OpenAPI Generator For Go Web Development

The Openapi Generator for Go API and Go web app development works surprisingly well, but somehow I found that it’s not so often mentioned. Recently I’ve tried it in one of my projects, and in my (limited) experience with it, I was pleasantly surprised by how good it was. With some setup, it could generate Go code with decent quality, and it’s fairly easy to use once you get a hang of it. Whether you’re building a standalone web-app from scratch or creating a service with REST API endpoints, openapi-generator might come up handy for you.

Using a generator might save much time to kick-start your web app project. And most importantly, I found that a good, well-defined, consistent API definition is so crucial to your development, testing, and most importantly, communication among teams and customers. I highly recommend that for any sizable project, you spend some quality time on writing a good API spec. It’ll become essential to your development workflow. I used to highly doubt this, and now I don’t think I can live without it.

And if you manually keep documentation, or API specifictions in sync with your Go code, you’ll have a hard time reviewing, checking, and testing between code and specs. The best way IMHO is to automate the process, by either generating the API code from spec, or the other around. Many toolings support either one of these, and openapi-generator is one of the really nice tools that I’m going to introduce in this blog post.

Openapi Generator supports many languages on the server as well as on the client side. And it has generator for different frameworks of Go. Right here I’m going to use go-server generator as an example. It uses the Gorilla framework for the server-side code.

For this blog post I’ve also made an example of code generation in my Github repo. I’ve generated the code, and implemented only one endpoint /books with example data:

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Reading BBoltDB

By trying to understand databases in general, I’ve recently dived into the source code of BBoltDB for a deeper understanding of its implementation. It’s a fairly small, nifty local KV store for source-code reading and studying, especially you’re a Golang programmer. At the same time it’s widely used and tested, embeded as the powerful storage engine for systems like Etcd and Consul. Though it’s only backed by one file, it’s capable of serving data size up to TB level.

Below is my notes for the very quick reading from its source code, hopefully providing anyone interested in data systems a quick glimpse of what’s under the hood. It’s not meant as an exhaustive and deep analysis.

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Building Applications With Cassandra: Experience And Gotchas

Recently I’ve summarized some experience on quickly getting started with Cassadra. And for this post I’d like to keep writing about some of our experience using and operating Cassandra. Hopefully it could be useful to you, and help you avoid future unwanted surprises.

Election and Paxos

Cassandra is always considered to be favoring the “AP” in “CAP” theorem, where it guarantees eventual consistency for availability and performance. But when really necessary, you can still leverage Cassandra’s built-in “Light-weight Transaction” for elections to determine a leader node in the cluster.

Basically, it works by writing to a table with your own lease:

INSERT INTO leases (name, owner) VALUES ('lease_master', 'server_1') IF NOT EXISTS;

The IF NOT EXISTS triggers the Cassandra built-in Light-weight Transaction and can be used to declare a consensus among a cluster. With a default TTL in the table, this can be used for leases control, or master election. For example:

CREATE table leases (
name text,
owner text,
) WITH default_time_to_live = 16;

So that the lease owner needs to keep writing to the lease row for heartbeats.

I’m not sure about the performance characteristics of Cassandra’s election behavior with other applications (etcd, Zookeeper, …) and it’ll be interesting to see a study. But since those are already more full-featured and well-understood in keeping consensus, I’d recommend delegating this behavior to them unless you’re stuck with Cassandra for your application.

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Building Applications With Cassandra: A Very Quick Guide

Cassandra Overview

Cassandra as an open-source NoSQL database has gained popularity in cloud and big data applications. Inspired by DynamoDB, it also has good latency, tunable consistency, easy to achieve scalability, and high-availability with cluster setup.

Our team’s been using Cassandra as the backend for an application we’ve been shipping to customers. We chose it for its high-availability setup, and good performance. We used to store time-series data and some simple configuration data as Key-value pairs. So it felt like a natural choice. And in our experience over time, it has proven to be highly capabable at serving our purposes.

With impressive availability, scalability, and read/write performance, Casandra also comes with its limitations. We cannot design data models the same way we did with traditional relational databases with SQL interface. And it doesn’t come with many of the guarantees from traditional databases, like consistency level, transactions, cascading deletion, etc. Like other NoSQL databases, Cassandra was designed to optimize batch write operations with good read and write latency. It fits applications without too much update/delete operations, especially ones with no high amounts of transactions.

So the best use cases for Cassandra can be:

  • You have a high volume of data with availability concerns.
  • Most data is sequential read/write or append, e.g.: logs, time-series, IoT applications, track records, messages, etc.
  • You don’t have complex data relations between data entities that requires high amount of transactions.

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Paper Reading: Ray: A Distributed Framework for Emerging AI Applications

Paper link:


Ray is a new and grossing distributed programming framework, with an ambitious plan to be the foundation of emerging AI/ML applications. In its own words, it aims to “provide a universal API for distributed computing”. Which means it needs to provide a programming interface that’s flexible enough for new applications, and a backend system designed to scale for elastic computing needs with some good performance. This paper (OSDI 18’) explains its API and architecture design to fulfill this goal. And I’ve found some very interesting points.

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Golang Channel Idioms

While learning Golang, I was fascinated with the power Golang’s goroutines and channels. Channel is a powerful tool to tackle synchronization problems in asynchronous programs. It acts as a bridge between async goroutines and can describe some complicated logic expressively. Together they can be powerful weapons in building async applications. On the other hand, when misused, they can be a nightmare to debug.

Here I’ve summarized a few of the valuable idioms of using Golang routines from multiple references as well as my own experience. They can serve as a helpful toolbox that comes in handy for similar problems. So that you don’t have to design them from scratch, which might help you avoid synchronization errors.

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Paper Reading: 150 Successful Machine Learning Models Deployed: 6 Lessons Learned At

Paper link:

Or download.

First published in KDD from, the paper described its lessons from deploying Machine Learning models in their production service. It provided some intriguing insights. I believe many are very valuable to understanding applying Machine Learning in real-world scenarios.

Here are some of my takeaways.

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Book Review: The Death of Expertise

The Death of Expertise: The Campaign Against Established Knowledge and Why It Matters

“The Death of Expertise” by Tom Nichols is a timely piece to the ongoing information endemic, especially in America. Quoting Issac Asimov:

There is a cult of ignorance in the United States, and there always has been. The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that “my ignorance is just a good as your knowledge.”

The book describes the author’s view of why experts are so important in a democracy and the relationship between expertise the public. And it also goes on to decry the ongoing decay in this relationship, where citizens are increasingly losing trust in experts, and experts are increasingly finding it difficult to communicate with their audience.

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Reading Summary: 11/06/2020


Edsger Dijkstra: The Man Who Carried Computer Science on His Shoulders

The often untold story behind a mastermind of Computer Science: Dijkstra, whose name has been an important algorithm widely used in GPS navigation.

The blog described a wise, hard-thinker, a great mind who made unparallel contributions to both Computer Science as a mathematical and logical view, as well as Software Engineering which focuses on building software and hardware components.

He’s most famous for his private reports, named “EWD”, and continued for more than forty years, describing his views on Computer Science and Software Engineering in general, and sometimes worked as reviews for others’ work. One of the most influencing “EWD” report was “Notes on Structured Programming,” which argued programming as a serious form of skill that demands intellectual rigor.

In 1972, Dijkstra received the ACM Turing Award, he was recognized for:

contributions to programming as a high, intellectual challenge; for eloquent insistence and practical demonstration that programs should be composed correctly, not just debugged into correctness; for illuminating perception of problems at the foundations of program design.

He has great passion for his art, and his strong personality sometimes sparked controversies. One of the most famous was the discussion on critiquing “GOTO” statements as harmful. It brought widespread, heated debate, yet Dijkstra’s view finally prevailed, and his insistence made a monumental change to programming paradigm.

There are much more interesting details around his personal and academic life in the original post, too long to be summarized here. For example, his had a mini-van in Austin, which he often drove to national parks with his wife, and it was named the “Touring Machine.” If you are passionate with computers and software, have a long weekend afternoon, it’s worth a good read.

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Paper Reading: Julia: Dynamism and Performance Reconciled by Design


The paper outlines the Julia programming language’s some most important design choices, and explains how they build a bridge between user-friendliness and performance.

The paper provided with a few benchmarks, to compare its performance with a C baseline, along with other dynamic languages like Python, MATLAB, JavaScript, and so on. While other dynamic programming languages suffer great performance loss, due to its dynamism, Julia can compete relatively close with the C/C++ baseline, with up to native performance in a few cases, most of the benchmarks are within 2x of C or C++, while Python can suffer more than 70x slower performance than C++.

This is significant, as it may eliminate the “prototype in dynamic language, then reimplement in static language for faster performance” cycle, eliminating extra time on coding to achieve efficiency without sacrificing much performance.

Some key takeouts from this paper:

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