Remove Benchmarking Remove Cache Remove Efficiency Remove Hardware
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. All these contribute significantly towards ensuring smooth functioning.

Metrics 130
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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

PostgreSQL performance optimization aims to improve the efficiency of a PostgreSQL database system by adjusting configurations and implementing best practices to identify and resolve bottlenecks, improve query speed, and maximize database throughput and responsiveness. What is PostgreSQL performance tuning?

Tuning 52
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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Compress objects, not cache lines: an object-based compressed memory hierarchy Tsai & Sanchez, ASPLOS’19. Existing cache and main memory compression techniques compress data in small fixed-size blocks, typically cache lines. Hotpads is a hardware-managed hierarchy of scratchpad-like memories called pads.

Cache 61
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MySQL Performance Tuning 101: Key Tips to Improve MySQL Database Performance

Percona

Enhanced Database Efficiency By adjusting configuration settings, you can markedly enhance the overall efficiency of your MySQL database. This results in expedited query execution, reduced resource utilization, and more efficient exploitation of the available hardware resources. Experiencing database performance issues?

Tuning 52
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MySQL Key Performance Indicators (KPI) With PMM

Percona

We will also discuss related configuration variables to consider that can impact these KPIs, helping you gain a comprehensive understanding of your MySQL server’s performance and efficiency. Query performance Query performance is a key performance indicator (KPI) in MySQL, as it measures the efficiency and speed of query execution.

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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. When available, it can use hardware level performance counters.

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Upcoming of the learned data structures

Abhishek Tiwari

Apart from indexes, super efficient sorting and join operations are some major areas come to my mind with immediate benefits of using learned data structure. More importantly, if this works out well, this could lead to a radical improvement in performance by leveraging hardware trends such as GPUs and TPUs. Learned indexes.