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Twitter anayltics #8
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Certainly! Let's plan out how to model a Mastodon feed system in Coq, then extract it to Rust for implementation. We'll start by modeling the core data structures and operations in Coq, then outline the process for extraction and implementation in Rust.
First, let's define some basic data structures and operations in Coq: Require Import String List.
Open Scope string_scope.
(* Basic data types *)
Inductive UserId : Type :=
| MakeUserId : string -> UserId.
Inductive PostId : Type :=
| MakePostId : string -> PostId.
Inductive Post : Type :=
| MakePost : PostId -> UserId -> string -> list string -> Post.
(* Feed as a list of posts *)
Definition Feed := list Post.
(* Operations *)
Parameter create_post : UserId -> string -> list string -> Post.
Parameter add_to_feed : Feed -> Post -> Feed.
Parameter remove_from_feed : Feed -> PostId -> Feed.
Parameter get_user_feed : Feed -> UserId -> Feed.
(* API representation *)
Inductive APIResponse : Type :=
| Success : Feed -> APIResponse
| Error : string -> APIResponse.
(* Main feed operations *)
Definition fetch_feed (user: UserId) : APIResponse :=
(* Implementation would go here *)
Success nil.
Definition post_to_feed (user: UserId) (content: string) (tags: list string) : APIResponse :=
(* Implementation would go here *)
Success nil.
(* Theorems and proofs *)
Theorem add_then_remove : forall (f: Feed) (p: Post),
remove_from_feed (add_to_feed f p) (match p with MakePost id _ _ _ => id end) = f.
Proof.
(* Proof implementation *)
Admitted.
(* More theorems and proofs would be added here *)
After modeling the system in Coq, we'll need to set up the extraction to Rust. This process isn't straightforward as Coq doesn't have built-in Rust extraction. We'll need to: a. Extract to OCaml first: Extraction Language OCaml.
Extraction "mastodon_feed.ml" fetch_feed post_to_feed. b. Manually translate the OCaml to Rust, or use a tool to assist in this translation.
In Rust, we'll create corresponding structures and implement the extracted functions: use serde::{Serialize, Deserialize};
#[derive(Debug, Serialize, Deserialize)]
struct UserId(String);
#[derive(Debug, Serialize, Deserialize)]
struct PostId(String);
#[derive(Debug, Serialize, Deserialize)]
struct Post {
id: PostId,
user_id: UserId,
content: String,
tags: Vec<String>,
}
type Feed = Vec<Post>;
enum APIResponse {
Success(Feed),
Error(String),
}
fn create_post(user_id: UserId, content: String, tags: Vec<String>) -> Post {
Post {
id: PostId(uuid::Uuid::new_v4().to_string()),
user_id,
content,
tags,
}
}
fn add_to_feed(mut feed: Feed, post: Post) -> Feed {
feed.push(post);
feed
}
fn remove_from_feed(feed: Feed, post_id: PostId) -> Feed {
feed.into_iter().filter(|post| post.id != post_id).collect()
}
fn get_user_feed(feed: &Feed, user_id: &UserId) -> Feed {
feed.iter().filter(|post| post.user_id == *user_id).cloned().collect()
}
async fn fetch_feed(user: UserId) -> Result<APIResponse, Box<dyn std::error::Error>> {
// Implement API call to Mastodon here
Ok(APIResponse::Success(vec![]))
}
async fn post_to_feed(user: UserId, content: String, tags: Vec<String>) -> Result<APIResponse, Box<dyn std::error::Error>> {
// Implement API call to Mastodon here
Ok(APIResponse::Success(vec![]))
}
To actually implement the Mastodon feed functionality, you'll need to use a Mastodon API client for Rust. You can use a library like use megalodon::{Megalodon, MegalodonClient};
async fn fetch_real_feed(client: &MegalodonClient, user: UserId) -> Result<APIResponse, Box<dyn std::error::Error>> {
let response = client.get_home_timeline().await?;
let feed: Feed = response.into_iter().map(|status| {
Post {
id: PostId(status.id),
user_id: UserId(status.account.id),
content: status.content,
tags: status.tags.into_iter().map(|tag| tag.name).collect(),
}
}).collect();
Ok(APIResponse::Success(feed))
}
async fn post_to_real_feed(client: &MegalodonClient, user: UserId, content: String, tags: Vec<String>) -> Result<APIResponse, Box<dyn std::error::Error>> {
let status = client.post_status(content, None).await?;
Ok(APIResponse::Success(vec![Post {
id: PostId(status.id),
user_id: user,
content: status.content,
tags,
}]))
}
Finally, you should create tests in Rust to ensure that the implementation satisfies the properties proven in Coq. For example: #[test]
fn test_add_then_remove() {
let mut feed = vec![];
let post = create_post(UserId("user1".to_string()), "content".to_string(), vec![]);
let post_id = post.id.clone();
feed = add_to_feed(feed, post);
feed = remove_from_feed(feed, post_id);
assert!(feed.is_empty());
} This plan outlines the process of modeling a Mastodon feed system in Coq, extracting it to Rust, and implementing it with actual Mastodon API integration. The Coq model provides a formal specification and proofs of key properties, while the Rust implementation provides a practical, efficient implementation that can interact with real Mastodon instances. |
@startuml
actor "Data Analyst" as Hero
participant "Current Analytics" as OW
participant "Need for Mastodon Insights" as Call
participant "Mastodon API Docs" as Mentor
participant "API Authentication" as Threshold
participant "Data Collection & Storage" as Tests
participant "Data Processing Challenge" as Ordeal
participant "Insights Dashboard" as Reward
participant "User Feedback" as RoadBack
participant "System Optimization" as Resurrection
participant "Deployed Analytics Platform" as Return
Hero -> OW: evaluate_current_tools
OW -> Call: identify_mastodon_analytics_gap
Call -> Hero: propose_new_project
Hero -> Call: initial_hesitation
Call -> Hero: recognize_potential_impact
Hero -> Mentor: study_mastodon_api
Mentor -> Hero: gain_api_knowledge
Hero -> Threshold: implement_oauth
Threshold -> Tests: begin_data_collection
Tests -> Hero: design_database_schema
Tests -> Hero: handle_rate_limits
Hero -> Ordeal: tackle_data_volume
Ordeal -> Hero: implement_batch_processing
Ordeal -> Hero: optimize_queries
Ordeal -> Reward: create_visualization_tools
Reward -> Hero: develop_dashboard
Reward -> RoadBack: release_beta_version
RoadBack -> Resurrection: analyze_performance_issues
Resurrection -> Hero: refactor_and_optimize
Resurrection -> Return: deploy_to_production
Return -> Hero: monitor_and_maintain
@enduml
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