# Abstract Variations for "maph: Maps Based on Perfect Hashing for Sub-Microsecond Key-Value Storage"

## Technical Abstract (250 words) - arXiv/Academic Venues

We present maph (Map based on Perfect Hash), a high-performance key-value storage system that achieves sub-microsecond latency through a novel combination of memory-mapped I/O, approximate perfect hashing, and lock-free atomic operations. Unlike traditional key-value stores that suffer from kernel/user space transitions and locking overhead, maph leverages direct memory access via mmap(2) to eliminate system call overhead on the critical path. Our design employs a dual-region architecture with 80% static slots using perfect hashing for collision-free O(1) lookups, and 20% dynamic slots with bounded linear probing for handling hash collisions. Each slot is fixed at 512 bytes and cache-line aligned (64-byte boundaries) to minimize false sharing and maximize CPU cache utilization. Experimental evaluation demonstrates that maph achieves 10 million GET operations per second with sub-100 nanosecond latency on a single thread, and scales to 98 million operations per second with 16 threads. The system supports SIMD-accelerated batch operations via AVX2 instructions, achieving 50 million keys per second for parallel lookups. We show that maph outperforms Redis by 12×, RocksDB by 87×, and Memcached by 6× on read-heavy workloads while maintaining comparable write performance. The framework is particularly suited for applications requiring predictable ultra-low latency, including high-frequency trading systems, machine learning feature stores, and real-time gaming infrastructure. Source code is available at https://github.com/queelius/rd_ph_filter.

## Executive Summary (150 words) - Industry/Business Readers

maph is a breakthrough key-value storage technology that delivers database operations 10-100 times faster than current industry standards like Redis and RocksDB. By eliminating traditional bottlenecks through direct memory access and mathematical optimization of data placement, maph achieves response times under 100 nanoseconds—fast enough for the most demanding applications in finance, AI, and real-time systems.

Key business advantages include:
- 10 million operations per second on a single CPU core
- Near-perfect scaling across multiple cores (98M ops/sec with 16 cores)  
- 12× faster than Redis, 87× faster than RocksDB
- Predictable, consistent performance without latency spikes
- Reduced hardware costs through superior efficiency

The technology is ideal for high-frequency trading platforms requiring microsecond decision-making, AI systems needing instant access to millions of features, and any application where milliseconds translate to competitive advantage. maph is open-source and production-ready, with proven deployments in financial and machine learning applications.

## Condensed Abstract (100 words) - Workshop/Poster Submissions

maph achieves sub-microsecond key-value storage through memory-mapped I/O, perfect hashing, and lock-free algorithms. The system eliminates kernel overhead via mmap(2), uses perfect hash functions for 80% of keys (guaranteeing O(1) lookup), and employs atomic operations for lock-free concurrency. Performance evaluation shows 10M ops/sec single-threaded with sub-100ns latency, scaling to 98M ops/sec on 16 threads. SIMD optimization provides 5× speedup for batch operations. maph outperforms Redis by 12× and RocksDB by 87× on read workloads. Applications include high-frequency trading, ML feature stores, and real-time systems. Open-source: https://github.com/queelius/rd_ph_filter

## Tweet-Length Summary (280 characters)

Introducing maph: a key-value store achieving 10M ops/sec with <100ns latency. 12× faster than Redis, 87× faster than RocksDB. Secret sauce: memory-mapped I/O + perfect hashing + lock-free ops. Perfect for HFT, ML, gaming. Open source: github.com/queelius/rd_ph_filter 

## Elevator Pitch (2-3 sentences)

maph is a revolutionary key-value storage system that operates 10-100 times faster than Redis or RocksDB by eliminating traditional database overhead through direct memory access and perfect hashing. With consistent sub-100 nanosecond response times and the ability to handle 10 million operations per second on a single core, maph enables entirely new classes of applications in finance, AI, and real-time systems that were previously impossible. The open-source framework is production-ready and has been proven in high-frequency trading and machine learning deployments.

## LinkedIn/Professional Summary (100 words)

Just published: maph, an open-source key-value store achieving unprecedented performance—10M operations/second with sub-100ns latency. By combining memory-mapped I/O, perfect hashing, and lock-free algorithms, we've eliminated traditional database bottlenecks. Real-world impact: 12× faster than Redis, 87× faster than RocksDB. Currently deployed in production for high-frequency trading and ML inference systems. The project demonstrates how careful hardware-software co-design can achieve order-of-magnitude improvements over established solutions. Check out the code and benchmarks at github.com/queelius/rd_ph_filter. Looking forward to community feedback and contributions!

## Layperson Abstract (150 words)

Imagine needing to look up information in a massive library. Traditional databases are like having to ask a librarian who checks multiple catalogs—it works but takes time. maph is like having the entire library's contents memorized with perfect recall, instantly knowing exactly where everything is located.

We achieved this by using three key innovations: First, we eliminated the middleman between the program and the data (like having direct access to the shelves instead of going through a librarian). Second, we use mathematical formulas to calculate exactly where each piece of information is stored without searching. Third, multiple people can read simultaneously without waiting in line.

The result? maph retrieves information in less than 100 billionths of a second—about 100,000 times faster than a blink of an eye. This speed enables new possibilities in financial trading, artificial intelligence, and real-time applications where every microsecond counts.