Tag Archives: node.js

Self-contained Node.js Deployment

While setting up a Node.js environment on an individual developer’s machine can be done in a casual manner and oftentimes can be tailored to the developer’s own taste, deploying Node.js applications on shared or production servers requires a little more planning in advance.

To install Node.js on a server, a straight forward approach is to just follow some quick-start instructions from an official source. For instance, assuming latest v.4.x of Node.js is the target version and CentOS Linux is the OS on the target server, the installation can be as simple as follows:

Software version: Latest versus Same

However, the above installation option leaves the version of the installed Node.js out of your own control. Although the major release would stick to v.4, the latest update to Node available at the time of the command execution will be installed.

There are debates about always-getting-the-latest versus keeping-the-same-version when it comes to software installation. My take is that on individual developer’s machine, you’re at liberty to go for ‘latest’ or ‘same’ to suit your own need (for exploring experimental features versus getting ready for production support). But on servers for staging, QA, or production, I would stick to ‘same’.

Some advocates of ‘latest’ even for production servers argue that not doing so could compromise security on the servers. It’s a valid concern but stability is also a critical factor. My recommendation is to keep version on critical servers consistent while making version update for security a separate and independently duty, preferably handled by a separate operations staff.

Onto keeping a fixed Node.js version

As of this writing, the latest LTS (long-term-support) release of Node.js is v.4.4.7. The next LTS (v.6.x) is scheduled to be out in the next quarter of the year. There are a couple of options. Again, let’s assume we’re on CentOS, and that it’s CentOS 7 64-bit. There are a couple of options.

Option 1: Build from source

As a side note, if you’re on CentOS 6 or older, you’ll need to update gcc and Python.

Option 2: Use pre-built binary

Note that both the above two options install a system-wide Node.js (which comes with the default package manager NPM) accessible to all legitimate users on the server host.

Node process manager

Next, install a process manager to manage processes of the Node app, providing features such as auto-restart. Two of the most prominent ones are forever and pm2. Let’s go with the slightly more robust one, pm2. Check for the latest version from the pm2 website and specify it in the npm install command:

Deploying self-contained Node.js

Depending on specific deployment requirements, one might prefer having Node confined to a local file structure that belongs to a designated user on the server host. Contrary to having a system-wide Node.js, this approach would equip each of your Node projects with its own Node.js binary and modules.

Docker, as briefly touched on in a previous blog, would be a good tool in such use case, but one can also handle it without introducing an OS-level virtualization layer. Here’s how Node.js can be installed underneath a local Node.js project directory:

Next, create simple scripts to start/stop the local Node.js app (assuming main Node app is app.js):

Script: $PROJDIR/bin/njsenv.sh (sourced by start/stop scripts)

Script: $PROJDIR/bin/start.sh

Script: $PROJDIR/bin/stop.sh

It would make sense to organize such scripts in, say, a top-level bin/ subdirectory. Along with the typical file structure of your Node app such as controllers, routes, configurations, etc, your Node.js project directory might now look like the following:

Packaging/Bundling your Node.js app

Now that the key Node.js software modules are in place all within a local $PROJDIR subdirectory, next in line is to shift the focus to your own Node app and create some simple scripts for bundling the app.

This blog post is aimed to cover relatively simple deployment cases in which there isn’t need for environment-specific code build. Should such need arise, chances are that you might already be using a build automation tool such as gulp, which was heavily used by a Node app in a recent startup I cofounded. In addition, if the deployment requirements are complex enough, configuration management/automation tools like Puppet, SaltStack or Chef might also be used.

For simple Node.js deployment that the app modules can be pre-built prior to deployment, one can simply come up with simple scripts to pre-package the app in a tar ball, which then gets expanded in the target server environments.

To better manage files for the packaging/bundling task, it’s a good practice to maintain a list of files/directories to be included in a text file, say, include.files. For instance, if there is no need for environment-specific code build, package.json doesn’t need to be included when packaging in the QA/production environment. While at it, keep also a file, exclude.files that list all the files/directories to be excluded. For example:

Below is a simple shell script which does the packaging/bundling of a localized Node.js project:

Run bundling scripts from within package.json

An alternative to doing the packaging/bundling with external scripts is to make use of npm’s features. The popular Node package manager comes with file exclusion rules based on files listed in .npmignore and .gitignore. It also comes with scripting capability that to handle much of what’s just described. For example, one could define custom file inclusion variable within package.json and executable scripts to do the packaging/bundling using the variables in the form of $npm_package_{var} like the following:

Here comes another side note: In the dependencies section, a version with prefix ~ qualifies any version with patch-level update (e.g. ~1.2.3 allows any 1.2.x update), whereas prefix ^ qualifies minor-level update (e.g. ^1.2.3 allows any 1.x.y update).

To deploy the Node app on a server host, simply scp the bundled tar ball to the designated user on the host (e.g. scp $NAME-$VERSION.tgz njsapp@:package/) use a simple script similar to the following to extract the bundled tar ball on the host and start/stop the Node app:

Deployment requirements can be very different for individual engineering operations. All that has been suggested should be taken as simplified use cases. The main objective is to come up with a self-contained Node.js application so that the developers can autonomously package their code with version-consistent Node binary and dependencies. A big advantage of such approach is separation of concern, so that the OPS team does not need to worry about Node installation and versioning.

Adopting Node.js In The Core Tech Stack

At a startup company, DwellAware, I’ve been with recently, I was tasked to build a web-centric application with a backend for comprehensive data analytics in the residential real estate space. Nevertheless, this post is not about the startup venture. It’s about Node.js, the technology stack chosen to power the application. Programming platforms considered at the beginning of the venture include Scala/Play, PHP/Laravel, Python/Twisted, Ruby/Sinatra and Javascript/Node.js.

Neither is it a blog post about comparing programming platforms. I’m going to simply state that Node.js was picked mainly for a few reasons:

  1. its lean-and-mean minimalist design principle is in line with how I would like to run things in general,
  2. its event-driven, non-blocking-I/O architecture is well suited for contemporary high-concurrency web-centric applications, and,
  3. keeping the entire web application to a single programming platform, since contemporary client-side features are heavily and ubiquitously implemented using Javascript anyway.

Is adopting Node.js a justifiable risk?

In fact, that was the original title of the blog post. I was going to blog about the necessary research for adopting Node.js as the tech stack for the core web application back in 2013. It never grew to more than a few bullet points and was soon buried deep down the priority to-do list.

Javascript has been used on the client side in web applications for a long time. Handling non-blocking events triggered by human activities on a web browser is one thing, dealing with split-second server events and I/O activities on the server side in a non-blocking fashion is a little different. Node.js’s underlying event-driven non-blocking architecture does help somewhat flatten the learning curve to Javascript developers.

Although new Node modules emerged almost daily to try address just about anything in any problem space one could think of, not many of them prove to be very useful, let alone production-grade. That was two years ago. Admittedly, a lot has changed over the past couple of years and Node has definitely become more mature everyday. By most standards, Node.js is still a relatively young technology though.

Anyway, let’s rewind back to Fall 2013.

Built on Google’s V8 Javascript engine, Node is a Javascript-based server platform designed to efficiently run I/O-intensive server applications. For a long time, Javascript was being considered a client-side-only technology. Node.js has made it a serious contender for server-side technology. The fact that prominent software companies such as Microsoft, eBay, LinkedIn, adopted Node.js in some of their products/services was more or less testimonial. While hypes about certain seemingly arbitrary technologies have always been a phenomenon in the Silicon Valley, I wouldn’t characterize the recent uprising of Javascript and Node a mere hype.

Node.js modules

Node by itself is just a barebone server, hence picking suitable modules was one of the upfront tasks. One of the core modules that was an essential part of Node’s middleware framework is Connect, which provides chaining of functions and enhances Node’s http module. ExpressJS further equips Node with rich web app features on top of Connect. To take advantage of multi-core/processor server configuration, Node offers a method child_process.fork() for spawning worker processes that are capable of communicating with their parent via built-in IPC (Inter-process Communication).

On build tool, we started out with Grunt then later shifted to Gulp partly for the speed due to Gulp’s streaming approach. But we were happy with Grunt as well. Node uses Jade as its default templating engine. We didn’t like the performance, so we evaluated a couple of alternate templating engines including doT.js and Swig, and were shocked to see performance gain in an order of magnitude. We promptly switched to Swig (with doT.js a close second).

On test framework, we used Mocha.js with assertion libray, Chai.js, which supports BDD (Behavior-driven Development) assertion style.

Data persistence, caching, content delivery, etc

A key part of our product offerings is about data intelligence, thus databases for both OLTP and warehousing are critical components of the technology stack. MongoDB has been a default database choice for many Node.js applications for good reasons. The emerging MEAN (MongoDB-ExpressJS-AngularJS-Node.js) framework hints the popularity of the Node-MongoDB combo. So Mongo was definitely a considered database. After careful consideration, we decided to go with MySQL. One consideration being that it wouldn’t be too hard to hire a DBA/devops with MySQL experience given its popularity. Both Node.js and MongoDB are relatively new products and we didn’t have in-house MongoDB experise at the time, so taming one beast (Node in this case) at a time was a preferred route.

There weren’t many Node-MySQL modules out there, though we managed to adopt a simplistic MySQL module that also provides simple connection pooling. Later on, due to the superior geospatial functionality of PostGIS available in the PostgreSQL ecosystem, we migrated from MySQL to PostgreSQL. Thanks to the vast Node.js module repository, there were Node-PostgreSQL modules readily available for connection pooling. To cache frequently referenced application data, we used Redis as a centralized cache store.

Besides dynamic content rendered by application, we were building a web presence also with a lot of static content of various types including images and certain client-side application data. To serve static web content, a few typical approaches, including using a proxy web server, content delivery network (CDN), have been reviewed. On proxy server, Nginx has been on its rise to overtake Apache to become the most popular HTTP server. Its minimalist design is kind of like Node’s. We did some load-testing of static content on Node which appears to be a rather efficient static content server. We decided a proxy server wasn’t necessary at least in the immediate term. As to CDN, we used Amazon’s CloudFront.

Score calculation & image processing

Part of the core value proposition of the product was to come up with objective scores in individual residential real estate properties and neighborhoods so as to help users to make intelligent choice in buying/selling their homes. As described in a previous blog post, a lot of data science work in a wide spectrum of areas (cost analysis, crime, schools, comfort, noise, etc) was performed to generate the scores.

Based on the computed scores, we then derived badges for qualified real estate properties in different areas (e.g. “Low Energy Bills”, “Safe Neighborhood”, “Top Rated Elementary School”). The badges were embedded in selected photos of individual real estate properties, which could then be fed back into the listings distribution cycle by resubmitting into the associated MLSes if the real estate agents/brokers chose to.

All the necessary score calculation and image processing for badges were done in the backend on a Python platform with PostgreSQL databases. Python Tornado servers were used as data service API along with basic caching for Node.js to consume data as presentation content.

Here’s a screen-shot of the Dwelling Page for a given real estate property, showing its DwellScore:

DwellAware DwellScore

Geospatial maps & search

For geographical maps and search, Google Maps API was extensively used from within Node.js. We gecoded in advance all real estate property addresses using the API as part of the backend data processing routine so as to take advantage of Google’s superior search capability.

To supplement the already pretty robust Google Maps search from within Node.js to better utilize our own geospatial data content, we experimented using an Elasticsearch module which comes with their n-gram lexical analyzer for fuzzy-match search. The test result was promising. An advantage of using such an autonomous search system is that it doesn’t directly tax on the Node.js server or the PostgreSQL database (e.g. pg_trgm) as traffic load increases.

Below is a screen-shot of the Search Page centering around San Diego:

DwellAware Search

Fast-forward to the present

As mentioned earlier, Node.js has evolved quite a bit over the past couple of years — the rather significant feature/performance improvements from the v0.10 to v0.12, the next LTS (long-term-support) release incorporating the latest V8 Javascript engine and ES6 ECMA features, the fork-off to io.js which later merged back to Node, …, all sound promising and exciting.

In conclusion, given the evident progress of Node’s development I’d say it’s now hardly a risk to adopt Node for building general web-centric applications, provided that your engineering team possesses sufficiently strong Javascript skills. It wasn’t a difficult decision for me two years ago to pick Node as the core technology stack, and would be an even easier one today.

For more screen-shots of the website, click here.