Database Reliability Engineering DESIGNING and OPERATING RESILIENT DATABASE SYSTEMS
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An Architect's Guide to Site Reliability Engineering Nathaniel T
An Architect's Guide to Site Reliability Engineering Nathaniel T. Schutta @ntschutta ntschutta.io https://content.pivotal.io/ ebooks/thinking-architecturally Sofware development practices evolve. Feature not a bug. It is the agile thing to do! We’ve gone from devs and ops separated by a large wall… To DevOps all the things. We’ve gone from monoliths to service oriented to microserivces. And it isn’t all puppies and rainbows. Shoot. A new role is emerging - the site reliability engineer. Why? What does that mean to our teams? What principles and practices should we adopt? How do we work together? What is SRE? Important to understand the history. Not a new concept but a catchy name! Arguably goes back to the Apollo program. Margaret Hamilton. Crashed a simulator by inadvertently running a prelaunch program. That wipes out the navigation data. Recalculating… Hamilton wanted to add error-checking code to the Apollo system that would prevent this from messing up the systems. But that seemed excessive to her higher- ups. “Everyone said, ‘That would never happen,’” Hamilton remembers. But it did. Right around Christmas 1968. — ROBERT MCMILLAN https://www.wired.com/2015/10/margaret-hamilton-nasa-apollo/ Luckily she did manage to update the documentation. Allowed them to recover the data. Doubt that would have turned into a Hollywood blockbuster… Hope is not a strategy. But it is what rebellions are built on. Failures, uh find a way. Traditionally, systems were run by sys admins. AKA Prod Ops. Or something similar. And that worked OK. For a while. But look around your world today. -
Chapter 6 Structural Reliability
MIL-HDBK-17-3E, Working Draft CHAPTER 6 STRUCTURAL RELIABILITY Page 6.1 INTRODUCTION ....................................................................................................................... 2 6.2 FACTORS AFFECTING STRUCTURAL RELIABILITY............................................................. 2 6.2.1 Static strength.................................................................................................................... 2 6.2.2 Environmental effects ........................................................................................................ 3 6.2.3 Fatigue............................................................................................................................... 3 6.2.4 Damage tolerance ............................................................................................................. 4 6.3 RELIABILITY ENGINEERING ................................................................................................... 4 6.4 RELIABILITY DESIGN CONSIDERATIONS ............................................................................. 5 6.5 RELIABILITY ASSESSMENT AND DESIGN............................................................................. 6 6.5.1 Background........................................................................................................................ 6 6.5.2 Deterministic vs. Probabilistic Design Approach ............................................................... 7 6.5.3 Probabilistic Design Methodology..................................................................................... -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Form Follows Function Model-Driven Engineering for Clinical Trials
Form Follows Function Model-Driven Engineering for Clinical Trials Jim Davies1, Jeremy Gibbons1, Radu Calinescu2, Charles Crichton1, Steve Harris1, and Andrew Tsui1 1 Department of Computer Science, University of Oxford Wolfson Building, Parks Road, Oxford OX1 3QD, UK http://www.cs.ox.ac.uk/firstname.lastname/ 2 Computer Science Research Group, Aston University Aston Triangle, Birmingham B4 7ET, UK http://www-users.aston.ac.uk/~calinerc/ Abstract. We argue that, for certain constrained domains, elaborate model transformation technologies|implemented from scratch in general- purpose programming languages|are unnecessary for model-driven en- gineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of soft- ware tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial pro- tocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automati- cally from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conduct- ing the trial instantiates such models in reporting observations|again by completing a form to create a conformant XML document, represent- ing the data gathered during that observation. Simple standard form management tools are all that is needed. -
Training-Sre.Pdf
C om p lim e nt s of Training Site Reliability Engineers What Your Organization Needs to Create a Learning Program Jennifer Petoff, JC van Winkel & Preston Yoshioka with Jessie Yang, Jesus Climent Collado & Myk Taylor REPORT Want to know more about SRE? To learn more, visit google.com/sre Training Site Reliability Engineers What Your Organization Needs to Create a Learning Program Jennifer Petoff, JC van Winkel, and Preston Yoshioka, with Jessie Yang, Jesus Climent Collado, and Myk Taylor Beijing Boston Farnham Sebastopol Tokyo Training Site Reliability Engineers by Jennifer Petoff, JC van Winkel, and Preston Yoshioka, with Jessie Yang, Jesus Climent Collado, and Myk Taylor Copyright © 2020 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Acquistions Editor: John Devins Proofreader: Charles Roumeliotis Development Editor: Virginia Wilson Interior Designer: David Futato Production Editor: Beth Kelly Cover Designer: Karen Montgomery Copyeditor: Octal Publishing, Inc. Illustrator: Rebecca Demarest November 2019: First Edition Revision History for the First Edition 2019-11-15: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781492076001 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Training Site Reli‐ ability Engineers, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
Reliability Engineering: Today and Beyond
Reliability Engineering: Today and Beyond Keynote Talk at the 6th Annual Conference of the Institute for Quality and Reliability Tsinghua University People's Republic of China by Professor Mohammad Modarres Director, Center for Risk and Reliability Department of Mechanical Engineering Outline – A New Era in Reliability Engineering – Reliability Engineering Timeline and Research Frontiers – Prognostics and Health Management – Physics of Failure – Data-driven Approaches in PHM – Hybrid Methods – Conclusions New Era in Reliability Sciences and Engineering • Started as an afterthought analysis – In enduing years dismissed as a legitimate field of science and engineering – Worked with small data • Three advances transformed reliability into a legitimate science: – 1. Availability of inexpensive sensors and information systems – 2. Ability to better described physics of damage, degradation, and failure time using empirical and theoretical sciences – 3. Access to big data and PHM techniques for diagnosing faults and incipient failures • Today we can predict abnormalities, offer just-in-time remedies to avert failures, and making systems robust and resilient to failures Seventy Years of Reliability Engineering – Reliability Engineering Initiatives in 1950’s • Weakest link • Exponential life model • Reliability Block Diagrams (RBDs) – Beyond Exp. Dist. & Birth of System Reliability in 1960’s • Birth of Physics of Failure (POF) • Uses of more proper distributions (Weibull, etc.) • Reliability growth • Life testing • Failure Mode and Effect Analysis -
Reliability: Software Software Vs
Reliability Theory SENG 521 Re lia bility th eory d evel oped apart f rom th e mainstream of probability and statistics, and Software Reliability & was usedid primar ily as a tool to h hlelp Software Quality nineteenth century maritime and life iifiblinsurance companies compute profitable rates Chapter 5: Overview of Software to charge their customers. Even today, the Reliability Engineering terms “failure rate” and “hazard rate” are often used interchangeably. Department of Electrical & Computer Engineering, University of Calgary Probability of survival of merchandize after B.H. Far ([email protected]) 1 http://www. enel.ucalgary . ca/People/far/Lectures/SENG521/ ooene MTTF is R e 0.37 From Engineering Statistics Handbook [email protected] 1 [email protected] 2 Reliability: Natural System Reliability: Hardware Natural system Hardware life life cycle. cycle. Aging effect: Useful life span Life span of a of a hardware natural system is system is limited limited by the by the age (wear maximum out) of the system. reproduction rate of the cells. Figure from Pressman’s book Figure from Pressman’s book [email protected] 3 [email protected] 4 Reliability: Software Software vs. Hardware So ftware life cyc le. Software reliability doesn’t decrease with Software systems time, i.e., software doesn’t wear out. are changed (updated) many Hardware faults are mostly physical faults, times during their e. g., fatigue. life cycle. Each update adds to Software faults are mostly design faults the structural which are harder to measure, model, detect deterioration of the and correct. software system. Figure from Pressman’s book [email protected] 5 [email protected] 6 Software vs. -
SUSE BU Presentation Template 2014
TUT7317 A Practical Deep Dive for Running High-End, Enterprise Applications on SUSE Linux Holger Zecha Senior Architect REALTECH AG [email protected] Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 2 Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 3 About REALTECH 1/2 REALTECH Software REALTECH Consulting . Business Service Management . SAP Mobile . Service Operations Management . Cloud Computing . Configuration Management and CMDB . SAP HANA . IT Infrastructure Management . SAP Solution Manager . Change Management for SAP . IT Technology . Virtualization . IT Infrastructure 4 About REALTECH 2/2 5 Our Customers Manufacturing IT services Healthcare Media Utilities Consumer Automotive Logistics products Finance Retail REALTECH Consulting GmbH 6 Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 7 The Inspiration for this Session • Several performance workshops at customers • Performance escalations at customer who migrated from UNIX (AIX, Solaris, HP-UX) to Linux • Presenting the experiences made at these customers in this session • Preventing the audience from performance degradation caused from: – Significant design mistakes – Wrong architecture assumptions – Having no architecture at all 8 Performance Optimization The False Estimation Upgrading server with CPUs that are 12.5% faster does not improve application -
Biostatistics (BIOSTAT) 1
Biostatistics (BIOSTAT) 1 This course covers practical aspects of conducting a population- BIOSTATISTICS (BIOSTAT) based research study. Concepts include determining a study budget, setting a timeline, identifying study team members, setting a strategy BIOSTAT 301-0 Introduction to Epidemiology (1 Unit) for recruitment and retention, developing a data collection protocol This course introduces epidemiology and its uses for population health and monitoring data collection to ensure quality control and quality research. Concepts include measures of disease occurrence, common assurance. Students will demonstrate these skills by engaging in a sources and types of data, important study designs, sources of error in quarter-long group project to draft a Manual of Operations for a new epidemiologic studies and epidemiologic methods. "mock" population study. BIOSTAT 302-0 Introduction to Biostatistics (1 Unit) BIOSTAT 429-0 Systematic Review and Meta-Analysis in the Medical This course introduces principles of biostatistics and applications Sciences (1 Unit) of statistical methods in health and medical research. Concepts This course covers statistical methods for meta-analysis. Concepts include descriptive statistics, basic probability, probability distributions, include fixed-effects and random-effects models, measures of estimation, hypothesis testing, correlation and simple linear regression. heterogeneity, prediction intervals, meta regression, power assessment, BIOSTAT 303-0 Probability (1 Unit) subgroup analysis and assessment of publication -
On the Performance Variation in Modern Storage Stacks
On the Performance Variation in Modern Storage Stacks Zhen Cao1, Vasily Tarasov2, Hari Prasath Raman1, Dean Hildebrand2, and Erez Zadok1 1Stony Brook University and 2IBM Research—Almaden Appears in the proceedings of the 15th USENIX Conference on File and Storage Technologies (FAST’17) Abstract tions on different machines have to compete for heavily shared resources, such as network switches [9]. Ensuring stable performance for storage stacks is im- In this paper we focus on characterizing and analyz- portant, especially with the growth in popularity of ing performance variations arising from benchmarking hosted services where customers expect QoS guaran- a typical modern storage stack that consists of a file tees. The same requirement arises from benchmarking system, a block layer, and storage hardware. Storage settings as well. One would expect that repeated, care- stacks have been proven to be a critical contributor to fully controlled experiments might yield nearly identi- performance variation [18, 33, 40]. Furthermore, among cal performance results—but we found otherwise. We all system components, the storage stack is the corner- therefore undertook a study to characterize the amount stone of data-intensive applications, which become in- of variability in benchmarking modern storage stacks. In creasingly more important in the big data era [8, 21]. this paper we report on the techniques used and the re- Although our main focus here is reporting and analyz- sults of this study. We conducted many experiments us- ing the variations in benchmarking processes, we believe ing several popular workloads, file systems, and storage that our observations pave the way for understanding sta- devices—and varied many parameters across the entire bility issues in production systems. -
Sql Server to Aurora Postgresql Migration Playbook
Microsoft SQL Server To Amazon Aurora with Post- greSQL Compatibility Migration Playbook 1.0 Preliminary September 2018 © 2018 Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offer- ings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without war- ranty of any kind, whether express or implied. This document does not create any warranties, rep- resentations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agree- ments, and this document is not part of, nor does it modify, any agreement between AWS and its cus- tomers. - 2 - Table of Contents Introduction 9 Tables of Feature Compatibility 12 AWS Schema and Data Migration Tools 20 AWS Schema Conversion Tool (SCT) 21 Overview 21 Migrating a Database 21 SCT Action Code Index 31 Creating Tables 32 Data Types 32 Collations 33 PIVOT and UNPIVOT 33 TOP and FETCH 34 Cursors 34 Flow Control 35 Transaction Isolation 35 Stored Procedures 36 Triggers 36 MERGE 37 Query hints and plan guides 37 Full Text Search 38 Indexes 38 Partitioning 39 Backup 40 SQL Server Mail 40 SQL Server Agent 41 Service Broker 41 XML 42 Constraints