New Go Billion Row Challenge w/ Great Optimizations | Prime Reacts
TLDRThe script details an ambitious project to optimize a Go program tackling the 1 billion row challenge, which involves reading a massive file and aggregating weather data. The author shares their journey from an initial 95-second run time to a significantly reduced 1.96 seconds through various optimizations, including file reading enhancements, scanner class manipulation, and leveraging Go's concurrency features. The process showcases the power of empirical testing and iterative improvements in low-level code optimization.
Takeaways
- ๐ The 1 billion row challenge is a programming task involving reading a large file, aggregating information, and computing statistics for each weather station mentioned.
- ๐ The challenge originated in Java but has been adapted to various programming languages, including Go.
- ๐ป The initial naive approach in Go took 95 seconds to process the 1 billion rows, which was surprisingly faster than expected.
- ๐ง Optimizations in Go involved using bytes instead of strings, manipulating buffer sizes, and reducing memory allocations.
- ๐ A significant speedup was achieved by using buffered scanning with an appropriately sized buffer, reducing the time to 6.7 seconds.
- ๐ Experimentation with different buffer sizes and file reading methods led to further improvements, with the use of file.Read and large buffer sizes yielding the best results.
- ๐ The use of goroutines for parallel processing was explored, with the main thread reading data and distributing it to worker goroutines for aggregation.
- ๐ ๏ธ Custom parsing of float values and avoiding unnecessary string conversions contributed to performance gains.
- ๐ง The final optimized solution in Go achieved a processing time of 1.96 seconds for the 1 billion row challenge.
- ๐ The article emphasizes the importance of empirical testing and iterative optimization in achieving high-performance results.
- ๐ฏ The process highlighted the trade-offs between readability and performance, as well as the learning curve involved in low-level optimizations.
Q & A
What is the 1 billion row challenge?
-The 1 billion row challenge is a programming task that involves developing a program capable of reading a file with 1 billion lines, aggregating the information contained in each line, and printing a report with the computed minimum, average, and maximum temperature reading for each respective weather station mentioned in the file.
What was the initial time taken by the naive approach to process the 1 billion rows?
-The initial naive approach to process the 1 billion rows took 95 seconds.
How did the author optimize the file reading process in Go?
-The author optimized the file reading process by using bytes instead of strings, reusing an internal buffer, and adjusting the buffer size. They also experimented with different buffer sizes and used the 'file.Read' function with larger buffers, which resulted in significant time reductions.
What was the impact of using a buffered scanner on the performance?
-Using a buffered scanner with a predefined byte object and a maximum number of elements for the buffer size improved the performance, reducing the processing time to 6.7 seconds from the initial 36 seconds.
How did the author handle the temperature data to optimize performance?
-The author converted the temperature data to integers instead of using floating-point numbers, which proved to be more efficient. They also developed a custom integer converter that keeps the decimal point and generates an integer equivalent of the float temperature.
What role did Go routines play in the optimization process?
-Go routines were used to process data chunks in parallel, aggregating data independently. The main thread then merged the information from each go routine to produce the final report, which helped in reducing the overall processing time.
How did the author address the issue of buffer overriding during file reading?
-The author copied the buffer data into another array to avoid the issue of the file.read buffer overriding the buffer during each reading. This change increased the processing time but ensured consistent data was being read.
What was the final result after all the optimizations?
-After all the optimizations, the final result was achieved in 1.96 seconds, which is a significant improvement from the initial 95 seconds.
What tool did the author use for identifying performance bottlenecks?
-The author used the 'go tool pprof' for identifying performance bottlenecks, which provided insights through a CPU profile and a flame graph.
How did the author manage the distribution of newline characters in the file reading process?
-The author created a 'trash bin' go routine that received the discarded parts from other go routines and tried to merge the individual parts to complete the line, ensuring concurrency consistency by using the same mutex used in reading the file.
What was the strategy for the final optimization to reach under 2 seconds?
-The final optimization involved delegating the reading task to the consumer go routines, eliminating the communication overhead and reducing memory allocation overhead by replacing the communication buffer with a fixed buffer and adjusting the number of workers and read buffer size.
Outlines
๐ Go Language Challenge: 1 Billion Row Reading
The speaker discusses their excitement about optimizing a Go program to read and process a file with 1 billion lines of data. The task involves aggregating weather station information and producing a report. The 1 Billion Row Challenge, originally a Java challenge, has expanded to multiple programming languages. The speaker aims to improve their Go solution beyond their initial attempt, which was influenced by Java solutions and focused on reading efficiency and low-level optimizations.
๐ข Initial Approach and File Reading Optimization
The speaker details their initial approach to the challenge, which involved simple file reading and processing. They were surprised by the relatively fast execution time of 95 seconds. The speaker then explores various optimizations, such as using bytes instead of strings, employing buffered scanning, and adjusting buffer sizes. These optimizations led to significant improvements, reducing the execution time to 6.7 seconds.
๐ Deep Dive into Optimization Techniques
The speaker continues their optimization journey by examining the internal workings of the scanner and exploring the use of file.read. They experiment with different buffer sizes and find that larger buffers can further reduce processing time. The speaker also considers the use of goroutines for parallel processing and discusses the overhead of communication between goroutines.
๐ Profiling and Further Optimization
The speaker uses a CPU profiler to identify bottlenecks in their code, focusing on memory allocations and the parse float function. They implement a bite split function to reduce memory allocations and make slice, resulting in a significant time reduction. The speaker also discusses the challenges of converting temperatures to integers while preserving the decimal point and the benefits of using custom scan functions.
๐ง Custom Hash Function and Buffer Management
The speaker develops a custom hash function to optimize map lookups and considers the use of a 'trash bin' goroutine to recover discarded parts of lines. They also experiment with eliminating the need for a name and temperature buffer by using a sub-slice of the read buffer, which further reduces the execution time to just over 2 seconds.
๐ Final Optimizations and Testing
The speaker conducts a grid test to fine-tune the read buffer size and the number of workers, achieving results in under two seconds. They recover the first and last lines of each chunk using a 'trash bin' goroutine and further optimize by removing the need for a separate name and temperature buffer. The final test, with the computer closed and everything turned off except the terminal, results in an impressive 1.96 seconds.
Mindmap
Keywords
๐ก1 billion row challenge
๐กGo (Golang)
๐กOptimization
๐กConcurrency
๐กBuffering
๐กProfiling
๐กHash Function
๐กData Aggregation
๐กEmpirical Testing
๐กFlame Graph
Highlights
The 1 billion row challenge involves developing a program capable of reading a file with 1 billion lines and aggregating information from each line.
The challenge originated as a Java challenge but has since expanded to include multiple programming languages.
Each line of the file contains a weather station name and a temperature reading in a specific format.
The expected output format is a report with minimum, average, and maximum temperatures for each station, sorted alphabetically by station name.
The 1 billion line file is approximately 13 gigabytes in size.
The official repository provides a script to generate synthetic data with random readings for those without access to the large database.
The initial naive approach to the challenge took 95 seconds to complete.
Optimizations began with file reading speed, aiming to understand the fastest possible file read time in Go.
Using bytes instead of strings for file reading reduced the execution time significantly due to less memory allocation and faster processing.
The use of a buffered scanner for reading lines led to a substantial decrease in processing time, down to 6.7 seconds.
Experimenting with different buffer sizes for file reading showed that larger buffers can significantly improve performance.
The implementation of a custom scan function that reads bytes until a newline character reduced the processing time further.
Converting temperature readings to integers instead of floats led to a significant efficiency improvement.
The use of Go's built-in FNV hash function for map lookups sped up processing by avoiding unnecessary string conversions.
Inlining functions and removing unnecessary function calls from the hot path improved overall performance.
Delegating the file reading task to worker Go routines eliminated communication overhead and reduced memory allocation.
The final optimized solution achieved a processing time of 1.96 seconds for the 1 billion row challenge.
The use of Go's CPU profiler (pprof) was instrumental in identifying bottlenecks and guiding optimization efforts.
The article demonstrates the power of empirical investigation and iterative optimization in improving code performance.