SQL Server Performance Tuning: Common Problems, Possible Solutions

Oct 8, 2024Download as PPTX, PDF0 likes49 views

Identifying which queries are running the slowest, or using the most resources is relatively well documented. However, once you identify the query you need to fix, what are you supposed to do next? This session will walk through a bunch of the most common performance problems and how you go about identifying those problems. From there, we'll discuss some possible solutions for those problems as a way to move you more quickly to a more highly performing database.

Grant Fritchey
DevOps Advocate
Microsoft Data Platform MVP
AWS Community Builder
About me
@gfritchey
grant@scarydba.com
scarydba.com

1. Learn common
performance problems
2. Understand possible
solutions
3. Discover methods of
validation
Agenda

• Unique constraints
• Foreign keys
• Check constraints
Missing, or Incorrect Constraints

• Wide columns
• Poor selectivity, especially on
leading edge
• Poor or missing clustered index
• Clustered index updated
frequently
• Columnstore
Index Design

• Cost Threshold for
Parallelism
• Max Degree of
Parallelism
Parallelism

• Non-sargeable filter
• Calculation in filter
• Optimizer hints
• Nesting views
• Object ownership
• Implicit data conversion
T-SQL Code

• Data skew
• Out of date
statistics
Parameter Sniffing

• Execution Plans
• DMVs
• Extended Events
• Query Store
• Mk I Eyeball
Validating Solutions

Photo Credits
• Crash | Arslan | Flickr
• crash | CSX and a empty semi flat bed. No injuries,
| Ruin Raider | Flickr
• Crash ! | I was quietly looking at a book and moved
slightly… | Flickr
• Crash | kozzmen | Flickr
• Crashed | Frankfurt 2011 | List_84 | Flickr
• Car crash | Driver walked away from this. | huggs2
| Flickr

This document discusses 8 cloud design patterns: External Configuration, Cache Aside, Federated Identity, Valet Key, Gatekeeper, Circuit Breaker, Retry, and Strangler. It provides an overview of each pattern, including what problem it addresses, when to use it, considerations, and examples of cloud offerings that implement each pattern. It aims to help developers understand and apply common best practices for cloud application design.

Cloud Design Patterns - Hong Kong CodeaholicsTaswar Bhatti

Static Analysis PrimerCoverity

Static analysis is a technique that analyzes source code without executing it to find useful information. It works by using tools to capture how code is compiled and then analyzes every path through the code using checkers to find defects like crashes, memory issues and security vulnerabilities. It is beneficial because it can find problems early in the development process when they are cheaper to fix, and the analysis can be done continuously even when developers are not working. Static analysis fits into the software development lifecycle by integrating with activities like code checking, nightly builds, testing and security audits to surface defects.

Cloud patterns at Carleton UniversityTaswar Bhatti

Software design with Domain-driven design Allan Mangune

The document discusses domain-driven software design and related concepts. It begins with an overview of monolithic architecture and modular monoliths. It then covers IdentityServer4 for centralized authentication. Next, it defines domain-driven design and the utility of domain models. It discusses ingredients for effective modeling like prototyping and collaboration. It also covers bounded contexts, entities, value objects, and repositories. Finally, it provides tips for optimizing entities and database operations.

Владимир Бронников (Senior .NET Developer, Perfectial) “Performance optimizat...DataArt

Análisis de las novedades del Elastic StackElasticsearch

UPenn on Rails introMat Schaffer

This document provides an overview and introduction to the Ruby on Rails web framework. It discusses the following key points: - Rails was developed by 37signals to build their BaseCamp application. It uses convention over configuration and opinionated defaults to provide a productive environment for web development. - The MVC pattern organizes a Rails app into models, views, and controllers. Models manage data, views display it, and controllers handle requests and coordinate between models and views. - Rails makes data access very simple with ActiveRecord. Models map directly to database tables, and CRUD (create, read, update, delete) operations are straightforward. - Features like the rails console and built-in

Demystifying Data Engineeringnathanmarz

This document discusses data engineering. It defines data engineering as software engineering focused on dealing with large amounts of data. It explains why data engineering has become important now due to advances in technology and economics. The document then discusses data engineering concepts like distributed systems, parallel processing, and databases. It provides an example of a data pipeline that collects tweets and processes them. Finally, it discusses qualities of an ideal data engineer.

Threat Modeling the CI/CD Pipeline to Improve Software Supply Chain Security ...Denim Group

The SolarWinds attack brought additional scrutiny software supply chain security, but concerns about organizations’ software supply chains have been discussed for a number of years. Development organizations’ shift to DevOps or DevSecOps has pushed teams to adopt new technologies in the build pipeline – often hosted by 3rd parties. This has resulted in build pipelines that expose a complicated and often uncharted attack surface. In addition, modern products also incorporate code from a variety of contributors – ranging from in-house developers, 3rd party development contractors, as well as an array open source contributors. This talk looks at the challenge of developing secure build pipelines. This is done via the construction of a threat model for an example software build pipeline that walks through how the various systems and communications along the way can potentially be misused by malicious actors. Coverage of the major components of a build pipeline – source control, open source component management, software builds, automated testing, and packaging for distribution – is used to enumerate likely attack surface exposed via the build process and to highlight potential controls that can be put in place to harden the pipeline against attacks. The presentation is intended to be useful both for evaluating internal build processes as well as to support the evaluation of critical external vendors’ processes.

Análisis del roadmap del Elastic StackElasticsearch

Disrupting Data Discoverymarkgrover

Lyft developed Amundsen, an internal metadata and data discovery platform, to help their data scientists and engineers find data more efficiently. Amundsen provides search-based and lineage-based discovery of Lyft's data resources. It uses a graph database and Elasticsearch to index metadata from various sources. While initially built using a pull model with crawlers, Amundsen is moving toward a push model where systems publish metadata to a message queue. The tool has increased data team productivity by over 30% and will soon be open sourced for other organizations to use.

Live Content: Finding new ways to publishckimrie

This document discusses 3 ideas for improving ExpressionEngine as a publishing platform: 1. Live Content - Allowing publishers to create fields dynamically as needed rather than using predefined field groups. 2. Client Side Data - Developing a JavaScript API and utility called EEJS to access ExpressionEngine data like channels and entries from the front-end templates. 3. Data Services - Creating a separate CodeIgniter application called the EE Data API to provide a RESTful API for ExpressionEngine data to power off-site apps and mobile apps.

How we integrate Machine Learning Algorithms into our IT Platform at Outfitte...OUTFITTERY

Outfittery's mission is to provide relevant fashion to men. In the past it was our stylists that put together the best outfits for our customers. But since about a year ago we started to rely more on intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer. Later we also provided easy configurability to steer processes. And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. All in all it's a nice setup that we are pretty happy with. It then really took us some time to realise that we actually had built a delivery platform to deliver just any pure function that our data scientists come up with - directly into our microservice landscape. We just now started to use it that way; we just put their R&D experiments directly into production... :-) This talk will guide you through this journey, explain how this platform is built, and what we do with it.

Elastic Stack roadmap deep diveElasticsearch

- Elastic provides a search and analytics platform called the Elastic Stack that includes the Elastic Stack, Beats data shippers, and Kibana analytics and visualization tools. - The presentation discussed updates to Elastic's products including performance improvements to search, new features for distributed search across data centers, and enhanced security options for authentication and authorization. - Elastic aims to provide customizable and extensible solutions for users to ingest, store, search, analyze and visualize large volumes of data from various sources.

GOTO Night: Decision Making Based on Machine LearningOUTFITTERY

Outfittery's mission is to provide relevant fashion to men. In the past it was our stylists that put together the best outfits for our customers. But since about a year ago we started to rely more on intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer. Later we also provided easy configurability to steer processes. And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. All in all it's a nice setup that we are pretty happy with. It then really took us some time to realise that we actually had built a delivery platform to deliver just any pure function that our data scientists come up with - directly into our microservices landscape. We just now started to use it that way; we just put their R&D experiments directly into production... :-) This talk will guide you through this journey, explain how this platform is built, and what we do with it.

Decision Making based on Machine Learning at Outfittery (W-JAX 2017)OUTFITTERY

Outfittery’s mission is to provide relevant fashion to men. In the past it was our stylists that put together the best outfits for our customers. But since about a year ago we started to rely more on intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer.Later we also provided easy configurability to steer processes.And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. All in all it’s a nice setup that we are pretty happy with. It then really took us some time to realise that we actually had built a delivery platform to deliver just any pure function that our data scientists come up with – directly into our microservices landscape. We just now started to use it that way; we just put their R&D experiments directly into production…  This talk will guide you through this journey, explain how this platform is built, and what we do with it.

Defcon 22-wesley-mc grew-instrumenting-point-of-sale-malwareDaveEdwards12

This document discusses improving the communication of malware analysis by supplementing written reports with demonstrations of the malware's capabilities. It presents a case study of instrumenting and demonstrating point-of-sale (POS) malware called JackPOS. Key points covered include setting up the malware's command and control infrastructure, instrumenting the malware's code to monitor its behavior, and demonstrating how it searches process memory for stolen credit card data. The goal is to make malware analysis more reproducible and educational for other analysts.

ML Platform 2018 Q2 Meetup - Search Relevance Debugging at LinkedInEing Ong

Webinar: Personalized Retail Search & Recommendations with FusionLucidworks

Fusion provides personalized retail search and recommendations through machine learning capabilities like clickstream auto-tuning, query intent classification, and recommendation models. It ingests diverse data sources, processes signals like clicks and purchases, and leverages these to improve relevancy, boost performance, and drive higher conversion. The document demonstrates how Fusion has helped large travel and home improvement retailers by increasing engagement, conversion, and revenues through personalized search experiences.

RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu

Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.

RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...Joaquin Delgado PhD.

This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking. To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.

Neo4j Theory and Practice - Tareq Abedrabbo @ GraphConnect London 2013Neo4j

Cloud Design PatternsTaswar Bhatti

SPS Toronto 2015Mike Maadarani

Mike Maadarani is an architect, trainer, and presenter with over 20 years of experience in app development and architecture. He provides consulting services focused on enterprise content management, search, and publishing sites. This document outlines the key steps in a SharePoint migration lifecycle including analyze, plan, execute, assess, and sustain. It discusses analyzing content, systems, and custom code, planning the target structure and remediation needs, executing the migration of services, sites and content, assessing the new environment, and sustaining it over time. People knowledge, realistic timelines, and commitment are identified as keys to a successful migration project.

Getting Ahead of Delivery Issues with Deep SDLC Analysis by Donald Belcham.NET Conf UY

Unit Testing Done RightBrian Fenton

The document discusses unit testing and best practices for writing effective unit tests. It defines what a unit test is and explores different philosophies for testing. It addresses challenges like flaky tests, testing private methods, and relying too heavily on code coverage metrics. The document provides examples of how to avoid these issues and write clean, focused unit tests that reliably test single units of code.

You Need a PostgreSQL Restore Plan PresentationGrant Fritchey

Over and over, you'll hear the need to have a plan in place for backing up your PostgreSQL databases. However, this truly has things completely backwards. The most important thing is not your ability to backup a database. Instead, your most important ability will be to restore a database. This session will explore the concepts that will enable you to create a viable restore plan for your PostgreSQL databases. We'll cover concepts like the Recovery Time Objective and Recovery Point Objective and how they help you, with your business, to define a PostgreSQL recovery plan. Once you have a recovery plan, building a database backup plan is simple. One of the most important things you'll ever do is successfully restore a database in an emergency. This session will help you prepare for that moment.

PostgreSQL Query Performance Monitoring for the Absolute BeginnerGrant Fritchey

You may know that your database is slow, or, you may be told that there are performance problems in the database. However, how do you know where the performance problems are? Which queries are running the slowest? Why are they running slow? This session will take you through the fundamental tools that are built right into PostgreSQL that can help you answer all these questions. We'll start with using queries against the Cumulative Statistics System. We'll begin an exploration of explain plans. Setup and guidance for how all these tools work will be provided along the way. You can finally know which queries are slow, and why they are slow.

More Related Content

Similar to SQL Server Performance Tuning: Common Problems, Possible Solutions (20)