Perfect Hash Function for Ultra-Low Latency Stock Data Retrieval
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Solution Overview
Problem
Conventional data management techniques for ultra-low latency trading systems face inefficiencies in storage and retrieval of stock-related information due to inefficient database indexes, leading to increased latency and disruptions, especially with varying symbol representations across financial exchanges and frequent intraday updates.
Innovation Solution
Implementing a method that uses a deterministic perfect hash function to enable high-speed storage and retrieval of stock-related information by identifying and programming registers and memory with parameters generated from retrieved data, including host, simulation, and hash state files, and employing active and inactive hashing banks for intraday updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional data management techniques with database indexes are used, then data storage and retrieval can be implemented, but latency increases and ultra-low latency performance cannot be achieved
Solution Approach 1:
The patent extracts the indexing function from conventional database systems and replaces it with a perfect hash function implementation. By removing the database index layer and using direct hash-based addressing, the system eliminates the overhead associated with conventional database indexing, achieving ultra-low latency performance while maintaining data storage and retrieval capabilities
Solution Approach 2:
The patent substitutes the mechanical database indexing system with a mathematical hash function-based system. Instead of using physical or logical database indexes that require traversal and comparison operations, the system uses perfect hash functions to directly compute memory addresses, replacing the mechanical indexing process with a deterministic mathematical transformation that operates in constant time
2Reliability
If frequent intraday updates are performed to handle varying symbol representations across exchanges, then data accuracy is maintained, but latency increases and trading disruptions occur
Solution Approach 1:
The patent performs preliminary actions by pre-processing and normalizing symbol representations from multiple exchanges during system initialization. By resolving symbol mapping issues before trading operations begin and using perfect hash functions to establish deterministic mappings, the system maintains data accuracy across exchanges without requiring frequent intraday updates, thus avoiding latency and trading disruptions
Data Source
AI summary
A method for providing high-speed storage and retrieval of information from a plurality of sources is disclosed. The method includes retrieving data from the sources; identifying a deterministic perfect hash function based on the retrieved data for stock symbols and corresponding stock information, the perfect hash function relating to a mapping of distinct elements in the corresponding stock information with the stock symbols; generating, by using the retrieved data, a parameter that relates to an instruction to calculate the identified perfect hash function, the parameter including a host file, a simulation file, and a hash state file; programming a register and a memory that are associated with the processor by using the parameter; and determining, via the programmed register and the programmed memory, a hash value for incoming stock symbols, the hash value relating to a symbol index that locates information corresponding to the incoming stock symbols.