Apache Cassandra at Instagram

Use Case Highlights

 • Instagram’s first deployment with Apache Cassandra was for storing audit information related to site integrity purposes; this was running on Redis in-memory.

• Implementing Apache Cassandra cut costs, to the point that they were paying around a 1/4 of what we were paying before.
• Instagram now uses Apache Cassandra for fraud detection, newsfeed, & inbox services.
• They found Apache Cassandra to be a perfect fit because of its high availability, write throughput, and linear scalability.


"Implementing Cassandra cut our costs to the point where we were paying around a quarter of what we were paying before. Not only that, but it also freed us to just throw data at the cluster because it was much more scalable and we could add nodes whenever needed."
-Rick Branson, Infrastructure Engineer at Instagram
Apache Cassandra at i2O Water

Use Case Highlights

• i2O Water records time-series data for multiple physical channels from their devices in the field, over a GPRS mobile phone network, through the internet and into Cassandra.


• Prior to using Apache Cassandra they had a traditional analysis technology using Microsoft SQL Server.


• They currently use Cassandra to save over 100 million litres of water per day for customers across the world.


"The other technologies we looked at were other column stores, both open-source and commercial, and by far and away Cassandra had the best reputation and had the best performance for the testing that we did."
-Mike Williams, Software Director at i2O Water
Apache Cassandra at Spotify

Use Case Highlights

• Spotify uses Cassandra to store data for their entire product catalog and key customer experience capabilities such as playlists, radio stations, notification popups, recommendation engine, and the customized lists of artists.


• To achieve the level of service, demanded by its 40 million+ active users, Spotify needed Apache Cassandra to keep up with its growth, without performance or availability issues.


• Spotify initially started out as a PostgreSQL shop.


“Cassandra gives us a level of trust that we won’t lose data. If there are bugs or crashes, we are confident it won’t lose our data, that is very important to us.”
-Axel Liljencrantz, Backend Engineer at Spotify
Apache Cassandra at Apple

Use Case Highlights

• 75,000 Nodes


• Data Size: 10′s of PetaBytes


• Ops/Sec: In the millions


• Largest Cluster: 1,000+ Nodes


• Versions: 1.2.x & 2.0.x


"We have been pushing Apache Cassandra to the limits in various areas; we have done a lot of contributions in those areas to improve performance and non-performance aspects of the database."
-Sankalp Kohli, Software Engineer at Apple
Apache Cassandra at The Weather Channel

Use Case Highlights

• The Weather Channel uses Apache Cassandra to support nearly every imaginable type of content: observations, forecasts, marine data, pollen, video content, ads, etc.


• They get ~100M transactions per day on average against their busiest Cassandra-backed service, with a heavy day seeing ~180-200M transactions.


• The Weather Channel has grown their node count from 3 to 36 in ~1 year’s time; this growth has happened incrementally.


“As the number one destination for weather we have a global brand that’s on 24×7. Downtime any time of day means someone isn’t getting served, which means lost revenue, credibility, and important notifications to people who are expecting us to tell them when the weather gets rough.”
-Robbie Strickland, Software Development Manager at The Weather Channel
Apache Cassandra at The New York Times

Use Case Highlights

• The New York Times uses Apache Cassandra to connect millions of devices with their services, route billions of messages and notifications, and remember every message.


• They used DynamoDB originally, but converted as it is not natively multi-data center.


• Open source, multi-region support, scalability, and reliability/availability were the primary criterion for chosing Apache Cassandra.


"Simplicity has helped us make nyt⨍aбrik global, reliable, fast, and efficient. It scales up and down on a minute’s notice to meet demand.”
-Michael Laing, Systems Architect at The New York Times
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