cheaper) to run those processing types and reduces the burdens of maintaining infrastructure, tools and frameworks. Spark is designed to be friendly for developers, offered language bindings to Python, Java, Scala, R (via SparkR) and SQL (SparkSQL), and of course shipped with ready to use libraries such as GraphX and MLLib. A growing supports from Deep Machine Learning practitioners are also happening, like H2O Sparkling Water, DL4J and Prediction.IO.  It also integrated closely with other big data tools, like Hadoop, YARN, HBase, Cassandra, Mesos, Hive, etc. Spark ecosystem is growing very fast.

Spark started in 2009 as a research project in UC Berkeley RAD Lab (AMPLab). The researchers in AMPLab that previously work with Hadoop MapReduce found that MapReduce was inefficient for iterative and interactive computing jobs. You can refer to some research papers for better scientific proofs, or following a thriving OSS developer community around Spark, including famous startups like DataBricks.

Let me share my hacking experiences on Apache Spark. Spark is written in Scala and requires JVM to run. If you want to work with Python later, you may need to install Python package like Anaconda that combine all frameworks you need for scientific computing – include the famous Jupiter Notebook. I started by downloading Spark binary then later source codes to build on my Mac machine. A straightforward maven based compilation took sometime (~24 minutes) till I can run spark shell. But I was impatient; so during the compilation I just downloaded and used the binary version (now version 1.4.0) to test some commands. The good fact was I can use Spark without Hadoop, even in my single Mac machine to practice its basic principles. When Spark was ready in my machine, I just followed the file (good habits of a geek) to test it, for example in Scala shell:

scala> sc.parallelize(1 to 1000).count()

or in Python shell:

>>> sc.parallelize(range(1000)).count()

Spark comes with several sample programs in the `examples` directory. To run one of them, I used `./bin/run-example <class> [params]`. Here for example for SparkPi:

./bin/run-example SparkPi

First thing I learnt about Spark was to make custom driver program that launches various parallel operations on my single machine Spark instance. The driver program (we can write in Python, Scala, Java, and R), contains main function and defines distributed datasets on the cluster, then applies data transformation actions to them. Spark shells are obvious examples of driver programs that access Spark through a SparkContext object, which represents a connection to a Spark’s computing cluster. In the any shell, a SparkContext is predefined for us as sc object, like in above examples. Spark default distribution (now version 1.4.0) provides spark-shell (for Scala) and pyspark (for Python) for interactive computing with sc object.

Second thing I learnt was about Spark’s main abstractions for working with distributed data, the RDD (Resilient Distributed Dataset), a distributed immutable collection of objects. In a clustered environment, each RDD is split into multiple partitions, which may be computed on different nodes. Programming in Spark is expressed as either creating new RDDs from data sources, transforming existing RDDs, or perform actions on RDDs to compute a result. Spark automatically distributes the data contained in RDDs across cluster and parallelizes the operations we want to perform on them.

RDDs can contain any type of Python, Java, Scala, or R (through SparkR) objects, including user- defined classes. Users can create RDDs in two ways: by loading an external dataset, or by distributing a collection of objects (e.g., a list or set) in their driver program. Once created, RDDs offer two types of operations: transformations and actions. Transformations construct a new RDD from a previous one. Spark context object directly provides a lot of functions to perform RDD transformations. Actions, on the other side, compute a result based on an RDD, and either returns it to the driver program or save it to external storage systems like HDFS, HBase, Cassandra, ElasticSearch etc.

For example – Python filtering of file:
>>> lines = sc.textFile(“”)
>>> pythonLines = lines.filter(lambda line: “Python” in line)
>>> pythonLines.first()

And Scala filtering version for the same file:
scala> val lines = sc.textFile(“”)
scala> val pythonLines = lines.filter(line => line.contains(“Python”))
scala> pythonLines.first()

Finally, third thing I learnt was about a lazy fashion of Spark execution. Although we can define new RDDs any time, Spark computes them only in a lazy fashion—that is, the first time they are used in an action. Spark’s RDDs are by default recomputed each time we run an action. To reuse an RDD in multiple actions, we can ask Spark to persist data in a number of different places using RDD.persist(). After computing it the first time, Spark will store the RDD contents in memory (partitioned across the machines in cluster), and reuse them in future actions. Persisting RDDs on disk instead of memory is also possible. The behavior of not persisting by default may again seem unusual, but it makes a lot of sense for big datasets: if you will not reuse the RDD, there’s no reason to waste storage space when Spark could instead stream through the data once and just compute the result. In real practice, we will often use persist() to load a subset of data into memory and query it repeatedly.

Example of persisting previous RDD in memory:

>>> pythonLines.persist
>>> pythonLines.count()
>>> pythonLines.first()

As this is just a quick intro to Spark, lot more to hack if you are curious. To learn more, read the official Spark programming guide. If you prefer MOOC style, I recommend eDX- BerkeleyX: CS100.1x Introduction to Big Data with Apache Spark from DataBricks. Books can also help your learning curve, you can try these :

  1. Learning Spark: Lightning-Fast Big Data Analysis
  2. Advanced Analytics with Spark: Patterns for Learning from Data at Scale
  3. Machine Learning with Spark

Lastly, hacking specific computation problems is always better way to learn. Good luck with your Spark hacking!

Data Science – Science or Art?

People called it sexiest job of 21st century, hot and growing field that needs millions or billions resources in future. But what is that? I found it confusing at the beginning, as there is ambiguity to split between substances of science and methodologies on solving scientific problems through data computations. Since the beginning, the purpose of computing is insight, not the data. Thus computing is, or at least should be, intimately bound up with both the source of scientific problems and the model that is going to be made of the answers, it is not a step to be taken in isolation from physical reality. As a “failed theoretical physicist” of course I am very biased.

The Venn diagram model that widely accepted (many books refers to it), defines data science as intersections between hacking skills, math and stats knowledge, and substantive expertise. Although I really want to argue it, I quickly realize “substantive expertise” is open for any area of scientific topics; hence I will again waste my time to argue in open area. Even after consulted to Wikipedia, that defines data science as extraction of knowledge from large volume of data that aren’t structured, I’m still deeply confused. Nevertheless, let it be my problem, not yours. It is well known that IT industry has “unique” behavior to give confusing names to same thing.

Assuming I can push myself to accept data science definition from Wikipedia (never in reality), how can I relate the science? In science, there is a set of rules (the fundamental laws of nature) in operation, and task of scientists is to figure out what the rules are, by observing the results (data) that occur when the rules are followed. Simply said – it is an attempt to “reverse-engineer hack” on machinery of the nature. Even in math, it’s the other way around, to choose the rules (or model) and discover the insights of choosing any particular set of models. There is a superficial similarity, which leads to my other confusion.

In science, the way we test a theory is to codify it as a set of models and then explore the consequences of those models – in effects; to predict what would happen if those models were true. People do same thing in math, and in fact, the way its done in math serves as a model for the way its done in science, sometime. But the big difference is: in science, as soon as our predictions conflict with experimental data from nature, we are done. We know that our models are wrong and need to modify it. In math, this kind of conflict is minimal, because there is no necessary connection between any theory and the world. As long as it is still interesting enough to induce mathematicians to keep work on it, then it will continue to be explored.

Data science – to what we know so far in IT industry refers to collection of tools and methods to get insights from data (not necessarily large or big), by analyzing it with various computation techniques and later communicate (or consume) the insights through visualization (or else). It typically deals with data that mostly un-structured, collected from users, computer systems or other like sensors, without single predefined formats. Long debates in online forums regarding its definitions, and as it is still hyped-up, it will takes more time till it finally landed to earth again. It may because of legacy of computer science, which also in debate for decades.

People who come from statistic or math background will argue that data science is mostly about statistical analysis on data using modern tools, languages, libraries and computing infrastructures. By hacking those technologies they can work to produce insights from data with statistical methods. On other case if their background is physics for instance, they will think of numerical methods or computer simulations to fit modeled hypothesis to experimental data. From computer scientists, who have explored areas of information retrieval, for example, will proudly claimed that finally machine learning has a better name. Ex-scientists who are good in programming and programmers who are good in statistics and scientific/numerical computing. All may true subjectively – but if you look around the reality, variety of languages, tools, methods, and techniques for data analytic leads to an art instead of science. Yes, data analytic art if you need a new name again (data artist probably better title?). But no, it is not attractive enough, as taken by digital artists previously. Disclaimer: In IT business, we are in high demand of new hype and jargon (Read Gartner Hype Cycle 2014). So lets stick with data science as normally accepted as growing trend.

What actually data scientists do? Is it covering collecting and pre-processing the data, formulating hypothesis, identifying algorithms/tools that fit, performing computation, communicating insights and creating abstractions for higher level business people? Yes, perhaps those all written in their resumes mixed between software/data engineering and data analytical tasks. As it still far from maturity, roles and responsibilities may change over time (I believe it will become business roles not only IT), new data sources will explode with other hypes (such as Internet of Almost Stupid Things), companies who crafting automation tools/frameworks/platforms will emerge and raise more funds to innovate faster. More and more things can happen as art has no end. The art of machine intelligence is still going on progress. If we found way to un-supervised machine intelligence, many other things can happen, including we may not need data scientists and let the machines work for us. We all need to respond (or just do nothing) to anticipate this new hyped-trend. I choose to enjoy the show by hacking it!.