Photo by on Marion Michele Unsplash If you are one of the happy readers of about + architecture, just keep reading because today we are going to improve our serverless with mongoose, connection pooling and monitoring. my article TDD Serverless toolkit If you haven’t yet, I strongly encourage you to read part #1 first, and get back once you are happy, up and running. _Today we are going to build the dream backend for your startup with TDD and Serverless._medium.com If TDD is Zen, adding Serverless brings Nirvana So if part #1 is OK for you but still need more, let’s see how we can make our DB perform fast and furious. Database model If we want our code to be easily maintainable, lambda functions and are our friends. But when dealing with the database, we clearly can do better to document and enforce a database schema for our backend. TDD The NPM package is the lightest way to implement a call to our database. However, we may want to consider using an ODM like , as the benefits we get are worth sacrificing just a bit of performance. We will be tweaking performance in a few minutes. mongodb Mongoose So, starting on the code from episode 1, let’s install Mongoose and get started: npm uninstall mongodbnpm install mongoose Ready! Now we can define our first model. As a team member joining the project I would clearly go to the folder if I checked out the code for the first time. So let’s create the folder and add a model for a user in . models models/user.js As you may notice, we are defining the three fields we used in the starter project as and and we are also definig an extra field which will be populated with the result of evaluating upon creation. String required created Date.now Now we can use our DB model object and invoke methods on it to perform operations on the database with type validation and schema compliance. We also get a more compact and cleaner code. For example, to find all users with we needed to do: mongodb const dbUsers = dbClient.db(dbName).collection(dbCollection)const result = await dbUsers.findOneAndUpdate({ _id: ObjectID(userId) }, { $set: newUser })return result.value._id Whereas now, the same with would look like: mongoose const User = require("./models/user.js")const result = await User.findByIdAndUpdate(userId, newUser)return result._id You see, shorter, quicker… and we get schema validation. But you may think: Right, let’s see what happens when we query an object created as : What about the extra overhead? { name: "John", lastName: "Smith", email: "john@example.com" } Objects returned by Mongoose are not the plain JS objects we would expect. They behave like JS Proxy objects, with internal getters, setters, caches and more. This happens in order to detect whether a field was updated and whether the object needs to be synced or not. All of this magic comes at a cost. However, mongoose allows us to enable the mode, skip the extra housekeeping and deal with plain JS objects. Schema validation will only be done when updating or creating documents. Queries will just pass the data. How that? lean await User.findByIdAndUpdate(userId, newUser).lean() Easy, right? So now, let’s rework our handlers and see how they look: Now, our beloved tests: See? We rewrote most our logic, but in just 132ms we know that we broke nothing. That’s what I meant by TDD and zen. Connection pooling But we still have room for improvement. As you just saw, our handler connects and disconnects from the DB server at every request. This is due to the constraints of the serverless architecture, the process starts from scratch at every request and exists once the event loop is empty. However, there is a small serverless trick that can be used to keep our process frozen when a response is sent, instead of exiting. This would allow us to keep our context and reuse database connections that were already established. . “Sounds good, show me the money” Sure, in your handler, set the following property to at the begining of the function: false exports.list = async (event, context) => { context.callbackWaitsForEmptyEventLoop = false // ...} Since now our function may already have DB connections available, we need to encapsulate our connection logic and ensure connectivity across executions: This is fine, but we need to do an extra step with our DB models. They need to be attached to the active mongoose connection, so the previous code will not work anymore. Let’s change and return the instead of registering it to the global context. models/user.js schema mongoose Then, we need a last step in our function: ensureDbConnected Now in our handlers we just need to replace the connection logic by a call to and remove the calls to . So our handlers now look like this: ensureDbConnected disconnect Every new developer on your team will have little to no problem in mastering such code. The gotcha However, now there is a small issue. If you nice, now the Node process does not just exit when tests are complete. We told serverless to freeze our context instead of letting the event loop be empty, so now the process will not exit because of that. This is okay if testing and deployment are performed by the project maintainer, but definitely becomes an issue if the testsuite is part of a pipeline that performs . CI/CD tasks There may well be better approaches, but the solution I’ve found to work in our projects is adding hooks to every test suite and force the process to exit when they are done. Create the file like this: test/test-completion-hooks.js We need to register them in every test suite (by now, ). The result change is very simple: test/user.spec.js There are two lines to include in every test suite, but using is the solution that I’ve found to work best. If you have a better approach to get around this limitation, I’ll be more than glad to read it from you on the comments :) addCompletionHooks So we have DB models, we keep connections open and our testing is working. What next? Webpack Let’s optimize the code we are shipping. It is no secret that can easily contain hunderds of megabytes of data that will be never used. However, serverless will package the main folder of your project, except for development dependencies in . node_modules node_modules Even if we only wrote 455 lines of code at the current point, running results in a 2.3Mb file, which expands into 11Mb of uncompressed data. serverless package We can surely do better by making use of webpack’s tree shaking capabilities. Let’s install the package: npm install -D serverless-webpack webpack At the time of writing, webpack version 4 is supported. Now we need to declare the plugin in > the declaration of : serverless.yml plugins above serverless-offline # ... plugins: serverless-webpack serverless-offline serverless-mocha-plugin Next, we need a config file for webpack, so we will create : webpack.config.js That’s all! Now we just need to again and now everything will be bundled into a single JS file. npm run deploy Comparison After all these changes, how well are we doing? Let’s use to see if we made any difference. Apache Bench Before we made any change, this was the result of making 1000 requests to the function: listUser $ ab -k -c 50 -n 1000 https://hkyod1qhuh.execute-api.eu-central-1.amazonaws.com/staging/users This is ApacheBench, Version 2.3 <$Revision: 1807734 $> [...] Concurrency Level: 50Time taken for tests: 39.649 secondsComplete requests: 1000 (Connect: 0, Receive: 0, Length: 423, Exceptions: 0) Keep-Alive requests: 1000Total transferred: 493011 bytesHTML transferred: 28317 bytes Time per request: 1982.445 [ms] (mean) Transfer rate: 12.14 [Kbytes/sec] received Failed requests: 423 Non-2xx responses: 423 Requests per second: 25.22 [#/sec] (mean) Time per request: 39.649 [ms] (mean, across all concurrent requests) Connection Times (ms)min mean[+/-sd] median maxConnect: 0 23 98.7 0 466Processing: 83 1753 2052.4 1001 7927Waiting: 83 1753 2052.4 1001 7927Total: 83 1775 2065.3 1001 7927 Percentage of the requests served within a certain time (ms)50% 100166% 160175% 227080% 307590% 593495% 612698% 668699% 6996100% 7927 (longest request) The benckmark is made upon a starter plan of MongoDB Atlas, which has a limit of 100 simultaneous connections. Even if we set a concurrency limit of 50, we see that 423 requests are still failing because they try to open a connection that surpasses this limit. Now let’s run the same with the improvements we made in this article and see how it goes: $ ab -k -c 50 -n 1000 https://hkyod1qhuh.execute-api.eu-central-1.amazonaws.com/staging/users This is ApacheBench, Version 2.3 <$Revision: 1807734 $> [...] Concurrency Level: 50Time taken for tests: 10.087 secondsComplete requests: 1000 Keep-Alive requests: 1000Total transferred: 465000 bytesHTML transferred: 2000 bytes Time per request: 504.335 [ms] (mean) Transfer rate: 45.02 [Kbytes/sec] received Failed requests: 0 Requests per second: 99.14 [#/sec] (mean) Time per request: 10.087 [ms] (mean, across all concurrent requests) Connection Times (ms)min mean[+/-sd] median maxConnect: 0 19 85.2 0 468Processing: 61 484 406.7 370 1079Waiting: 61 484 406.7 370 1079Total: 61 503 399.9 487 1079 Percentage of the requests served within a certain time (ms)50% 48766% 88375% 91780% 92790% 97295% 98598% 100199% 1010100% 1079 (longest request) Simple and clear, the actual . And the number of failed requests (because of no DB connection) . throughput is 4x drops to zero There is no remarkable performance improvement in adding webpack alone. Keep it if you like to store lighter files on S3, but you can also disable the plugin and achieve faster response/rebuild times while developing. Note: Needless to say: before jumping into production, do your own distributed benckmarks with a scalable DB hosting plan and setting a and much higher number of connections. Monitoring Monitoring lambda functions is a subject that could well cover another article by itself. However, there are some tools that we can take advantage of, out of the box. Let’s have a look at the lambda we have been benchmarking: . Open , click the function on the list and select the “Monitoring” tab. listUsers AWS Lambda You’ll be presented with a few charts giving you an overview of the function’s status. To check the status of the DB cluster, we can also refer to , open our cluster and open the metrics section. MongoDB Atlas If we got into a scenario in which we would like to diagnose a problem, we can get back to the Lambda function console and click on the “View logs in CloudWatch” button. Let’s click on the latest log stream: Now we can explore execution times, billed times, memory usage, etc. If you need to investigate the internals of a function’s execution, you can make use of , , etc. Let’s add some logging on our lambda function: console.log console.err console.log("> I AM AN EVENT PRINTED BY console.log()") console.error("> AND I AM AN ERROR REPORTED BY console.error()", new Error("Error message")) If we deploy our code and open the latest log stream entry, we will be able to see them: There is room to go deeper in this subject. Depending on your needs, you can have a look at , along with , or check . [serverless-plugin-tracing](https://github.com/alex-murashkin/serverless-plugin-tracing) AWS X-Ray Dashbird Wrap up If you want to experiment with the code of the article, I have updated the repo in a dedicated branch: https://github.com/ledfusion/serverless-tdd-starter/tree/part-2 This concludes part #2 of my article on how to achieve nirvana by using TDD, along with Serverless. I hope that you found it useful, and don’t forget to clap, comment, share and smile if you want more and more :) BTW: I am available to help you grow your projects. Feel free to find me on https://jordi-moraleda.github.io/ Thanks for your read.
