{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "Python One Billion Row Challenge - From 10 Minutes to 4 Seconds",
  "keywords": "性能优化,大数据",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/python-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e/",
  "publish_date": "2024-05",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "9a421c1f9c07",
  "collected_at": "2026-10-01T21:06:30",
  "title": "Python One Billion Row Challenge - From 10 Minutes to 4 Seconds | Towards Data Science",
  "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Python One Billion Row Challenge - From 10 Minutes to 4 Seconds\nThe one billion row challenge is exploding in popularity. How well does Python stack up?\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nMay 8, 202410 min read\n![Photo by Alina Grubnyak on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0W7gkPGcHZpQGe03W-scaled.jpg)\nPhoto by [Alina Grubnyak](https://unsplash.com/@alinnnaaaa?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nThe question of how fast a programming language can go through and aggregate [1 billion rows](https://1brc.dev) of data has been gaining traction lately. **Python** , not being the most performant language out there, naturally **doesn't stand a chance** - especially since the currently [top-performing Java implementation](https://www.morling.dev/blog/1brc-results-are-in/) takes only 1.535 seconds!\nThe fundamental [rule](https://1brc.dev/#rules-and-limits) of the challenge is that **no external libraries are allowed**. My goal for today is to start by obeying the rules, and then see what happens if you use external libraries and better-suited file formats.\n**I've run all the scripts 5 times and averaged the results.**\nAs for the hardware, I'm using a _16\" M3 Pro Macbook Pro with 12 CPU cores and 36 GB of RAM_. Your results may vary if you decide to run the code, but hopefully, you should see similar percentage differences between implementations.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n### Code\n  * [GitHub Repo - 1 Billion Row Challenge in Python](https://github.com/darioradecic/python-1-billion-row-challenge/blob/main/data/convertToParquet.py)\n\n\n* * *\n## What is the 1 Billion Row Challenge?\nThe idea behind the 1 Billion Row Challenge (1BRC) is simple - go through a `.txt` file that contains arbitrary temperature measurements and calculate summary statistics for each station (min, mean, and max). The only issues are that you're working with 1 billion rows and that the data is stored in an uncompressed `.txt` format (13.8 GB).\nThe dataset is generated by the `data/createMeasurements.py` script on my [GitHub repo](https://github.com/darioradecic/python-1-billion-row-challenge/blob/main/data/createMeasurements.py). I've copied the script from the [source](https://github.com/ifnesi/1brc#submitting), just to have everything in the same place.\nOnce you generate the dataset, you'll end up with a 13.8 GB semicolon-separated text file with two columns - **station name** and **measurement**.\nAnd at the end of the aggregation process, you should end up with something like this:\n![Image 1 - Sample results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1cv6lEftD62U7Gb4I7go5Dw.png)\nImage 1 - Sample results (image by author)\nThe actual output format is somewhat different from one implementation to the other, but this is the one I found proposed by the [official Python repo](https://github.com/ifnesi/1brc#submitting).\nYou now know what the challenge is, so let's dive into the implementation next!\n## 1 Billion Row Challenge - Pure Python Implementation\nThis is the only section in which I plan to obey the challenge rules. The reason is simple - Python doesn't stand a chance with its standard library, and everyone in the industry relies heavily on third-party packages.\n### Single-Core Implementation\nBy far the easiest one to implement. You go through the text file and keep track of the measurements in a dictionary. It's simple for min and max calculations, but mean requires keeping track of the count and then dividing the results.\npython\n\n```\n# https://github.com/ifnesi/1brc#submitting\n# Modified the multiprocessing version\n\ndef process_file(file_name: str):\n    result = dict()\n\n    with open(file_name, \"rb\") as f:\n        for line in f:\n            location, measurement = line.split(b\";\")\n            measurement = float(measurement)\n            if location not in result:\n                result[location] = [\n                    measurement,\n                    measurement,\n                    measurement,\n                    1,\n                ]\n            else:\n                _result = result[location]\n                if measurement < _result[0]:\n                    _result[0] = measurement\n                if measurement > _result[1]:\n                    _result[1] = measurement\n                _result[2] += measurement\n                _result[3] += 1\n\n    print(\"{\", end=\"\")\n    for location, measurements in sorted(result.items()):\n        print(\n            f\"{location.decode('utf8')}={measurements[0]:.1f}/{(measurements[2] / measurements[3]) if measurements[3] !=0 else 0:.1f}/{measurements[1]:.1f}\",\n            end=\", \",\n        )\n    print(\"bb} \")\n\nif __name__ == \"__main__\":\n    process_file(\"data/measurements.txt\")\n```\n\n### Multi-core implementation\nThe same idea as before, but now you need to split the text file into equally sized chunks and process them in parallel. Here, you compute the statistics for each chunk and then combine the results.\npython\n\n```\n# Code credits: https://github.com/ifnesi/1brc#submitting\n\nimport os\nimport multiprocessing as mp\n\ndef get_file_chunks(\n    file_name: str,\n    max_cpu: int = 8,\n) -> list:\n    \"\"\"Split flie into chunks\"\"\"\n    cpu_count = min(max_cpu, mp.cpu_count())\n\n    file_size = os.path.getsize(file_name)\n    chunk_size = file_size // cpu_count\n\n    start_end = list()\n    with open(file_name, \"r+b\") as f:\n\n        def is_new_line(position):\n            if position == 0:\n                return True\n            else:\n                f.seek(position - 1)\n                return f.read(1) == b\"n\"\n\n        def next_line(position):\n            f.seek(position)\n            f.readline()\n            return f.tell()\n\n        chunk_start = 0\n        while chunk_start < file_size:\n            chunk_end = min(file_size, chunk_start + chunk_size)\n\n            while not is_new_line(chunk_end):\n                chunk_end -= 1\n\n            if chunk_start == chunk_end:\n                chunk_end = next_line(chunk_end)\n\n            start_end.append(\n                (\n                    file_name,\n                    chunk_start,\n                    chunk_end,\n                )\n            )\n\n            chunk_start = chunk_end\n\n    return (\n        cpu_count,\n        start_end,\n    )\n\ndef _process_file_chunk(\n    file_name: str,\n    chunk_start: int,\n    chunk_end: int,\n) -> dict:\n    \"\"\"Process each file chunk in a different process\"\"\"\n    result = dict()\n    with open(file_name, \"rb\") as f:\n        f.seek(chunk_start)\n        for line in f:\n            chunk_start += len(line)\n            if chunk_start > chunk_end:\n                break\n            location, measurement = line.split(b\";\")\n            measurement = float(measurement)\n            if location not in result:\n                result[location] = [\n                    measurement,\n                    measurement,\n                    measurement,\n                    1,\n                ]  # min, max, sum, count\n            else:\n                _result = result[location]\n                if measurement < _result[0]:\n                    _result[0] = measurement\n                if measurement > _result[1]:\n                    _result[1] = measurement\n                _result[2] += measurement\n                _result[3] += 1\n    return result\n\ndef process_file(\n    cpu_count: int,\n    start_end: list,\n) -> dict:\n    \"\"\"Process data file\"\"\"\n    with mp.Pool(cpu_count) as p:\n        # Run chunks in parallel\n        chunk_results = p.starmap(\n            _process_file_chunk,\n            start_end,\n        )\n\n    # Combine all results from all chunks\n    result = dict()\n    for chunk_result in chunk_results:\n        for location, measurements in chunk_result.items():\n            if location not in result:\n                result[location] = measurements\n            else:\n                _result = result[location]\n                if measurements[0] < _result[0]:\n                    _result[0] = measurements[0]\n                if measurements[1] > _result[1]:\n                    _result[1] = measurements[1]\n                _result[2] += measurements[2]\n                _result[3] += measurements[3]\n\n    # Print final results\n    print(\"{\", end=\"\")\n    for location, measurements in sorted(result.items()):\n        print(\n            f\"{location.decode('utf8')}={measurements[0]:.1f}/{(measurements[2] / measurements[3]) if measurements[3] !=0 else 0:.1f}/{measurements[1]:.1f}\",\n            end=\", \",\n        )\n    print(\"bb} \")\n\nif __name__ == \"__main__\":\n    cpu_count, *start_end = get_file_chunks(\"data/measurements.txt\", max_cpu=12)\n    process_file(cpu_count=cpu_count, start_end=start_end[0])\n```\n\n### PyPy implementation\nLeverages the multiprocessing implementation to a large extent, but uses PyPy instead of CPython. This allows you to use a just-in-time compiler for your Python code. Great in all cases that don't depend on CPython extensions.\npython\n\n```\n# Code credits: https://github.com/ifnesi/1brc#submitting\n\nimport os\nimport multiprocessing as mp\n\ndef get_file_chunks(\n    file_name: str,\n    max_cpu: int = 8,\n) -> list:\n    \"\"\"Split flie into chunks\"\"\"\n    cpu_count = min(max_cpu, mp.cpu_count())\n\n    file_size = os.path.getsize(file_name)\n    chunk_size = file_size // cpu_count\n\n    start_end = list()\n    with open(file_name, \"r+b\") as f:\n\n        def is_new_line(position):\n            if position == 0:\n                return True\n            else:\n                f.seek(position - 1)\n                return f.read(1) == b\"n\"\n\n        def next_line(position):\n            f.seek(position)\n            f.readline()\n            return f.tell()\n\n        chunk_start = 0\n        while chunk_start < file_size:\n            chunk_end = min(file_size, chunk_start + chunk_size)\n\n            while not is_new_line(chunk_end):\n                chunk_end -= 1\n\n            if chunk_start == chunk_end:\n                chunk_end = next_line(chunk_end)\n\n            start_end.append(\n                (\n                    file_name,\n                    chunk_start,\n                    chunk_end,\n                )\n            )\n\n            chunk_start = chunk_end\n\n    return (\n        cpu_count,\n        start_end,\n    )\n\ndef _process_file_chunk(\n    file_name: str,\n    chunk_start: int,\n    chunk_end: int,\n    blocksize: int = 1024 * 1024,\n) -> dict:\n    \"\"\"Process each file chunk in a different process\"\"\"\n    result = dict()\n\n    with open(file_name, \"r+b\") as fh:\n        fh.seek(chunk_start)\n\n        tail = b\"\"\n        location = None\n        byte_count = chunk_end - chunk_start\n\n        while byte_count > 0:\n            if blocksize > byte_count:\n                blocksize = byte_count\n            byte_count -= blocksize\n\n            index = 0\n            data = tail + fh.read(blocksize)\n            while data:\n                if location is None:\n                    try:\n                        semicolon = data.index(b\";\", index)\n                    except ValueError:\n                        tail = data[index:]\n                        break\n\n                    location = data[index:semicolon]\n                    index = semicolon + 1\n\n                try:\n                    newline = data.index(b\"n\", index)\n                except ValueError:\n                    tail = data[index:]\n                    break\n\n                value = float(data[index:newline])\n                index = newline + 1\n\n                if location not in result:\n                    result[location] = [\n                        value,\n                        value,\n                        value,\n                        1,\n                    ]  # min, max, sum, count\n                else:\n                    _result = result[location]\n                    if value < _result[0]:\n                        _result[0] = value\n                    if value > _result[1]:\n                        _result[1] = value\n                    _result[2] += value\n                    _result[3] += 1\n\n                location = None\n\n    return result\n\ndef process_file(\n    cpu_count: int,\n    start_end: list,\n) -> dict:\n    \"\"\"Process data file\"\"\"\n    with mp.Pool(cpu_count) as p:\n        # Run chunks in parallel\n        chunk_results = p.starmap(\n            _process_file_chunk,\n            start_end,\n        )\n\n    # Combine all results from all chunks\n    result = dict()\n    for chunk_result in chunk_results:\n        for location, measurements in chunk_result.items():\n            if location not in result:\n                result[location] = measurements\n            else:\n                _result = result[location]\n                if measurements[0] < _result[0]:\n                    _result[0] = measurements[0]\n                if measurements[1] > _result[1]:\n                    _result[1] = measurements[1]\n                _result[2] += measurements[2]\n                _result[3] += measurements[3]\n\n    # Print final results\n    print(\"{\", end=\"\")\n    for location, measurements in sorted(result.items()):\n        print(\n            f\"{location.decode('utf-8')}={measurements[0]:.1f}/{(measurements[2] / measurements[3]) if measurements[3] !=0 else 0:.1f}/{measurements[1]:.1f}\",\n            end=\", \",\n        )\n    print(\"bb} \")\n\nif __name__ == \"__main__\":\n    cpu_count, *start_end = get_file_chunks(\"data/measurements.txt\", max_cpu=12)\n    process_file(cpu_count, start_end[0])\n```\n\n### 1BRC Pure Python Results\nAs for the results, well, take a look for yourself:\n![Image 2 - Pure Python implementation results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1bVZUAMh9c7HvDJLncxDXCQ.png)\nImage 2 - Pure Python implementation results (image by author)\nThat's pretty much all you can squeeze out of Python's standard library. Even the PyPy implementation is over 11 times slower than the fastest Java implementation. So no, Python won't win any speed contest any time soon.\n## Speeding Things Up - Using 3rd Party Python Libraries\nBut what if you rely on third-party libraries? I said it before and I'll say it again - it's against the rules of the competition - but I'm beyond it at this point. I just want to make it run faster. No one's going to restrict me to Python's standard library on my day job anyway.\n### Pandas\nA must-know data analysis library for any Python data professional. Not nearly the fastest one, but has a far superior ecosystem. I've used Pandas 2.2.2 with the PyArrow engine when reading the text file.\npython\n\n```\nimport pandas as pd\n\ndf = (\n    pd.read_csv(\"data/measurements.txt\", sep=\";\", header=None, names=[\"station_name\", \"measurement\"], engine=\"pyarrow\")\n        .groupby(\"station_name\")\n        .agg([\"min\", \"mean\", \"max\"])\n)\ndf.columns = df.columns.get_level_values(level=1)\ndf = df.reset_index()\ndf.columns = [\"station_name\", \"min_measurement\", \"mean_measurement\", \"max_measurement\"]\ndf = df.sort_values(\"station_name\")\n\nprint(\"{\", end=\"\")\nfor row in df.itertuples(index=False):\n    print(\n        f\"{row.station_name}={row.min_measurement:.1f}/{row.mean_measurement:.1f}/{row.max_measurement:.1f}\",\n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### Dask\nAlmost identical API to Pandas, but is lazy evaluated. You can use Dask to scale Pandas code across CPU cores locally or across machines on a cluster.\npython\n\n```\nimport dask.dataframe as dd\n\ndf = (\n    dd.read_csv(\"data/measurements.txt\", sep=\";\", header=None, names=[\"station_name\", \"measurement\"], engine=\"pyarrow\")\n        .groupby(\"station_name\")\n        .agg([\"min\", \"mean\", \"max\"])\n        .compute()\n)\n\ndf.columns = df.columns.get_level_values(level=1)\ndf = df.reset_index()\ndf.columns = [\"station_name\", \"min_measurement\", \"mean_measurement\", \"max_measurement\"]\ndf = df.sort_values(\"station_name\")\n\nprint(\"{\", end=\"\")\nfor row in df.itertuples(index=False):\n    print(\n        f\"{row.station_name}={row.min_measurement:.1f}/{row.mean_measurement:.1f}/{row.max_measurement:.1f}\",\n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### Polars\nSimilar to Pandas, but has a multi-threaded query engine written in Rust and offers order of operation optimization. [I've written about it previously](https://medium.com/towards-data-science/pandas-vs-polars-time-to-switch-932d62e7e829).\npython\n\n```\n# Code credits: https://github.com/ifnesi/1brc#submitting\n\nimport polars as pl\n\ndf = (\n    pl.scan_csv(\"data/measurements.txt\", separator=\";\", has_header=False, with_column_names=lambda cols: [\"station_name\", \"measurement\"])\n        .group_by(\"station_name\")\n        .agg(\n            pl.min(\"measurement\").alias(\"min_measurement\"),\n            pl.mean(\"measurement\").alias(\"mean_measurement\"),\n            pl.max(\"measurement\").alias(\"max_measurement\")\n        )\n        .sort(\"station_name\")\n        .collect(streaming=True)\n)\n\nprint(\"{\", end=\"\")\nfor row in df.iter_rows():\n    print(\n        f\"{row[0]}={row[1]:.1f}/{row[2]:.1f}/{row[3]:.1f}\", \n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### **DuckDB**\nOpen-source, embedded, in-process, relational OLAP DBMS that is typically orders of magnitude faster than Pandas. You can use it from the shell or over 10 different programming languages. In most of them, you can choose between a traditional analytical interface and a SQL interface. [I've written about it previously](https://medium.com/codex/this-pandas-alternative-is-350x-faster-when-processing-100-million-rows-3f6ff992182d).\npython\n\n```\n\nimport duckdb\n\nwith duckdb.connect() as conn:\n    data = conn.sql(\"\"\"\n        select\n            station_name,\n            min(measurement) as min_measurement,\n            cast(avg(measurement) as decimal(8, 1)) as mean_measurement,\n            max(measurement) as max_measurement\n        from read_csv(\n            'data/measurements.txt',\n            header=false,\n            columns={'station_name': 'varchar', 'measurement': 'decimal(8, 1)'},\n            delim=';',\n            parallel=true\n        )\n        group by station_name\n        order by station_name\n    \"\"\")\n\n    print(\"{\", end=\"\")\n    for row in sorted(data.fetchall()):\n        print(\n            f\"{row[0]}={row[1]}/{row[2]}/{row[3]}\",\n            end=\", \",\n        )\n    print(\"bb} \")\n```\n\n### 1BRC Third-Party Library Results\nThe results are interesting, to say at least:\n![Image 3 - Python data analysis libraries runtime results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1QaHJRbOqN5TcxvKYBgDpMg.png)\nImage 3 - Python data analysis libraries runtime results (image by author)\nPandas is slow - no surprises here. Dask offers pretty much the same performance as multi-core Python implementation, but with around 100 lines of code less. Polars and DuckDB reduce the runtime to below 10 seconds, which is impressive!\n## Going One Step Further - Ditching .txt for .parquet\nThere's still one bit of performance gain we can squeeze out, and that's changing the data file format. The `data/convertToParquet.py` file in the [repo](https://github.com/darioradecic/python-1-billion-row-challenge/blob/main/data/convertToParquet.py) will do just that.\nThe idea is to go from uncompressed and unoptimized 13.8 GB of text data to a compressed and columnar-oriented 2.51 GB [Parquet](https://towardsdatascience.com/csv-files-for-storage-no-thanks-theres-a-better-option-72c78a414d1d) file.\nThe libraries remain the same, so it doesn't make sense to explain them again. I'll just provide the source code:\n### Pandas\npython\n\n```\nimport pandas as pd\n\ndf = (\n    pd.read_parquet(\"data/measurements.parquet\", engine=\"pyarrow\")\n        .groupby(\"station_name\")\n        .agg([\"min\", \"mean\", \"max\"])\n)\ndf.columns = df.columns.get_level_values(level=1)\ndf = df.reset_index()\ndf.columns = [\"station_name\", \"min_measurement\", \"mean_measurement\", \"max_measurement\"]\ndf = df.sort_values(\"station_name\")\n\nprint(\"{\", end=\"\")\nfor row in df.itertuples(index=False):\n    print(\n        f\"{row.station_name}={row.min_measurement:.1f}/{row.mean_measurement:.1f}/{row.max_measurement:.1f}\",\n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### Dask\npython\n\n```\nimport dask.dataframe as dd\n\ndf = (\n    dd.read_parquet(\"data/measurements.parquet\")\n        .groupby(\"station_name\")\n        .agg([\"min\", \"mean\", \"max\"])\n        .compute()\n)\n\ndf.columns = df.columns.get_level_values(level=1)\ndf = df.reset_index()\ndf.columns = [\"station_name\", \"min_measurement\", \"mean_measurement\", \"max_measurement\"]\ndf = df.sort_values(\"station_name\")\n\nprint(\"{\", end=\"\")\nfor row in df.itertuples(index=False):\n    print(\n        f\"{row.station_name}={row.min_measurement:.1f}/{row.mean_measurement:.1f}/{row.max_measurement:.1f}\",\n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### Polars\npython\n\n```\nimport polars as pl\n\ndf = (\n    pl.scan_parquet(\"data/measurements.parquet\")\n        .group_by(\"station_name\")\n        .agg(\n            pl.min(\"measurement\").alias(\"min_measurement\"),\n            pl.mean(\"measurement\").alias(\"mean_measurement\"),\n            pl.max(\"measurement\").alias(\"max_measurement\")\n        )\n        .sort(\"station_name\")\n        .collect(streaming=True)\n)\n\nprint(\"{\", end=\"\")\nfor row in df.iter_rows():\n    print(\n        f\"{row[0]}={row[1]:.1f}/{row[2]:.1f}/{row[3]:.1f}\", \n        end=\", \"\n    )\nprint(\"bb} \")\n```\n\n### DuckDB\npython\n\n```\nimport duckdb\n\nwith duckdb.connect() as conn:\n    data = conn.sql(\"\"\"\n        select\n            station_name,\n            min(measurement) as min_measurement,\n            cast(avg(measurement) as decimal(8, 1)) as mean_measurement,\n            max(measurement) as max_measurement\n        from parquet_scan('data/measurements.parquet')\n        group by station_name\n        order by station_name\n    \"\"\")\n\n    print(\"{\", end=\"\")\n    for row in sorted(data.fetchall()):\n        print(\n            f\"{row[0]}={row[1]}/{row[2]}/{row[3]}\",\n            end=\", \",\n        )\n    print(\"bb} \")\n```\n\n### 1BRC with Parquet Results\nIt looks like we have a clear winner:\n![Image 4- Data analysis libraries on Parquet format runtime results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1B5ZBjkDA5aWNk5L9K2WswQ.png)\nImage 4- Data analysis libraries on Parquet format runtime results (image by author)\nThe DuckDB implementation on the Parquet file format reduced the runtime to below 4 seconds! It's still about 2.5x times slower than the fastest Java implementation (on `.txt`), but it's something to be happy with.\n* * *\n## Conclusion\nIf there's one visualization to remember from this article, it has to be the following one:\n![Image 5 - Average runtime results for all approaches \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/17P7VbhsrMIUQLXsyz13ZSg.png)\nImage 5 - Average runtime results for all approaches (image by author)\nSure, only the first three columns obey the official competition rules, but I don't care. Speed is speed. All is fair in love and Python performance optimization.\nPython will never be as fast as Java or any other compiled language - that's the fact. The question you have to answer is _how fast is fast enough_. For me, less than 4 seconds for 1 billion rows of data is well below that margin.\n_What are your thoughts on the 1 Billion Row Challenge? Did you manage to implement a faster solution?_ Let me know in the comment section below.\n**Read next:**\n> [**DuckDB and AWS - How to Aggregate 100 Million Rows in 1 Minute**](https://towardsdatascience.com/duckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657095692_93hujy.png)\nWritten By\n## [Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nhttps://writingfordevs.substack.com\n[See all from Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\n[Benchmark](https://towardsdatascience.com/tag/benchmark/)[Coding](https://towardsdatascience.com/tag/coding/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Programming](https://towardsdatascience.com/tag/programming/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F&title=Python%20One%20Billion%20Row%20Challenge%20-%20From%2010%20Minutes%20to%204%20Seconds)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F&title=Python%20One%20Billion%20Row%20Challenge%20-%20From%2010%20Minutes%20to%204%20Seconds)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F&text=Python%20One%20Billion%20Row%20Challenge%20-%20From%2010%20Minutes%20to%204%20Seconds)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/05/1ar3IyIB8Pk7HIc4fIus69g.jpeg)](https://towardsdatascience.com/improve-your-gradient-descent-the-epic-quest-for-the-optimal-stride-4711dc6f5dba/)\n## [Improve your Gradient Descent: The Epic Quest for the Optimal Stride](https://towardsdatascience.com/improve-your-gradient-descent-the-epic-quest-for-the-optimal-stride-4711dc6f5dba/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nTechniques for Optimizing Step-Size/Learning Rate in Gradient Descent for Machine Learning\n[Naman Agrawal](https://towardsdatascience.com/author/namanagr03/)\nMay 23, 202313 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0On5LyPzQkgK5iVD_.jpg)](https://towardsdatascience.com/the-math-behind-lstm-9069b835289d/)\n## [The Math Behind LSTM](https://towardsdatascience.com/the-math-behind-lstm-9069b835289d/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nDive into LSTMs, understand their mathematics, and implement them from scratch\n[Cristian Leo](https://towardsdatascience.com/author/cristianleo120/)\nMay 1, 202426 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0pJt9UGHYaelofMxo-scaled.jpg)](https://towardsdatascience.com/building-a-named-entity-extractor-for-covid-19-risk-factors-cb9bf9022b5e/)\n## [Building a named entity extractor for COVID-19 risk factors](https://towardsdatascience.com/building-a-named-entity-extractor-for-covid-19-risk-factors-cb9bf9022b5e/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHow to extract information from a huge corpus of data\n[Maria Mestre](https://towardsdatascience.com/author/dataqa/)\nJanuary 17, 20225 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/11/0hRvek9zkP1NYL3TA-scaled.jpg)](https://towardsdatascience.com/had-your-treats-time-for-data-science-tricks-28ad59f9b59d/)\n## [Had Your Treats? Time for Data Science Tricks](https://towardsdatascience.com/had-your-treats-time-for-data-science-tricks-28ad59f9b59d/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nOur weekly selection of must-read Editors' Picks and original features\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nNovember 2, 20233 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/08/0H-NPJXpUJHNptEHo-scaled.jpg)](https://towardsdatascience.com/why-using-learning-rate-schedulers-in-nns-may-be-a-waste-of-time-8fa20339002b/)\n## [Why Using Learning Rate Schedulers In NNs May Be a Waste of Time](https://towardsdatascience.com/why-using-learning-rate-schedulers-in-nns-may-be-a-waste-of-time-8fa20339002b/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nHint: Batch size is the key and it might not be what you think!\n[Andy Wang](https://towardsdatascience.com/author/andy-wang/)\nAugust 5, 20228 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1IL0TaIk5KbB5PNWX4aiv1w.png)](https://towardsdatascience.com/anchors-away-more-python-regular-expressions-you-wish-you-knew-8a7780ac54e9/)\n## [Anchors Away! More Python Regular Expressions You Wish You Knew](https://towardsdatascience.com/anchors-away-more-python-regular-expressions-you-wish-you-knew-8a7780ac54e9/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSecrets to working with text using advanced regular expressions tools in Python\n[Drew Seewald](https://towardsdatascience.com/author/realdrewdata/)\nOctober 26, 202011 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/04/1vgrkCK7-VLWBpmuKOWScZw.png)](https://towardsdatascience.com/whats-the-difference-between-a-data-scientist-research-scientist-and-an-applied-scientist-30c04190c1fa/)\n## [What's the Difference Between a Data Scientist, Research Scientist, and an Applied Scientist?](https://towardsdatascience.com/whats-the-difference-between-a-data-scientist-research-scientist-and-an-applied-scientist-30c04190c1fa/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nDemystifying the similarities and differences between data science roles\n[Terence Shin, MSc, MBA](https://towardsdatascience.com/author/terenceshin/)\nApril 30, 20203 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0FG8CGqieNiQ0QLuD.jpg)](https://towardsdatascience.com/data-warehouses-for-distributed-applications-7e34679f74/)\n## [Data Warehouses for Distributed Applications](https://towardsdatascience.com/data-warehouses-for-distributed-applications-7e34679f74/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nWhat are cloud data warehouses and how to implement them at scale\n[Mostafa Ibrahim](https://towardsdatascience.com/author/mostafaibrahim18/)\nJanuary 31, 20225 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1CR0ec-gowB583cq4Ed2IrQ-scaled.jpeg)](https://towardsdatascience.com/predicting-market-value-of-fifa-soccer-players-with-regression-5d79aed207d9/)\n## [Predicting the Market Value of FIFA Soccer Players with Regression](https://towardsdatascience.com/predicting-market-value-of-fifa-soccer-players-with-regression-5d79aed207d9/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA case study with Linear, LASSO, Ridge, Elastic Net and Polynomial Regression.\n[Tan Pengshi Alvin](https://towardsdatascience.com/author/tanpengshi/)\nSeptember 21, 20207 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "metadata": {
    "title": "Python One Billion Row Challenge - From 10 Minutes to 4 Seconds | Towards Data Science",
    "description": "The one billion row challenge is exploding in popularity. How well does Python stack up?",
    "keywords": null,
    "author": "Dario Radečić",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Python One Billion Row Challenge - From 10 Minutes to 4 Seconds",
    "og:description": "The one billion row challenge is exploding in popularity. How well does Python stack up?",
    "og:url": "https://towardsdatascience.com/python-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0W7gkPGcHZpQGe03W-scaled.jpg",
    "og:image:width": "2560",
    "og:image:height": "1707",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Dario Radečić",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "10 min read",
    "article:published_time": "2024-05-08T16:12:43.000Z",
    "article:modified_time": "2024-05-08T16:12:43.000Z",
    "article:author": "https://towardsdatascience.com/author/radecicdario/"
  },
  "paywall": {
    "listed_paid": true,
    "detected": false,
    "marker": "",
    "public_only": true,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 200,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_000.txt",
      "chars": 1203
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_001.txt",
      "chars": 3673
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_002.txt",
      "chars": 4441
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_003.txt",
      "chars": 633
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_004.txt",
      "chars": 661
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_005.txt",
      "chars": 685
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_006.txt",
      "chars": 758
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_007.txt",
      "chars": 580
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_008.txt",
      "chars": 590
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_009.txt",
      "chars": 537
    },
    {
      "language": "txt",
      "file": "attachments/9a421c1f9c07/code/block_010.txt",
      "chars": 586
    }
  ],
  "images": [
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0W7gkPGcHZpQGe03W-scaled.jpg",
      "alt": "Photo by Alina Grubnyak on Unsplash",
      "local_path": "attachments/9a421c1f9c07/images/000.jpg",
      "bytes": 660708,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1cv6lEftD62U7Gb4I7go5Dw.png",
      "alt": "Image 1 - Sample results (image by author)",
      "local_path": "attachments/9a421c1f9c07/images/001.png",
      "bytes": 677854,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1bVZUAMh9c7HvDJLncxDXCQ.png",
      "alt": "Image 2 - Pure Python implementation results (image by author)",
      "local_path": "attachments/9a421c1f9c07/images/002.png",
      "bytes": 23778,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1QaHJRbOqN5TcxvKYBgDpMg.png",
      "alt": "Image 3 - Python data analysis libraries runtime results (image by author)",
      "local_path": "attachments/9a421c1f9c07/images/003.png",
      "bytes": 25138,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/1B5ZBjkDA5aWNk5L9K2WswQ.png",
      "alt": "Image 4- Data analysis libraries on Parquet format runtime results (image by author)",
      "local_path": "attachments/9a421c1f9c07/images/004.png",
      "bytes": 24674,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/17P7VbhsrMIUQLXsyz13ZSg.png",
      "alt": "Image 5 - Average runtime results for all approaches (image by author)",
      "local_path": "attachments/9a421c1f9c07/images/005.png",
      "bytes": 35800,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2023/05/1ar3IyIB8Pk7HIc4fIus69g.jpeg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/006.jpeg",
      "bytes": 508839,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0On5LyPzQkgK5iVD_.jpg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/007.jpg",
      "bytes": 363910,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0pJt9UGHYaelofMxo-scaled.jpg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/008.jpg",
      "bytes": 385723,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2023/11/0hRvek9zkP1NYL3TA-scaled.jpg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/009.jpg",
      "bytes": 301087,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2022/08/0H-NPJXpUJHNptEHo-scaled.jpg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/010.jpg",
      "bytes": 686666,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1IL0TaIk5KbB5PNWX4aiv1w.png",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/011.png",
      "bytes": 45115,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/04/1vgrkCK7-VLWBpmuKOWScZw.png",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/012.png",
      "bytes": 490614,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/0FG8CGqieNiQ0QLuD.jpg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/013.jpg",
      "bytes": 84987,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1CR0ec-gowB583cq4Ed2IrQ-scaled.jpeg",
      "alt": "",
      "local_path": "attachments/9a421c1f9c07/images/014.jpeg",
      "bytes": 247452,
      "downloaded": true
    }
  ],
  "audio": [],
  "videos": [
    {
      "src": "https://www.youtube.com/c/TowardsDataScience",
      "quality": "unavailable",
      "local_path": "",
      "note": "无1080p且无720p，未交付更低清晰度"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/radecicdario/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/csv-files-for-storage-no-thanks-theres-a-better-option-72c78a414d1d"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/duckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/benchmark/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/coding/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/programming/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/improve-your-gradient-descent-the-epic-quest-for-the-optimal-stride-4711dc6f5dba/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/namanagr03/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/the-math-behind-lstm-9069b835289d/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/deep-learning/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@alinnnaaaa"
    },
    {
      "type": "external",
      "href": "https://unsplash.com"
    },
    {
      "type": "external",
      "href": "https://1brc.dev"
    },
    {
      "type": "external",
      "href": "https://www.morling.dev/blog/1brc-results-are-in/"
    },
    {
      "type": "external",
      "href": "https://1brc.dev/"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-data-scientist"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/computer-science"
    },
    {
      "type": "external",
      "href": "https://github.com/darioradecic/python-1-billion-row-challenge/blob/main/data/convertToParquet.py"
    },
    {
      "type": "external",
      "href": "https://github.com/darioradecic/python-1-billion-row-challenge/blob/main/data/createMeasurements.py"
    },
    {
      "type": "external",
      "href": "https://github.com/ifnesi/1brc"
    },
    {
      "type": "external",
      "href": "https://medium.com/towards-data-science/pandas-vs-polars-time-to-switch-932d62e7e829"
    },
    {
      "type": "external",
      "href": "https://medium.com/codex/this-pandas-alternative-is-350x-faster-when-processing-100-million-rows-3f6ff992182d"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=Python+One+Billion+Row+Challenge+-+From+10+Minutes+to+4+Seconds&u=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=Python+One+Billion+Row+Challenge+-+From+10+Minutes+to+4+Seconds&url=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=Python+One+Billion+Row+Challenge+-+From+10+Minutes+to+4+Seconds&url=https%3A%2F%2Ftowardsdatascience.com%2Fpython-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-14T11:11:48Z",
  "source_url": "https://towardsdatascience.com/python-one-billion-row-challenge-from-10-minutes-to-4-seconds-0718662b303e/",
  "delivery_status": "excluded_paid",
  "discovery_status": "excluded_paid",
  "missing_reason": "按原约定排除付费/会员全文"
}