Scalegrid
DECEMBER 21, 2023
Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring. To monitor Redis® instances effectively, collect Redis metrics focusing on cache hit ratio, memory allocated, and latency threshold.
Dynatrace
NOVEMBER 28, 2022
The new Amazon capability enables customers to improve the startup latency of their functions from several seconds to as low as sub-second (up to 10 times faster) at P99 (the 99th latency percentile). This can cause latency outliers and may lead to a poor end-user experience for latency-sensitive applications.
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High Scalability
FEBRUARY 17, 2021
This is guest post by Sachin Sinha who is passionate about data, analytics and machine learning at scale. 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. Again Yugabyte latency is quite high. Author & founder of BangDB.
Dynatrace
DECEMBER 27, 2022
Dynatrace provides a centralized approach for establishing, instrumenting, and implementing SLOs that uses full-stack observability , topology mapping, and AI-driven analytics. Latency is the time that it takes a request to be served. Availability. Define SLOs for each service. Reliability.
The Netflix TechBlog
JANUARY 25, 2023
The service should be able to serve real-time, aka UI, applications so CRUD and search operations should be achieved with low latency. All data should be also available for offline analytics in Hive/Iceberg. Search Latency Our client applications are studio UI applications so they expect low latency for the search queries.
Dynatrace
APRIL 7, 2023
Now, customers can use streamed responses to build more responsive applications by sending partial responses to clients as the response becomes available. Customers can use AWS Lambda Response Streaming to improve performance for latency-sensitive applications and return larger payload sizes. Return larger payload sizes.
Dynatrace
DECEMBER 4, 2023
Resource consumption: Observing computational resource availability and saturation, whether deployed in cloud-native environments like Kubernetes or CPU-enabled servers. Data quality and drift: Monitoring the quality and characteristics of training and runtime data to detect significant changes that might impact model accuracy.
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