v3.1.0 (final)

v3.1.0

• Reduced Calls to global map
• Implemented a function to determine chunk bounds

Processing Time: 59.6s. Tested with a Ryzen 5800x3d and 32 gigs Ram

I got this down to 59 Seconds and achieved my goal of getting it to under 1 minute. I am pretty happy with that for a single day session of coding. Further improvements could be made, and if I would continue working on it I would probably directly use a syscall with mmap and use the 8-byte hash of id as a key for an unsafe maphash. And maybe write some tests.

v3.0.0

v3.0.0

Key Enhancements

• Parallel File Processing: Implemented an advanced parallel processing approach where the input file is divided into chunks and processed independently in parallel, drastically reducing I/O bottleneck.
• Optimized Memory Management: Refined memory usage by processing data in chunks and employing local maps for data aggregation to reduce memory overhead.
• Improved Data Aggregation: Enhanced the efficiency of data aggregation through the use of sharded data structures, minimizing lock contention.

Processing Time: 1m3s. Tested with a Ryzen 5800x3d and 32 gigs Ram

v2.0.0

v2.0.0

Version 2.0 of the One Billion Row Challenge Processor introduces significant optimizations, leading to a substantial reduction in processing time. This release focuses on enhancing concurrency handling and reducing contention, along with other performance improvements.

Performance Enhancements

• Concurrent Map Implementation: Introduced a sharded concurrent map to reduce lock contention. This allows for more efficient updates to the data structure in a multi-threaded environment.
• Hash-Based Sharding: Implemented hash-based sharding for distributing data across multiple shards, further reducing the chance of lock conflicts.
• Optimized String Processing: Refined the string handling logic to minimize overhead during file parsing.
• Buffer Size Adjustments: Tuned the buffer sizes for channels to balance throughput and memory usage.
• Efficient Data Aggregation: Streamlined the data aggregation process for improved efficiency.

Processing Time 5m19s. Tested with a Ryzen 5800x3d

v1.1.0

Recent Optimizations (v1.1.0)

The program has undergone several optimizations to improve its processing time:

• Concurrency Model Improved: Implemented a worker pool pattern for dynamic goroutine management and balanced workload distribution.
• Buffered Channels: Increased channel buffer sizes to reduce blocking and increase throughput.
• Batch Processing: Process multiple lines of data in a single goroutine to reduce overhead.
• I/O Enhancements: Adjusted file reading for larger chunks to reduce I/O bottlenecks.

Processing Time: 6m53s. Tested with a Ryzen 5800x3d

v1.0.0

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