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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. Similarly, an increased throughput signifies an intensive workload on a server and a larger latency.

Metrics 130
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Faster time to value with enhanced handling of OneAgent runtime data

Dynatrace

Storage mount points in a system might be larger or smaller, local or remote, with high or low latency, and various speeds. Sometimes these locations landed on mount points which, due to capacity, availability, or access constraints, weren’t well suited for large runtime storage. See details below. See details below.

Storage 146
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Top 10 Tips for Making the Spark + Alluxio Stack Blazing Fast

DZone

In addition, compute and storage are increasingly being separated causing larger latencies for queries. Alluxio is leveraged as compute-side virtual storage to improve performance. But to get the best performance, like any technology stack, you need to follow the best practices. The first few tips are related to locality.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. Our engineering teams tuned their services for performance after factoring in increased resource utilization due to tracing.

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How digital experience monitoring helps deliver business observability

Dynatrace

With DEM solutions, organizations can operate over on-premise network infrastructure or private or public cloud SaaS or IaaS offerings. STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables).

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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 203
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A case for managed and model-less inference serving

The Morning Paper

Making queries to an inference engine has many of the same throughput, latency, and cost considerations as making queries to a datastore, and more and more applications are coming to depend on such queries. The following figure highlights how just one of these variables, batch size, impacts throughput and latency on ResNet50.