Topic: Speedtest: Benchmark Your Apache Kafka®️
Abstract: In this session, Mark will talk about running benchmarking utilities for Apache Kafka; to determine how much MB/sec a cluster can handle; how to set up automated benchmark runs (including the repo), and using this to find and optimize client-side producer configuration properties
Citi Tech Talk: Monitoring and Performanceconfluent
The objective of the engagement is for Citi to have an understanding and path forward to monitor their Confluent Platform and
- Platform Monitoring
- Maintenance and Upgrade
IBM MQ - better application performanceMarkTaylorIBM
Presented in Feb 2015 at Interconnect
This presentation is aimed at helping application developers understand how to best use MQ features for higher performance.
Migrating from a monolith to microservices – is it worth it?Katherine Golovinova
IURII IVON, EPAM Solution Architect, Microsoft Competency Center Expert.
The term ‘microservices’ has become so popular that many people see it as a silver bullet for all architectural problems, or at least as a trend that should be followed. If your project is a monolith today, does it make sense to move towards microservices? This presentation overviews painful issues to be considered when migrating from a monolith to microservice architecture, ways to solve them, and ideas on the feasibility of such migration.
Learn how to improve the performance of your Cognos environment. We cover hardware and server specifics, architecture setup, dispatcher tuning, report specific tuning including the Interactive Performance Assistant and more. See the recording and download this deck: https://senturus.com/resources/cognos-analytics-performance-tuning/
Senturus offers a full spectrum of services for business analytics. Our Knowledge Center has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: https://senturus.com/resources/
Adding Value in the Cloud with Performance TestRodolfo Kohn
System quality attributes such performance, scalability, and availability are among the main concerns for cloud application developers and product managers. There are many examples of notable system failures that show how a company business can be affected during key events like a Cyber Monday. However, many difficulties come up when a team intends to consciously manage these type of quality attributes during development and operations. It is possible to group these difficulties in two main aspects: human aspects and technical aspects. During this presentation, I will share main technical difficulties we had to deal with in the last seven years working with different cloud services as well as key technical performance, scalability, and availability issues we were able to find and solve. It is about cases that are relevant through different products, technologies, and teams.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2y2yPiS.
Colin McCabe talks about the ongoing effort to replace the use of Zookeeper in Kafka: why they want to do it and how it will work. He discusses the limitations they have found and how Kafka benefits both in terms of stability and scalability by bringing consensus in house. He talks about their progress, what work is remaining, and how contributors can help. Filmed at qconsf.com.
Colin McCabe is a Kafka committer at Confluent, working on the scalability and extensibility of Kafka. Previously, he worked on the Hadoop Distributed Filesystem and the Ceph Filesystem.
Citi Tech Talk: Monitoring and Performanceconfluent
The objective of the engagement is for Citi to have an understanding and path forward to monitor their Confluent Platform and
- Platform Monitoring
- Maintenance and Upgrade
IBM MQ - better application performanceMarkTaylorIBM
Presented in Feb 2015 at Interconnect
This presentation is aimed at helping application developers understand how to best use MQ features for higher performance.
Migrating from a monolith to microservices – is it worth it?Katherine Golovinova
IURII IVON, EPAM Solution Architect, Microsoft Competency Center Expert.
The term ‘microservices’ has become so popular that many people see it as a silver bullet for all architectural problems, or at least as a trend that should be followed. If your project is a monolith today, does it make sense to move towards microservices? This presentation overviews painful issues to be considered when migrating from a monolith to microservice architecture, ways to solve them, and ideas on the feasibility of such migration.
Learn how to improve the performance of your Cognos environment. We cover hardware and server specifics, architecture setup, dispatcher tuning, report specific tuning including the Interactive Performance Assistant and more. See the recording and download this deck: https://senturus.com/resources/cognos-analytics-performance-tuning/
Senturus offers a full spectrum of services for business analytics. Our Knowledge Center has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: https://senturus.com/resources/
Adding Value in the Cloud with Performance TestRodolfo Kohn
System quality attributes such performance, scalability, and availability are among the main concerns for cloud application developers and product managers. There are many examples of notable system failures that show how a company business can be affected during key events like a Cyber Monday. However, many difficulties come up when a team intends to consciously manage these type of quality attributes during development and operations. It is possible to group these difficulties in two main aspects: human aspects and technical aspects. During this presentation, I will share main technical difficulties we had to deal with in the last seven years working with different cloud services as well as key technical performance, scalability, and availability issues we were able to find and solve. It is about cases that are relevant through different products, technologies, and teams.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2y2yPiS.
Colin McCabe talks about the ongoing effort to replace the use of Zookeeper in Kafka: why they want to do it and how it will work. He discusses the limitations they have found and how Kafka benefits both in terms of stability and scalability by bringing consensus in house. He talks about their progress, what work is remaining, and how contributors can help. Filmed at qconsf.com.
Colin McCabe is a Kafka committer at Confluent, working on the scalability and extensibility of Kafka. Previously, he worked on the Hadoop Distributed Filesystem and the Ceph Filesystem.
(ATS4-PLAT03) Balancing Security with access for DevelopmentBIOVIA
Administrators of Pipeline Pilot servers wish to have a controlled environment to ensure that ownership and access is properly identified and enforced. Protocol developers desire the ability to quickly easily publish protocols and updates for production use. End-users need deployed applications to be tested and maintained. It is important to establish policies that ensure that these often-conflicting needs are met in a balanced way appropriate for your environment. In this session we will discuss the commonly reported pain points and outline the types of policies and procedures that that can help bring harmony. Be prepared to discuss your own experiences!
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
Discover how to avoid common pitfalls when shifting to an event-driven architecture (EDA) in order to boost system recovery and scalability. We cover Kafka Schema Registry, in-broker transformations, event sourcing, and more.
AWS re:Invent 2016: Amazon CloudFront Flash Talks: Best Practices on Configur...Amazon Web Services
In this series of 15-minute technical flash talks you will learn directly from Amazon CloudFront engineers and their best practices on debugging caching issues, measuring performance using Real User Monitoring (RUM), and stopping malicious viewers using CloudFront and AWS WAF.
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...HostedbyConfluent
If your data platform is powered only by batch data processing, you know you are always trailing your customer. Your databases aren’t always up to date. Your inability to have a synchronized data flow across systems leads to operational inefficiencies. And, your dreams of running advanced real-time AI and ML applications can’t be fulfilled. However, you might be wary of the implications of turning your product into an event-driven one. In this presentation we’ll share our experience transforming our CDP-based marketing orchestration engine to be both real-time and highly scalable with the Kafka ecosystem. We will look into how we saved resources with Connect when ingesting and syncing data with NoSQL databases, data warehouses and third-party platforms. What we did to turn ksqlDB into our data transformation, aggregation and querying hub, reducing latency and costs. How Streams helps us activate multiple real-time applications such as building identity graphs, updating materialized views in high frequency for efficient real-time lookups and inferencing machine learning models. Finally, we will look at how Confluent Cloud solved our pre-rollout sizing and scaling questions, significantly reducing time-to-market.
Making your PostgreSQL Database Highly AvailableEDB
High Availability is one of the most important requirements for mission-critical database systems. It is important for business continuity.
Enterprises cannot afford an outage of mission-critical applications, as mere minutes of downtime can cost millions of dollars in lost revenue.
Therefore making a database environment highly available is typically one of the highest priorities and poses significant challenges/questions to enterprises and database administrators.
What you will learn at this webinar:
- Database high availability basics in PostgreSQL
- How to design your environment for high availability
- High availability options available for PostgreSQL
- What EDB can offer to help enterprises meet their high availability requirements
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with A...confluent
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® is providing developers a critically important component as they build and modernize applications to cloud-native architecture.
This talk will explore:
• Why cloud-native platforms and why run Apache Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Demo: Running Apache Kafka as a Streaming Platform on Kubernetes
Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LMEconfluent
Confluent Platform is supporting London Metal Exchange’s Kafka Centre of Excellence across a number of projects with the main objective to provide a reliable, resilient, scalable and overall efficient Kafka as a Service model to the teams across the entire London Metal Exchange estate.
Uber has one of the largest Kafka deployment in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a "logical cluster". The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation. In particular, Client control. Inside Uber there are a large of applications and clients on Kafka, and it's challenging to migrate a topic with live consumers between clusters. Coordinations with the users are usually needed to shift their traffic to the migrated cluster. Cluster federation enables much control of the clients from the server side by enabling consumer traffic redirection to another physical cluster without restarting the application. Scalability: With federation, the Kafka service can horizontally scale by adding more clusters when a cluster is full. The topics can freely migrate to a new cluster without notifying the users or restarting the clients. Moreover, no matter how many physical clusters we manage per topic type, from the user perspective, they view only one logical cluster. Availability: With a topic replicated to at least two clusters we can tolerate a single cluster failure by redirecting the clients to the secondary cluster without performing a region-failover. This also provides much freedom and alleviates the risks for us to carry out important maintenance on a critical cluster. Before the maintenance, we mark the cluster as a secondary and migrate off the live traffic and consumers. We will present the details of the architecture and several interesting technical challenges we overcame.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Beyond REST and RPC: Asynchronous Eventing and Messaging PatternsClemens Vasters
In this session you will learn about when and why to use asynchronous communication with and between services, what kind of eventing/messaging infrastructure you can use in the cloud and on the edge, and how to make it all work together.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
(ATS4-PLAT03) Balancing Security with access for DevelopmentBIOVIA
Administrators of Pipeline Pilot servers wish to have a controlled environment to ensure that ownership and access is properly identified and enforced. Protocol developers desire the ability to quickly easily publish protocols and updates for production use. End-users need deployed applications to be tested and maintained. It is important to establish policies that ensure that these often-conflicting needs are met in a balanced way appropriate for your environment. In this session we will discuss the commonly reported pain points and outline the types of policies and procedures that that can help bring harmony. Be prepared to discuss your own experiences!
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...ScyllaDB
Discover how to avoid common pitfalls when shifting to an event-driven architecture (EDA) in order to boost system recovery and scalability. We cover Kafka Schema Registry, in-broker transformations, event sourcing, and more.
AWS re:Invent 2016: Amazon CloudFront Flash Talks: Best Practices on Configur...Amazon Web Services
In this series of 15-minute technical flash talks you will learn directly from Amazon CloudFront engineers and their best practices on debugging caching issues, measuring performance using Real User Monitoring (RUM), and stopping malicious viewers using CloudFront and AWS WAF.
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...HostedbyConfluent
If your data platform is powered only by batch data processing, you know you are always trailing your customer. Your databases aren’t always up to date. Your inability to have a synchronized data flow across systems leads to operational inefficiencies. And, your dreams of running advanced real-time AI and ML applications can’t be fulfilled. However, you might be wary of the implications of turning your product into an event-driven one. In this presentation we’ll share our experience transforming our CDP-based marketing orchestration engine to be both real-time and highly scalable with the Kafka ecosystem. We will look into how we saved resources with Connect when ingesting and syncing data with NoSQL databases, data warehouses and third-party platforms. What we did to turn ksqlDB into our data transformation, aggregation and querying hub, reducing latency and costs. How Streams helps us activate multiple real-time applications such as building identity graphs, updating materialized views in high frequency for efficient real-time lookups and inferencing machine learning models. Finally, we will look at how Confluent Cloud solved our pre-rollout sizing and scaling questions, significantly reducing time-to-market.
Making your PostgreSQL Database Highly AvailableEDB
High Availability is one of the most important requirements for mission-critical database systems. It is important for business continuity.
Enterprises cannot afford an outage of mission-critical applications, as mere minutes of downtime can cost millions of dollars in lost revenue.
Therefore making a database environment highly available is typically one of the highest priorities and poses significant challenges/questions to enterprises and database administrators.
What you will learn at this webinar:
- Database high availability basics in PostgreSQL
- How to design your environment for high availability
- High availability options available for PostgreSQL
- What EDB can offer to help enterprises meet their high availability requirements
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with A...confluent
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® is providing developers a critically important component as they build and modernize applications to cloud-native architecture.
This talk will explore:
• Why cloud-native platforms and why run Apache Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Demo: Running Apache Kafka as a Streaming Platform on Kubernetes
Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LMEconfluent
Confluent Platform is supporting London Metal Exchange’s Kafka Centre of Excellence across a number of projects with the main objective to provide a reliable, resilient, scalable and overall efficient Kafka as a Service model to the teams across the entire London Metal Exchange estate.
Uber has one of the largest Kafka deployment in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a "logical cluster". The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation. In particular, Client control. Inside Uber there are a large of applications and clients on Kafka, and it's challenging to migrate a topic with live consumers between clusters. Coordinations with the users are usually needed to shift their traffic to the migrated cluster. Cluster federation enables much control of the clients from the server side by enabling consumer traffic redirection to another physical cluster without restarting the application. Scalability: With federation, the Kafka service can horizontally scale by adding more clusters when a cluster is full. The topics can freely migrate to a new cluster without notifying the users or restarting the clients. Moreover, no matter how many physical clusters we manage per topic type, from the user perspective, they view only one logical cluster. Availability: With a topic replicated to at least two clusters we can tolerate a single cluster failure by redirecting the clients to the secondary cluster without performing a region-failover. This also provides much freedom and alleviates the risks for us to carry out important maintenance on a critical cluster. Before the maintenance, we mark the cluster as a secondary and migrate off the live traffic and consumers. We will present the details of the architecture and several interesting technical challenges we overcame.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Beyond REST and RPC: Asynchronous Eventing and Messaging PatternsClemens Vasters
In this session you will learn about when and why to use asynchronous communication with and between services, what kind of eventing/messaging infrastructure you can use in the cloud and on the edge, and how to make it all work together.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
3. Understand and tune
• Producers
• Consumers
• Brokers
Producer tuning is key
• Efficient batching is essential
for overall performance
Focus on fundamentals
• Large impact & gains
• Advanced topics e.g. in
• Tail Latency at Scale with
Apache Kafka
Where to begin?
3
4. Service goals and
tradeoffs
4
Non-performance objectives
• Business requirements take
priority
• Durability, availability and
ordering?
Performance objectives
• Trade off between throughput
and latency
Example approach
• Set configuration to ensure data
durability
• Optimize for throughput
Throughput Latency
Availability
Durability
payments
logging
Next Best
Offer
Centralized
Kafka
5. Agenda
5
01. Introduction
Setting the scene & review of relevant terminology
02. Producers
Deep dive into producer internals.
Why is producer behavior key for cluster performance?
03. Consumers
Understand fetching and consumer group behavior.
04. Brokers, Zookeepers and Topics
How are requests handled? Why does Zookeeper matter?
05. Optimising and Tuning Client Applications
Key parameters to consider for different service goals.
06. Summary
Summary and outlook.
6. Identify your
service goal
Throughput, latency,
durability, or availability
Understand
Kafka
internals
Producer, Consumer
and Broker behavior
Configure
cluster and
clients
Ensure service goals are
met
Benchmark,
monitor, and
tune
Iterative procedure to
drive performance
It is a journey...
8. Producer
8
acks=1
enable.idempotence=false
max.request.size=1MB
retries=MAX_INT
delivery.timeout.ms=2min
max.in.flight.requests.
per.connection=5
Serializer
● Retrieves and
caches schemas
from Schema
Registry
Partitioner
● Java client uses
murmur2 for
hashing
● If key not
provided
performs round
robin
● If keys
unbalanced it will
overload one
leader
Sender thread
● Batches grouped
by destination
broker into
requests
● Multiple batches
to different
partitions
potentially in the
same producer
request
Record accumulator
● Buffer per partition,
seldom used partitions
may not achieve high
batching
● If many producers are in
the same JVM, memory
and GC could become
important
● Sticky partitioner could
be used to increase
batches in the case of
round robin
(KIP-408/KIP-794)
Compression
● At batch level
● Allows faster transfer to
the broker
● Reduces the inter
broker replication load
● Reduces page cache &
disk space utilization on
brokers
● Gzip is more CPU
intensive, Snappy is
lighter, LZ4/ZStd are a
good balance*
compress.type=none
batch.size=16KB
buffer.memory=32MB
max.block.ms=60s
record batch request
batch.size=16KB
linger.ms=0
buffer.memory=32MB
max.block.ms=60s
compress.type=none
9. Batching is key
to overall performance
9
Benefits to batching
● Reduced network bandwidth
○ producer to broker
○ broker to broker (replication)
○ broker to consumer
● Less storage requirements on broker disks
● Reduced CPU requirement due to fewer
requests
From Tail Latency at Scale with Apache Kafka
“Batching reduces the cost of each record by
amortizing costs on both the clients and
brokers.
Generally, bigger batches reduce processing
overhead and reduce network and disk IO, which
improves network and disk utilization.”
10. Start the demo
environment
10
in docker-compose (on my mac)
1 * zookeeper
5 * brokers
1 * Squid proxy (sends JMX metrics to Health+)
Not starting:
schema registry
connect
ksqlDB
REST Proxy
Confluent Control Center
11. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 11
12. Kafka performance
test tools
12
kafka-producer-perf-test
--num-records 1000000
--record-size 1000
--topic demo-perf-topic
--throughput 10000
--print-metrics
--producer-props bootstrap.servers=kafka:9092
acks=all batch.size=300000 linger.ms=100
compression.type=lz4
Overview
● CLI tools to write & read sample data
to/from topics
● Helpful to enhance understanding of
parameters & impact
Disclaimer
● Performance numbers are not
representative for specific customer use
cases!
○ Random test data is reused
● Use case specific performance testing is
required
kafka-consumer-perf-test
kafka-producer-perf-test
13. Most significant producer performance metrics
Metric Meaning MBean
record-size-avg Avg record size kafka.producer:type=producer-metrics,client-id=([-.w]+)
batch-size-avg
Avg number of bytes sent per partition
per-request
kafka.producer:type=producer-metrics,client-id=([-.w]+)
bufferpool-wait-ratio
Faction of time an appender waits for
space allocation
kafka.producer:type=producer-metrics,client-id=([-.w]+)
compression-rate-avg
Avg compression rate for a topic.
Compressed / uncompressed batch size
kafka.producer:type=producer-topic-metrics,client-id=([-.w]+),to
pic=([-.w]+)
record-queue-time-avg
Avg time (ms) record batches spent in
the send buffer
kafka.producer:type=producer-metrics,client-id=([-.w]+)
request-latency-avg Avg request latency (ms) kafka.producer:type=producer-metrics,client-id=([-.w]+)
produce-throttle-time-avg
Avg time (ms) a request was throttled
by a broker
kafka.producer:type=producer-metrics,client-id=([-.w]+)
record-retry-rate
Avg per-second number of retried record
sends for a topic
kafka.producer:type=producer-topic-metrics,client-id=([-.w]+),to
pic=([-.w]+)
Overview Java metrics & librdkafka statistics
16. Consumers
Partitions
● Basis for scalability
● No partition will be assigned to more than one consumer in the same group
Key parameters
# of partitions
fetch.min.bytes=1
fetch.max.wait.ms=500ms
max.partition.fetch.bytes=10MB
fetch.max.bytes=50MB
max.poll.records=500
max.poll.interval.ms=5min
auto.commit.interval.ms=5s (if being used)
17. Key positions in each
partition
17
Log end offset
• Latest data added to the partition
• Position of the producer
• Not accessible to consumers
High watermark
• Offsets up to the watermark can be
consumed
• Data has been replicated to all insync
replicas
Current position
• Specific to consumer instances
• Current message being processed in
poll-loop
Last committed offset
• Last position persisted in the
__consumer_offsets topic
0 1 2 3 4 5 6 7 8 9 10 11 12
Last
committed
offset
Current
position of
consumer
High
watermark
Log end
offset
18. Consumer groups
Consumer
Any Broker
(bootstrap)
Coordinator
Broker
Find coordinator
Coordinator details
Join consum
er group
Leader details
Sync group
Partition assignm
ent
Rebalances
● Every time a new consumer joins or
leaves (fails) the group
● Until Kafka 2.4 “stop the world” event
(solved in KIP-429)
● Consider setting group.instance.id
to minimize rebalances (KIP-345)
Partition assignment
● Based on
partition.assignment.strategy
● Options: Range (default), round robin,
sticky, cooperative sticky
● Is customizable
Heartbeat
heartbeat.interval.ms=3s
session.timeout.ms=10s
group.initial.
rebalance.delay.ms=3s
19. Selected consumer performance metrics
Metric Meaning MBean
fetch-latency-avg Avg time taken for a fetch request kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-
.w]+)
fetch-size-avg Avg number of bytes fetched per request kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-
.w]+)
commit-latency-avg Avg time commit request kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.w
]+)
rebalance-latency-total Total time taken for group rebalances kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.
w]+)
fetch-throttle-time-avg Avg throttle time (ms) kafka.consumer:type=consumer-fetch-manager-metrics,client-id=([-
.w]+)
Overview Java metrics and librdkafka statistics
20. Consumer
Benchmarking
20
(1) Start with most simple test: Without any
tuning, we get extremely good results
Highlights:
● 10M messages in less than 30 seconds
● 1Gb data retrieved
● 325 Mb/s
Conclusion:
● Tuning producer is key, if it is correctly
tuned, there (can be) almost no tuning
required on consumer side
21. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 21
22. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 22
24. Overview
Brokers and Zookeeper
24
Request lifecycle in broker
● How are produce & fetch requests
handled?
● How can inefficient batching impact
performance?
● How to identify where time is spent during
request handling?
Controller, leaders, and Zookeeper
● How is the Controller elected?
● How are broker failures detected?
● Why does the partition count matter for
the recovery time after a controller failure?
(Next 8 slides skipped)
25. 04. Optimizing and Tuning
Client Applications
https://docs.confluent.io/cloud/current/client-apps/optimizing/index.html#optimizing-and-tuning
27. Recommendations
27
Benchmarking
● Benchmark all applications with a significant & representative load
● Consider a test cluster with
the applications requirements configured (either it is durability, availability or any other)
real data (size, schema, serialization format, ...)
● Test the different parameters to see the impact in the test data (throughput, latency, ...) considering
different configurations (batch size, compression, linger, ...)
● Evaluate the traffic and leave space for growth when determining the number of partitions
● Low volume applications may need care too
● Re-evaluate after major changes in application or message content (JSON size, ...) and volume
Monitoring
● Should be used to identify bottlenecks in running clusters
● Client monitoring is as important as broker monitoring
28. Conclusion
28
Resources
● Optimizing Your Apache Kafka®
Deployment
● Optimizing and Tuning
● White paper
Optimization approach
● Determine service goals
● Understand Kafka’s internals
● Configure clients & cluster
● Benchmark, monitor & tune
Continue the conversation
● How to monitor the cluster & clients?
● Integration with external systems
● Tuning of Kafka Streams & ksqlDB
applications?