This document discusses distributed tracing and how it can help solve problems caused by microservices. It covers what distributed tracing is, how it works, popular implementations like OpenTracing and Zipkin, and best practices for using distributed tracing. OpenTracing is introduced as a vendor-neutral standard that helps library developers implement tracing and defines common formats for propagating traces between services. Code examples are provided for collecting trace data using OpenTracing and Zipkin.
Completing a transition to a microservices-based architecture makes every software engineer feel good. You can be proud of requests spanning multiple individual services, each with isolated single responsibility. Exactly as you dreamed it would be.
In the course of this transition however, you will have also created several new problems. Among these is a whole new level of complexity related to understanding the behavior of the application when troubleshooting a problem. If you have ever wrestled with pinpointing the exact root cause during a post-mortem, this talk is for you.
We will show you how capturing the runtime transparency of the distributed and dynamic architecture is possible. Better yet, we will cover both simple and advanced examples about how taking this route gives you an objective and evidence-based ability to zoom in to the problem.
After attending the talk you will understand how distributed tracing will help your team during incident response and post-mortems.
Register today to learn more:
What are distributed traces
Different ways to add distributed tracing to your production services
How the distributed traces expose the runtime architecture of your microservices in production.
Examples of how a distributed trace highlights a problem
Advanced examples of how distributed traces map root causes to real user impact
With distributed tracing, we can track requests as they pass through multiple services, emitting timing and other metadata throughout, and this information can then be reassembled to provide a complete picture of the application’s behavior at runtime - Read more in https://blog.buoyant.io/2016/05/17/distributed-tracing-for-polyglot-microservices/ and https://www.rookout.com/
Distributed tracing - get a grasp on your productionnklmish
Slides from my presentation on distributed tracing, explaining what is latency and why it matters. We took a look at openzipkin and its concepts like how the core annotations works, what are tags/logs, etc. Followed by a demo application created using golang and java (spring boot , spring cloud sleuth zipkin) . You can find source code here
https://github.com/nklmish/go-distributed-tracing-demo
https://github.com/nklmish/java-distributed-tracing-demo
Tracing Micro Services with OpenTracingHemant Kumar
Tracing in the world of micro services has become a standard with people using distributed tracers like Zipkin, Jaeger, Appdash etc. But, with so many different tracers, its confusing to choose one tracer and then painful to replace a tracer. That's where OpenTracing comes in. OT provides a consistent, vendor-neutral API to allow users to choose whatever distributed tracer they need and can change the tracer with just an O(1) operation.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk covers the fundamental concepts of observability and then demonstrates how to instrument your applications using the OpenTelemetry libraries.
Completing a transition to a microservices-based architecture makes every software engineer feel good. You can be proud of requests spanning multiple individual services, each with isolated single responsibility. Exactly as you dreamed it would be.
In the course of this transition however, you will have also created several new problems. Among these is a whole new level of complexity related to understanding the behavior of the application when troubleshooting a problem. If you have ever wrestled with pinpointing the exact root cause during a post-mortem, this talk is for you.
We will show you how capturing the runtime transparency of the distributed and dynamic architecture is possible. Better yet, we will cover both simple and advanced examples about how taking this route gives you an objective and evidence-based ability to zoom in to the problem.
After attending the talk you will understand how distributed tracing will help your team during incident response and post-mortems.
Register today to learn more:
What are distributed traces
Different ways to add distributed tracing to your production services
How the distributed traces expose the runtime architecture of your microservices in production.
Examples of how a distributed trace highlights a problem
Advanced examples of how distributed traces map root causes to real user impact
With distributed tracing, we can track requests as they pass through multiple services, emitting timing and other metadata throughout, and this information can then be reassembled to provide a complete picture of the application’s behavior at runtime - Read more in https://blog.buoyant.io/2016/05/17/distributed-tracing-for-polyglot-microservices/ and https://www.rookout.com/
Distributed tracing - get a grasp on your productionnklmish
Slides from my presentation on distributed tracing, explaining what is latency and why it matters. We took a look at openzipkin and its concepts like how the core annotations works, what are tags/logs, etc. Followed by a demo application created using golang and java (spring boot , spring cloud sleuth zipkin) . You can find source code here
https://github.com/nklmish/go-distributed-tracing-demo
https://github.com/nklmish/java-distributed-tracing-demo
Tracing Micro Services with OpenTracingHemant Kumar
Tracing in the world of micro services has become a standard with people using distributed tracers like Zipkin, Jaeger, Appdash etc. But, with so many different tracers, its confusing to choose one tracer and then painful to replace a tracer. That's where OpenTracing comes in. OT provides a consistent, vendor-neutral API to allow users to choose whatever distributed tracer they need and can change the tracer with just an O(1) operation.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk covers the fundamental concepts of observability and then demonstrates how to instrument your applications using the OpenTelemetry libraries.
Everything You wanted to Know About Distributed TracingAmuhinda Hungai
In the age of microservices, understanding how applications are executing in a highly distributed environment can be complicated. Looking at log files only gives a snapshot of the whole story and looking at a single service in isolation simply does not give enough information. Each service is just one side of a bigger story. Distributed tracing has emerged as an invaluable technique that succeeds in summarizing all sides of the story into a shared timeline. Yet deploying it can be quite challenging, especially in the large scale, polyglot environments of modern companies that mix together many different technologies. During this session, we will take a look at patterns and means to implement Tracing for services. After introducing the basic concepts we will cover how the tracing model works, and how to safely use it in production to troubleshoot and diagnose issues.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk gives an overview of the OpenTelemetry project and then outlines some production-proven architectures for improving the observability of your applications and systems.
Tracing 2000+ polyglot microservices at Uber with Jaeger and OpenTracingYuri Shkuro
Slides from my talk & demo at Go NYC Meeetup 19-Jan-2017.
We present Jaeger, Uber’s open source distributed tracing system, featuring Go backend, React based UI, and OpenTracing API support. We show examples of instrumenting application code for tracing and using distributed context propagation to attribute backend resource usage to top level consumers.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk covers the latest concepts in observability and then demonstrates how to configure and deploy various OpenTelemetry components to effectively meet your SLO's.
Everyone wants observability into their system, but find themselves with too many vendors and tools, each with its own API, SDK, agent and collectors.
In this talk I will present OpenTelemetry, an ambitious open source project with the promise of a unified framework for collecting observability data. With OpenTelemetry you could instrument your application in a vendor-agnostic way, and then analyze the telemetry data in your backend tool of choice, whether Prometheus, Jaeger, Zipkin, or others.
I will cover the current state of the various projects of OpenTelemetry (across programming languages, exporters, receivers, protocols), some of which not even GA yet, and provide useful guidance on how to get started with it.
Observability in Java: Getting Started with OpenTelemetryDevOps.com
Our software is more complex than ever: applications must be reliable, predictable, and easy to use to meet modern expectations. As developers, this means our responsibilities have grown while the things we can control have stayed the same. In order to better understand our systems and create truly modern software, we need observability.
This workshop will walk through what observability means for Java developers and how to achieve it in our systems with the least amount of work using the open source observability project OpenTelemetry.
This presentation shows you the basic concept of distributed tracing and Opentracing. And you can see the sample hands-on application (HotROD) of Jaeger
This presentation has been presented at the "Vienna DevOps & Security Meetup" in 2021.
It discusses the state of monitoring, what Opentelemetry is and why should you care about it.
Concepts and basics are discussed and presented in a full example extracting traces, metrics and logs.
Demo: https://github.com/secustor/opentelemetry-meetup
"Introducing Distributed Tracing in a Large Software System", Kostiantyn Sha...Fwdays
Software systems are growing in size and complexity when the business is growing, and sometimes it is hard to figure out what is going on. Various teams make different changes for different business capabilities. Distributed Tracing is a useful way to look under the hood and see for yourself what operations are being performed, what services are used in a certain use case, and how performant are they. In this talk, I will present what Distributed Tracing is and how we introduced it into our software system with some tips and tricks on what you should focus on if you want to do the same.
Everything You wanted to Know About Distributed TracingAmuhinda Hungai
In the age of microservices, understanding how applications are executing in a highly distributed environment can be complicated. Looking at log files only gives a snapshot of the whole story and looking at a single service in isolation simply does not give enough information. Each service is just one side of a bigger story. Distributed tracing has emerged as an invaluable technique that succeeds in summarizing all sides of the story into a shared timeline. Yet deploying it can be quite challenging, especially in the large scale, polyglot environments of modern companies that mix together many different technologies. During this session, we will take a look at patterns and means to implement Tracing for services. After introducing the basic concepts we will cover how the tracing model works, and how to safely use it in production to troubleshoot and diagnose issues.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk gives an overview of the OpenTelemetry project and then outlines some production-proven architectures for improving the observability of your applications and systems.
Tracing 2000+ polyglot microservices at Uber with Jaeger and OpenTracingYuri Shkuro
Slides from my talk & demo at Go NYC Meeetup 19-Jan-2017.
We present Jaeger, Uber’s open source distributed tracing system, featuring Go backend, React based UI, and OpenTracing API support. We show examples of instrumenting application code for tracing and using distributed context propagation to attribute backend resource usage to top level consumers.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk covers the latest concepts in observability and then demonstrates how to configure and deploy various OpenTelemetry components to effectively meet your SLO's.
Everyone wants observability into their system, but find themselves with too many vendors and tools, each with its own API, SDK, agent and collectors.
In this talk I will present OpenTelemetry, an ambitious open source project with the promise of a unified framework for collecting observability data. With OpenTelemetry you could instrument your application in a vendor-agnostic way, and then analyze the telemetry data in your backend tool of choice, whether Prometheus, Jaeger, Zipkin, or others.
I will cover the current state of the various projects of OpenTelemetry (across programming languages, exporters, receivers, protocols), some of which not even GA yet, and provide useful guidance on how to get started with it.
Observability in Java: Getting Started with OpenTelemetryDevOps.com
Our software is more complex than ever: applications must be reliable, predictable, and easy to use to meet modern expectations. As developers, this means our responsibilities have grown while the things we can control have stayed the same. In order to better understand our systems and create truly modern software, we need observability.
This workshop will walk through what observability means for Java developers and how to achieve it in our systems with the least amount of work using the open source observability project OpenTelemetry.
This presentation shows you the basic concept of distributed tracing and Opentracing. And you can see the sample hands-on application (HotROD) of Jaeger
This presentation has been presented at the "Vienna DevOps & Security Meetup" in 2021.
It discusses the state of monitoring, what Opentelemetry is and why should you care about it.
Concepts and basics are discussed and presented in a full example extracting traces, metrics and logs.
Demo: https://github.com/secustor/opentelemetry-meetup
"Introducing Distributed Tracing in a Large Software System", Kostiantyn Sha...Fwdays
Software systems are growing in size and complexity when the business is growing, and sometimes it is hard to figure out what is going on. Various teams make different changes for different business capabilities. Distributed Tracing is a useful way to look under the hood and see for yourself what operations are being performed, what services are used in a certain use case, and how performant are they. In this talk, I will present what Distributed Tracing is and how we introduced it into our software system with some tips and tricks on what you should focus on if you want to do the same.
Umm, how did you get that number? Managing Data Integrity throughout the Data...John Kinmonth
We live at the intersection of data and people. Data integrity is a function of the decisions that people make throughout the data lifecycle.
Dave De Noia, Pointmarc lead solution architect in data management, gives his take on the processes and people that affect data integrity throughout organizations at DRIVE 2014 (Data, Reporting, Intelligence, and Visualization Exchange)
Whether you're a retailer merging web analytics data with offline numbers or a healthcare company adding new data management software, De Noia explains how to avoid logic wobble and establish shared data structures.
About Dave:
Dave De Noia lives in the balance of chaos and order inherent to working with data. Starting his career at Microsoft building analyses in both SQL and big data environments, Dave later moved onto Redfin where he created and managed data infrastructure for analysis and reporting projects. Dave now serves as the senior solution and data architect at Pointmarc, a Bellevue-based digital analytics consultancy, where he helps some of the world’s largest brands get value from their data. Naturally functioning as a bridge between business and technical teams, Dave’s professional passion lies at the intersection of data and people.
About Pointmarc:
Pointmarc is a leading digital analytics agency providing actionable marketing insight and analytics platform instrumentation services for Fortune 500 clients within retail, technology, financial, media and pharmaceutical industries. With offices in Seattle, Boston, San Francisco and Portland, Pointmarc’s immersive approach to analytics empowers businesses to dive deeper into their data.
Email info@pointmarc.com for more information on data management or analytics instrumentation, and follow @pointmarc on Twitter for the latest in analytics.
State of Florida Neo4j Graph Briefing - Cyber IAMNeo4j
Identity is based on relationships. Graph databases ensure those connections are current, scoped to actual requirements, and secure. David Rosenblum will discuss how customers from large financial institutions to smart home security systems are IAM enabled with Neo4j.
How often do you learn that you must deliver a new capability or correct a network issue and it needed to be implemented yesterday? Do you get extra resources to make it happen? Do these asks cut into your personal time?
Now, imagine a world where you can not only easily execute these asks but one where you can also prove to your leaders how the network can be a competitive advantage and help your company make money.
The latest World Wide Technology (WWT) TEC37 webinar focuses on the operational challenges in the data center that can be addressed by software-defined networking (SDN), including how WWT is implementing SDN, as well as the cost benefits of adopting a programmable network.
The data streaming processing paradigm and its use in modern fog architecturesVincenzo Gulisano
Invited lecture at the University of Trieste.
The lecture covers (briefly) the data streaming processing paradigm, research challenges related to distributed, parallel and deterministic streaming analysis and the research of the DCS (Distributed Computing and Systems) groups at Chalmers University of Technology.
DCEU 18: From Monolith to MicroservicesDocker, Inc.
Jeff Nickoloff - Co-founder, Topple
Growth can be challenging to address once monolithic systems begin to fail under strain or internal software development processes begin to slow the release cadence. Many organizations are looking to microservices architecture to solve these application issues, whether they plan to write new applications or rewrite the monoliths into microservices. This talk will highlight the common technical and cultural issues that will make microservice architectures a challenge to adopt and maintain. Issues include impact of Dunbar's Number and Conway's Law, build-time vs runtime continuous integration, evolution of testability, API versioning impact, logistics overhead, artifact management, and strategies for iteration in a distributed environment. Attendees will learn: - How and why microservice architectures and ownership end up falling along organizational lines (and why that is a good thing) - How we can learn from monolith tooling to inform our tooling in a microservice environment - How you can achieve operational excellence at scale taking a logistical approach with Docker.
Neo4j GraphTalks Oslo - Graph Your Business - Rik Van Bruggen, Neo4jNeo4j
The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.
NET Systems Programming Learned the Hard Way.pptxpetabridge
What is a thread quantum and why is it different on Windows Desktop and Windows Server? What's the difference between a blocking call and a blocking flow? Why did our remoting benchmarks suddenly drop when we moved to .NET 6? When should I try to write lock-free code? What does the `volatile` keyword mean?
Welcome to the types of questions my team and I are asked, or ask ourselves, on a regular basis - we're the makers of Akka.NET, a high performance distributed actor system library and these are the sorts of low-level questions we need to answer in order to build great experiences for our own users.
In this talk we're going to learn about .NET systems programming, the low level components we hope we can take for granted, but sometimes can't. In particular:
- The `ThreadPool` and how work queues operate in practice;
- Synchronization mechanisms - including `lock`-less ones;
- Memory management, `Span<T>`, and garbage collection;
- `await`, `Task`, and the synchronization contexts; and
- Crossing user-code and system boundaries in areas such as sockets.
This talk will help .NET developers understand why their code works the way it does and what to do in scenarios that demand high performance.
DotNext 2020 - When and How to Use the Actor Model and Akka.NETpetabridge
The actor model is an old computer science concept, originating in 1973 and it laid dormant is largely a thought experiment for most of its history until the rise of the Internet. Now in the era of cheap, commodity cloud computing the actor model is staging a major comeback across all programming languages and runtimes, both for building distributed systems and for creating reactive mobile or desktop applications.
In this talk, we will introduce the actor model through the use of Akka.NET, the most popular distributed actor model framework in .NET. We'll talk about what sorts of problems it solves well when you should use it, and what are some of the adoption costs and overhead involved in using a tool like Akka.NET.
By the time you're finished with this talk, you should be familiar with most of the major Akka.NET and actor model concepts, basic Akka.NET syntax, and some ideas for how you might be able to use actors in your place of work. This talk is intended for developers, architects, and team leads.
.NET Conf 2019 When and How to Use the Actor Model: an Introduction to Akka...petabridge
In the era of cheap, commodity cloud computing the actor model is ascendant across all programming languages and runtimes, both for building distributed systems and for creating reactive mobile or desktop applications.
Continuous Deployment with Akka.Cluster and Kubernetes (Akka.NET)petabridge
In this 60 minute long webinar Petabridge and Akka.NET co-founder Aaron Stannard you will learn about how companies ranging from the Fortune 500 to brand new startups are changing the way the build .NET applications to leverage the very latest offerings from Microsoft and the .NET open source community.
You'll learn how and why companies are moving their applications onto .NET Core; rearchitecting them to use Akka.NET for fault tolerance, scalability, and the ability to respond to customers in real-time; containerizing them with Docker; putting everything together using Kubernetes for orchestration on-premise or on the cloud with Azure Container Services.
This session will provide an overview of how all of these technologies fit together and why companies are adopting them.
The .NET platform's last major releases prominently feature improved concurrency tools and keywords, and that's no accident. In a world where developers are expected to build applications that are consistently responsive across a wide range of devices, user experiences, and workloads knowing how to take full advantage of the power of concurrent programming is essential.
But let's face it: even with nice tools like the TPL and async / await, multi-threaded concurrent programming has typically been a bottomless pit of despair, Heisenbugs, and lessons learned the hard way.
None of this is true with Akka.NET and the actor model: a powerful programming methodology that makes building concurrent applications easy, fun, and much more powerful than what we had before.
Introduction to Akka.NET and Akka.Clusterpetabridge
Demands and expectations for .NET developers have never been higher.
We're expected increase customer value against higher and higher expectations; deliver our services and content across a greater variety of devices; retain, analyze, and use ever-growing volumes of data faster; and to do all of this while being available 24/7.
That's a tall order, but it's one that is being done successfully by .NET companies all over the world - right now.
In this webinar, lead by Petabridge CEO Aaron Stannard, we're going to cover how the obvious ways of scaling software in the past are doomed to fail and how .NET shops are developing and deploying their own distributed systems to tackle these problems efficiently and effectively using Akka.NET and Akka.Cluster.
Automed .NET Performance Testing with NBenchpetabridge
Not long ago in Akka.NET-land we had an issue occur where users noticed a dramatic drop in throughput in Akka.Remote’s message processing pipeline - and to make matters worse, this occurred in a production release of AKka.NET!
Yikes, how did that happen?
The answer is that although you can use unit tests and code reviews to detect functional problems with code changes and pull requests, using those same mechanisms to detect performance problems with code is utterly ineffective. Even skilled developers who have detailed knowledge about the internals of the .NET framework and CLR are unable to correctly predict how changes to code will impact its performance.
Hence why we developed NBench - a .NET performance-testing, stress-testing, and benchmarking framework for .NET applications that works and feels a lot like a unit test.
We're all distributed systems devs now: a crash course in distributed program...petabridge
Going forward, every developer who works in server-side development will be expected to understand the fundamental concepts that drive the design of distributed systems. It's a matter of when, not if.
In this talk we'll dive into concepts such as the CAP theorem, eventual consistency, microservices, event-driven architectures – and how to apply each of these tools to build effective, resilient, distributed systems.
.NET has been, historically, an expensive ecosystem to play in.
Because to play in it, you have to do so by Microsoft’s rules.
This… is what the experience of developing in .NET has been like, until the recent past.
Today and into the near future, we have an exciting opportunity to reinvent ourselves, our careers, our products, our companies, our entire ecosystem, and the experience of being a .NET developer.
As users of NServiceBus, we’re already familiar with using message-oriented systems to decouple our systems and create reliable collaboration between our services. But what could be possible if you made this message-oriented behavior the basis of your entire application?
In this talk, we’ll introduce Akka.NET and the actor model and show you how to use it to create massively scalable, stateful applications using many of the same messaging patterns you’re already familiar with from NServiceBus. We’ll talk about how to use the Akka.NET distributed actor framework to create more responsive, scalable, and intuitive applications by bringing message-oriented behavior to your entire stack via actors and many of the messaging patterns you already use.
OSS From the Outside In - A Personal Journey With Akka.NETpetabridge
(From .NET Unboxed Conference 2015)
A year ago, I'd never sent in an open-source PR in my life. Today, I collaborate on Akka.NET with awesome developers and end users in 20+ countries every week. How the heck did THAT happen?!
- How do you get involved in a project and build credibility?
- How do you grok all the new ideas you need?
- Once you're involved, how do you build community around the project so people
actually USE the damn thing?
This is a talk about how a complete outsider gets into open-source quickly, and what possibilities that opens up for you personally and in your career.
This is a brief introduction to Akka.Cluster:
- what is Akka.Cluster?
- what does it do for me / why should I care?
- when do I use it?
- how do I use it?
Akka.NET: Concurrency Without the Pain (Intro to the Actor Model)petabridge
We will introduce and explain the mind-bending actor model from the ground up. This will teach the foundational concepts for understanding the processing framework (Akka, ported to .NET from the JVM) that powers organizations like Walmart, Hootsuite, Apache Spark, Twitter, LinkedIn, and many more.
Attendees will walk away understanding all the fundamental concepts of what makes an actor systems so scalable and powerful, and all the foundational knowledge they need to start creating and deploying actor systems in the wild to solve hard problems.
"How do I do transactions across a distributed system?" is a very common question. This talk taught the basic conceptual model, which can then be customized and applied to specific use cases and consistency requirements.
This was a conceptual overview and demo of how to approach making a streaming ETL system using the Actor Model and Akka.NET for scalable, realtime processing.
2 12-2015 - Cassandra Day LA - Using DataStax Enterprise and Actor Systems fo...petabridge
Cassandra and DataStax Enterprise have made it a lot easier to build distributed applications by providing a solid persistence layer that distributes well, is highly available, and comes with an intuitive programming model.
So what if you could have all of the nice things DataStax Enterprise provides to you at your application layer too?
1. An intuitive programming model;
2. High availability;
3. Reliability; and
4. The ability to decouple application design from network topology.
Well it turns out you can do this easier than ever before using the Actor Model and Akka.NET. This talk explains how.
http://getakka.net/
http://petabridge.com/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. Distributed Tracing: How the
Pros Debug Concurrent and
Distributed Systems
By Aaron Stannard,
CEO, Petabridge
2. What We’re Going to Cover
• Microservices and common “people”
problems they cause
• How distributed tracing solves some of
these problems
• What distributed tracing is and how it
works
• The OpenTracing standard and its
implementations
• Distributed tracing best practices
11. OpenTracing
• Vendor-neutral standard
for facilitating distributed
tracing
• Enforces a common
lexicon across all tracing
products
• Helps library and
framework maintainers
implement tracing
• Defines common carrier
formats for propagating
traces between services
16. Best Practices
• Standardize on carrier formats inside your
services
– i.e. B3 HTTP headers, dictionary formats
• Introduce tracing at the infrastructure level,
if you can
– i.e. inject into HTTP request processing
pipeline
• Use OpenTracing IScopeManager
– Automatically resolves current active Span
• Have logging infrastructure inject events
into active span
Petabridge is in the process of releasing its first distributed tracing product….
So I wanted to share some insights with you about this technology and how it’s changing the way we debug and troubleshoot software in production.
Monoliths have long been the default way of building systems, but increasingly people are moving more and more towards distributed and microservice architectures.
This trend probably isn’t going to reverse itself any time soon, fundamentally because micoservices offer us greater scalability at the team level.
Specifically, microservices are for scaling teams, not products.
We can take our microservice architecture and partition our organization responsible for producing the complete software product along the lines of each independent service.
This gives our companies and teams the benefits of encapsulation and implementation transparency. If I’m on the Service 1 team, I don’t need to know how the Service 2 team is implemented. I only need to be aware of the messaging contracts that the public endpoints of Service 2 accept.
As long as those contracts are honored or are changed in a version-aware fashion, microservices allow all of these different teams to observe their own independent release cycles and, in theory, their own preferred technology stacks.
The benefits of microservices aren’t without costs, however. While microservices promise greater flexibility and independence for each participating development team, one of the immediate costs is that most traditional monitoring and tracing infrastructure breaks immediately upon moving away from a monolithic design to a microservices design.
In a monolith, your monitoring infrastructure only needs to tell a single story from the perspective of one process – all of the data on why a request succeeded or failed is right there inside the same process.
In microservices this is no longer true – we lose “coherence” around the flow of information across process boundaries. If Service 1 receives a request and, in turn, has to issue a request to Service 2 and it never receives a response back… It won’t be obvious why, because those implementation details are privately encapsulated inside the remote microservice instance.