Issue #18: 10 Reads, A Handcrafted Weekly Newsletter for Humans

The total time to read this newsletter is 130 minutes.
Fortune favors the prepare mind. — Louis Pasteur
  1. Three Sales Mistakes Software Engineers Make: 15 mins read. This post by PipelineDB folks talk about three mistakes sales mistakes software engineers make. I myself find it difficult when I have to take part in any sales initiative. The truth is we all have to sell. Sales do not always mean selling a product. It could be as simple as sharing your idea with the audience. It requires social skills that most software engineers lack. This post talk about three sales mistakes:
    1. Building a product before validating the market for it. This is part of the lean philosophy. I don’t think it is always feasible that you will have an audience with which you can validate your idea. So, I think in some cases it makes sense to building a functional MVP and drive from there. The MVP should not take more than 3 months.
    2. Talking instead of listening. The key message here is that listen to your audience and ask open ended questions. Some example of questions you can ask:
    3. How do you think about this problem?
      1. To what extent is this a priority?
      2. Why are you interested in this topic?
      3. .. Etc
    4. Mistaking interest for demand. Until you get money in your account your work is not done. IBM salespeople use BANT to qualify sales lead.
      1. Do they have enough budget to purchase the product?
      2. Do they have the authority to make the purchase?
      3. Do they need your product?
      4. Will the transaction be completed in a timeline that is acceptable to you?
  2. Amazon’s HQ2 Spectacle Isn’t Just Shameful—It Should Be Illegal: 20 mins read. This is yet another story of corporate getting things the way they want. They take billions of dollar subsidy from the government and return quite less. It is true in all parts of the world. In India, we have seen loan worth crores of rupees given to corporate. When the time comes to return back, system allows them to get away easily. All governments are hand in glove with corporates. We just don’t matter.
  3. Cloud Computing without Containers: 15 mins read. This looks interesting. I thought containers are the best we can go. But, as mentioned in this post, there are other possibilities like Isolates that can provider more efficient and economic alternatives.  This post does a good job comparing how Isolates compare against AWS Lambda that underneath uses containers. I will dig deeper into it more. Overall, a great post by Cloudfare folks.
  4. Things I learned from working at Shopify: 10 mins read. This is a great post by Budi Tanrim, a software engineer at Shopify. In this post, he talked about why he left an amazing job at Shopify to go back to Indonesia. Few points from his post that resonated with me:
    1. Come with a learning mindset: I often go for consulting assignments and there is always a tendency in me to come up with solutions before understanding the problem statement well enough. As he mentioned in his post, try to first understand the context and then think about solution.
    2. Be comfortable with being uncomfortable: When life events do not go as planned don’t get uncomfortable. If you get stressed then things will get more worse. It is always better to take a step back and think about the situation again.
    3. Prepare before presenting your work: This is an essential if you want to make an impact. Many time right words doesn’t come out at the right time so preparation helps a lot.
    4. Make decision log to have firmer decision: I also started doing it but I am not consistent. I agree with Budi that it is essential to document your decisions. The time you spent today in documenting your decision will serve you tomorrow when you might need to explain your rationale.
  5. What would a message-oriented programming language look like?: 10 mins read. Author thinks answer is Erlang. I have not worked with Erlang but I have used Akka with Scala in the past. Erlang like Akka is based on Actor model and when you work with actor model different objects communicate with each other using message passing. So, may be the answer is the language that has support for Actor model.
  6. Lessons from the data lake, part 1: Architectural decisions: 15 mins read.This post by AutoTrader engineers goes over the architectural decisions they took in building their own data lake. They started with on-premise solution but soon faced series of operational issues. The author writes Cluster computing on-premises is hard and expensive, cloud is easier. After failing with on-premise solution, they decided to build a new solution using Amazon S3 and Apache Spark delivered through AWS EMR solution. They used Terraform for provisioning cloud resources. They build five different zones to impose structure on their lake. They confined data to five ‘zones’ – in practice, five S3 buckets – named transient, raw, refined, user and trusted.They used Apache Avro for achieving schema on read.
  7. There’s Seldom Any Traffic on the High Road: 5 mins read. Another meaningful post on Farnam Street. This post makes an important point of not reacting when someone behaves rudely of you. As author writes, She was being rude. Yes. But that wasn’t the best version of her. I see the value of learning this skill. Making enemies is expensive. Sometimes you don’t even realize how expensive.
  8. Peeking under the hood of redesigned Gmail: 15 mins read. This post does a good analysis of performance issues with new Gmail interface. Using the tools available in Google Chrome, author was able to find possible reasons for bad performance of Gmail. It is sad that Gmail team does not use the facilities provided by Google’s own browser. I will recommend reading this article as you can apply the same learning for your website as well.
  9. Dealing with significant Postgres database bloat — what are your options? 15 mins read. When data is updated or deleted in Postgres, new data is written. The old data then needs to be vacuumed. That unvacuumed data is known as bloat. Here’s a look at how you can deal with it.
  10. Scalability Worst Practices: 10 mins read. This is an old blog published in 2008. The worst practices are still applicable today. So, give it a read.

Thinking about software system in terms of reliability, scalability, and maintainability

A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable. – Leslie Lamport

Last six months I was building pricing engine for a client. The application was built using multiple components:

  1. We had a change data capture pipeline built using AWS Kinesis that read data from IBM DB2 and writes to PostgreSQL to keep database in sync with changes happening in the source system
  2. We were storing denormalised documents in AWS ElastiCache i.e. Redis
  3. We had a batch job that was doing one time load of the PostgreSQL database
  4. We had a near cache that helped us process our worst requests in few hundred milliseconds

When you build a system using multiple independent components then you have to keep in mind that you are building a data system that it needs to provide certain guarantees. In our case, we had to guarantee:

  1. AWS ElastCache i.e Redis will be updated with changes happening in the source system in less than 30 seconds
  2. Near-cache will be invalidated and updated with latest data so that clients accessing the system will get consistent results. Keeping a global cache like Redis is easier to keep in sync than keeping near-cache in sync. We came up with a novel way to keep near cache in sync with the global cache.
  3. Data will not be lost by our change data capture pipeline. If processing of a message failed then we retry the message
  4. There will be times when different data components i.e. PostgreSQL, Redis, and near-cache will have different state. But, eventually it should become consistent
  5. That there will be a mechanism to observe state of the system at any point of time

Like it or not systems that we are building are becoming more and more distributed. This means there are many more ways they can fail. To help build software systems that meets the end goal, we should keep following three concerns in our mind. These should be defined as clearly as possible so that every team member keep these in mind while building software systems.

  1. Reliability
  2. Scalability
  3. Maintainability

Continue reading “Thinking about software system in terms of reliability, scalability, and maintainability”