Presentation: Counting is Hard: Probabilistic Algorithms for View Counting at Reddit

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 10:55am - 11:45am

Day of week: Wednesday

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Abstract

While counting votes has always been a core feature of Reddit's platform, only recently did we begin counting and displaying view numbers. In this talk, we explain the challenges of building a view counting system at scale, and how we used probabilistic counting algorithms to make scaling easier.

Speaker: Krishnan Chandra

Data Engineer @Reddit

Krishnan is a data engineer at Reddit, and has been working in data engineering for 4 years. Before joining Reddit, Krishnan worked on backend engineering at Optimizely and LinkedIn. He holds bachelor's degrees in computer science and math from the University of Illinois at Urbana-Champaign.

Find Krishnan Chandra at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.