Go runtime related problems in TiDB production environment About me ● Arthur Mao(毛康力), Senior Engineer@PingCAP ● TiDB core developer (top3 contributor) ● GitBook about golang internals (@tiancaiamao) not exhausted, but where is the bottleneck? Description ● The table is wide in the customer environment ○ CPU usage is higher with less columns ● The deployment does not consider NUMA ○ CPU usage is

Focused on version control and automated environment creation – time reduced from 8 weeks to 1 day b. ENABLE ON-DEMAND CREATION OF DEV, TEST, AND PRODUCTION ENVRIONMENTS i. Major contributing cause behaves in a production-like environment ii. Don’t just document the environment specifications 1. Create a common build mechanism to create it on demand 2. Codified in automated environment build process everything, everything is checked into version control 1. Application code & dependencies 2. Environment scripts & creation tools 3. DB scripts and reference data 4. Containers 5. Automated tests

operations and automation forward by “light-years” and had performed huge number of production deployments. g. INJECT PRODUCTION FAILURES TO ENABLE RESILIENCE AND LEARNING i. Crumple zones in cars – build in issues away from critical areas. ii. Michael Nygard, author of Release It! Design and Deploy Production-Ready Software, “If you do not design your failure modes, then you will get whatever unpredictable—and operations influence components used in production or have the ability to not be responsible for unsupported platforms ii. Systematically review production infrastructure and services for items that

within each element of their programs, thus helping them to succeed in an increasingly complex environment. Agile has emerged as the leading industry software development methodology, and has seen growing Some practices can even be applied to non-IT acquisitions. Program managers should evaluate the environment, constraints, and objectives to determine the right structure and methods to apply. Agile requires offers a set of principles that can help reduce cycle times and risks to deliver IT in a complex environment. “The US joint force will be smaller and leaner. But its great strength will be that it will

3. The last option to install is creating a django CMS project manually into a local virtual environment by running the djangocms command. This will create a new Django project set up for running django when you add django CMS to a Django project. Production-ready: django CMS quickstart The django CMS quickstart project is a template for a Docker-based production- ready setup. Especially if you know your in its setup.py. Important We strongly recommend doing all of the following steps in a virtual environment [https://docs.python.org/3/library/venv.html]. You ought to know how to create, activate and dispose

simultaneously. Key Measures Mean-time to production: the average time it takes from when new software features are required until they are running in production. Average lead-time: how long it takes for into the production environment. Deployment frequency: how often a new release can be deployed into the production environment. Production failure rate: how often software fails during production. Mean-time Mean-time to recovery: how long it takes applications in the production stage to recover from failure. Key Principles to implementing a successful DevSecOps approach  Remove bottlenecks (including human

times more frequent) ii. Code and change deployment lead time (two hundred times faster) iii. Production deployments (sixty times higher change success rate) iv. Mean time to restore service (168 times releases, where an entire year’s worth of code that Development has worked on is released to production deployment. Like in manufacturing, this large batch release creates sudden, high levels of WIP This validates our common experience that the larger the change going into production, the more difficult the production errors are to diagnose and fix, and the longer they take to remediate. iii.

• Focus on strategies / How to think to achieve DI in the real world without undue warping of Production Code (or just giving up) • Talk is rooted in a real-world system not theory9 Bloomberg Dependency Testing : Set up a (barely) functional full environment : • Set up toy configs , databases, components, etc. to have a usually barely functional “Full environment” and test • Testing is not unit testing switching of functionality • Allows limited Testing • No code changes/contamination in actual production application required • The code using the dependent functionality has no say in which implementation

Interfaces 3 As fast as (or even faster) than double 4 User Experience 5 Framework Basics 6 Environment, compatibility, next steps CppCon 2020 | A Physical Units Library For the Next C++ Agenda 31 Interfaces 3 As fast as (or even faster) than double 4 User Experience 5 Framework Basics 6 Environment, compatibility, next steps CppCon 2020 | A Physical Units Library For the Next C++ Agenda In QUANTITIES OF LINEAR ALGEBRA TYPES 72CppCon 2020 | A Physical Units Library For the Next C++ ENVIRONMENT, COMPATIBILITY, NEXT STEPS 73• Concept • Class NTTPs • Consistent and Defaulted Comparison

[https://www.django-cms.org/slack]. 1. Installing django CMS We’ll get started by setting up our environment. 1.1. Requirements django CMS requires Django 1.11 or newer, and Python 2.7 or 3.3 or newer. This cms.org/en/latest/index.html#software-version-requirements-and-release-notes] 1.2. Your working environment We’re going to assume that you have a reasonably recent version of virtualenv installed and that that you have some basic familiarity with it. 1.2.1. Create and activate a virtual environment Linux python3.6 -m venv env # Python 2 usage: virtualenv env source env/bin/activate Windows py -m venv