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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 251
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. Message Broker vs. Distributed Event Streaming Platform RabbitMQ functions as a message broker, managing message confirmation, routing, storage, and delivery within a queue. What is RabbitMQ?

Latency 147
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Mastering Disk Space Management with MongoDB® Storage Engines

Scalegrid

MongoDB offers several storage engines that cater to various use cases. The default storage engine in earlier versions was MMAPv1, which utilized memory-mapped files and document-level locking. Choosing the appropriate storage engine can have a significant impact on application performance.

Storage 130
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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.

Latency 239
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Edgar: Solving Mysteries Faster with Observability

The Netflix TechBlog

This difference has substantial technological implications, from the classification of what’s interesting to transport to cost-effective storage (keep an eye out for later Netflix Tech Blog posts addressing these topics). As a request flows between services, each distinct unit of work is documented as a span.

Latency 298
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Unlock the power of contextual log analytics

Dynatrace

There is no need to think about schema and indexes, re-hydration, or hot/cold storage. OpenPipeline’s high-performance filtering and preprocessing provide full ingest and storage control for the Dynatrace platform. Keep in mind that Dynatrace Grail is schema-on-read and indexless, built with scaling in mind.

Analytics 304
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Data ingestion pipeline with Operation Management

The Netflix TechBlog

But we cannot search or present low latency retrievals from files Etc. This API finds all Elasticsearch documents with ID1 and marks isAnnotationOperationActive=FALSE. Using memcache allows us to keep latencies for our search low (most of our queries are less than 100ms). This is obviously very expensive.

Media 272