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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. offers the Software Watchdog specifically designed for this purpose.

Metrics 130
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How to Assess MySQL Performance

HammerDB

Among the different components of modern software solutions, the database is one of the most critical. Benchmarking the target Two of the more popular database benchmarks for MySQL are HammerDB and sysbench. We used the first processor socket for the MySQL database and the second socket for the benchmark (sysbench or HammerDB).

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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. CHERI also rethinks the way that pointers and memory work, but the goal here is memory protection.

C++ 61
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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Looking across a set of eight Java benchmarks, we find that only two of them are array dominated, the rest having between 40% to 75% of the heap footprint allocated to objects, the vast majority of which are small. Consider a B-Tree node from the B-tree Java benchmark: Uncompressed, it’s memory layout looks like (a) below. Evaluation.

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

Percona

This results in expedited query execution, reduced resource utilization, and more efficient exploitation of the available hardware resources. A finely tuned database processes queries more efficiently, leading to swifter results. MySQL relies heavily on the availability of hardware resources to perform at its best.

Tuning 52
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MySQL Key Performance Indicators (KPI) With PMM

Percona

Indexing efficiency Monitoring indexing efficiency in MySQL involves analyzing query performance, using EXPLAIN statements, utilizing performance monitoring tools, reviewing error logs, performing regular index maintenance, and benchmarking/testing. This KPI is also directly related to Query Performance and helps improve it.