The Twitter Engineering Blog

Information from Twitter's engineering team about our technology, tools and events.

Open Sourcing Twitter Heron

We’re open sourcing Heron, our real-time stream processing engine which has been powering all of Twitter’s real-time analytics for over two years.

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Manhattan software deployments: how we deploy Twitter’s large scale distributed database

We elaborate on the challenges involved in conducting software deployments for our distributed database and share our approach to solving them.

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Introducing Omnisearch

Our search infrastructure team is building a new information retrieval system to power the next generation of relevance-based, personalized products.

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The release of Pants 1.0

After five years of development, open source project Pants 1.0.0 is here and we can’t wait for you to check it out.

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Overview of the Twitter Cloud Platform: Compute

We recently held a meetup event called #compute where our engineers spoke about how we build and operate the Twitter Cloud Platform: Compute at scale

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Twitter goes to #NSBE42

A summary of Twitter’s time at the National Society of Black Engineers 42nd Annual Convention in Boston.

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Resilient ad serving at Twitter-scale

“Adaptive quality factor” is a technique used to make the ad server resilient and scalable, and at the same time achieve revenue optimality.

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Observability at Twitter: technical overview, part II

As one of the most critical infrastructure at Twitter, Observability provides highly scalable data collection and visualization services. This blog post gives overview of our architecture and shares our experience in developing and operating our systems.

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Observability at Twitter: technical overview, part I

As one of the most critical infrastructure at Twitter, observability provides highly scalable data collection and visualization services. Our post gives overview of our architecture and shares our experience in developing and operating our systems.

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Strong consistency in Manhattan

We explore lessons we learned while adding strong consistency to Manhattan and describe several problems that had to be solved along the way (implementing TTLs in a strongly consistent manner, doing distributed log truncations).

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