Fetch Requests and Controller Actions: Connecting the Frontend to the Backend

Introduction 💻

What does it mean to be a full-stack developer? Well, according to W3Schools, it means: “a person who can develop both client and server software”.

While I do agree with their definition, I would add that full-stack developers are basically match-makers 🥰. On their own, the frontend and backend are independent and don’t necessarily care about each other. Thus, it is the full-stack developer’s responsibility to make that connection happen and “match them up”. In other words, I believe that full-stack developers are people who know how to send and handle data from the frontend to the backend (and vice versa).

With a JavaScript frontend and a Rails backend, the way to make this connection is through fetch requests and controller actions. To further understand how they work together, read on!

Refresher on Fetch Requests 🤔🧐🤨

(Think you have a good handle on fetch requests? Skip to the next section!)

“What is fetch?” from the movie Mean Girls

…Not quite 😅

Fetch requests are a huge part of request/response cycles, especially for sending data. With fetch requests we can “get” or “show” information, “post” new information, “patch” (or update) information, and “destroy” information. The basic syntax is as follows (from MDN):

fetch('http://example.com/movies.json')
.then(response => response.json())
.then(data => console.log(data));

#ruby-on-rails #javascript #guides-and-tutorials #full-stack #code-newbie

What is GEEK

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PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

In our previous posts in this series, we spoke at length about using PgBouncer  and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !

The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.

It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

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Performance Testing

While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.

Testing Conditions

All of the PostgreSQL benchmark tests were run under the following conditions:

1. Initialized pgbench using a scale factor of 100.
2. Disabled auto-vacuuming on the PostgreSQL instance to prevent interference.
3. No other workload was working at the time.
4. Used the default pgbench script to run the tests.
5. Used default settings for both PgBouncer and Pgpool-II, except max_children*. All PostgreSQL limits were also set to their defaults.
6. All tests ran as a single thread, on a single-CPU, 2-core machine, for a duration of 5 minutes.
7. Forced pgbench to create a new connection for each transaction using the -C option. This emulates modern web application workloads and is the whole reason to use a pooler!

We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:

• For PgBouncer, we installed it on the same box as the PostgreSQL server(s). This is the configuration we use in our managed PostgreSQL clusters. Since PgBouncer is a very light-weight process, installing it on the box has no impact on overall performance.
• For Pgpool-II, we tested both when the Pgpool-II instance was installed on the same machine as PostgreSQL (on box column), and when it was installed on a different machine (off box column). As expected, the performance is much better when Pgpool-II is off the box as it doesn’t have to compete with the PostgreSQL server for resources.

Throughput Benchmark

Here are the transactions per second (TPS) results for each scenario across a range of number of clients:

#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling

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Backends For Frontends — BFF

— Keep learning, keep building and keep growing :)

Summary

A Backend for Frontend (BFF) architecture can be used to create backends for client-facing mobile or web apps. BFF’s can help support an application with multiple clients while at the same time moving the system into a less-coupled state than a monolith system. This code pattern helps teams iterate features faster and have control over the backends for mobile apps without affecting the experience for a corresponding mobile or web app.

What is it?

The backends-for-frontends architectural pattern describes a world in which each client application has its own server-side component — a _backend _for a particular frontend.

This pattern is highly applicable if you have multiple client interfaces with significantly different needs that all consume the same underlying resources. The most common real-world example is an application that has both a web and a mobile client.

To understand why backends-for-frontends is useful, let’s walk through some evolutions in web architecture.

Multiple clients with a single general-purpose server

Simplicity is great, right? Actually, it is…but only up to a point. If your application is small enough this architecture can absolutely work! However, monoliths tend to break down with scale. You might hear your teams start to say things like…

• Our server is so bloated! Client-specific control flow is littered everywhere and we are struggling to add features without introducing side effects.
• I can’t commit any changes without merge conflicts. There are N teams changing this exact piece of code; some that we barely talk to!
• Builds and tests are taking forever to run and it’s going to take us days to debug that one intermittent test failure.

These types of problems catalyzed the rise of microservices.

#micro-frontends #bff #frontends #backends