--- title: Best Apache Cassandra posts of 2024 | daily.dev description: The most upvoted Apache Cassandra posts of 2024, curated by the daily.dev community. canonical: https://daily.dev/tags/apache-cassandra/best-of/2024 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/apache-cassandra/best-of/2024 og:type: website og:site_name: daily.dev og:title: Best Apache Cassandra posts of 2024 | daily.dev og:description: The most upvoted Apache Cassandra posts of 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Apache Cassandra — 2024 1. 1 [](/posts/how-netflix-manages-238-million-memberships--zupmyqjex "How Netflix Manages 238 Million Memberships?") Article ByteByteGo · 2y How Netflix Manages 238 Million Memberships? Netflix manages 238 million memberships through a microservices architecture that ensures high availability and scalability. The platform includes stages like signup, plan changes, renewals, and payment issues, with core components using databases like CockroachDB and Cassandra. The use of Change Data Capture (CDC) patterns helps in tracking historical data for debugging and analytics, ensuring robustness and resilience. The architecture supports large volumes of data and requests, crucial for handling the global scale and diverse offerings of Netflix. 559 6 2. 2 [](/posts/how-instagram-scaled-to-2-5-billion-users-sjamkrudl "How Instagram Scaled to 2.5 Billion Users") Article Community Picks · 2y How Instagram Scaled to 2.5 Billion Users Learn how Instagram scaled its infrastructure to handle 2.5 billion users. Discover the problems faced during hypergrowth and the technologies used to handle scalability. 125 2 3. 3 [](/posts/how-uber-scaled-cassandra-for-tens-of-millions-of-queries-per-second--sanx3xtef "How Uber Scaled Cassandra for Tens of Millions of Queries Per Second?") Article ByteByteGo · 2y How Uber Scaled Cassandra for Tens of Millions of Queries Per Second? Uber's Cassandra database service supports tens of millions of queries per second, petabytes of data, and tens of thousands of nodes, ensuring high reliability and low latency for mission-critical workloads. The dedicated Cassandra team manages daily operations, implements new features, and ensures 99.99% availability. Uber's custom framework and service discovery mechanisms enable efficient lifecycle management and real-time node discovery. Key challenges tackled include unreliable node replacement, lightweight transaction errors, and data inconsistency issues, which were addressed through various engineering improvements. 71 4. 4 [](/posts/how-reddit-built-a-metadata-store-that-handles-100k-reads-per-second-rwa6wcblw "How Reddit built a Metadata Store that Handles 100k Reads per Second") Article Quastor Daily · 2y How Reddit built a Metadata Store that Handles 100k Reads per Second Reddit developed a media metadata store that handles 100k reads per second to accommodate their rapidly growing user base. They opted for Sharded Postgres over Cassandra due to easier management and fewer denormalization issues. The migration involved dual writes, backfill data, and dual reads. Their scaling strategies include table partitioning using the pg\_partman extension and data denormalization to optimize query efficiency. 49 1 5. 5 [](/posts/how-discord-stores-trillions-of-messages-am5k14ral "How Discord Stores Trillions of Messages") Article Hacker News · 2y How Discord Stores Trillions of Messages Discord initially used Cassandra to store its rapidly growing number of messages but faced significant performance issues as the database scaled. After experimenting with and seeing improvements, Discord decided to switch to ScyllaDB. This new database, written in C++, offered better performance and reduced maintenance complexity, addressing many issues encountered with Cassandra. To manage traffic spikes and improve read efficiency, Discord also implemented intermediary data services using Rust, which allowed for safer and more efficient concurrency. The transition has significantly enhanced performance and stability, enabling Discord to handle trillions of messages reliably. 40 1 6. 6 [](/posts/introducing-netflix-timeseries-data-abstraction-layer-z9owa9bij "Introducing Netflix TimeSeries Data Abstraction Layer") Article Netflix TechBlog · 2y Introducing Netflix TimeSeries Data Abstraction Layer Netflix has introduced a TimeSeries Data Abstraction Layer designed to handle vast amounts of temporal data with millisecond access latency. Key features include efficient data partitioning, flexible storage integration (using Apache Cassandra and Elasticsearch), and scalability to manage high-throughput, immutable temporal event data. This abstraction layer optimizes storage and query efficiency, addressing issues like global read/write operations, tunable configurations, bursty traffic management, and cost efficiency. It plays a vital role in various Netflix services like user interaction tracking, feature rollout analysis, and asset impression tracking. 29 7. 7 [](/posts/how-reddit-built-a-metadata-store-that-handles-100k-reads-per-second-hgxlphbzh "How Reddit built a Metadata Store that Handles 100k Reads per Second") Article Quastor Daily · 2y How Reddit built a Metadata Store that Handles 100k Reads per Second Reddit built a scalable metadata store to handle over 100k reads per second, choosing Postgres over Cassandra due to manageability and flexibility challenges with the latter. They implemented dual writes, backfill data, dual reads, and other strategies for data migration. To achieve high performance, they utilized table partitioning and denormalization. Reddit's current setup on AWS Aurora Postgres delivers low latency without the need for a read-through cache. 21 [See all Apache Cassandra archives](/tags/apache-cassandra/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/tags/apache-cassandra/best-of/2024#page","url":"https://daily.dev/tags/apache-cassandra/best-of/2024","name":"Best Apache Cassandra Posts of 2024","description":"The most upvoted Apache Cassandra posts of 2024, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/apache-cassandra/best-of/2024#items","numberOfItems":7,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/how-netflix-manages-238-million-memberships--zupmyqjex","name":"How Netflix Manages 238 Million Memberships?"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/how-instagram-scaled-to-2-5-billion-users-sjamkrudl","name":"How Instagram Scaled to 2.5 Billion Users"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/how-uber-scaled-cassandra-for-tens-of-millions-of-queries-per-second--sanx3xtef","name":"How Uber Scaled Cassandra for Tens of Millions of Queries Per Second?"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/how-reddit-built-a-metadata-store-that-handles-100k-reads-per-second-rwa6wcblw","name":"How Reddit built a Metadata Store that Handles 100k Reads per Second"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/how-discord-stores-trillions-of-messages-am5k14ral","name":"How Discord Stores Trillions of Messages"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/introducing-netflix-timeseries-data-abstraction-layer-z9owa9bij","name":"Introducing Netflix TimeSeries Data Abstraction Layer"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/how-reddit-built-a-metadata-store-that-handles-100k-reads-per-second-hgxlphbzh","name":"How Reddit built a Metadata Store that Handles 100k Reads per Second"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Tags","item":"https://daily.dev/tags"},{"@type":"ListItem","position":3,"name":"Apache Cassandra","item":"https://daily.dev/tags/apache-cassandra"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/tags/apache-cassandra/best-of"},{"@type":"ListItem","position":5,"name":"2024"}]}]} ```