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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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HammerDB v4.0 New Features Pt1: TPROC-C & TPROC-H

HammerDB

compared to previous releases is that the workload names have changed from TPC-C and TPC-H to TPROC-C and TPROC-H respectively and therefore a key question is how are the v4.0 The simple answer is nothing, the workloads are exactly the same workloads derived from the TPC-C and TPC-H specifications and HammerDB v4.0

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

The Morning Paper

CheriABI: enforcing valid pointer provenance and minimizing pointer privilege in the POSIX C run-time environment Davis et al., 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 ). ASPLOS’19. We answer this question affirmatively.

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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. usr/bin/install -c -m 644./pg_stat_statements--1.4.sql./pg_stat_statements--1.8--1.9.sql./pg_stat_statements--1.7--1.8.sql./pg_stat_statements--1.6--1.7.sql./pg_stat_statements--1.5--1.6.sql./pg_stat_statements--1.4--1.5.sql. src/port -L././src/common

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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.

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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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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

Key areas include: Configuration parameter tuning : This tuning involves altering variables such as memory allocation, disk I/O settings, and concurrent connections based on specific hardware and requirements. This not only results in cost savings by minimizing hardware requirements but also has the potential to decrease cloud expenses.

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