Welcome to Instant-Search ⚡

Deep dive into cache-related concepts with an application allowing you to search for software development tags.

Feel the impact of three-tier caching while you type. ⌨️

Every keystroke flows through LRU → Redis → Postgres, and the UI streams live hit/miss counters and latency charts so you can watch hot prefixes bubble up the stack in real time. The entire demo fits comfortably inside free-tier quotas and seeds itself from a single CSV in seconds.

Under the hood:

1. L1: lru-cache (key-value in-memory store, per instance, short term)
2. L2: Redis (key-value store, shared, long term)
3. DB (L3): Postgres (materialized view, shared, persisted)

1. Render Web Service, Free tier
2. 750 instance-hours / month; spins down after 15 minutes of idle time.
3. Bandwidth: 100 GB / month
4. Build: 500 min / month
5. Custom Domains.
6. Managed TLS certificates.

1. Backend and Frontend: Next.js.
2. Charts and visualizations: Chart.js.

1. lru-cache
2. Type: In-process cache, part of the Backend and Frontend instance.
3. Straightforward, battle-tested implementation.
4. Single-flight behavior is available via fetch() and SWR patterns.
5. Easy to add TTL jitter by wrapping .set() with a jittered TTL generator.

1. Upstash Redis, Free tier
2. Type: Lambda (Serverless)
3. One free DB per account.
4. Commands/API Calls: 500K / month
5. Bandwidth: 10 GB / month
6. Storage: 256 MB

1. Supabase PostgreSQL, Free tier
2. Type: Instance
3. Free projects are paused after 1 week of inactivity; Max 2 active projects.
4. Commands/API Calls: Unlimited
5. Bandwidth: 5 GB / month
6. Storage: 500 MB DB, 1 GB files
7. RAM: 500 MB

1. Seed DB with data from StackOverflow: Tags with Popularity query
   1. Table name: tags
   2. Columns: TagName,Mentions,SynonymsCount,SynonymsJSON,ExcerptID,Excerpt,WikiID,Wiki
2. Create a Materialized View
   1. A DB object that stores the result of a query on disk (or in memory) so it can be read like a regular table.
   2. Break down the tags corpus to prefixes
   3. Will allow time complexity of O(1) for reads
   4. Table name: suggestions
   5. Columns: prefix,suggestions
   6. Cap prefix length to 6 characters to keep row count reasonable (50k tags × 6 prefixes = 300k rows)
   7. Order the suggestions based on Mentions
   8. prefix needs to be text PRIMARY KEY so can create a btree index on it
   9. Limit to 10 suggestions
   10. Example:

       j,[javascript,java,jquery...] 

3. (optional) Setup automatic refresh for the Materialized View every 24 hours