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Next-level interaction and customization of data visualizations in Dynatrace Dashboards and Notebooks

Dynatrace

To achieve the best visual outcome, we recommend experimenting with the available customization options. Go to our documentation to learn more about implementing honeycomb visualizations on your dashboards or notebooks. The functionality is automatically available in all time-series charts in the Dashboards and Notebook apps.

Latency 246
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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

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. It follows a push-based approach, ensuring messages are distributed to consumers as soon as they become available.

Latency 147
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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. The latency table shows that 99th percentile latency for Yugabyte is quite high compared to others (lower is better). Again Yugabyte latency is quite high. Conclusion.

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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

What is the availability, configurability, and efficacy of each? ?️ Plotted on the same horizontal axis of 1.6s, the waterfalls speak for themselves: 201ms of cumulative latency; 109ms of cumulative download. 4,362ms of cumulative latency; 240ms of cumulative download. And do any of our previous decisions dictate our options?

Cache 348
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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. To learn more, see our documentation.

Metrics 246
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Dynatrace automatically monitors OpenAI ChatGPT for companies that deliver reliable, cost-effective services powered by generative AI

Dynatrace

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. With these latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.