{"repo":"Abhisheklearn12/tsdb","free":true,"listed":false,"github":"https://github.com/Abhisheklearn12/tsdb","clone":"git clone https://github.com/Abhisheklearn12/tsdb.git","description":"In-memory time series database for learning purpose only","language":"Rust","stars":11,"topics":["database","distributed","distributed-systems","time-series"],"license":null,"category":"databases-storage","readme_excerpt":"In-memory time series database implemented in Rust Based on Facebook's Gorilla TSDB research paper (VLDB 2015) Note : This is an educational implementation to deeply understand time series compression, in-memory storage optimization, and distributed monitoring systems. It implements the core algorithms from the paper. It's just the poc to implement this concept in my main Database project and I really enjoyed every bit of this. Readme will keep on updating as I rectify and filled my knowledge gaps. --- Features Delta-of-Delta Timestamp Compression - Compresses regular intervals to 1 bit per timestamp XOR-Based Float Compression - Exploits similarity in consecutive values (12x compression) chunks of 2 hours of data - Optimal compression efficiency (proven in paper) In-Memory Storage - Sub-millisecond query latency Time Series Map (TSmap) - Efficient O(1) lookups with fast scanning Correlation Analysis - Find related metrics (PPMCC-based) Zero-Copy Design - Rust's ownership eliminates GC overhead Paper's Production Statistics (Facebook 2015) - 2 billion unique time series - 700 million data points per minute - 1.37 bytes per data point (vs 16 bytes uncompressed = 12x compression ) - 73x faster queries than HBase (500ms → 7ms) - 26 hours of data in memory --- Project Structure --- Build & Run Note: For Quick Re-run --- Architecture Memory Layout (§4.2) --- Example Output Sample run (from cargo run --release ): --- What I Learned 🔹 How I Thought About It I started with the quest","default_branch":null,"files":null,"tree":[],"storefront":"/r/Abhisheklearn12","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Abhisheklearn12/tsdb/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}