Migrate from Apify Reddit Scraper to Synchronous REST API (<1s Latency)

import os
import requests

API_KEY = os.environ["REDDITDATAAPI_KEY"]  # rd_live_... (instant at /signup)
HEADERS = {"x-api-key": API_KEY}
BASE = "https://api.redditdataapi.io"

# 1. Replaces Actor search input: {"queries": ["supabase OR clerk"], "sort": "new", "timeFilter": "week"}
search_res = requests.get(
    f"{BASE}/v1/posts/search",
    headers=HEADERS,
    params={"query": "supabase OR clerk", "sort": "new", "time_filter": "week", "limit": 50},
    timeout=10,
)
search_res.raise_for_status()
posts = search_res.json()["data"]["items"]  # ~180ms sync JSON (0 Credits if 0 matches!)

# 2. Replaces Actor subreddit feed: {"subreddits": ["SaaS"], "sort": "hot", "maxItemsPerTarget": 50}
feed_res = requests.get(
    f"{BASE}/v1/subreddit/posts",
    headers=HEADERS,
    params={"subreddit": "SaaS", "sort": "hot", "limit": 50},
    timeout=10,
)
feed_posts = feed_res.json()["data"]["items"]

# 3. Replaces Actor comment crawl: {"postIdsOrUrls": ["1u0z4vz"], "maxItemsPerTarget": 200}
comments_res = requests.get(
    f"{BASE}/v1/posts/comments",
    headers=HEADERS,
    params={"post_id": "1u0z4vz", "sort": "top", "limit": 200},
    timeout=10,
)
comment_tree = comments_res.json()["data"]["items"]  # Flattened with depth & parent_id
print(f"Fetched {len(posts)} mentions, {len(feed_posts)} feed posts, {len(comment_tree)} comments")

const API_KEY = process.env.REDDITDATAAPI_KEY!; // rd_live_...
const BASE = "https://api.redditdataapi.io";
const headers = { "x-api-key": API_KEY };

// 1. Keyword & brand mention search (/v1/posts/search) — replaces async Actor run + dataset fetch
const searchUrl = new URL(`${BASE}/v1/posts/search`);
searchUrl.searchParams.set("query", "apify alternative OR reddit scraper");
searchUrl.searchParams.set("sort", "new");
searchUrl.searchParams.set("time_filter", "month");
searchUrl.searchParams.set("limit", "50");

const searchRes = await fetch(searchUrl, { headers });
if (!searchRes.ok) throw new Error(`HTTP ${searchRes.status}`);
const { data: searchData, meta } = await searchRes.json();
console.log(`Got ${searchData.items.length} posts in ${meta.latency_ms}ms (credits: ${meta.credits_used})`);

// 2. Subreddit feed (/v1/subreddit/posts)
const subRes = await fetch(`${BASE}/v1/subreddit/posts?subreddit=LocalLLaMA&sort=hot&limit=25`, { headers });
const { data: subData } = await subRes.json();

// 3. Nested 200-comment tree (/v1/posts/comments)
const treeRes = await fetch(`${BASE}/v1/posts/comments?post_id=1u0z4vz&sort=top&limit=200`, { headers });
const { data: treeData } = await treeRes.json();
console.log(subData.items[0]?.title, treeData.items.length);

# 1) Keyword & Brand Mention Search (replaces queries[] + timeFilter + maxPostsPerQuery)
curl -sS "https://api.redditdataapi.io/v1/posts/search?query=supabase+OR+clerk&sort=new&time_filter=week&limit=50" \
  -H "x-api-key: $REDDITDATAAPI_KEY"

# 2) Subreddit Feed (replaces subreddits[] / startUrls + sort + maxItemsPerTarget)
curl -sS "https://api.redditdataapi.io/v1/subreddit/posts?subreddit=SaaS&sort=hot&limit=50" \
  -H "x-api-key: $REDDITDATAAPI_KEY"

# 3) Full 200-Comment Nested Tree (replaces postIdsOrUrls[] comment crawl)
curl -sS "https://api.redditdataapi.io/v1/posts/comments?post_id=1u0z4vz&sort=top&limit=200" \
  -H "x-api-key: $REDDITDATAAPI_KEY"

{
  "code": 0,
  "provider": "https://redditdataapi.io",
  "status": "ok",
  "msg": "success",
  "data": {
    "items": [
      {
        "id": "1u0z4vz",
        "post_id": "t3_1u0z4vz",
        "subreddit": "SaaS",
        "title": "How we cut social listening ingestion latency from 38s to 180ms",
        "selftext": "Switched our hourly container jobs to incremental ?since= REST polling...",
        "author": "reallehnert",
        "score": 142,
        "upvote_ratio": 0.96,
        "num_comments": 47,
        "created_utc": 1762324800,
        "created_at": "2026-11-05T06:40:00.000Z",
        "permalink": "https://www.reddit.com/r/SaaS/comments/1u0z4vz/how_we_cut_social_listening/",
        "url": "https://www.reddit.com/r/SaaS/comments/1u0z4vz/how_we_cut_social_listening/"
      }
    ],
    "count": 1,
    "cursor": "t3_1u0z4vz",
    "has_more": false
  },
  "meta": {
    "endpoint": "/v1/posts/search",
    "credits_used": 2,
    "cache_hit": false,
    "latency_ms": 184
  }
}