本文为摘录(或转载)，侵删，原文为： attachments/pdf/c/red_hat_enterprise_linux-7-performance_tuning_guide-en-us.pdf

1 TOOLS

1.1 top

1.2 ps

1.3 vmstat – Virtual Memory Statistics

1.4 sar – System Activity Repoter

1.5 perf

1.6 tubostat

1.7 iostat

1.8 irqbalance

1.9 ss – socket statistics

1.10 numastat

1.11 numad

本文为摘录(或转载)，侵删，原文为： https://linuxopsys.com/topics/check-shared-memory-in-linux

1 RTFM…

1.1 ipcs

1.2 ipcrm

2 Check shared memory in Linux

Print active shared memory segments using -m option.

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# ipcs -m
------ Shared Memory Segments --------
key shmid owner perms bytes nattch status
0x00000000 65536 root 600 393216 2 dest
0x00000000 98305 root 600 393216 2 dest

where,

本文为摘录(或转载)，侵删，原文为： https://www.cppstories.com/2023/initializer_list_improvements/

1 Referencing local array

std::initializer_list 在使用中会转化成为常量对象的匿名局部数组：

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std::initializer_list<int> wrong() { // for illustration only!
    return { 1, 2, 3, 4};
}
int main() {
    std::initializer_list<int> x = wrong();
}

The above code is equivalent to the following:

本文为摘录(或转载)，侵删，原文为： attachments/pdf/c/p3346-gong.pdf

本文为摘录(或转载)，侵删，原文为： https://www.highgo.ca/2021/01/12/the-mapping-of-oid-and-relfilenode-in-pg/

A table in PostgreSQL has a relfilenode value, which specifies the file name of the table on disk (except foreign table and partition table). In general, this value can be found in the relfilenode field of the pg_class table, but there are some specific tables whose query result in the relfilenode field is 0. This blog will explore the kernel processing of these special tables relfilenode.

本文为摘录(或转载)，侵删，原文为： attachments/pdf/3/The Vertica Analytic Database- C-Store 7 Years Later (p1790_andrewlamb_vldb2012).pdf

1 ABSTRACT

• Vertica 是 C-Store 的商业化成果

2 BACKGROUND

2.1 Design Overview

2.1.1 Design Goals

• Designed for analytic workloads rather than for transactional workloads

本文为摘录(或转载)，侵删，原文为： attachments/pdf/6/p2613-guagliardo.pdf

1 INTRODUCTION

• NULL 用于表示不完整的数据

2 DESIGN AND METHODOLOGY

本文为摘录(或转载)，侵删，原文为： attachments/pdf/7/p3402-wang.pdf

1 ABSTRACT

• UDO:

  • Universal Database Optimization, 通用数据库优化
  • 为数据库的某种工作负载所做的特殊优化
  • 包含多种调优：从索引选择到系统参数

• 基于强化学习

本文为摘录(或转载)，侵删，原文为： attachments/pdf/0/Understanding Quantum Technologies Olivier Ezratty.pdf

1 Why quantum computing

• to solve complex problems that are and will stay inaccessible to classical computers.
• problems whose solutions scale exponentially in computing time on classical machines.

https://motherduck.com/blog/big-data-is-dead/

谷歌的大数据工程师乔丹·蒂加尼 （Jordan Tigani），最近直言不讳地说：“大数据已死”。

他认为，大数据时代已经结束了，大数据的存储和分析，作为一个技术问题已经解决了。 用户已经不必担心数据大小了，再多的数据都不是问题。