New Go Billion Row Challenge w/ Great Optimizations | Prime Reacts

23 Mar 202439:42

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.


  • πŸš€ The 1 billion row challenge is a programming task involving reading a large file, aggregating information, and computing statistics for each weather station mentioned.
  • πŸ“ˆ The challenge originated in Java but has been adapted to various programming languages, including Go.
  • πŸ’» The initial naive approach in Go took 95 seconds to process the 1 billion rows, which was surprisingly faster than expected.
  • πŸ”§ Optimizations in Go involved using bytes instead of strings, manipulating buffer sizes, and reducing memory allocations.
  • 🌟 A significant speedup was achieved by using buffered scanning with an appropriately sized buffer, reducing the time to 6.7 seconds.
  • πŸ“Š 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.
  • πŸ”„ The use of goroutines for parallel processing was explored, with the main thread reading data and distributing it to worker goroutines for aggregation.
  • πŸ› οΈ Custom parsing of float values and avoiding unnecessary string conversions contributed to performance gains.
  • πŸ”§ The final optimized solution in Go achieved a processing time of 1.96 seconds for the 1 billion row challenge.
  • πŸ“ˆ The article emphasizes the importance of empirical testing and iterative optimization in achieving high-performance results.
  • 🎯 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 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.



πŸš€ 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.


🐒 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.


πŸ” Deep Dive into Optimization Techniques

The speaker continues their optimization journey by examining the internal workings of the scanner and exploring the use of 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.


πŸ“ˆ 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.


πŸ”§ 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.


🏁 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.



πŸ’‘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 of data, aggregating information from each line, and printing a report with results. In the context of the video, this challenge is used to test and optimize the performance of a Go program.

πŸ’‘Go (Golang)

Go, also known as Golang, is a statically typed, compiled programming language designed at Google. It is known for its simplicity, efficiency in handling concurrent processes, and strong support for software engineering principles. In the video, Go is the chosen language to tackle the 1 billion row challenge.


Optimization in the context of programming refers to the process of improving the performance, efficiency, or effectiveness of a software program. This can involve reducing execution time, minimizing memory usage, or enhancing the readability and maintainability of the code. In the video, the speaker focuses on optimizing a Go program to handle the 1 billion row challenge more efficiently.


Concurrency in programming is the concept of managing the execution of multiple tasks simultaneously. In Go, this is achieved through goroutines, which are lightweight threads managed by the Go runtime. The video explores using concurrency to parallelize the processing of data in the 1 billion row challenge.


Buffering in the context of file I/O refers to the practice of temporarily storing data in a buffer to improve the efficiency of read and write operations. The video discusses the use of buffering to optimize file reading speeds, which is a critical aspect of the 1 billion row challenge.


Profiling is the process of measuring the performance of a program, typically to identify bottlenecks or areas where improvements can be made. In the video, the speaker uses a CPU profiler to analyze the performance of the Go program and find optimization opportunities.

πŸ’‘Hash Function

A hash function is a mathematical algorithm that takes an input (or 'key') and returns a fixed-size string of bytes. Hash functions are used in various programming tasks, such as indexing data in a hash table. In the video, the speaker discusses using a custom hash function to optimize the lookup of data in a map.

πŸ’‘Data Aggregation

Data aggregation is the process of collecting and grouping data from multiple sources and summarizing it to provide a comprehensive view. In the context of the 1 billion row challenge, data aggregation involves computing minimum, maximum, and average temperatures for each weather station from the lines of the file.

πŸ’‘Empirical Testing

Empirical testing involves conducting experiments to gather data and observe the behavior of a system or program. In software development, this can mean running tests with different configurations to determine the most efficient approach. The video showcases the speaker's use of empirical testing to optimize their Go program.

πŸ’‘Flame Graph

A flame graph is a visual representation of a program's profile, typically used to display the percentage of time spent in different parts of the code. It helps developers identify performance bottlenecks by showing a hierarchical structure of function calls and their relative time spent. In the video, the speaker refers to a flame graph generated by the CPU profiler.


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.