{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "DuckDB and AWS - How to Aggregate 100 Million Rows in 1 Minute",
  "keywords": "DuckDB,AWS,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/duckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79/",
  "publish_date": "2024-04",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "a7a9de655a46",
  "collected_at": "2026-10-01T21:10:05",
  "title": "DuckDB and AWS - How to Aggregate 100 Million Rows in 1 Minute | Towards Data Science",
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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# DuckDB and AWS - How to Aggregate 100 Million Rows in 1 Minute\nProcess huge volumes of data with Python and DuckDB - An AWS S3 example.\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nApril 25, 20244 min read\n![Photo by Growtika on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/0MXB9w3QFy1uve2sH-scaled.jpg)\nPhoto by [Growtika](https://unsplash.com/@growtika?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nWhen companies need a secure, performant, and scalable storage solution, they tend to gravitate toward the cloud. One of the most popular platforms in the game is AWS S3 - and for a good reason - it's an industry-leading object storage solution that can serve as a data lake.\nThe question is - **Can you aggregate S3 bucket data without downloading it? And can you do it fast?**\nThe answer is **yes** to both questions. DuckDB allows you to connect to your S3 bucket directly via the `httpfs` extension. You'll learn how to use it today by aggregating around 111 million rows split between 37 [Parquet](https://towardsdatascience.com/csv-files-for-storage-no-thanks-theres-a-better-option-72c78a414d1d) files.\n**Spoiler alert:** It will take you around a minute.\n_Note:_ I wrote this post because I was searching for a more performant Pandas alternative. My goal was to perform analysis on large datasets locally instead of opting for cloud solutions. I have no affiliations with DuckDB or AWS.\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Data Engineer](https://roadmap.sh/data-engineer) roadmaps.\n## AWS S3 Setup\nFirst things first, you'll need an AWS account and an S3 bucket. You'll also want to create an IAM user for which you can generate an access key.\nAs for the data, I've downloaded _Yellow Taxi_ data [Parquet](https://towardsdatascience.com/csv-files-for-storage-no-thanks-theres-a-better-option-72c78a414d1d) files from January 2021 to January 2024 from the following link:\n  * **New York City Taxi and Limousine Commission (TLC) Trip Record Data** was accessed on April 18, 2024, from [https://www.[nyc.gov](https://www.nyc.gov/home/terms-of-use.page)/site/tlc/about/tlc-trip-record-data.page](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). The data is free to use. License info can be found on nyc.gov.\n\n\nThis is what it looks like when loaded into an S3 bucket:\n![Image 1 - Parquet files in an S3 bucket \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/17JeqeO0aXLgSIR-vSxbnFg.png)\nImage 1 - Parquet files in an S3 bucket (image by author)\nThe bucket now contains 37 Parquet files taking 1.79 GB of space and containing over 111 million rows.\n## DuckDB AWS S3 Setup\nSetup on the Python end requires the `duckdb` library and the `httpfs` extension for DuckDB. Assuming you have the library installed (simple `pip` installation), import it, and create a new connection:\npython\n\n```\nimport duckdb\n\nconn = duckdb.connect()\n```\n\n### DuckDB httpfs Extension\nThe `httpfs` [extension](https://duckdb.org/docs/extensions/httpfs/overview.html), among other things, allows you to write/read files to and from a given AWS S3 bucket.\nInstall it and load it with the following Python command (run the installation only once):\npython\n\n```\nconn.execute(\"\"\"\n    INSTALL httpfs;\n    LOAD httpfs;\n\"\"\").df()\n```\n\nYou should see a success message like this:\n![Image 2 - Installing and loading httpfs extension \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1PZ4JT345uOKGAgDDnFkuzg.png)\nImage 2 - Installing and loading httpfs extension (image by author)\n### DuckDB S3 Configuration\nAs for the S3 configuration, provide the region, access key, and secret access key to DuckDB:\npython\n\n```\nconn.execute(\"\"\"\n    SET s3_region = '<your-region>';\n    SET s3_access_key_id = '<your-access-key>';\n    SET s3_secret_access_key = '<your-secret-key>';\n\"\"\").df()\n```\n\nYou should see a success message once again:\n![Image 3 - DuckDB S3 configuration \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/17T6zNrvOob9FWkkfFxdvwg.png)\nImage 3 - DuckDB S3 configuration (image by author)\nAnd that's it! You can now query S3 data directly from DuckDB.\n## Python and DuckDB - How to Get Data From AWS\nThis section will show how long it takes to run two queries - simple count and aggregation - from 37 Parquet files stored on S3.\n### Query #1 - Simple Count\nTo read Parquet data from an S3 bucket, use the `parquet_scan()` function and provide a glob path to all Parquet files stored in the root path. Just remember to change the bucket name:\npython\n\n```\nres_count = conn.execute(\"\"\"\n    select count(*)\n    from parquet_scan('s3://<your-bucket-name>/*.parquet');\n\"\"\").df()\n\nres_count\n```\n\nGetting a count of over 111 million takes only 7 seconds:\n![Image 4 - DuckDB count results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1FP6CdUWE5mKM5inRu7J7dw.png)\nImage 4 - DuckDB count results (image by author)\n### Query #2 - Monthly Summary Statistics\nAnd now let's calculate summary statistics over all Parquet files. The goal is to get counts, sums, and averages for certain columns grouped on a monthly level:\npython\n\n```\nres_agg = conn.execute(\"\"\"\n    select \n        period,\n        count(*) as num_rides,\n        round(avg(trip_duration), 2) as avg_trip_duration,\n        round(avg(trip_distance), 2) as avg_trip_distance,\n        round(sum(trip_distance), 2) as total_trip_distance,\n        round(avg(total_amount), 2) as avg_trip_price,\n        round(sum(total_amount), 2) as total_trip_price,\n        round(avg(tip_amount), 2) as avg_tip_amount\n    from (\n        select\n            date_part('year', tpep_pickup_datetime) as trip_year,\n            strftime(tpep_pickup_datetime, '%Y-%m') as period,\n            epoch(tpep_dropoff_datetime - tpep_pickup_datetime) as trip_duration,\n            trip_distance,\n            total_amount,\n            tip_amount\n        from parquet_scan('s3://duckdb-bucket-20240422/*.parquet')\n        where trip_year >= 2021 and trip_year <= 2024\n    )\n    group by period\n    order by period\n\"\"\").df()\n\nres_agg\n```\n\nIt takes just over a minute to perform this aggregation:\n![Image 5 - DuckDB aggregation results \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1iMpuUc2zpELhxQ8d4io2Sg.png)\nImage 5 - DuckDB aggregation results (image by author)\nAs a frame of reference, the same operation took [under 2 seconds](https://medium.com/codex/this-pandas-alternative-is-350x-faster-when-processing-100-million-rows-3f6ff992182d) when files were saved on a local disk. Pandas took over 11 minutes for the same task.\n* * *\n## Summing up DuckDB and AWS in Python\nOverall, if you have huge volumes of data stored on S3, DuckDB is your friend. You can analyze and aggregate data in no time without file downloads - which might not be allowed at all due to privacy and security concerns.\nYou can also use DuckDB as an analysis and **aggregation layer between two AWS S3 storage layers**. Raw data goes in, aggregated data goes out - hopefully, into another bucket or subdirectory.\n**Read next:**\n> [**How to Train a Decision Tree Classifier... In SQL**](https://towardsdatascience.com/how-to-train-a-decision-tree-classifier-in-sql-e29f37835f18)\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[Aws S3](https://towardsdatascience.com/tag/aws-s3/)[Data Engineering](https://towardsdatascience.com/tag/data-engineering/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Duckdb](https://towardsdatascience.com/tag/duckdb/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fduckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79%2F&title=DuckDB%20and%20AWS%20-%20How%20to%20Aggregate%20100%20Million%20Rows%20in%201%20Minute)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fduckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79%2F&title=DuckDB%20and%20AWS%20-%20How%20to%20Aggregate%20100%20Million%20Rows%20in%201%20Minute)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fduckdb-and-aws-how-to-aggregate-100-million-rows-in-1-minute-3634eef06b79%2F&text=DuckDB%20and%20AWS%20-%20How%20to%20Aggregate%20100%20Million%20Rows%20in%201%20Minute)\n\n\nTowards Data Science is a community publication. 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