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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 247
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. Its design prioritizes high availability and efficient data transfer with minimal overhead, making it a practical choice for handling real-time data pipelines and distributed event processing. What is RabbitMQ?

Latency 147
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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 165
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Dynatrace Managed turnkey Premium High Availability for globally distributed data centers (Early Adopter)

Dynatrace

Dynatrace Managed is intrinsically highly available as it stores three copies of all events, user sessions, and metrics across its cluster nodes. The network latency between cluster nodes should be around 10 ms or less. Turnkey high availability across globally distributed data centers. Dynatrace news.

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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 208
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Reducing Your Database Hosting Costs: DigitalOcean vs. AWS vs. Azure

Scalegrid

Since database hosting is more dependent on memory (RAM) than storage, we are going to compare various instance sizes ranging from just 1GB of RAM up to 64GB of RAM so you can see how costs vary across different application workloads. Is my database cluster still highly available? Does it affect latency? EC2 instances.

Azure 344
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Best Practices for Scaling RabbitMQ

Scalegrid

Implementing clustering and quorum queues in RabbitMQ significantly improves load distribution and data redundancy, ensuring high availability and fault tolerance for messaging services. Classic queues can be used in clusters, emphasizing their behavior during node failures, particularly regarding durability and availability.