Faithlife’s Software Developer Interview Process

The goal of this blog post is to prepare you to get a software development job at Faithlife! If you have additional questions after reading this article, check out this blog post that outlines FAQs during Faithlife’s Software Development Interview Process.

Our interview process typically takes one to two months from the initial interview to offer. If you have other interviews close to the offer stage, please let us know! Most of the time, we can make the interview process speedier to accommodate you.

1. Phone Screen (1–2 rounds)

The initial phone screen is with a recruiter, engineering manager, or software development group lead and lasts 30–60 minutes. You can anticipate a mix of behavioral and technical questions.

2. Digital Pair Programming #1

This stage consists of a 60–90 minute pair programming session. You’ll be able to choose the language you’re most comfortable with. Before the interview, you’ll receive an email with information that outlines the coding problem you’ll be working on. While we’d like you to have your IDE ready, you shouldn’t write code ahead of time.

For Faithlife de México Interviews:

In addition to the pair programming, you can anticipate a 30–60 minute meeting with some of our Faithlife de México employees.

3. Virtual Onsite (Pair Programming #2 + Development Team Meeting + Development Team Management Meeting)

The virtual on-site consists of a 90-minute pair programming session followed by a 90-minute meeting with some of our Faithlife employees and a 30-minute meeting with Development Team Management.

In the second pair programming, we want to see you write code in an unfamiliar situation. Most of the time, we choose the IDE and language you’ll code in and don’t provide the coding question ahead of time. As in the first pair programming, you’ll want to use Chrome (not Safari) when linking to the video call.

Faithlife’s Software Development interview process is designed to gradually increase in difficulty. Our goal is not to trip you up but to see a holistic view of your soft skills and technical abilities.

4. Offer

Before an offer, we extend a preliminary offer which includes benefits information + offer specifics. After agreeing to the preliminary offer, you’ll receive the official offer. We then run a background check, speak with references, and finalize a start date. Once that’s completed, you’re officially a Faithlife employee! Congratulations!

We value your input! If you have recommendations on how we can improve our interview process, please email

Posted by Maria Smithson on May 19, 2021

Recruitment Frequently Asked Questions

What is it like to work at Faithlife?

Each person’s experience is unique. Faithlife’s Glassdoor page provides a holistic view of the company culture. You can also check out the following video:

Working at Faithlife

What is it like to work at Faithlife de México?

En Faithlife de México incorporamos la cultura laboral de Faithlife USA cumpliendo el marco legal de México. Nuestros desarrolladores mexicanos forman parte de diversos equipos estadounidenses trabajando de forma remota desde México. Valoramos la calidad de vida y familiar de cada desarrollador, destacando el trato íntegro hacia nuestros colaboradores.

At Faithlife de México we incorporate the work culture of Faithlife USA in compliance with the Mexican legal framework. Our Mexican developers are part of various U.S. teams working remotely from Mexico. We value the quality of life and family life of each developer, highlighting the integrity of our employees.

Does Faithlife allow remote work?

Yes! Faithlife allows remote work in these states.

Our goal is for remote work to be as collaborative, enjoyable, and flexible as possible. Here are some of the ways our remote workers stay connected:

  1. Monthly Software Development Lunch Groups specifically for remote workers!
  2. Virtual Tech Talks every 2–3 months.
  3. Weekly Virtual Demo Days that provide the opportunity to see what’s happening in the tech side of the company and showcase the cool things you’re working on.
  4. Virtual launch celebrations.
  5. Wellness activities and giveaways.
  6. Holiday gifts.

What does the “ideal” candidate look like for this role?

We don’t have a “one size fits all” approach. For technical roles, we look for people who are excited to grow and learn new technologies. We also look for experience utilizing our tech stack (.NET, JavaScript, React, MySQL). For software development leadership roles, we look for candidates who have experience leading a team or teams while remaining hands-on in the code.

What’s included in the interview process?

Check out this blog post to see Faithlife’s Software Development Interview Process in its entirety.

Is there anything specific I should prepare for prior to the interview?

We recommend the following:

  1. Read through our Glassdoor reviews to get a sense of what it’s like to work at Faithlife.
  2. Look through our products listed on — is there a specific product you’d like to work on? If so, tell the person/people you interview with!
  3. We recommend answering behavioral interview questions utilizing the STAR interview format. Here’s an article that describes how to use STAR.
  4. You’re interviewing us just as much as we’re interviewing you! Prior to your interview, we recommend that you write down the questions you’d like to ask us.
  5. Have fun! We’re excited to get to know you better and have designed our interview process to showcase you at your best. Part of the reason we let developers use their favorite IDE in the coding challenge is because we want you to feel comfortable, confident, and excited to code. Best of luck in your interview! We’re rooting for you.

What language will I use for pair programming?

We have two separate pair programming sessions done at different stages in the interview process.

In the first pair programming, you’re able to choose the language that you’re most comfortable with. Prior to the interview, you’ll receive an email with information that outlines the coding problem you’ll be working on. While we’d like you to have your IDE ready, you shouldn’t write code ahead of time.

In the second pair programming, we want to see you write code in an unfamiliar situation. Most of the time, we choose the IDE and language you’ll code in and don’t provide the coding question ahead of time. As in the first pair programming, you’ll want to use Chrome (not Safari) when linking to the video call.

Faithlife’s Software Development interview process is designed to gradually increase in difficulty. Our goal is not to trip you up but to see a holistic view of your soft skills and technical abilities.

Still have questions? Check out this blog post to see Faithlife’s Software Development Interview Process in its entirety.

What is Faithlife’s Tech Stack?

We primarily develop using C#, JavaScript/TypeScript, React, MySQL, Elasticsearch, Redis, and Azure. If you don’t have experience with one of the listed technologies, still apply! We don’t expect anyone to have experience with each of the technologies in our tech stack.

What does it take to be successful in this role?

We look for people who live out our HOAGIES values. HOAGIES stands for:

Core Values

Honesty — Speak the truth with courage and kindness.
Openness — Share generously, keep asking questions.
Awesomeness — Provoke delighted exclamations: make the customer giggle.
Growth — Onward, upward: growing the church, growing each other.
Initiative — The person you’re waiting for is you.
Elegance — Everything you need and nothing more.
Shipping — Our products can’t help anyone until they have them.

Department Values

In addition to our company values (HOAGIES), Faithlife has Software Development Values.

Responsible for planning, managing, and delivery of tasks and projects.

Ownership — Getting the job done
Effectiveness — Doing the right job
Efficiency — Doing the job right

Working together, sharing responsibility, and growing co-workers.

Participation — Sharing your perspective
Collaboration — Working together
Leadership — Growing from others

Building elegant software that equips the Church.

Direction — Seeing the purpose
Breadth — Growing your skillset
Depth — Mastering skills

What’s the salary range for this role?

Salaries for each role are reviewed annually to ensure Faithlife stays competitive. The exact salary for this role depends on your background/experience and isn’t something that can be assessed this early in the interview process.

Prior to your first phone interview, please determine what your desired salary range is. At the end of the first phone interview, you’ll have the opportunity to tell the interviewer your desired salary range.

Posted by Maria Smithson on May 18, 2021

Using Fiddler to inspect Terraform Azure requests

When using Terraform to configure Azure resources, you may get an unhelpful error message (from terraform apply) similar to the following:

module.<name>.<resource>.<name>: Creating...
│ Error: Error creating/updating Some Thing. Component: Failure sending request:
  StatusCode=409 -- Original Error: autorest/azure: Service returned an error. Status=<nil> <nil>

Setting TF_LOG='DEBUG' will dump the HTTP responses to stderr, but it’s extremely verbose and hard to find the information you’re looking for. A much easier way to view HTTP responses is by using a HTTP debugger such as Fiddler.

There are a few setup steps you need to perform to enable this:

  1. Find your Fiddler certificate. Run certmgr.msc and in Intermediate Certification Authorities > Certificates, find DO_NOT_TRUST_FiddlerRoot.
  2. Right-click > All Tasks > Export, export as Base-64 encoded X.509 (.CER). Save it to a temporary file.
  3. Open C:\Program Files (x86)\Microsoft SDKs\Azure\CLI2\Lib\site-packages\certifi\cacert.pem in a text editor running as Administrator.
  4. Paste the contents of the CER file (from step 2) to the end of the file; save it.

Then, to debug Terraform requests:

  1. Run Fiddler; stop capturing all traffic.
  2. Set HTTP(S) proxies: $env:HTTP_PROXY=''; $env:HTTPS_PROXY=''
  3. Run terraform apply and watch the requests stream into Fiddler.
  4. Now it’s easy to select a single failing request and inspect the response body.

Posted by Bradley Grainger on April 21, 2021

Ignoring ASP.NET Core static file requests in New Relic

We use New Relic for monitoring ASP.NET Core web applications. In one web service, we want to ignore requests for static files; there are a large number of static file requests (that are served quickly), which artificially lowers the average response time and error rate, and they’re unnecessary noise in New Relic.

One way would be to install the New Relic API NuGet package, then explicitly ignore the transaction:

app.UseStaticFiles(new StaticFileOptions
    OnPrepareResponse = context =>
        if (context.File.Name == "file-to-ignore.txt")

Another way would be to use custom instrumentation. This is an XML file that specifies method names to match and ignore.

My first approach ignored Microsoft.AspNetCore.StaticFiles.StaticFileMiddleware.Invoke. Unfortunately, the static file middleware will be called on every request (if you put UseStaticFiles before UseMvc), so this method will always be invoked and every request will be ignored.

I tried to refine this by ignoring a method that’s only called when a static file is served:

<match assemblyName="Microsoft.AspNetCore.StaticFiles" className="Microsoft.AspNetCore.StaticFiles.StaticFileContext">
    <exactMethodMatcher methodName="SendAsync" />
    <exactMethodMatcher methodName="SendStatusAsync" />

However, the static file requests were still being tracked. Inspecting the New Relic log file revealed this line:

[Error] 2020-01-07 21:51:50 Skipping sequential layout method: (Module: C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App\2.2.6\Microsoft.AspNetCore.StaticFiles.dll, AppDomain: clrhost)[Microsoft.AspNetCore.StaticFiles]Microsoft.AspNetCore.StaticFiles.StaticFileContext.SendStatusAsync(System.Int32)

“Sequential layout” made me think of the LayoutKind enum. When a coworker pointed out that StaticFileContext is a struct, I made the assumption that New Relic can’t profile methods belonging to a struct, at least when [StructLayout(LayoutKind.Sequential)] is applied, which is the C# compiler default. (I don’t know why it has this limitation. Searching for the log message above provided no results; maybe it will now at least return this blog post?)

So I next searched for a method (on a class) that is only invoked when StaticFileContext sends a static file, and found LoggerExtensions.

I finally settled on this custom instrumentation to ignore HEAD requests that are handled by StaticFileMiddleware:

    <tracerFactory name="NewRelic.Agent.Core.Tracer.Factories.IgnoreTransactionTracerFactory">
        <match assemblyName="Microsoft.AspNetCore.StaticFiles" className="Microsoft.AspNetCore.StaticFiles.LoggerExtensions">
            <exactMethodMatcher methodName="Handled" /> <!-- .NET Core 3.0 -->
            <exactMethodMatcher methodName="LogHandled" /> <!-- .NET Core 2.2.6 -->

Use with caution: there is no logging that a request is being ignored due to custom instrumentation, so you can (and I did!) waste a lot of time tracking down missing requests that are due to this. The code-based approach at the top of this post is likely to be better in most situations.

Posted by Bradley Grainger on January 08, 2020

Integrating Dependabot with GitHub Enterprise using Jenkins

Dependabot is a GitHub bot that scans your repositories for outdated dependencies, then opens PRs to update them. Although the code supports GitHub Enterprise, the hosted version of Dependabot only supports This post will walk you through setting up dependabot-core so that it can scan for outdated dependencies in a GitHub Enterprise repository, using Jenkins to run it.

Firstly, most of the credit should go to the dependabot team who have made the core code freely available, published a Docker image containing it, and written a script that automates the process.

Secondly, this assumes you have a GitHub Enterprise instance available at, and Jenkins at Change the URLs as appropriate in the following instructions.

1. Set Up Credentials

You will need both GitHub Enterprise and personal access tokens. You can create these at and Select the repo scope when creating the tokens. PRs opened by Dependabot will be created by the GitHub user who creates the tokens.

These credentials need to be stored safely in Jenkins. Go to and create two new Secret text credentials (one per PAT) as follows:

  • Scope: Global
  • Secret: (paste personal access token here)
  • ID: dependabot-github-enterprise / dependabot-github-com (ID based on PAT)
  • Description: Dependabot PAT for GitHub Enterprise / Dependabot PAT for GitHub (name based on PAT)

2. Create the Jenkins Job

The Dependabot build can run for one package manager at a time. These instructions configure it for NuGet. The other available options are listed here; adjust the script as necessary.

At Jenkins, create a new Freestyle job named Dependabot-NuGet with the following configuration:

  • GitHub project > Project url:
  • This project is parameterized
    • Add a String Parameter:
      • Name: PROJECT_PATH
      • Description: GitHub repository name, including org
  • Restrict where this project can be run: (This job requires a Linux build node that has access to Docker, select as appropriate)
  • Source Code Management: Git
    • Repository URL:
  • Build Environment
    • Use secret text(s) or file(s) - Add the following Bindings
      • Secret text
        • Credentials: Dependabot PAT for GitHub Enterprise
      • Secret text
        • Variable: GITHUB_ACCESS_TOKEN
        • Credentials: Dependabot PAT for GitHub
  • Build
    • Execute shell
docker pull dependabot/dependabot-core:latest
docker run -v "$(pwd):/home/dependabot/dependabot-script" -w /home/dependabot/dependabot-script dependabot/dependabot-core bundle install -j 3 --path vendor
docker run -v "$(pwd):/home/dependabot/dependabot-script" -w /home/dependabot/dependabot-script -e -e GITHUB_ENTERPRISE_ACCESS_TOKEN=$GITHUB_ENTERPRISE_ACCESS_TOKEN -e GITHUB_ACCESS_TOKEN=$GITHUB_ACCESS_TOKEN -e PACKAGE_MANAGER=nuget -e PROJECT_PATH=$PROJECT_PATH dependabot/dependabot-core bundle exec ruby ./generic-update-script.rb

Click Save to create the job.

3. Run the Jenkins Job

At, Click Build with Parameters. You will be prompted for PROJECT_PATH; enter MyOrg/MyRepo. Click Build to start.

The build should run, and list all the out-of-date dependencies it found; it will open a PR on GitHub Enterprise for each outdated dependency.

Posted by Bradley Grainger on December 13, 2019

Being Like Water

I often forget just how young the software craft is. The landscape moves quickly, and many I’ve known have difficulty keeping up with the shifting ground beneath them. A couple of years ago I stumbled onto Huyen Tue Dao’s talk Be Like Water. She highlights the importance of being adaptable and reminds us that skills to build good software aren’t necessarily coupled to the systems we work with any given day. Shortly after joining Faithlife, a colleague shared Dan McKinley’s essay Choose Boring Technology, which tempers the virtue of adaptability in a helpful way. Some of these ideas stick with me today in some form or another and have substantially influenced the direction of the Faithlife Android app’s systems under the hood. In the year since I last wrote about the Faithlife app, several things have changed. Many have stayed the same.


Overall, our high level architecture is pretty similar when compared to the previous post. The notable difference is that we have come to adopt more of a Model-View-ViewModel (MVVM) paradigm.

Current Architecture Overview

Instead of contract interfaces facilitating precise communication between the view and presentation/business logic in the presenter, the view takes inputs from the user and requests that the viewmodel perform some action. The view is notified of new information by observing properties on the viewmodel.

Android Jetpack

Jetpack continues to be a huge boon to our small team’s productivity. We make use of several components like navigation, viewmodel, and livedata and continue to look to Jetpack components first when we have need of a supplement to the framework itself. Google forms the vessel for Android development, so using their materials will make us more adaptable in the future with less hassle.

ViewModel & LiveData

ViewModel has made handling the lifecycle issues of the old days much less painful. There were solutions before (like headless fragments, shudder) for handling orientation changes well, but none provide the simplicity of ViewModel. Recently, some nice Kotlin coroutine features were added to the class that make structured concurrency easier than ever.

LiveData<T> make observing changes in viewmodel state a breeze by providing observability in a lifecycle-aware way. I wish the API had a more idiomatic affordance for setting values on the main thread with coroutines. Currently, you either have to call postValue or use withContext(Dispatchers.Main) and set value property directly. The latter is more idiomatic, the former is a little safer as it’s impossible to forget to set values on the main thread. We’ve made a habit of the latter since our viewmodel suspend functions are typically called from the main dispatcher context anyway. It’s a small concern for now.

Data Binding

The Jetpack data binding library is something we’re gravitating away from. It has some nice qualities that definitely encourage a separation of view and logic concerns. However, the build time impact is—while not huge by any means—considerable. We decided to try the system some time after making the decision to adopt the MVVM paradigm. Before then, we just modified view properties directly in the LiveData observers we registered in their parent Fragment. The code generator for data binding generates some impressively elaborate Java code, but doesn’t play all that well with Kotlin. One example of this is that to use a top-level extension function in a data binding expression, you had to be aware that the function was compiled into a class that had the name of the file in which the function was defined appended with ‘Kt’. Then you had to know the way extension functions work. The object being extended (the receiver) is passed as the first argument of the function, then all the other arguments are passed.

For example, to use an AliasKind extension defined in MentionExtensions.kt:

    	<import type="" />
            type="" />

    		android:text="@{MentionExtensionsKt.getDisplayName(aliasKind, context)}"

Slightly modified markup from an internal discussion on the subject

The data binding expression language also had magic variables that you could seemingly reference out of nowhere (like context). None of this is insurmountable, but we’ve agreed that manipulating views in observer listeners is better.

We’re likely going to try out the relatively new view binding system that seems to be a more type-safe way to get references to views from framework components. Adios findViewById.

We’re also keeping an eye on Jetpack Compose. Data binding expressions are a step in the wrong direction considering the new UI toolkit doesn’t use any markup at all.

Dagger 2

We’re also continuing to improve our use of Dagger as a facilitator of dependency injection. We never got into Dagger-Android as it seemed like it was somehow both more magical and constraining than it was worth. Google has shown they agree recently by announcing its deprecation. We’ve worked toward isolating components to subgraphs where possible and have used relatively recent support for setting custom fragment factories on any FragmentManager, in order to make dependency injection more possible in framework components where passing arguments to constructors was historically a bad idea. Our fragments and viewmodels can be passed dependencies via simple @Inject annotated constructors.

All of this makes isolating tests much easier, encourages reuse among different systems, and makes the separation of concerns much clearer among components in the system.

More Than Water

Engineering requires balance. Every choice is a trade-off. Two of our company values, shipping and elegance, characterize this tension well. We were early adopters of Kotlin coroutines and Jetpack Navigation. We didn’t find much value in Koin. We saw promise in databinding so we gave it a shot, but we’re reconsidering so that we might more easily pick up Compose down the road. Choosing how you invest your effort is a tremendously important skill for building good software. We don’t always nail it, but we certainly aspire to. We are adaptable, but we’re more than water; aware of our environment we strive to make choices that will put us in the best position to build a great product.

Posted by Justin Brooks on October 30, 2019

Improving WPF Text Display Performance

Logos 8 is a WPF Windows desktop application. It includes fonts that are embedded in the application itself (instead of being installed in the system’s Fonts folder). When we added a Chinese Bible to the Logos library, we bundled Noto Sans CJK for display of the Chinese text. But we found that drawing a screen of text would take multiple seconds (even on a high-end machine) and allocate gigabytes of RAM.

Time usage of ReadFileFragment

Memory usage of ReadFileFragment

The time is spent in WPF functions called by DWrite. (These are also the same functions responsible for the excessive memory allocation.) DirectWrite is a hardware-accelerated text layout and rendering subsystem in Windows. It should provide high-performance text rendering, but it’s clearly not in this situation.

Like many Win32 APIs, DirectWrite is built with COM, a 25-year-old technology for creating software components. DWrite provides COM interfaces that an application can call to render text, and also defines interfaces that a client can implement to extend DWrite, e.g., by providing a custom font loader.

WPF implements a number of DWrite COM interfaces to make fonts embedded in .NET assemblies available to DWrite:

Our primary problem is with IDWriteFontFileStream and now, thanks to Microsoft making WPF open source, then recently adding all the code, we can see exactly where the problem lies. ReadFileFragment allocates a buffer, copies unmanaged memory into it, pins the buffer, then returns its address to DWrite (source code link).

This is almost a worst-case scenario for the .NET garbage collector: hundreds of megabytes of memory are being allocated, every buffer is being pinned so the GC can’t compact the heap, and the buffers are probably living long enough (at least across multiple native/managed interop calls) to make it out of gen 0.

Moreover, it’s completely unnecessary. IDWriteFontFileStream only needs to return a pointer to a portion of the font (no copying is necessary), which is simple if the font is already loaded in memory. And it is: embedded fonts are concatenated in the “Resources” section of the assembly, and every .NET DLL is mapped into the virtual address space of the process.

I wrote an implementation of IDWriteFontFileStream that is initialised with a pointer to the beginning of the font data. ReadFileFragment becomes simply a pointer addition and assignment: no allocation, no memcpy, extremely low overhead.

Getting a pointer to the beginning of the font data is somewhat trickier. We can parse the PE header to find the .text section that contains the CLR Header. From this we can find the offset to the embedded resources and use ResourceReader.cs as a guide for parsing the binary format. This will give us the address of each font file in memory, and enough information to construct a pack URI for each font.

We now just have to find a way to replace WPF’s inefficient FontFileStream with our optimised version. The interface-based nature of COM works to our advantage here. If we can replace the IDWriteFactory interface that WPF calls into when it calls RegisterFontCollectionLoader, we could substitute our own IDWriteFontCollectionLoader implementation, and ultimately return the efficient stream from IDWriteFontFileLoader::CreateStreamFromKey.

I first looked into replacing WPF’s reference to the IDWriteFactory object, e.g., by using reflection to change a private static field. But it was created by a static constructor and didn’t seem possible to intercept early enough in the application’s lifetime.

Instead, I found a great library for Windows API hooking: EasyHook. This let me override the DWriteCreateFactory method exported from DWrite.dll for our application’s process. We hook the API very early in startup, before WPF has called it. Then, when WPF does call it, we instantiate the real DWriteFactory but return our own custom implementation of IDWriteFactory that simply forwards most API calls to the real factory.

The two calls that aren’t forwarded directly are RegisterFontCollectionLoader and RegisterFontFileLoader. Instead, another proxy object is created (around WPF’s loaders) and the proxy is registered with the real DWrite. Finally, when DWrite calls IDWriteFontFileLoader::CreateStreamFromKey on our proxy, we examine the “reference key” that’s supplied as a font identifier. If we don’t recognise it, we forward the call to the original loader. But if it’s a pack URI that matches one created for our assembly resources (above), an optimised IDWriteFontFileStream is created and returned instead.

The results are incredible: instead of displaying one page of text every 2–3 seconds, we can now refresh the display dozens of times per second. Managed allocations have been eliminated, there are no native/managed interop calls from DWrite to the font loader, and CPU usage has been reduced by at least 100×.

Updated profile with optimised code

Faithlife isn’t using WPF on .NET Core yet but if we do, we’ll consider contributing this back to dotnet/wpf so that we don’t have to use hacks like API hooking and so that all WPF applications can benefit.

(The code mentioned in this post was primarily written in 2017, extending code we first wrote in 2015 to integrate HarfBuzz with DWrite, so structuring it to be contributed to an open source project was not a concern at the time.)

Posted by Bradley Grainger on June 03, 2019

Model-View-Presenter on Android with Kotlin Coroutines

Kotlin just released a major version that brings coroutines out of experimental status. We’ve been using coroutines for around six months now and have learned quite a bit about how to use coroutines and how not to. The coroutines library is a powerful tool and seems to be on the rise in popularity. There is a lot of great info out there about using coroutines in MVVM projects and in projects that make heavy use of Android Jetpack’s Architecture Components. Instead, we’d like to share how we are using coroutines in a model-view-presenter architected application.

Disclaimer: I’ll assume you’re familiar with coroutines at a high level. If you need an intro, there are plenty of resources online that would serve you better than I would. I’ll link some in the Extras section.

Our Architecture at a Glance

MVP is a tried and true architecutral pattern for Android. Recently we’ve been successfully leveraging new technologies, like coroutines and architecture components, to make MVP nicer than ever. Our current implementation is a stepping stone toward a better future that embraces a reactive paradigm with coroutines as the bedrock.

Current Architecture Overview


There are certainly ways we can improve our architecture. We know we have a way to go yet, but we’ve found success on the road to a clean architecture.

Structured Concurrency

A driving principle of coroutines development is that coroutines are like lightweight threads. The early experimental versions of coroutines were a bit of a wild west that encouraged creating new coroutines all the time. A typical presenter might look something like this previous to coroutines version 0.26.

class PlaylistPresenter : PlaylistContract.Presenter {

    private var job: Job? = null

    override fun long fetchSongs() {
        // The default context was 'CommonPool' which 
        // delegated the work to a pool of non-ui threads.
        // A new job is created for this coroutine.
        job = launch {
            val songs = fetchSongsFromNetwork()
            withContext(UI) {

    override fun cleanup() {
        // imagine tracking many job objects for multiple
        // coroutines executing simultaneously. _shudder_

Unlike threads, which are often created in a global scope, coroutines can be tightly scoped to the entities that own them. Instead of firing coroutines into the ether and hoping everything goes well, the team working on coroutines introduced a better way with kotlinx.coroutines 0.26.

The CoroutineScope interface facilitates constraining a coroutine’s lifetime by an object with lifetime semantics.

class Presenter : PlaylistContract.Presenter, CoroutineScope {

    private val job = Job()
    override val coroutineContext: CoroutineContext = job + Dispatchers.IO
    override fun long fetchSongs() {
        // This launch uses the coroutineContext defined
        // by the coroutine presenter.
        launch {
            val songs = fetchSongsFromNetwork()
            withContext(Dispatchers.Main) {

    override fun cleanup() {
        // By default, every coroutine initiated in this context
        // will use the job and dispatcher specified by the 
        // coroutineContext.
        // The coroutines are scoped to their execution environment.

Now the presenter is a CoroutineScope, and coroutines started in this scope can more appropriately be cleaned up or cancelled when the presenter is no longer necessary.

A View-Presenter Contract for Coroutines

Fortunately, generalizing the scope impementation to all presenters in a project is fairly straightforward.

interface BaseContract {
    interface View
    interface Presenter<T: View> {
        fun takeView(view: T) {
            this.view = view

        fun releaseView() {
            this.view = null

        var view: T? = null

interface CoroutineContract {
    interface View : BaseContract.View, CoroutineScope {
        private val job = Job()
        override val coroutineContext: CoroutineContext = job + Dispatchers.Main
    abstract class Presenter<T: View> : BaseContract.Presenter<T>, CoroutineScope {
        private val job = Job()
        override val coroutineContext: CoroutineContext = job + Dispatchers.IO

        override fun releaseView() {

View methods will be called from the presenter. They should be modified with suspend and, importantly, they are responsible for changing the coroutine execution context back to the main thread via withContext(this.coroutineContext) where appropriate.

Presenter methods return are also suspend methods. Since presenters are coroutine scopes, you can easily use a coroutine builder on the presenter like launch to call the suspend methods on the presenter.

Example MVP Interaction


Isolating and testing presenters on the JVM is easy if a few rules are followed:

Rules for Presenters
  1. Avoid direct references to the Android framework. If necessary, they reference framework components through the view interface. This is antithetical to the passive view philosophy, but the trade-off is worth sealing the Android framework in a box for testing purposes.
  2. Presenter methods that are called by the view are suspending functions. Those functions should always be called within the presenter’s coroutine scope.
  3. takeView is the cue for the presenter that ui is ready for information. releaseView is the cue for the presenter that the ui is being torn down. Hard dependencies on the MVP view’s lifecycle are discouraged and should use the contract interfaces to communicate if necessary.

Android Test is out now and provides hope for a future where testing on the JVM can involve the Android framework. Fingers crossed.

The Future

We aspire to a more reactive variant of the MVP pattern. The good news is that we have several paths to making our reactive aspirations a reality.

Future Architecture

A possible future using Room for a local data store that serves as an authority on data presented to the user. Maybe Room will support coroutine channels by the time we take this on. Maybe we’ll use LiveData. Who knows?

Dagger 2, Retrofit, Lifecycle, & Coroutines make the foundation of our stack, but we’re always evaluating technologies that can help us write better code faster.


We like Kotlin a lot. As it currently stands, the app is exclusively written in the language. If you’re an Android developer and find this interesting, we’re hiring!

We have some exciting things planned for the Faithlife App.

Thanks to Logan Ash and Jacob Peterson for reviewing early versions of this post.

Update 01/19/2019

An earlier version of this post suggested that presenter methods should return an instance of Job so that tests can synchronize on the called coroutine in order to prevent race conditions. We ran into some problems with the way exceptions of child coroutines within a runBlocking coroutine builder are handled. runBlocking immediately cancels child coroutines when they thrown an exception. After some consideration, we determined that the presenter should be responsible for exactly how the methods are called, just with the fact that they are suspending.

The structured concurrency updates and the CoroutineScope interface helped us achieve this separation. We can start presenter coroutines in the presenter’s scope from the view and in tests we wrap the entire test in a runBlocking coroutine builder so that suspend methods are executed synchronously.

We’re happier with the new system in it’s flexibility to treat suspending functions differently depending on the context in which they are called.

Posted by Justin Brooks on November 05, 2018

Making renders faster with the React 16.5 profiler

React 16.5 recently shipped, which added support for some new Profiling tools. We recently used these tools to identify a major source of slow render performance. is a web application powered by React 16.3. The homepage consists of a reverse-chronological timeline of posts. We received some reports that interactions with posts (such as replying) caused the browser to lag, depending on how far down the post was on the page. The further down the page the post was, the more lag occurred.

After updating React to 16.5 on a local copy of Faithlife, our next step was to start profiling and capture what components were re-rendering. Below is a screenshot of what the tools showed us clicking the ‘Like’ button on any post:

Slow renders screenshot

The blue blocks below NewsFeed show render being called on all the posts in the feed. If there were 10 items loaded, NewsFeedItem and all its children would get rendered 10 times. This can be fine for small components, but if the render tree is deep, rendering a component and its children unnecessarily can cause performance problems. As a user scrolls down on the page, more posts get loaded in the feed. This causes render to get called for posts all the way at the top, even though they haven’t changed!

This seemed like a good time to try changing NewsFeedItem to extend PureComponent, which will skip re-rendering the component and its children if the props have not changed (a shallow comparison is used for this check).

Unfortunately applying PureComponent was not enough - profiling again showed that unnecessary component renders were still happening. We then uncovered two issues preventing us from leveraging PureComponent’s optimizations:

First roadblock: Use of children props.

We had a component that looked something like this:

<NewsFeedItem contents={item.contents}>
  <VisibilitySensor itemId={} onChange={this.handleVisibilityChange} />

This compiles down to:

  { contents: item.contents },
  React.createElement(VisibilitySensor, { itemId:, onChange: this.handleVisibilityChange })

Because React creates a new instance of VisibilitySensor during each render, the children prop always changes, so making NewsFeedItem a PureComponent would make things worse, since a shallow comparison in shouldComponentUpdate may not be cheap to run and will always return true.

Our solution here was to move VisibilitySensor into a render prop and use a bound function:


class NewsFeedItemWithHandlers extends PureComponent {
  // The arrow function needs to get created outside of render, or the shallow comparison will fail
  renderVisibilitySensor = () => (

  render() {

Because the bound function only gets created once, the same function instance will be passed as props to NewsFeedItem.

Second roadblock: Inline object created during render

We had some code that was creating a new instance of a url helper in each render:

getUrlHelper = () => new NewsFeedUrlHelper(

	urlHelper={this.getUrlHelper()} // new object created with each method call

Since getUrlHelper is computed from props, there’s no point in creating more than one instance if we can cache the previous result and re-use that. We used memoize-one to solve this problem:

import memoizeOne from 'memoize-one';

const memoizedUrlHelper = memoizeOne(
	(moreItemsUrlTemplate, pollItemsUrlTemplate, updateItemsUrlTemplate) =>
		new NewsFeedUrlHelper({

// in the component
getUrlHelper = memoizedUrlHelper(

Now we will create a new url helper only when the dependent props change.

Measuring the difference

The profiler now shows much better results: rendering NewsFeed is now down from ~50ms to ~5ms!

Better renders screenshot

PureComponent may make your performance worse

As with any performance optimization, it’s critical to measure the how changes impact performance.

PureComponent is not an optimization that can blindly be applied to all components in your application. It’s good for components in a list with deep render trees, which was the case in this example. If you’re using arrow functions as props, inline objects, or inline arrays as props with a PureComponent, both shouldComponentUpdate and render will always get called, because new instances of those props will get created each time! Measure the performance of your changes to be sure they are an improvement.

It may be perfectly fine for your team to use inline arrow functions on simple components, such as binding onClick handlers on button elements inside a loop. Prioritize readability of your code first, then measure and add performance optimizations where it makes sense.

Bonus experiment

Since the pattern of creating components just to bind callbacks to props is pretty common in our codebase, we wrote a helper for generating components with pre-bound functions. Check it out on our Github repo.

You can also use windowing libraries, such as react-virtualized to avoid rendering components that aren’t in view.

Thanks to Ian Mundy, Patrick Nausha, and Auresa Nyctea for providing feedback on early drafts of this post.

Posted by Dustin Masters on September 21, 2018

Converting BibleWorks Hebrew

We recently announced that we’ll support importing BibleWorks notes into Logos 7. This is mostly a matter of converting RTF into our internal format, a fairly well-understood process.

The one wrinkle is supporting BibleWorks Greek and Hebrew fonts. BibleWorks didn’t support Unicode for many years; instead it used the Bwhebl and Bwgrkn 8-bit fonts to simulate Greek and Hebrew characters.

In Unicode, b and β and ב are all separate characters (that in some cases are all supported by a single font). With 8-bit fonts, one uses Latin characters (a, b, c) but changes the font so that “b” looks like β or ב. Behind-the-scenes, κύριος is stored as ku,rioj and אֲדֹנָ֣י as yn’ådoa}. This makes text processing more difficult as you can no longer perform a search for Greek or Hebrew using the Unicode versions of those characters. It also means the user must have these specific fonts installed and can’t change their preferred Greek or Hebrew font. For a good customer experience, we needed to convert the characters for users who had BibleWorks notes predating Unicode support.

The Greek was relatively straightforward, but Hebrew presented a bigger challenge. Not only is BibleWorks Hebrew stored using Latin characters, it’s also stored in display (i.e., left-to-right) order. In Unicode, characters are stored in logical order (which is right-to-left for fragments of Hebrew text); the display system will lay them out correctly. The string needs to be reversed, but with a catch: in both BibleWorks Hebrew and in Unicode, Hebrew vowels and accents are entered after the character that they’re positioned on top of. We can’t naively reverse the entire string; we have to reverse it one grapheme cluster at a time.

Moreover, Unicode has a concept of bidirectional mirroring in which “neutral” characters are replaced by their mirrored versions in a RTL run; for example ( will be displayed as ) in right-to-left text. When reversing the string, these characters need to be replaced by their mirrored version.

Finally, the documentation we found gave BibleWorks Hebrew characters as decimal numbers representing entries in an 8-bit font; due to the way we were reading the RTF source of BW Notes, these bytes had already gone through a Windows-1252 to Unicode conversion, so our character map had to be based off the Unicode characters that corresponded to Windows 1252 bytes.

Initial input 191 121 110 39 229 100 111 97 125 192
Decode Windows-1252 to Unicode ¿ y n å d o a } À
Untransliterate ( י נ ָ ֣ ד ֹ א ֲ )
Reverse grapheme clusters ) א ֲ ד ֹ נ ָ ֣ י (
Flip punctuation ( א ֲ ד ֹ נ ָ ֣ י )

The final result: (אֲדֹנָ֣י)

Our complete BibleWorks Hebrew mapping table is available here.

Posted by Bradley Grainger on August 15, 2018