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Why you should benchmark your database using stored procedures

HammerDB

HammerDB uses stored procedures to achieve maximum throughput when benchmarking your database. HammerDB has always used stored procedures as a design decision because the original benchmark was implemented as close as possible to the example workload in the TPC-C specification that uses stored procedures.

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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. <code> 127.0.0.1:6379> <code> 127.0.0.1:6379>

Metrics 130
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How to Assess MySQL Performance

HammerDB

GHz 4th Generation Intel Xeon Scalable processors (code-named Sapphire Rapids) Up to 20% higher compute performance than z1d instances Up to 50 Gbps of networking speed Up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (EBS) We can also verify these capabilities by running some simple benchmarks on the different subsystems.

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HammerDB v4.3 New Features Pt1: Graphical Metrics for PostgreSQL

HammerDB

This enables the user to compare and contrast performance across different benchmark scenarios. The events are colour coded and indexed in the graph to the wait event groups. Metrics view for benchmark. PostgreSQL Graphical Metrics. Install pg_stat_statements and pg_sentinel extensions.

Metrics 62
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CheriABI: enforcing valid pointer provenance and minimizing pointer privilege in the POSIX C run-time environment

The Morning Paper

Last week we saw the benefits of rethinking memory and pointer models at the hardware level when it came to object storage and compression ( Zippads ). The protections are hardware implemented and cannot be forged in software. code is not given access to excessive capabilities. ASPLOS’19. CHERI implementation.

C++ 61
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What Adrian Did Next?—?Part 2?—?Sun Microsystems

Adrian Cockcroft

I became the Sun UK local specialist in performance and hardware, and as Sun transitioned from a desktop workstation company to sell high end multiprocessor servers I was helping customers find and fix scalability problems. We had specializations in hardware, operating systems, databases, graphics, etc.

Tuning 52
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Upcoming of the learned data structures

Abhishek Tiwari

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. The benchmarking was performed using 3 real-world data sets (weblogs, maps, and web-documents), and 1 synthetic dataset (lognormal). Learned indexes.