99.9% of Your Traces are Trash

•

0 likes•53 views

Distributed tracing is still finding its footing in many organizations today, one challenge to overcome is the data volume - keeping 100% of your traces is expensive and unnecessary. Enter sampling - head vs tail how do you decide? Let’s review the tradeoffs associated with different types of sampling strategies and how they can be mixed and matched.

Technology

HOSTED BY
99.9% of your traces are trash 🚮
Paige Cruz
Senior Developer Advocate at Chronosphere

Distributed tracing is still finding its footing in many organizations
today, one challenge to overcome is the data volume - keeping
100% of your traces is expensive and unnecessary. Enter sampling -
head vs tail how do you decide? Let’s look at the design of Sifter and
get familiar with why tail-based sampling is the way to enact a
cost-effective tracing solution while actually increasing the system’s
observability.

A Very Brief History of Tracing
2010
Google
publishes
Dapper paper
Dapper

A Very Brief History of Tracing
2012
Company
formerly known
as Twitter
releases Zipkin
Zipkin

A Very Brief History of Tracing
2015
Uber releases
Jaeger
Jaeger
CNCF accepts
OT
OpenTracing

A Very Brief History of Tracing
2017
Google promotes
OpenCensus
OpenCensus

A Very Brief History of Tracing
2019
Baggage!
W3C
OC + OT = OTel
OpenTelemetry

A Very Brief History of Tracing
2021
Tracing for all!
OTel GA

A Very Brief History of Tracing
2010
Dapper
2015
Jaeger
2019
W3C
2017
OpenCensus
2021
OTel GA
2012
Zipkin
OpenTracing OpenTelemetry

How to scale
Collectors?
What sampling
strategy makes
sense?
How do I get
other teams to
adopt tracing?

Factors that make tracing $$$$
■ High throughput = more traces generated
■ # of services instrumented
● more services = more spans
■ More spans = bigger traces
■ # of labels
● More labels = bigger spans
■ Egress costs sending to vendor
■ Cross-AZ costs & storage costs (self-hosting)

In practice, sampling
rates can be as low as
0.01%

No Sampling
■ Best for very low throughput services/endpoints
■ Speciﬁc high impact time-boxed use cases
● A/B testing of a new endpoint
● Customer troubleshooting
● etc…

Head Based Sampling
■ Eﬃcient for systems with high throughput
■ Minimal impact on service performance
■ Lower ﬁnancial cost
■ Easy to get started with
■ Tends to be conﬁgured statically requiring
re-deployments to take effect

Tail Based Sampling
■ Works best for
● systems with low throughput
● systems with high throughput combined with
head based sampling
■ Very conﬁgurable + easy to reason about
■ Higher ﬁnancial and operational cost

Which traces should we keep?
Which traces should we discard?
What constitutes an “interesting” trace?

Edge case code
paths
Infrequent
request types
Anomalous events

P99 Conf Template
#1A1047 #00E5FF
#753bf0 #FF2CDF
#2B53F9
Font usage Share Tech or Roboto
Color Palette
Table
Column 1 Column 2 Column 3 Column 5
Data 1 Data 2 Data 3 Data 4
Data 5 Data 6 Data 7 Data 8
#667EEA

P99 Conf Template
#1A1047
r26 g16 b71
c100 m100 y34 b45
Pantone 275c
#00E5FF
r0 g229 b255
c60 m0 y9 b0
#753bf0
r117 g59 b240
c79 m77 y0 b0
#FF2CDF
r255 g44 b223
C31 m78 y0 b0
#2B53F9
r38 g24 b250
c91 m75 y0 b0
Pantone 2727c
Color Palette Details
#667EEA
r102 g126 b234
c60 m35 y0 b5
Pantone 659c

P99 Conf Template
<here is some code>
<styling>
<use consolas for font when displaying code>
<don’t go below 12pt font size>

Slide title with white background
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer auctor eros eu
faucibus sodales. Nunc dictum, urna id blandit pretium, mauris velit pulvinar ligula,
interdum blandit sem tortor eget dolor.
■ Bullet 1
● Bullet 2
■ Bullet 3

Paige Cruz
paigerduty@chronosphere.io
@paigerduty@hachyderm.io
www.paigerduty.com
Thank you! Let’s connect.

Similar to 99.9% of Your Traces are Trash

Slides from my talk at MLOps World' 21 Deploying AI models in production and scaling the ML services is still a big challenge. In this talk we will cover details of how to deploy your AI models, best practices for the deployment scenarios, and techniques for performance optimization and scaling the ML services. Come join us to learn how you can jumpstart the journey of taking your PyTorch models from Research to production.

Scaling AI in production using PyTorch

geetachauhan

Building scalable cloud-native applications (Sam Vanhoutte at Codit Azure Paa...

Codit

The name WALD-stack stems from the four technologies it is composed of, i.e. a cloud-computing Warehouse like Snowflake or Google BigQuery, the open-source data integration engine Airbyte, the open-source full-stack BI platform Lightdash, and the open-source data transformation tool DBT. Using a Formula 1 Grand Prix dataset, I will give an overview of how these four tools complement each other perfectly for analytics tasks in an ELT approach. You will learn the specific uses of each tool as well as their particular features. My talk is based on a full tutorial, which you can find under https://waldstack.org.

WALD: A Modern & Sustainable Analytics Stack

Florian Wilhelm

Mike Tuche, CEO of Talend: Enabling the Data Driven Enterprise

Talend

#Interactive Session by Seema Kohli, "Test Leadership in the Era of Artificia...

Agile Testing Alliance

[Paris merge world tour] Perforce Keynote

Perforce

Innovative mobile operators need to mine the vast troves of unstructured data now available to them to help develop compelling customer experiences and uncover new revenue opportunities. In this webinar, you’ll learn how HDB’s in-database analytics enable advanced use cases in network operations, customer care, and marketing for better customer experience. Join us, and get started on your advanced analytics journey today!

Pivotal - Advanced Analytics for Telecommunications

Hortonworks

Machine Learning with Cosmos and Spark - 28 October 2020 Corresponding webinar recording: https://youtu.be/isugbtZWU4I This webinar presents an introduction to data engineering with FIWARE using Apache Spark ready for big data deployments. You will learn how to perform real-time predictions step-by-step through a real use case. Chapter: Processing Difficulty: 3 Audience: Core Speaker: Sonsoles Lopez, Andres Muñoz and Joaquin Salvachua (Universidad Politécnica de Madrid - UPM)

FIWARE Wednesday Webinars - Machine Learning with Cosmos and Spark

FIWARE

Data analytics at a petabyte scale final

Ori Reshef

E2E Life Cycle Intelligence & Channel Strategy @ TPC

David Porter

JMRA Ray Poynter 2017

Ray Poynter

Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward? Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.

Data Culture Series - Keynote & Panel - Reading - 12th May 2015

Jonathan Woodward

External Aerodynamic Optimization Using ANSYS Mesh Morphing

Marco E. Biancolini

Lean kanban India 16

Marcio Sete

The proliferation of connected devices and sensors is leading the Digital Transformation. By 2020 there will be over 20 billion connected devices. Data from these devices need to be ingested at extreme speeds in order to be analyzed before the data decays. The life cycle of the data is critical in revealing what insight can be revealed and how quickly they can be acted upon. In this session we will look at the past, present and future architecture trends streaming analytics. We will look at how to turn all the data from devices into actionable insights and dive into recommendations for streaming architecture depending on the data streams and time factor of the data. We will also discuss how to manage all the sensor data, understand the life cycle cost of the data, and how to scale capacity and capability easily with a modern infrastructure strategy.

Future Architecture of Streaming Analytics: Capitalizing on the Analytics of ...

DataWorks Summit

Charles sonigo - Demuxed 2018 - How to be data-driven when you aren't Netflix...

Charles Sonigo

As a penniless academic I wanted to do "big data" for science. Open source, Python, and simple patterns were the way forward. Staying on top of todays growing datasets is an arm race. Data analytics machinery —clusters, NOSQL, visualization, Hadoop, machine learning, ...— can spread a team's resources thin. Focusing on simple patterns, lightweight technologies, and a good understanding of the applications gets us most of the way for a fraction of the cost. I will present a personal perspective on ten years of scientific data processing with Python. What are the emerging patterns in data processing? How can modern data-mining ideas be used without a big engineering team? What constraints and design trade-offs govern software projects like scikit-learn, Mayavi, or joblib? How can we make the most out of distributed hardware with simple framework-less code?

Building a cutting-edge data processing environment on a budget

Gael Varoquaux

Outside-in Planning Processes. A Case for Change at ASCM Conference

SupplychainInsights

As the focus in software development shifts from adoption of Agile and DevOps to sophistication of approach and consistency of execution, so too does the industry’s understanding of testing’s role in the development cycle. More enterprises understand that testers need to work in constant collaboration with developers, which is shaping how teams test, as well as expanding the systems and environments they have to test in. Before 2019 draws to an end, we take stock of all the milestones testing crossed, as well as the ones that still lie ahead. Join us as we explain why we think you should be mobilizing your team for these trends in the coming year: AI-powered testing comes of age; The rise of “smart” testing and the shrinking of test case libraries; Test automation and RPA’s convergence and its far-reaching benefits; And more, including developments for IOT, Big Data, Security, and Cloud Services.

2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead

DevOps.com

OpenSIPS Summit, Open Source Telecom Software Survey 2022

Alan Quayle

Similar to 99.9% of Your Traces are Trash (20)

Scaling AI in production using PyTorch

Building scalable cloud-native applications (Sam Vanhoutte at Codit Azure Paa...

WALD: A Modern & Sustainable Analytics Stack

Mike Tuche, CEO of Talend: Enabling the Data Driven Enterprise

#Interactive Session by Seema Kohli, "Test Leadership in the Era of Artificia...

[Paris merge world tour] Perforce Keynote

Pivotal - Advanced Analytics for Telecommunications

FIWARE Wednesday Webinars - Machine Learning with Cosmos and Spark

Data analytics at a petabyte scale final

E2E Life Cycle Intelligence & Channel Strategy @ TPC

JMRA Ray Poynter 2017

Data Culture Series - Keynote & Panel - Reading - 12th May 2015

External Aerodynamic Optimization Using ANSYS Mesh Morphing

Lean kanban India 16

Future Architecture of Streaming Analytics: Capitalizing on the Analytics of ...

Charles sonigo - Demuxed 2018 - How to be data-driven when you aren't Netflix...

Building a cutting-edge data processing environment on a budget

Outside-in Planning Processes. A Case for Change at ASCM Conference

2020 Testing Trends: Top Predictions for QA Teams to Watch, Join, and Lead

OpenSIPS Summit, Open Source Telecom Software Survey 2022

More from Paige Cruz

Curious about containers beyond Docker? There’s a new generation of containers on the scene, Podman! Supporting secure, rootless containers for Kubernetes microservices, it was designed and built with the cloud in mind. Benefitting from the lessons learned out in the open from Docker, this next generation of containers will quickly become a trusted daily driver in your dev workflow. Covering what you need to know as an end-user from the UI to the backend, sharing a real world use case leveraging Podman for open source observability workshops https://o11y-workshops.gitlab.io. Paige will share how Podman and the adorable seal mascots Caitlín, Maighréad and Róisín have transformed her local development!

Power Up with Podman - Kubernetes Community Day LA

Paige Cruz

Distributed tracing is still finding its footing in many organizations today, despite being an industry concept for 14 years! One challenge to overcome is managing the data volume. Keeping 100% of traces is expensive and unnecessary - enter sampling. From head-based, tail-based or mixing and matching there is a buffet of configuration options available with OpenTelemetry. Let’s review the tradeoffs associated with different sampling configurations and their impact to the cost and value of your tracing data.

99.99% of Your Traces Are (Probably) Trash (SRECon NA 2024).pdf

Paige Cruz

OTel Orientation: How to Train Teams (OTel in Practice)

Paige Cruz

The signal to noise ratio can be frustratingly high for on-call alerts and downright mystifying when joining a new company or rotation. If you treat PagerDuty like the boy who cried wolf you’ve certainly mired in monitoring debt. Monitoring debit is tragically common and with investment mitigatable! Just how much of a risk are false positives? The data shows that a high number of false alerts trains workers to assume most alerts will be false. Repetition of the same alerts causes even greater alert fatigue. For clinicians the likelihood of acknowledging an alert dropped 30% for each reminder. Paige will share the nuts and bolts of how to start auditing your own alerts to begin your journey towards an on-call oasis.

Avoiding Alert Bankruptcy and Burnout

Paige Cruz

Many things can go awry on the journey from pull request (PR) open to merge to production deployment. Issues can arise from the application code, layers of YAML configuration, underlying infrastructure or pipeline logic itself. How can distributed tracing and trace-derived metrics bring developers and operators together for troubleshooting paradise? I’ll unpack a deploy gone bad from both vantage points, gaining an empathy for the engineer who needs to deploy their changes and an ops engineer who is responsible for keeping the system up and running. With signals from OpenTelemetry I will show how increasing the observability of your deploy system can facilitate better collaboration and quicker troubleshooting.

Tracing Adventures from PR - Production

Paige Cruz

Threat modeling is a key part of securing your cloud computing environment and is unique to each organization's people, practices and processes. Without a threat model you could end up going on wild goose chases and miss the forest for the trees. This talk will introduce the concept of threat modeling including how to get started, guiding questions and recap findings from the Argo Project Threat Model from 2021. Learn how and why to start threat modeling today!

Threat Modeling in the Cloud

Paige Cruz

There’s a reason “i test in prod” isn’t a cheeky take but a lived reality. And that reason is there is no place like production. Not local dev or staging or other environment. This became clear when I deployed a tiny config change that passed all checks, reviews and pre-production environments that triggered a SEV-1. Examining each step of the journey from PR to production I uncovered the snafu that had occurred (unsurprisingly it relates to overwriting key blocks on nested YAML files). I’ll share how difficult it was to reconstruct the chain of events in the system compared to the ideal case of a highly observable system and how to share your own incident learnings since we all test in prod!

There's No Place Like Production

Paige Cruz

The misunderstanding that devops is a tool or team or role has tragically increased the gulf between developers and operators.If you have been unofficially on-call 24/7, toiled away in turmoil, managed initiatives across multiple silos sans project manager with a directive to “influence without authority”...surprise you have lived the contradiction that is feral devops! Consequences of letting this fester are: burn out, learned helplessness and missing out on the speed that happens when developers and operators communicate openly. Taming feral devops is a journey you can start by gathering feedback on the status quo, iterating on onboarding and training and holistically examining on-call health.Whether you’re an individual contributor, manager or leader you will find ideas to experiment with to “domesticate” devops in your organization

Taming Feral DevOps

Paige Cruz

Cognitive apprenticeship is the philosophy that it is more effective to learn in context and real-world situations compared to following a tutorial in a sandboxed environment. In a nutshell it is “learning-through-guided-experience” and shines when teaching problem-solving processes experts use to handle complex tasks like say…investigating an alert by spelunking in production observability and monitoring data. Learn how Alert Triage Hour of Power became a can’t miss meeting of camaraderie and system surprises!

SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power

Paige Cruz

For years tech companies have chased the fabled “single pane of glass” , the one observability tool to understand your system from north to south and east to west. Leafing through promo materials promising instant insights and seamless turnkey integrations you’d think increasing system observability is as easy as assembling a Lego set. In my experience chasing the “single pane of glass” translates to “pain in the ass”. Survey data supports this revealing the majority of engineers cite tool sprawl as a minor or non-existent problem despite relying on several tools. As alluring as the siren call of “single pane of glass” is, let's be practical and examine how to best observe systems across a myriad of tooling. From the telemetry buffet of metrics, events, traces and logs learn when to reach for which type and ways to bridge the gaps with links and enhance with context to free yourself from the fool’s errand of a “single pane of glass”.

Pushing Observability Uphill - The Single “Pain” of Glass

Paige Cruz

Curious about containers? There’s a new generation of containers on the scene, Podman! Supporting secure, rootless containers for Kubernetes microservices, it was designed and built with the cloud in mind. Benefitting from the lessons learned out in the open from Docker, this next generation of containers will quickly become a trusted daily driver in your dev workflow. Covering what you need to know as an end-user from the UI to the backend, sharing a real world use case leveraging Podman for open source observability workshops https://o11y-workshops.gitlab.io. Paige will share how Podman and the adorable seal mascots Caitlín, Maighréad and Róisín have transformed her local development!

Power Up with Podman

Paige Cruz

* Who/What/Where/When/Why of openTelemetry * Demystifying observability terms (telemetry, instrumentation, cardinality, percentile, observability) * Nuts and Bolts of Instrumentation * Tour of the Sample App * Automatic instrumentation with openTelemetry * Explore OOTB response time metric on charts * Manually add a label to the metric and review new facets to query by * Explore an OOTB trace * Manually instrument and add a span and review new level of visibility

Intro to Instrumentation

Paige Cruz

Are you collecting just about every metric under the sun and the kitchen sink too? Understanding the cost of collecting metrics and the usefulness of those metrics is the only way to scale in a cloud native world. You can’t get away with just collecting everything as you grow. How can you make decisions about what to collect, what to drop, what to aggregate while still being able to alert, triage, remediate? Gain immediate insights into high cost data (DPPS), when to drop time series data, and how to determine when the value of that data is at its lowest.

From Cardinal(ity) Sins to Cost-Efficient Metrics Aggregation

Paige Cruz

Today we are caught between the 2nd and 3rd waves of system observability - the proprietary approaches of the past are fading fast in favor of open source instrumentation. Pioneering projects like OpenTelemetry and Prometheus are leading the way providing a path for competitors to cooperate for the benefit of the community. Let's look back at the 3 waves of observability to understand how we got here, the vision for the future and ways you can join us for the journey to liberate telemetry for all!

3rd Wave Observability: Open or Bust

Paige Cruz

More from Paige Cruz (14)

Power Up with Podman - Kubernetes Community Day LA

99.99% of Your Traces Are (Probably) Trash (SRECon NA 2024).pdf

OTel Orientation: How to Train Teams (OTel in Practice)

Avoiding Alert Bankruptcy and Burnout

Tracing Adventures from PR - Production

Threat Modeling in the Cloud

There's No Place Like Production

Taming Feral DevOps

SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power

Pushing Observability Uphill - The Single “Pain” of Glass

Power Up with Podman

Intro to Instrumentation

From Cardinal(ity) Sins to Cost-Efficient Metrics Aggregation

3rd Wave Observability: Open or Bust

Recently uploaded

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

wesley chun

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

TrustArc

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Presentation on how to chat with PDF using ChatGPT code interpreter

naman860154

Tech Trends Report 2024 Future Today Institute.pdf

hans926745

BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Delhi Call girls

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Delhi Call girls

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.

IAC 2024 - IA Fast Track to Search Focused AI Solutions

Enterprise Knowledge

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

CNv6 Instructor Chapter 6 Quality of Service

giselly40

How to convert PDF to text with Nanonets

naman860154

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Joaquim Jorge

The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Igalia

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

Recently uploaded (20)

Powerful Google developer tools for immediate impact! (2023-24 C)

Data Cloud, More than a CDP by Matt Robison

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

Strategies for Landing an Oracle DBA Job as a Fresher

Presentation on how to chat with PDF using ChatGPT code interpreter

Tech Trends Report 2024 Future Today Institute.pdf

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Exploring the Future Potential of AI-Enabled Smartphone Processors

IAC 2024 - IA Fast Track to Search Focused AI Solutions

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

CNv6 Instructor Chapter 6 Quality of Service

How to convert PDF to text with Nanonets

Artificial Intelligence: Facts and Myths

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Automating Google Workspace (GWS) & more with Apps Script

A Domino Admins Adventures (Engage 2024)

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

99.9% of Your Traces are Trash

1. HOSTED BY 99.9% of your traces are trash 🚮 Paige Cruz Senior Developer Advocate at Chronosphere

2. Distributed tracing is still finding its footing in many organizations today, one challenge to overcome is the data volume - keeping 100% of your traces is expensive and unnecessary. Enter sampling - head vs tail how do you decide? Let’s look at the design of Sifter and get familiar with why tail-based sampling is the way to enact a cost-effective tracing solution while actually increasing the system’s observability.

3. Tracing Today

4. A Very Brief History of Tracing 2010 Google publishes Dapper paper Dapper

5. A Very Brief History of Tracing 2012 Company formerly known as Twitter releases Zipkin Zipkin

6. A Very Brief History of Tracing 2015 Uber releases Jaeger Jaeger CNCF accepts OT OpenTracing

7. A Very Brief History of Tracing 2017 Google promotes OpenCensus OpenCensus

8. A Very Brief History of Tracing 2019 Baggage! W3C OC + OT = OTel OpenTelemetry

9. A Very Brief History of Tracing 2021 Tracing for all! OTel GA

10. A Very Brief History of Tracing 2010 Dapper 2015 Jaeger 2019 W3C 2017 OpenCensus 2021 OTel GA 2012 Zipkin OpenTracing OpenTelemetry

11. that’s me!

12.

13.

14.

15. How to scale Collectors? What sampling strategy makes sense? How do I get other teams to adopt tracing?

16.

17. Broken Economics of Tracing

18.

19. I can’t even

20. Factors that make tracing $$$$ ■ High throughput = more traces generated ■ # of services instrumented ● more services = more spans ■ More spans = bigger traces ■ # of labels ● More labels = bigger spans ■ Egress costs sending to vendor ■ Cross-AZ costs & storage costs (self-hosting)

21.

22. In practice, sampling rates can be as low as 0.01%

23. Heads or Tails? Sampling Strategies

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34. No Sampling ■ Best for very low throughput services/endpoints ■ Speciﬁc high impact time-boxed use cases ● A/B testing of a new endpoint ● Customer troubleshooting ● etc…

35. Head

36. 🚮

37. Rate Limit TRACES

38. Probabilistic

39. Targeted

40. Adaptive

41. Head Based Sampling ■ Efficient for systems with high throughput ■ Minimal impact on service performance ■ Lower financial cost ■ Easy to get started with ■ Tends to be configured statically requiring re-deployments to take effect

42. Tail

43. 🚮

44. Tail Based Sampling ■ Works best for ● systems with low throughput ● systems with high throughput combined with head based sampling ■ Very conﬁgurable + easy to reason about ■ Higher ﬁnancial and operational cost

45.

46. Which traces should we keep? Which traces should we discard? What constitutes an “interesting” trace?

47.

48.

49. Edge case code paths Infrequent request types Anomalous events

50.

51.

52. Tomorrow’s Tracing

53.

54.

55.

56. P99 Conf Template #1A1047 #00E5FF #753bf0 #FF2CDF #2B53F9 Font usage Share Tech or Roboto Color Palette Table Column 1 Column 2 Column 3 Column 5 Data 1 Data 2 Data 3 Data 4 Data 5 Data 6 Data 7 Data 8 #667EEA

57. P99 Conf Template #1A1047 r26 g16 b71 c100 m100 y34 b45 Pantone 275c #00E5FF r0 g229 b255 c60 m0 y9 b0 #753bf0 r117 g59 b240 c79 m77 y0 b0 #FF2CDF r255 g44 b223 C31 m78 y0 b0 #2B53F9 r38 g24 b250 c91 m75 y0 b0 Pantone 2727c Color Palette Details #667EEA r102 g126 b234 c60 m35 y0 b5 Pantone 659c

58. P99 Conf Template <here is some code> <styling> <use consolas for font when displaying code> <don’t go below 12pt font size>

59. Slide title with white background Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer auctor eros eu faucibus sodales. Nunc dictum, urna id blandit pretium, mauris velit pulvinar ligula, interdum blandit sem tortor eget dolor. ■ Bullet 1 ● Bullet 2 ■ Bullet 3

60. Slide title with white background Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer auctor eros eu faucibus sodales. Nunc dictum, urna id blandit pretium, mauris velit pulvinar ligula, interdum blandit sem tortor eget dolor. ■ Bullet 1 ● Bullet 2 ■ Bullet 3

61. Slide title with white background Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer auctor eros eu faucibus sodales. Nunc dictum, urna id blandit pretium, mauris velit pulvinar ligula, interdum blandit sem tortor eget dolor. ■ Bullet 1 ● Bullet 2 ■ Bullet 3

62. Centered Large Text

63.

64.

65.

66.

67. Clear slide for diagram with caption

68. Paige Cruz paigerduty@chronosphere.io @paigerduty@hachyderm.io www.paigerduty.com Thank you! Let’s connect.

99.9% of Your Traces are Trash

Recommended

Recommended

More Related Content

Similar to 99.9% of Your Traces are Trash

Similar to 99.9% of Your Traces are Trash (20)

More from Paige Cruz

More from Paige Cruz (14)

Recently uploaded

Recently uploaded (20)

99.9% of Your Traces are Trash