pgx VS go

Sometimes, backend developers tend to opt for an ORM library because it provides an abstraction between your app and the database and thus there is little or no need to write raw queries and migrations which is nice. However, if you want to get better at writing queries (SQL for example), you need to learn how to build your repositories without an ORM. To open a database handle, you can either do it directly from the database driver or do it from database/sql with the driver passed into it. I will be opening the connection with database/sql together with pgx which is a driver and toolkit for PostgreSQL. Walk with me.

The infrastructure layer contains the concrete implementation of the repository domain interface ActivityRepository in the struct DbActivityRepository. This repository implementation uses the Postgres driver pgx and plain SQL to store the activity in the database. It uses the database transaction from the context, since the transaction was initiated by the application service.

For building the RESTful Point of Sale service API, I've considered and selected a combination of technologies that would work seamlessly together. For handling HTTP requests and responses, using the Gin HTTP web framework would make sense because I think it seems complete and popular among Go community too. To ensure data integrity and persistence, I'm using PostgreSQL database with pgx as the database driver, the reason I choose PostgreSQL because it is the most popular relational database to use in production and offers efficient Go integration. I'm also implementing caching using Redis with go-redis client library, which provides powerful in-memory data storage capabilities.

If you are willing to commit to working only with Postgres, I highly recommend pgx. Be sure you get the latest version github.com/jackc/pgx/v5. This gives you the full power of interacting with Postgres without going through an intermediate lowest-common-denominator library.

It uses pg package and pgx driver under the hood.

Starting off, is there any reason you're calling out to the CLI, instead of just using a Postgres driver like pgx? Shelling out to the command line should always be a last resort where possible as a software engineer.

For maintaining state I use PostgreSQL. Driver: https://github.com/jackc/pgx (I use the pgxpools) Along with Sqlc for generating database models and allowing me to focus on just building queries in DBeaver. https://sqlc.dev/

An example of what I'm looking for can be found here https://github.com/jackc/pgx/wiki/Getting-started-with-pgx-through-database-sql/c9f798b4d9a500fcf93931df2464af969d68f516

Except pgx recommends using their native interface, not database/sql, for performance and extra features [0], so it's not that simple in practice.

[0]: https://github.com/jackc/pgx#choosing-between-the-pgx-and-da...

pgx is “PostgreSQL driver and toolkit for Go”. Take a look at https://github.com/jackc/pgx

The description indicates it is not production ready, and is archived at the same time.

If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.

For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).

Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).

There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.

A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.

I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles

The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.