Exposed
FrameworkBenchmarks
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Exposed | FrameworkBenchmarks | |
---|---|---|
28 | 366 | |
7,879 | 7,378 | |
1.2% | 1.1% | |
9.5 | 9.8 | |
2 days ago | 7 days ago | |
Kotlin | Java | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Exposed
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Drizzle is just as unready for prime-time as Prisma, what else is there?
So is it like Exposed for Kotlin which is a typesafe Kotlin DSL for building sql queries? I’ve been looking for something like this in typescript! https://github.com/JetBrains/Exposed
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Database Testing with Testcontainers and Kotlin Exposed ORM
object TestDatabase { private val mySQLContainer: MySQLContainer = MySQLContainer("mysql:8.0.26").apply { withDatabaseName("test-db") withUsername("test-user") withPassword("test-password") start() // Start the container } init { val config = HikariConfig().apply { jdbcUrl = mySQLContainer.jdbcUrl username = mySQLContainer.username password = mySQLContainer.password driverClassName = "com.mysql.cj.jdbc.Driver" maximumPoolSize = 10 } val dataSource = HikariDataSource(config) // This doesn't connect to the database but provides a descriptor for future use // In the main app, we would do this on system start up // https://github.com/JetBrains/Exposed/wiki/Database-and-DataSource Database.connect(dataSource) // Create the schema transaction { SchemaUtils.create(Users) } } }
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I'm creating a REST API using KTOR. What's the best ORM to go with KTOR ?
Exposed SQL is pretty good.
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speed of a left join with millions of rows
I am using Kotlins Exposed DSL to access my postgreSQL database. I am running an SQL query which involves doing a one-to-many left join, with the first table having tens of thousands of rows and the second table having millions of rows. The database is stored locally on the machine. I would expect the returned query to be less than one thousand rows.
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Return a nested list of child table using Kotlin Exposed Dao
I understand that `referrersOn` and `referencedOn` can be used, however in the documentations StarWars example, the child table needs to have a column indicating the parent_id row it is joined to. This does not work in my case as my child table can be associated to multiple parent_ids.
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Replicating Jetbrains Exposed Star Wars Example in IntelliJ Scratch file
I am following this wiki to learn kotlin exposed. It contains an example of 'Referencing' using Star Wars data. I would like to create a scratch and/or console file in IntelliJ which reproduces this simple example. However When I try to run the code I get errors (see below). Is there something I am missing here?
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How are you all handling database persistence?
Exposed by JetBrains https://github.com/JetBrains/Exposed
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Why people don't like Java?
Alternatively there are... hybrid solutions like Kotlin's https://github.com/JetBrains/Exposed or https://jdbi.org/ that don't quite... do all the heavy lifting for querying but allow you to sorta stitch queries together manually.
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Using PostgreSQL as an Append-only Datastore with Kotlin and Exposed
In our last episode (https://youtu.be/Uza\_dWsNMUs) we worked out how to save stock items in PostgreSQL (https://www.postgresql.org/) using the JetBrains Exposed library (https://github.com/JetBrains/Exposed). This time we refactor our existing file-based storage, extracting an interface that we can implement with files, in-memory, or with Exposed. For the database version, instead of replacing items in a table when they change, we choose to implement an append-only datastore. This keeps all the old versions of every row, using a query to select the latest versions when we want to see the current state. This has the advantage that we can rebuild the state of our system if things go wrong, and may also be faster than amending when we consider transactions. This is part 62 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin. You can see the whole series as a playlist https://www.youtube.com/playlist?list=PL1ssMPpyqociJNwykAOB9\_KEZVW7BW7m2 and the code on GitHub https://github.com/dmcg/gilded-rose-tdd If you like this, you’ll probably like my book Java to Kotlin, A Refactoring Guidebook (http://java-to-kotlin.dev). It's about far more than just the syntax differences between the languages - it shows how to upgrade your thinking to a more functional style. I have some free time between producing videos and working for team Gilded Rose. If you like these videos I'd like to work with you - please get in touch - [email protected]
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Writing to db
I have used hikari and exposed to do this in the past with postgres, although other dialects are supported.
FrameworkBenchmarks
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Why choose async/await over threads?
Neat. Thanks for sharing!
Interestingly, may-minihttp is faring very well in the TechEmpower benchmark [1], for whatever those benchmarks are worth. The code is also surprisingly straightforward [2].
[1] https://www.techempower.com/benchmarks/
[2] https://github.com/TechEmpower/FrameworkBenchmarks/blob/mast...
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Ntex: Powerful, pragmatic, fast framework for composable networking services
ntex was formed after a schism in actix-web and Rust safety/unsafety, with ntex allowing more unsafe code for better performance.
ntex is at the top of the TechEmpower benchmarks, although those benchmarks are not apples-to-apples since each uses its own tricks: https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s...
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A decent VS Code and Ruby on Rails setup
Ruby is slow. Very slow. How much you may ask? https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s... fastest Ruby entry is at 272th place. Sure, top entries tend to have questionable benchmark-golfing implementations, but it gives you a good primer on the overhead imposed by Ruby.
It is also not early 00s anymore, when you pick an interpreted language, you are not getting "better productivity and tooling". In fact, most interpreted languages lag behind other major languages significantly in the form of JS/TS, Python and Ruby suffering from different woes when it comes to package management and publishing. I would say only TS/JS manages to stand apart with being tolerable, and Python sometimes too by a virtue of its popularity and the amount of information out there whenever you need to troubleshoot.
If you liked Go but felt it being a too verbose to your liking, give .NET a try. I am advocating for it here on HN mostly for fun but it is, in fact, highly underappreciated, considered unsexy and boring while it's anything but after a complete change of trajectory in the last 3-5 years. It is actually the* stack people secretly want but simply don't know about because it is bundled together with Java in the public perception.
*productive CLI tooling, high performance, works well in a really wide range of workloads from low to high level, by far the best ORM across all languages and back-end framework that is easier to work with than Node.JS while consuming 0.1x resources
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The Erlang Ecosystem [video]
Although that seems to have improved in recent years.
https://www.techempower.com/benchmarks/#hw=ph&test=json§...
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Ruby 3.3
RoR and whatever C++ based web backend there is count as a valid comparison in my book. But comparing the languages itself is maybe a bit off.
On a side note, you can actually compare their performance here if you’re really curious. But take it with a grain of salt since these are synthetic benchmarks.
https://www.techempower.com/benchmarks
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API: Go, .NET, Rust
Most benchmarks you'll find essentially have someone's thumb on the scale (intentionally or unintentionally). Most people won't know the different languages well enough to create comparable implementations and if you let different people create the implementations, cheating happens. The TechEmpower benchmarks aren't bad, but many implementations put their thumb on the scale (https://www.techempower.com/benchmarks). For example, a lot of the Go implementations avoid the GC by pre-allocating/reusing structs or allocate arrays knowing how big they need to be in advance (despite that being against the rules). At some point, it becomes "how many features have you turned off." Some Go http routers (like fasthttp and those built off it like Atreugo and Fiber) aren't actually correct and a lot of people in the Go community discourage their use, but they certainly top the benchmarks. Gin and Echo are usually the ones that are well-respected in the Go community.
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Rage: Fast web framework compatible with Rails
There is certainly a lot of speculation in Techempower benchmarks and top entries can utilize questionable techniques like simply writing a byte array literal to output stream instead of constructing a response, or (in the past) DB query coalescing to work around inherent limitations of the DB in case of Fortunes or DB quries.
And yet, the fastest Ruby entry is at 274th place while Rails is at 427th.
https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s...
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Node.js – v20.8.1
oh what machine? with how many workers? doing what?
search for "node" on this page: https://www.techempower.com/benchmarks/#section=data-r21
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Strong typing, a hill I'm willing to die on
JustJS would like a word https://www.techempower.com/benchmarks/#section=data-r20&tes...
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Rust vs Go: A Hands-On Comparison
In terms of RPS, this web service is more-or-less the fortunes benchmark in the techempower benchmarks, once the data hits the cache: https://www.techempower.com/benchmarks/#section=data-r21
Or, at least, they would be after applying optimizations to them.
In short, both of these would serve more rps than you will likely ever need on even the lowest end virtual machines. The underlying API provider will probably cut you off from querying them before you run out of RPS.
What are some alternatives?
Ktorm - A lightweight ORM framework for Kotlin with strong-typed SQL DSL and sequence APIs.
zio-http - A next-generation Scala framework for building scalable, correct, and efficient HTTP clients and servers
SQLDelight - SQLDelight - Generates typesafe Kotlin APIs from SQL
drogon - Drogon: A C++14/17 based HTTP web application framework running on Linux/macOS/Unix/Windows [Moved to: https://github.com/drogonframework/drogon]
kotlin-jpa-specification-dsl - This library provides a fluent DSL for querying spring data JPA repositories using spring data Specifications (i.e. the JPA Criteria API), without boilerplate code or a generated metamodel.
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
requery - requery - modern SQL based query & persistence for Java / Kotlin / Android
LiteNetLib - Lite reliable UDP library for Mono and .NET
kwery - Kwery is an SQL library for Kotlin
C++ REST SDK - The C++ REST SDK is a Microsoft project for cloud-based client-server communication in native code using a modern asynchronous C++ API design. This project aims to help C++ developers connect to and interact with services.
kotlin-nosql - NoSQL database query and access library for Kotlin
SQLBoiler - Generate a Go ORM tailored to your database schema.