The Three Pillars of Scalability

The three pillars of scalability are statelessness, idempotency, and coding to interfaces.

If you keep the above three in mind, your application can scale a long way with your users. Of course, I am not implying these are the only three things to keep in mind while designing scalable applications.

low-angle-photograph-of-the-parthenon-during-daytime-164336 (1)

Statelessness:

If an application does not store persistent state locally, one can scale it by adding servers.

Let us take the example of an application that requires users to sign in. Once a user signs in, the application has to remember that this particular user has logged in. You have the option of storing the logged-in state of the users in the application servers’ memory. When a subsequent request comes, the application looks up in its memory and acts accordingly.

If you are following the above scheme, you are storing the persistent state locally—in servers’ memory. The upside of this approach is its simplicity. The downside is that you cannot elastically scale the application by dynamically adding and removing application servers based on the load.

To figure out whether your application is stateless or not, ask the question: If the next request landed on a different instance of the server, will my operation fail? If the answer is yes, the application is not stateless.

Idempotency:

An operation is said to be idempotent if it produces the same result when executed multiple times.

Example:

a, b = 1, 2

a + b is idempotent—irrespective of how many times you execute this, the result is always 3.

a++ is not—each time you execute this, you get a different result.

If your application is idempotent, you can retry failed requests. 

Applications can fail momentarily, especially under load. When this happens, ideally, you should retry the failed request. But you can do this only if the application is idempotent. With idempotency, you do not have the unintended side effect of retrying a request.

You are trying to create a user. You hit the user creation API. For some reason, you do not get a response; this could be due to anything—a temporary network glitch, an application error, or something else. The bottom line is that you are not sure whether the user is created or not. If the application is not idempotent, you cannot retry the request. One might end up creating multiple users with the same identity. Not so, if the application is idempotent. One can retry with abandon.

Coding to interfaces:

Coding to interfaces lets us swap components.

You are using a cache in your application. Instead of using the cache provider’s API directly, you hide it behind an interface of your own. In the future, when you have a deluge of users, if you find the cache lacking, you can swap it with a performant cache without incurring tons of maintainability. You can do this only if you decouple your application from the specific cache provider’s API and abstract it out.

Conclusion:

It is tough to foresee scalability problems. Following the above generic principles will help you to develop adaptable applications that you can cheaply scale while buying time to create sophisticated scaling strategies specific to your needs.


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How do I Know I am Right?

TLDR; there is no way.

One thing the Coronavirus crisis has made vivid is that no one knows anything for sure. The fact that no one knows anything bubbles up every time there is a crisis. This time though, due to the severity of the mess, it is stark; in your face.

Experts used to say that eating fat is unhealthy. Now, not so much. Not long back, scientists used to believe that the adult human brain is static—once we enter adulthood, our intelligence stops improving. Today, everyone talks about brain plasticity—how the brain keeps growing with the right input even in adulthood and adapts well into old age. The scientific community is staring at a replication crisis—researchers are not able to consistently reproduce experimental results. Marshmallow experiment—one of the most cited psychology experiments, is under doubt.

meme

When I started putting my thoughts in public, I was hesitant. I always had a voice in the back of my mind asking: How do you know you are right? I face the same when someone comes to me for advice. I am guarded with my advice.

How do I reconcile with this?

I have benefitted immensely from the thoughts of others. I am thankful to all these people who take the pain to put their ideas in front of everyone, especially in the current environment where trolling is given. Today, it is fashionable to call anyone and everyone a virtue signaller. Thankfully, I have not gone through the trolling experience as I have a tiny audience.

What is wisdom?

Wisdom is knowledge synthesized with life experiences. By studying, observing, and paying attention, one gains knowledge. One accumulates life experiences by doing. When you mesh the two together and contemplate, you gain wisdom. If no one broadcasted their thoughts, the world would be a sad place.

Strong opinions, Weakly held

An excellent framework for better thinking is: Strong opinions, weakly held. The idea is not to be married to your views. In the face of disconfirming evidence, update your beliefs. Interestingly, even this maxim is under debate.

There is no way for you to be a hundred percent sure of anything; this applies when you give and receive advice. The best you can do is color your knowledge with your life experiences and share it with others in the hope that the other person takes something positive out of it—a small way for you to give back to the society.

Always be skeptical.


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Should I or Should I Not

This post walks you through a framework for adopting new technologies. Microservices is a placeholder in this post. It is a generic framework that you can apply to any new technology that you are planning to adopt.

Should we do microservices?

The above question plagues the minds of software developers.

Popular programming culture made microservices the de facto way to build software. Now, many are second-guessing their choice.

Here is a post from Segment on why they consolidated their microservices into a monolith.

Microservices is archetypical of software trends following the Hype cycle. In the Hype cycle, microservices has passed the “Peak of Inflated Expectations.”

Gartner_Hype_Cycle.svg
Hype Cycle

A framework for making new technology choices

Before adopting any new technology, you have to:

  1. Clearly define the problem you are trying to solve with the novel technology.
  2. Understand how the new technology solves the problem.
  3. Build perspective by studying the evolution of the technology.
  4. List the supporting structures needed to make the new technology work.

[ Click to Tweet (can edit before sending): https://ctt.ac/zcw9I ]

The above may sound meh, but taking the pain to define them to the T is the key to the success of new technology adoption.

dominos-dots-fun-game-585293

Clearly define the problem you are trying to solve

Nailing down the problem is the first step. You would be surprised by the number of people who try to solve a problem without defining it formally.

When the problem that you are trying to solve is vague, it becomes tough to find a solution to it. How many times has it happened to you that you describe a problem to someone, and in the process of doing so, you get closer to the solution?

Clearly define the problem that you are trying to solve with microservices. Is it a performance problem with the application? Are you trying to increase the productivity of the team with microservices?

When you do this, sometimes you find non-disruptive ways to solve the problem. Better communication between teams might be the solution, not microservices.

Understand how the new technology solves the problem

Understanding how the new technology solves the problem will help you to evaluate it objectively. Defining the problem, as stated in the first step of the framework, is essential for this.

There are two broad reasons for microservices adoption—technical and logistical.

Technical

The application has grown in complexity and has workloads vying for different types of resources. You are not doing justice to any of these workloads by packing them in a monolith. For example, some workloads might be CPU intensive, some IO heavy, and the others hungry for memory. By extracting each of these workloads into a microservice, you have the freedom to host them in different servers conducive to their demands.

The application has grown in complexity and has workloads better solved in different programming languages. Breaking the monolith into microservices gives you the ability to code them in the programming language of your choice.

Logistical

The application has evolved as well as your company. Different teams are responsible for different areas of the application. You want these teams to be independent. If you break the monolith into microservices that mimic the team structure, you will achieve this independence. These teams can work independently without stepping on each other’s toes, thus being more productive.

Build perspective by studying the evolution of the technology

When you try to dig up the history, keep in mind that you are not going after the rigorous academic definition of the term, but the cultural context of its evolution. The common definition of a term may not match with its formal description. For example, when people say microservices, they are usually referring to Services Oriented Architecture(SOA) and not microservices in particular.

Microservices exploded due to big companies like Amazon and Netflix evangelizing(maybe unintentionally) them. These companies have thousands of employees and divisions. Once you understand this and build a perspective, you will naturally ask, is this applicable to me? If you are a small startup that can count your tech team with one hand, in all probability, the answer is no. It is tough to build this perspective without studying the evolution of the technology.

Supporting structures needed to make the new technology work

Whenever you introduce a new technology, you might have to make some changes to the way you work. Some of these changes might be inconsequential, and others extensive.

For microservices to be successful, you will have to invest in tooling. You will have to have a robust monitoring system because, with microservices, you are treading into distributed computing where failure is a given. I will stop here as this requires a post in itself.

In many circumstances, these changes might be far-reaching negating the benefits of the new technology. Be keenly aware of this trade-off.

Summary

Doing this might sound time-consuming, but it pays off by preventing unmitigated disasters down the line, once you are in the middle of adopting the new technology. Many new technology choices bomb because someone did not do the above painstakingly enough.


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Hype Cycle image By Jeremykemp at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=10547051.

Photo by Miguel Á. Padriñán from Pexels

Let go of Stereotypes

The key to building a great team lies in ejecting the stereotypical portrayal of the role from your mind, objectively figuring out the qualities needed for success in the role, and ruthlessly going after that.

handmade-ceramics-pottery-workshop-22823

What is the stereotype of a leader?

A charismatic extrovert who can spellbind an audience with her talk.

Leadership is not about how charismatic you are or how good you are at public speaking. Popular culture has narrowly defined leadership to be so.

In the book, The Little Book of Talent, the author Daniel Coyle writes: Most great teachers/coaches/mentors do not give long-winded speeches. They do not give sermons or long lectures. Instead, they give short, unmistakably clear directions; they guide you to a target.

What is the stereotype of a developer?

This twitter thread does an excellent job of it.

Being a good developer is not about which editor you use or how socially awkward you are. These are urban legends devoid of any real substance.

Leaders and developers come in all shapes and sizes. Take a step back and think of all the great people you have worked with. Do they stick to the stereotypes associated with their role? Can you pigeonhole them into a mold?

The movie, Money Ball, is the best illustration of this line of thinking. The plot of the film revolves around the real-life story of a manager who assembles a successful baseball team analytically by ignoring the mythical stereotypes associated with what makes one a successful baseball player. This approach of building the team was not a cakewalk for him; he met with resistance from all for his radically different line of thinking.

Peter Thiel talks of startup hiring as finding the talent which the market has mispriced(I am paraphrasing this from memory).

If you stick to stereotypes while hiring and promoting, you are:

  1. Artificially restricting the available talent pool.
  2. Pursuing the same set of people that everyone else is.
  3. Going after qualities that you do not need.

We are sympathetic to underdogs, but we do not bet on them. Doing so is the not so secret strategy for building a great team.


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Wild Wild World of External Calls

Today, while developing software, external calls are a given—your code talks to external HTTP services, databases, and caches. These external communications happen over networks that are fast and work well most of the time. Once in a while, networks do show their true color—they become slow, congested, and unreliable. Even the external services can get overloaded, slow down, and start throwing errors. The code one writes to interface with external services should be able to stand steady under these circumstances.

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In this post, I will go through some of the basics one should keep in mind while calling external services. I will use the Python Requests library to demonstrate this with external HTTP calls. The concepts remain almost the same irrespective of the programming language, library, or the kind of external service. This post is not a Python Requests tutorial.


I have created a Jupyter Notebook so that you can read and run the code interactively. Click here, then click on the file WildWildWorldOfExternalCalls.ipynb to launch the Jupyter Notebook. If you are not familiar with executing code in a Jupyter Notebook, read about it here. You can find the Notebook source here.


Let us call api.github.com using Requests.

import requests
r = requests.get("https://api.github.com")

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call.py
hosted with ❤ by GitHub

External calls happen in two stages. First, the library asks for a socket connection from the server and waits for the server to respond. Then, it asks for the payload and waits for the server to respond. In both of these interactions, the server might choose not to respond. If you do not handle this scenario, you will be stuck indefinitely, waiting on the external service.

Timeouts to the rescue. Most libraries have a default timeout, but it may not be what you want

import requests
r = requests.get("https://api.github.com", timeout=(3.2, 3.2))

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call-timeout.py
hosted with ❤ by GitHub

The first element in the timeout tuple is the time we are willing to wait to establish a socket connection with the server. The second is the time we are willing to wait for the server to respond once we make a request.

Let us see the socket timeout in action by connecting to github.com on a random port. Since the port is not open(hopefully), github.com will not accept the connection resulting in a socket timeout.

import requests
from timeit import default_timer as timer
from requests import exceptions as e
start = timer()
try:
requests.get("https://api.github.com:88", timeout=(3.4, 20))
except e.ConnectTimeout:
end = timer()
print("Time spent waiting for socket connection -", end start, "Seconds")
start = timer()
try:
requests.get("https://api.github.com:88", timeout=(6.4, 20))
except e.ConnectTimeout:
end = timer()
print("Time spent waiting for socket connection -", end start, "Seconds")

The output.

Time spent waiting for socket connection – 3.42826354 Seconds
Time spent waiting for socket connection – 6.4075264999999995 Seconds

As you can see from the output, Requests waited till the configured socket timeout to establish a connection and then errored out.

Let us move onto the read timeout.

We will use httpbin service, which lets us configure read timeouts.

import requests
from timeit import default_timer as timer
from requests import exceptions as e
try:
start = timer()
r = requests.get("https://httpbin.org/delay/9", timeout=(6.4, 6))
except e.ReadTimeout:
end = timer()
print("Timed out after", end start, "Seconds")

view raw
call-read-timeout.py
hosted with ❤ by GitHub

The output.

Timed out after 6.941002429 Seconds

In the above, we are asking httpbin to delay the response by 9 seconds. Our read timeout is 6 seconds. As you can see from the output, Requests timed out after 6 seconds, the configured read timeout.

Let us change the read timeout to 11 seconds. We no longer get a ReadTimeout exception.

import requests
r = requests.get("https://httpbin.org/delay/9", timeout=(6.4, 11))

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call-read-timeout.py
hosted with ❤ by GitHub

A common misconception about the read timeout is that it is the maximum time the code spends in receiving/processing the response. That is not the case. Read timeout is the time between the client sending the request and waiting for the first byte of the response from the external service. After that, if the server keeps on responding for hours, our code will be stuck reading the response.

Let me illustrate this.

import requests
from timeit import default_timer as timer
from requests import exceptions as e
start = timer()
r = requests.get("https://httpbin.org/drip?duration=30&delay=0", timeout=(6.4, 6))
end = timer()
print("Time spent waiting for the response – ", end start, "Seconds")

The output.

Time spent waiting for the response – 28.210101459 Seconds

We are asking httpbin to send data for 30 seconds by passing the duration parameter. Requests read timeout is 15 seconds. As evident from the output, the code spends much more than 15 seconds on the response.

If you want to bound the processing time to 15 seconds, you will have to use a thread/process and stop the execution after 15 seconds.

import requests
from multiprocessing import Process
from timeit import default_timer as timer
def call():
r = requests.get("https://httpbin.org/drip?duration=30&delay=0", timeout=(6.4, 20))
p = Process(target=call)
start = timer()
p.start()
p.join(timeout=20)
p.terminate()
end = timer()
print("Time spent waiting for the response – ", end start, "Seconds")

The output.

Time spent waiting for the response – 20.012269603 Seconds

Even though we receive the HTTP response for 30 seconds, our code terminates after 20 seconds.

In many real-world scenarios, we might be calling an external service multiple times in a short duration. In such a situation, it does not make sense for us to open the socket connection each time. We should be opening the socket connection once and then re-using it subsequently.

import requests
import logging
logging.basicConfig(level=logging.DEBUG)
for _ in range(5):
r = requests.get('https://api.github.com')

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call-repeat.py
hosted with ❤ by GitHub

The output.

DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496

As you can see from the output, Requests started a new connection each time; this is inefficient and non-performant.

We can prevent this by using HTTP Keep-Alive as below. Using Requests Session enables this.

import requests
import logging
logging.basicConfig(level=logging.DEBUG)
s = requests.Session()
for _ in range(5):
r = s.get('https://api.github.com')

The output.

DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496

Now, Requests established the socket connection only once and re-used it subsequently.

In a real-world scenario, where multiple threads call external services simultaneously, one should use a pool.

import requests
from requests.adapters import HTTPAdapter
import threading
import logging
logging.basicConfig(level=logging.DEBUG)
s = requests.session()
def call(url):
s.get(url)
s.mount("https://", HTTPAdapter(pool_connections=1, pool_maxsize=2))
t0 = threading.Thread(target=call, args=("https://api.github.com", ))
t1 = threading.Thread(target=call, args=("https://api.github.com", ))
t0.start()
t1.start()
t0.join()
t1.join()
t2 = threading.Thread(target=call, args=("https://api.github.com", ))
t3 = threading.Thread(target=call, args=("https://api.github.com", ))
t2.start()
t3.start()
t2.join()
t3.join()

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call-pool.py
hosted with ❤ by GitHub

The output.

DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.github.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (2): api.github.com:443
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496
DEBUG:urllib3.connectionpool:https://api.github.com:443 “GET / HTTP/1.1” 200 496

As we have created a pool of size two, Requests created only two connections and re-used them, even though we made four external calls.

Pools also help you to play nice with external services as external services have an upper limit to the number of connections a client can open. If you breach this threshold, external services start refusing connections.

When calling an external service, you might get an error. Sometimes, these errors might be transient. Hence, it makes sense to re-try. The re-tries should happen with an exponential back-off.

Exponential back-off is a technique in which clients re-try failed requests with increasing delays between the re-tries. Exponential back-off ensures that the external services do not get overwhelmed, another instance of playing nice with external services.

import requests
from urllib3.util.retry import Retry
from requests.adapters import HTTPAdapter
import logging
logging.basicConfig(level=logging.DEBUG)
s = requests.Session()
retries = Retry(total=3,
backoff_factor=0.1,
status_forcelist=[500])
s.mount("https://", HTTPAdapter(max_retries=retries))
s.get("https://httpbin.org/status/500")

view raw
call-retry.py
hosted with ❤ by GitHub

The output.

DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): httpbin.org:443
DEBUG:urllib3.connectionpool:https://httpbin.org:443 “GET /status/500 HTTP/1.1” 500 0
DEBUG:urllib3.util.retry:Incremented Retry for (url=’/status/500′): Retry(total=2, connect=None, read=None, redirect=None, status=None)
DEBUG:urllib3.connectionpool:Retry: /status/500
DEBUG:urllib3.connectionpool:https://httpbin.org:443 “GET /status/500 HTTP/1.1” 500 0
DEBUG:urllib3.util.retry:Incremented Retry for (url=’/status/500′): Retry(total=1, connect=None, read=None, redirect=None, status=None)
DEBUG:urllib3.connectionpool:Retry: /status/500
DEBUG:urllib3.connectionpool:https://httpbin.org:443 “GET /status/500 HTTP/1.1” 500 0
DEBUG:urllib3.util.retry:Incremented Retry for (url=’/status/500′): Retry(total=0, connect=None, read=None, redirect=None, status=None)
DEBUG:urllib3.connectionpool:Retry: /status/500
DEBUG:urllib3.connectionpool:https://httpbin.org:443 “GET /status/500 HTTP/1.1” 500 0

In the above, we are asking httpbin to respond with an HTTP 500 status code. We configured Requests to re-try thrice, and from the output, we can see that Requests did just that.

Client libraries do a fantastic job of abstracting all the flakiness from external calls and lull us into a false sense of security. But, all abstractions leak at one time or the other. These defenses will help you to tide over these leaks.

No post on external services can be complete without talking about the Circuit Breaker design pattern. Circuit Breaker design pattern helps one to build a mental model of many of the things we talked about and gives a common vocabulary to discuss them. All programming languages have libraries to implement Circuit Breakers. I believe Netflix popularised the term Circuit Breaker with its library Hystrix.

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Image by RENE RAUSCHENBERGER from Pixabay

Centralization and Decentralization

Top management loves centralization. Rank and file prefer decentralization.

Why?

Imagine you are the CEO of a company with multiple teams. 

Teams need software to do their work. When the need arises for a software, someone from each of the team talks to the software company negotiates a price and procures the software. 

As a CEO, you observe this and see it as a duplication of effort – wastage of time, energy, and resources. You think you can improve efficiency by centralizing the software procurement process. Only one team will be doing the work – the software procurement team. Also, this team will be able to negotiate a better price due to multiple orders, remove redundancy, manage licenses better, and block unnecessary software spends.

Since software cost is a real expense, you can quantify the gain from this exercise.

black-ceiling-wall-161043

What about the downside?

Earlier, each team could independently procure the software they saw fit. Now, the individual teams have to go through the centralized procurement team and justify the need; this leads to back and forth and delays. The delay affects the cadence of work leading to employee dissatisfaction. Employee dissatisfaction leads to reduced quality of work, which in turn negatively affects the bottom line.

It is not easy to quantify the second-order effects of centralization, sometimes impossible.

The CEO, due to the broad nature of her work, sees the duplication everywhere. She also witnesses the expenses as a result of this; it is in her face. She wants to eliminate this and bring efficiency and cost-saving to the organization. Hence, she champions centralization. 

The rank and file are hands-on; they have to deal with the management policies to do their work. They experience the second-order effects of centralization day in and out. They instinctually develop anti-centralization spidey sense

Unline the rank and file; the CEO does not have the ringside view of the second-order side effects of centralization. The rank and file do not see the duplications the CEO sees because they do not have the same expansive look like that of the CEO.

Centralization efforts have a quantifiable impact. If not entirely measurable, you can do some mental gymnastics to get an idea.

The downsides of centralization are unquantifiable. The unquantifiable plays a crucial role in success, sometimes much more than the quantifiable.

Morgan Housel calls this the McNamara Fallacy.

McNamara Fallacy: A belief that rational decisions can be made with quantitative measures alone, when in fact the things you can’t measure are often the most consequential. Named after Defense Secretary McNamara, who tried to quantify every aspect of the Vietnam War.

Let us flip the earlier scenario. Imagine that the centralized procurement team does bring in efficiency and reduce cost, albeit at a minor loss of productivity. The software procurement expense as a whole is never on the mind of the rank and file; the rank and file do not look at it as closely as the CEO; it is not always on their face. Hence, the rank and file still view centralization as a bane, even when it brings in advantages.

The consensus is that a decentralized way of working trumps a centralized approach; this applies to the military too. Jocko Willink, a prolific US Navy Seal, champions decentralized command. 

There are valid cases for centralization, especially when the talent required to do something is in short supply, and there are legitimate gains to be had from economies of scale. But, when you centralize, think hard of the unquantifiable second-order effects of the decision.

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Working Hard To Be Lazy

The programming world heralds laziness as one of the virtues of a programmer.

Larry Wall, the creator of Perl, says – Most of you are familiar with the virtues of a programmer. There are three, of course: laziness, impatience, and hubris.

What no one tells you is that this laziness does not come for free; one has to work hard to imbibe this trait.

 

work-47200_640

 

In practical terms, what does being lazy translate to?

  1. Doing as little as possible, never more than needed.
  2. Instead of doing things yourself, delegating to well-established tools, libraries, and frameworks.

Let us work with some concrete examples.

You want to parse a CSV file.

You think: let me load the file, parse it line by line, and split each line on a comma. You roll up your sleeves and code this. You feel smug having solved the problem yourself without anyone’s help.

Trouble starts when the CSV you parse has a header. Now you add an if condition to detect the first line. Later, someone uploads a CSV separated by a tab instead of a comma. You add another if condition to accommodate this. Another person uploads a CSV which has quoted fields. You start doubting yourself and ask how many such “unknown unknows” are there when it comes to parsing a CSV?

Unknown unknowns are risks that come from situations that are so unexpected that they would not be considered.

CSV parsing might have a lot of “unknown unknowns” for you – a person who is not well versed with the intricacies of CSV format. But there are experts out there who know the CSV format and have written libraries to handle all the edge cases and surprises that it might throw. You hedge your “unknown unknown” risk by delegating the CSV parsing to one of these libraries.

In short, be lazy, do as little as possible, and delegate to well-established libraries.

“Fools say that they learn by experience. I prefer to profit by others experience.” 

― Otto von Bismarck

Let us consider another scenario.

You want to store a counter in a database. One approach is: when you want to increment the count, you get the current count from the database, add one to it and store the new count back in the database.

Do you see the problem with this approach?

What if many threads are doing this in parallel? You will end up with a wrong count. A better approach is to delegate the task of incrementing the count to the database by leveraging SQL’s arithmetic operators. This approach makes the counter increment atomic. Many threads trying to increment the count is no longer a concern.

By doing less yourself and delegating the task of incrementing the counter to the database, you have saved yourself from bugs.

Why is this hard work?

This sort of thinking does not come easy; you have to work hard to identify where what and to whom you can delegate the work.

Dunning-Kruger effect might have a role to play in this. We believe we are the experts and best suited to do things.

In the field of psychology, the Dunning–Kruger effect is a cognitive bias in which people assess their cognitive ability as greater than it is. It is related to the cognitive bias of illusory superiority and comes from the inability of people to recognize their lack of ability.

While coding, most of the time, you are solving a problem that someone else has already solved, probably in a different context. Be aware of your biases and always question: Is this something I have to code myself, or can I offload this to an already written, well established and well-tested library or framework?

“Learn from the mistakes of others. You can’t live long enough to make them all yourself.”

― Eleanor Roosevelt

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The Million Dollar Question

What is the point of life?

All of us have pondered over this question. Luminaries have devoted their lives in the pursuit of an answer to this question. Philosophers have written voluminous texts trying to answer this question.

I am no Yogi, but that does not disqualify me from trying to answer this profound question. Beware, my answer might leave you with a feeling of meh.

During a holiday, a group of us friends played a weird game of football. We were randomly dribbling the ball, passing, and tackling each other – no teams, rules, goals, and referees. This pointless pursuit of the ball was fun.

What is the difference between kids and adults?

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Kids involve themselves in pointless pursuits. They are always engaged in one activity or the other. These consume them. We, the self-critical adults, try to see a point in everything. Few things consume us.

Give a cardboard box to a kid. She can keep herself occupied with the box for hours—an adult dreads at the thought of this.

When a child is young, she loves to draw irrespective of whether she is good at drawing or not. As she grows older, she pursues drawing only if she finds herself good at it. Enter adulthood, she becomes self-critical and continues her hobby only if she sees a point in it.

As an adult, try to remember the last time you were engaged in and consumed by a pointless activity.

A child actively indulges in role-play, creating stories in her head and acting it out. An adult passively watches role play in tv-series and movies. A child plays a variety of games. An adult passively enjoys sports watching others play.

As we age, we move from an active to a passive life. We try to seek a point in everything.

A child has no time to search for meaning. She is busy indulging herself in everything. The activity is the end; it is not a means to an end. I believe the same goes for life.

The point of life is not to search for meaning but to indulge in it. It is a pointless existence, and there is a joy to be had in understanding this. It is liberating.

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Murphy’s Law Of Software Abstractions

All software abstractions, sooner or later, leak.

When this happens, it hurts.

To drive a car, you need not know how it works internally. The mechanics of an automobile is well abstracted from the driver. Similarly, software libraries, tools, and frameworks promise abstraction to the engineers using them. They promise that one can use them effectively without delving into the internals.

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Let me tell you a story of uWSGI, processes, and threads.

uWSGI is a container for running web applications. It is popular in the Python world. Global interpreter lock makes concurrency muddy in Python applications. A way around this is to spawn multiple processes. While starting uWSGI, one can configure the number of processes to spawn to service concurrent web requests. The master uWSGI process starts, initializes, and loads the Python application code, and then forks the configured number of child processes. One caveat while forking is that the child process does not inherit any of the threads created in the parent process. Since uWSGI loads and initializes the Python application and then forks, the child process will not inherit any threads created during initialization in the parent process.

This bit us hard.

We were initializing a Kafka library on application load. This library internally created background threads that aggregate and push messages to a Kafka broker. The child processes uWSGI forked did not have these threads. Hence, our payloads were not flowing to Kafka.

In 99% of the cases, an application developer need not bother about how uWSGI forks and creates child processes. uWSGI abstracts this well, and the application developer can go on with her regular day to day work willfully ignorant of these abstractions. The same goes for the Kafka library. One need not pry open the library to figure out how it aggregates the messages and sends them to Kafka. In this particular case, the abstractions leaked and bit us. We spent a couple of days debugging.

One of the principles of good software design is that it should be easy to reason about.

You can interpret the above in different ways. A short interpretation is that when you look at a piece of code, it should be easy to figure it out in one’s head. Whatever you need to reason about the code should be in it and apparent – no hidden surprises. Leaky abstractions break this principle.

The uWSGI incident is fresh in my mind, but I have seen this to be a recurring pattern.

This is the problem with abstractions and magic technologies. When the abstractions leak, they bite us in ways we do not expect, and at times we do not anticipate.

  1. Be skeptical of technologies that claim to be magic. Do not pay only lip service to KISS, follow it in spirit. Think twice before incorporating such technologies into your stack.
  2. Figuring out the anti-patterns beforehand is as crucial as figuring out the best practices. All technologies come with a list of dont’s – grok them.
  3. Even though it may sound like an oxymoron, invest time and effort in going beyond the abstraction and peel the layers. At the least, build a minimal mental model.
  4. If you are a library developer, documenting how things can go wrong is as important as highlighting the rosy use cases; this is the least you can do to help your fellow craftsmen. Map the minefields, so the one does not accidentally trip on them.

1. uWSGI is a fantastic piece of software. I am not trying to diss on uWSGI at all.

2. For our particular problem, we disabled the default forking behavior of uWSGI. We enabled, lazyload, which loads the Python code after the fork rather than before.

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Charlatans and Us

Charlatan – a person, falsely claiming to have special knowledge or skill.

“How do we hire amazing engineers fast?” is a question people ask me often.

When someone asks me the question, they usually expect a profound answer, which will cure all their hiring pains. Hiring, especially good people, is a long, involved, and arduous process. There are no deep secrets to this. But, this is not what people want to hear because they already know this. Instead, they expect a magic potion, a hack, which will wipe out all the hiring woes. My standard answer to the hiring question is along the lines of – “I do not have any tricks up my sleeve to help you with that.”

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When someone is expecting a profound answer, and you do not have any, it is very tempting to come up with one. When the other person is seeking enlightenment, and all you have is mundaneness, you feel like an amateur and a buzz kill.

Charlatans start like this. People expect magical answers from them; they do not have any. Still, the expectation from others is so high that they start coming up with one, and then it becomes a self-fulfilling prophecy. Also, once you do this multiple times, you start drinking your own kool-aid. You do not even recognize that you are a charlatan. You genuinely start believing that you are a messiah.

Growth is a catch-22 problem. You need to endure pain to grow. You are not ready to experience pain unless you see the growth. But you do not see growth unless you suffer pain. Charlatans, with their quick and simple hacks, give us hope of disproportionate returns by investing little effort, hence the demand for charlatans.

The rich and the famous are often called charlatans. We hold the rich and the famous accountable for lofty morals and weave stories of their impeccable character. We forget that they are just like us – winging through life, taking shortcuts, not knowing what is happening or where they are heading. When the rich and the famous know of these high expectations, which a lot of them do not have(there is nothing wrong with this), they artificially try to mold themselves on these lines. In today’s age of social media, where virtue signaling is just a click away, it is getting easier to do this. When you fake it, it can only go so far. One day, the cloak falls, and the grandness built on flimsy appearances comes tumbling down. We then start calling these celebrities charlatans, but little we introspect on our role in them turning out to be charlatans.

As much as we like to blame charlatans for their deception, a significant part of the blame rests on us for creating them. It is our unholy expectations and quick reward-seeking nature that gives rise to charlatans.

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