How Confluent Completes the Event Streaming Platform (Addison Huddy & Dan Ros...HostedbyConfluent
Apache Kafka fundamentally changes how organizations build and deploy a universal data pipeline that is scalable, reliable, and durable enough to meet the needs of digital-first organizations. However, as powerful as Kafka is today, it’s not an event-streaming platform - and getting it there on your own is a long, complicated, and expensive process. Earlier this year Confluent announced Project Metamorphosis - our plan to bring the best characteristics of cloud native systems to Apache Kafka. Since May we’ve begun transforming Confluent Cloud and Confluent Platform to do just that.
Join two of our Product Managers, Dan Rosanova and Addison Huddy to: Learn how we’ve evolved Confluent Cloud with the first phase of Project Metamorphosis releases
See how Confluent Platform 6.0 brings these transformational, cloud-like qualities to self-managed Kafka
Get a sneak peak of our next Metamorphosis theme and how it impacts your Kafka and event-streaming strategy.
Give Your Confluent Platform Superpowers! (Sandeep Togrika, Intel and Bert Ha...HostedbyConfluent
Whether you are a die-hard DC comic enthusiast, mad for Marvel, or completely clueless when it comes to comic books, at the end of the day each of us would love to possess the superpower to transform data in seconds versus minutes or days. But architects and developers are challenged with designing and managing platforms that scale elastically and combine event streams with stored data, to enable more contextually rich data analytics. This made even more complex with data coming from hundreds of sources, and in hundreds of terabytes, or even petabytes, per day.
Now, with Apache Kafka and Intel hardware technology advances, organizations can turn massive volumes of disparate data into actionable insights with the ability to filter, enrich, join and process data instream. Let's consider Information Security. IT leaders need to ensure all company data and IP is secured against threats and vulnerabilities. A combination of real-time event streaming with Confluent Platform and Intel Architecture has enabled threat detection efforts that once took hours to be completed in seconds, while simultaneously reducing technical debt and data processing and storage costs.
In this session, Confluent and Intel architects will share detailed performance benchmarking results and new joint reference architecture. We’ll detail ways to remove Kafka performance bottlenecks, and improve platform resiliency and ensure high availability using Confluent Control Center and Multi-Region Clusters. And we’ll offer up tips for addressing challenges that you may be facing in your own super heroic efforts to design, deploy, and manage your organization’s data platforms.
Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, QlikHostedbyConfluent
Qlik is an industry leader across its solution stack, both on the Data Integration side of things with Qlik Replicate (real-time CDC) and Qlik Compose (data warehouse and data lake automation), and on the Analytics side with Qlik Sense. These two “sides” of Qlik are coming together more frequently these days as the need for “always fresh” data increases across organizations.
When real-time streaming applications are the topic du jour, those companies are looking to Apache Kafka to provide the architectural backbone those applications require. Those same companies turn to Qlik Replicate to put the data from their enterprise database systems into motion at scale, whether that data resides in “legacy” mainframe databases; traditional relational databases such as Oracle, MySQL, or SQL Server; or applications such as SAP and SalesForce.
In this session we will look in depth at how Qlik Replicate can be used to continuously stream changes from a source database into Apache Kafka. From there, we will explore how a purpose-built consumer can be used to provide the bridge between Apache Kafka and an analytics application such as Qlik Sense.
Better Kafka Performance Without Changing Any Code | Simon Ritter, AzulHostedbyConfluent
Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. For mission-critical applications, how do you ensure that the performance delivered is the performance required? This is especially important as Kafka is written in Java and Scala and runs on the JVM. The JVM is a fantastic platform that delivers on an internet scale.
In this session, we'll explore how making changes to the JVM design can eliminate the problems of garbage collection pauses and raise the throughput of applications. For cloud-based Kafka applications, this can deliver both lower latency and reduced infrastructure costs. All without changing a line of code!
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...HostedbyConfluent
Cloud is changing the world; Kubernetes is changing the world; real-time event streaming is changing the world. In this talk we explore some of best practices to synergistically combine the power of these paradigm shifts to achieve a much greater return on your Kafka investments. From declarative deployments, zero-downtime upgrades, elastic scaling to self-healing and automated governance, learn how you can bring the next level of speed, agility, resilience, and security to your Kafka implementations.
Navigating the obdervability storm with Kafka | Jose Manuel Cristobal, AdidasHostedbyConfluent
When all your stores are closed, e-commerce becomes your bigger store… and the most challenging. That means a myriad of systems orchestrated to make it happen, all of them scaling out accordingly and implementing Observability and SRE practices to support this growth, preserving stability and reliability.
How can we detect problems, root causes and react? How can we predict those problems?
HOLMES is the adidas solution to accelerate problem detection, giving a holistic view of technical systems through metrics and logs democratisation.
In this talk, we'll show how Kafka technology stack allows adidas to support the ingestion and offload of all logs and metrics of the company. A platform which adoption has skyrocketed during 2020, supporting 100 Billion messages per day.
The main takeaway will be the explanation of a cutting-edge solution based on kafka technology stack (kafka, Kafka Streams and Kafka Connect) for demanding throughput ecosystem.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...HostedbyConfluent
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
How Confluent Completes the Event Streaming Platform (Addison Huddy & Dan Ros...HostedbyConfluent
Apache Kafka fundamentally changes how organizations build and deploy a universal data pipeline that is scalable, reliable, and durable enough to meet the needs of digital-first organizations. However, as powerful as Kafka is today, it’s not an event-streaming platform - and getting it there on your own is a long, complicated, and expensive process. Earlier this year Confluent announced Project Metamorphosis - our plan to bring the best characteristics of cloud native systems to Apache Kafka. Since May we’ve begun transforming Confluent Cloud and Confluent Platform to do just that.
Join two of our Product Managers, Dan Rosanova and Addison Huddy to: Learn how we’ve evolved Confluent Cloud with the first phase of Project Metamorphosis releases
See how Confluent Platform 6.0 brings these transformational, cloud-like qualities to self-managed Kafka
Get a sneak peak of our next Metamorphosis theme and how it impacts your Kafka and event-streaming strategy.
Give Your Confluent Platform Superpowers! (Sandeep Togrika, Intel and Bert Ha...HostedbyConfluent
Whether you are a die-hard DC comic enthusiast, mad for Marvel, or completely clueless when it comes to comic books, at the end of the day each of us would love to possess the superpower to transform data in seconds versus minutes or days. But architects and developers are challenged with designing and managing platforms that scale elastically and combine event streams with stored data, to enable more contextually rich data analytics. This made even more complex with data coming from hundreds of sources, and in hundreds of terabytes, or even petabytes, per day.
Now, with Apache Kafka and Intel hardware technology advances, organizations can turn massive volumes of disparate data into actionable insights with the ability to filter, enrich, join and process data instream. Let's consider Information Security. IT leaders need to ensure all company data and IP is secured against threats and vulnerabilities. A combination of real-time event streaming with Confluent Platform and Intel Architecture has enabled threat detection efforts that once took hours to be completed in seconds, while simultaneously reducing technical debt and data processing and storage costs.
In this session, Confluent and Intel architects will share detailed performance benchmarking results and new joint reference architecture. We’ll detail ways to remove Kafka performance bottlenecks, and improve platform resiliency and ensure high availability using Confluent Control Center and Multi-Region Clusters. And we’ll offer up tips for addressing challenges that you may be facing in your own super heroic efforts to design, deploy, and manage your organization’s data platforms.
Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, QlikHostedbyConfluent
Qlik is an industry leader across its solution stack, both on the Data Integration side of things with Qlik Replicate (real-time CDC) and Qlik Compose (data warehouse and data lake automation), and on the Analytics side with Qlik Sense. These two “sides” of Qlik are coming together more frequently these days as the need for “always fresh” data increases across organizations.
When real-time streaming applications are the topic du jour, those companies are looking to Apache Kafka to provide the architectural backbone those applications require. Those same companies turn to Qlik Replicate to put the data from their enterprise database systems into motion at scale, whether that data resides in “legacy” mainframe databases; traditional relational databases such as Oracle, MySQL, or SQL Server; or applications such as SAP and SalesForce.
In this session we will look in depth at how Qlik Replicate can be used to continuously stream changes from a source database into Apache Kafka. From there, we will explore how a purpose-built consumer can be used to provide the bridge between Apache Kafka and an analytics application such as Qlik Sense.
Better Kafka Performance Without Changing Any Code | Simon Ritter, AzulHostedbyConfluent
Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. For mission-critical applications, how do you ensure that the performance delivered is the performance required? This is especially important as Kafka is written in Java and Scala and runs on the JVM. The JVM is a fantastic platform that delivers on an internet scale.
In this session, we'll explore how making changes to the JVM design can eliminate the problems of garbage collection pauses and raise the throughput of applications. For cloud-based Kafka applications, this can deliver both lower latency and reduced infrastructure costs. All without changing a line of code!
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...HostedbyConfluent
Cloud is changing the world; Kubernetes is changing the world; real-time event streaming is changing the world. In this talk we explore some of best practices to synergistically combine the power of these paradigm shifts to achieve a much greater return on your Kafka investments. From declarative deployments, zero-downtime upgrades, elastic scaling to self-healing and automated governance, learn how you can bring the next level of speed, agility, resilience, and security to your Kafka implementations.
Navigating the obdervability storm with Kafka | Jose Manuel Cristobal, AdidasHostedbyConfluent
When all your stores are closed, e-commerce becomes your bigger store… and the most challenging. That means a myriad of systems orchestrated to make it happen, all of them scaling out accordingly and implementing Observability and SRE practices to support this growth, preserving stability and reliability.
How can we detect problems, root causes and react? How can we predict those problems?
HOLMES is the adidas solution to accelerate problem detection, giving a holistic view of technical systems through metrics and logs democratisation.
In this talk, we'll show how Kafka technology stack allows adidas to support the ingestion and offload of all logs and metrics of the company. A platform which adoption has skyrocketed during 2020, supporting 100 Billion messages per day.
The main takeaway will be the explanation of a cutting-edge solution based on kafka technology stack (kafka, Kafka Streams and Kafka Connect) for demanding throughput ecosystem.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...HostedbyConfluent
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
Safer Commutes & Streaming Data | George Padavick, Ohio Department of Transpo...HostedbyConfluent
The Ohio Department of Transportation has adopted Confluent as the event driven enabler of DriveOhio, a modern Intelligent Transportation System. DriveOhio digitally links sensors, cameras, speed monitoring equipment, and smart highway assets in real time, to dynamically adjust the surface road network to maximize the safety and efficiency for travelers. Over the past 24 months the team has increased the number and types of devices within the DriveOhio environment, while also working to see their vendors adopt Kafka to better participate in data sharing.
Supercharge Your Real-time Event Processing with Neo4j's Streams Kafka Connec...HostedbyConfluent
Do your event streams use connected-data domains such as fraud detection, live logistics routing, or predicting network outages? How can you maintain the analysis and leverage those connections real-time?
Graph databases differ from traditional, tabular ones in that they treat connections between data as first class citizens. This means they are optimized for detecting and understanding these relationships – providing insight at speed and at scale.
By combining event streams from Kafka along with the power of the Neo4j graph database for interrogating and investigating connections, you make real-time, event-driven intelligent insight a reality.
Neo4j Streams integrates Neo4j with Apache Kafka event streams, to serve as a source of data, for instance Change Data Capture or a sink to ingest any kind of Kafka event into your graph. In this session we’ll show you how to get up and running with Neo4j Streams to show you how to sink and source between graphs and streams.
Let's begin a very unusual Kafka Summit by reflecting about change. Changes we've seen in the software engineering world and changes we've seen in Kafka. We'll also talk about things that don't change - like great software design and architecture. We'll dive deep into two huge changes that are happening in the Kafka community right now - and the possibilities they open for the future.
Beyond the Brokers | Emma Humber and Andrew Borley, IBMHostedbyConfluent
While Kafka has guarantees around the number of server failures a cluster can tolerate, to avoid service interruptions, or even data loss, it is prudent to have infrastructure in place for when an environment becomes unavailable during a planned or unplanned outage.
This talk describes the architectures available to you when planning for an outage. We will examine configurations including active/passive and active/active as well as availability zones and debate the benefits and limitations of each. We will also cover how to set up each configuration using the tools in Kafka.
Whether downtime while you fail over clients to a backup is acceptable or you require your Kafka clusters to be highly available, this talk will give you an understanding of the options available to mitigate the impact of the loss of an environment.
Technical breakout during Confluent’s streaming event in Munich, presented by Sam Julian, Chief Cloud Engineer at E.On SE. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Enhancing Apache Kafka for Large Scale Real-Time Data Pipeline at Tencent | K...HostedbyConfluent
In this session we share our experience of building a real-time data pipelines at Tencent PCG - one that handles 20 trillion daily messages with 700 clusters and 100Gb/s bursting traffic from a single app. We discuss our roadmap of enhancing Kafka to break its limits in terms of scalability, robustness and cost of operation.
We first built a proxy layer that aggregates physical clusters in a way agnostic to the clients. While this architecture solves many operational problems, it requires significant development to stay future-proof. With retrospection with our customer and careful study of the ongoing work from the community, we then designed a region federation solution in the broker layer, which allows us to deploy clusters at a much larger scale than previously possible, while at the same time providing better failure recovery and operability. We discuss how we make this development compatible with KIP-500 and KIP-405, and the two KIP (693, 694) that we submitted for discussion.
Twitter’s Apache Kafka Adoption Journey | Ming Liu, TwitterHostedbyConfluent
Until recently, the Messaging team at Twitter had been running an in-house build Pub/Sub system, namely EventBus (built on top of Apache DistributedLog and Apache Bookkeeper, and similar in architecture to Apache Pulsar) to cater to our pubsub needs. In 2018, we made the decision to move to Apache Kafka by migrating existing use cases as well as onboarding new use cases directly onto Apache Kafka. Fast forward to today, Kafka is now an essential piece of Twitter Infrastructure and processes over 200M messages per second. In this talk, we will share the learning and challenges in our journey moving to Apache Kafka.
One Click Streaming Data Pipelines & Flows | Leveraging Kafka & Spark | Ido F...HostedbyConfluent
The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Taming a massive fleet of Python-based Kafka apps at Robinhood | Chandra Kuch...HostedbyConfluent
Robinhood uses Kafka in every line of its business, from stock and crypto trading to clearing and data analytics. One interesting aspect of our architecture is that many of our microservices leveraging Kafka are written in Python. When you combine Python's relatively slow performance coupled, its reliance on process-based parallelism and Robinhood’s scale, the result is a massive fleet of application processes producing to and consuming from our Kafka clusters. This fleet generates an atypical workload on Kafka that warrants a deeper investment in scalability and reliability.
This talk discusses our investments in Kafka infrastructure for a large-scale Python-based environment:
kafkahood: our librdkafka-based client library wrapper that codifies best practices, sane defaults and deep client-side observability.
kafkaproxy: a Rust-based sidecar proxy that reduces connection fan-in from Python gunicorn worker pools to our Kafka clusters.
We'll also present challenges we encountered along the way and share our learnings with the audience.
Distributed Enterprise Monitoring and Management of Apache Kafka (William McL...HostedbyConfluent
Managing a distributed system like Apache Kafka can be extremely challenging, especially when you try to approach monitoring and managing from a single centralized GUI approach. In this talk come here and see a demo of a more decoupled approach to Kafka management and Kafka Monitoring where data is centralized but access is is distributed to scale to enterprise deployments, CICD pipelines and much much more.
Deploying Kafka Streams Applications with Docker and Kubernetesconfluent
(Gwen Shapira + Matthias J. Sax, Confluent) Kafka Summit SF 2018
Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Kafka Streams is not only scalable, but fully elastic allowing for dynamic scale-in and scale-out as the library handles state migration transparently in the background. By running Kafka Streams applications on Kubernetes, you will be able to use Kubernetes powerful control plane to standardize and simplify the application management—from deployment to dynamic scaling.
In this technical deep dive, we’ll explain the internals of dynamic scaling and state migration in Kafka Streams. We’ll then show, with a live demo, how a Kafka Streams application can run in a Docker container on Kubernetes and the dynamic scaling of an application running in Kubernetes.
Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...HostedbyConfluent
One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions.
Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions.
In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.
In the last two years, Netflix has seen a mass migration to Spark from Pig and other MR engines. This talk will focus on the challenges of that migration and the work that has made it possible. This will include contributions that Netflix has made to Spark to enable wider adoption and on-going projects to make Spark appeal to a broader range of analysts, beyond data and ML engineers.
Speaker Ryan Blue
dA Platform is a production-ready platform for stream processing with Apache Flink®. The Platform includes open source Apache Flink, a stateful stream processing and event-driven application framework, and dA Application Manager, a central deployment and management component. dA Platform schedules clusters on Kubernetes, deploys stateful Flink applications, and controls these applications and their state.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
The Good, the Bad and the Ugly of Migrating Hundreds of Legacy Applications ...Josef Adersberger
Running applications on Kubernetes can provide a lot of benefits: more dev speed, lower ops costs, and a higher elasticity & resiliency in production. Kubernetes is the place to be for cloud native apps. But what to do if you’ve no shiny new cloud native apps but a whole bunch of JEE legacy systems? No chance to leverage the advantages of Kubernetes? Yes you can!
We’re facing the challenge of migrating hundreds of JEE legacy applications of a major German insurance company onto a Kubernetes cluster within one year. We're now close to the finish line and it worked pretty well so far.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way. We'll provide our answers to life, the universe and a cloud native journey like:
- What technical constraints of Kubernetes can be obstacles for applications and how to tackle these?
- How to architect a landscape of hundreds of containerized applications with their surrounding infrastructure like DBs MQs and IAM and heavy requirements on security?
- How to industrialize and govern the migration process?
- How to leverage the possibilities of a cloud native platform like Kubernetes without challenging the tight timeline?
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
Safer Commutes & Streaming Data | George Padavick, Ohio Department of Transpo...HostedbyConfluent
The Ohio Department of Transportation has adopted Confluent as the event driven enabler of DriveOhio, a modern Intelligent Transportation System. DriveOhio digitally links sensors, cameras, speed monitoring equipment, and smart highway assets in real time, to dynamically adjust the surface road network to maximize the safety and efficiency for travelers. Over the past 24 months the team has increased the number and types of devices within the DriveOhio environment, while also working to see their vendors adopt Kafka to better participate in data sharing.
Supercharge Your Real-time Event Processing with Neo4j's Streams Kafka Connec...HostedbyConfluent
Do your event streams use connected-data domains such as fraud detection, live logistics routing, or predicting network outages? How can you maintain the analysis and leverage those connections real-time?
Graph databases differ from traditional, tabular ones in that they treat connections between data as first class citizens. This means they are optimized for detecting and understanding these relationships – providing insight at speed and at scale.
By combining event streams from Kafka along with the power of the Neo4j graph database for interrogating and investigating connections, you make real-time, event-driven intelligent insight a reality.
Neo4j Streams integrates Neo4j with Apache Kafka event streams, to serve as a source of data, for instance Change Data Capture or a sink to ingest any kind of Kafka event into your graph. In this session we’ll show you how to get up and running with Neo4j Streams to show you how to sink and source between graphs and streams.
Let's begin a very unusual Kafka Summit by reflecting about change. Changes we've seen in the software engineering world and changes we've seen in Kafka. We'll also talk about things that don't change - like great software design and architecture. We'll dive deep into two huge changes that are happening in the Kafka community right now - and the possibilities they open for the future.
Beyond the Brokers | Emma Humber and Andrew Borley, IBMHostedbyConfluent
While Kafka has guarantees around the number of server failures a cluster can tolerate, to avoid service interruptions, or even data loss, it is prudent to have infrastructure in place for when an environment becomes unavailable during a planned or unplanned outage.
This talk describes the architectures available to you when planning for an outage. We will examine configurations including active/passive and active/active as well as availability zones and debate the benefits and limitations of each. We will also cover how to set up each configuration using the tools in Kafka.
Whether downtime while you fail over clients to a backup is acceptable or you require your Kafka clusters to be highly available, this talk will give you an understanding of the options available to mitigate the impact of the loss of an environment.
Technical breakout during Confluent’s streaming event in Munich, presented by Sam Julian, Chief Cloud Engineer at E.On SE. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Enhancing Apache Kafka for Large Scale Real-Time Data Pipeline at Tencent | K...HostedbyConfluent
In this session we share our experience of building a real-time data pipelines at Tencent PCG - one that handles 20 trillion daily messages with 700 clusters and 100Gb/s bursting traffic from a single app. We discuss our roadmap of enhancing Kafka to break its limits in terms of scalability, robustness and cost of operation.
We first built a proxy layer that aggregates physical clusters in a way agnostic to the clients. While this architecture solves many operational problems, it requires significant development to stay future-proof. With retrospection with our customer and careful study of the ongoing work from the community, we then designed a region federation solution in the broker layer, which allows us to deploy clusters at a much larger scale than previously possible, while at the same time providing better failure recovery and operability. We discuss how we make this development compatible with KIP-500 and KIP-405, and the two KIP (693, 694) that we submitted for discussion.
Twitter’s Apache Kafka Adoption Journey | Ming Liu, TwitterHostedbyConfluent
Until recently, the Messaging team at Twitter had been running an in-house build Pub/Sub system, namely EventBus (built on top of Apache DistributedLog and Apache Bookkeeper, and similar in architecture to Apache Pulsar) to cater to our pubsub needs. In 2018, we made the decision to move to Apache Kafka by migrating existing use cases as well as onboarding new use cases directly onto Apache Kafka. Fast forward to today, Kafka is now an essential piece of Twitter Infrastructure and processes over 200M messages per second. In this talk, we will share the learning and challenges in our journey moving to Apache Kafka.
One Click Streaming Data Pipelines & Flows | Leveraging Kafka & Spark | Ido F...HostedbyConfluent
The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Taming a massive fleet of Python-based Kafka apps at Robinhood | Chandra Kuch...HostedbyConfluent
Robinhood uses Kafka in every line of its business, from stock and crypto trading to clearing and data analytics. One interesting aspect of our architecture is that many of our microservices leveraging Kafka are written in Python. When you combine Python's relatively slow performance coupled, its reliance on process-based parallelism and Robinhood’s scale, the result is a massive fleet of application processes producing to and consuming from our Kafka clusters. This fleet generates an atypical workload on Kafka that warrants a deeper investment in scalability and reliability.
This talk discusses our investments in Kafka infrastructure for a large-scale Python-based environment:
kafkahood: our librdkafka-based client library wrapper that codifies best practices, sane defaults and deep client-side observability.
kafkaproxy: a Rust-based sidecar proxy that reduces connection fan-in from Python gunicorn worker pools to our Kafka clusters.
We'll also present challenges we encountered along the way and share our learnings with the audience.
Distributed Enterprise Monitoring and Management of Apache Kafka (William McL...HostedbyConfluent
Managing a distributed system like Apache Kafka can be extremely challenging, especially when you try to approach monitoring and managing from a single centralized GUI approach. In this talk come here and see a demo of a more decoupled approach to Kafka management and Kafka Monitoring where data is centralized but access is is distributed to scale to enterprise deployments, CICD pipelines and much much more.
Deploying Kafka Streams Applications with Docker and Kubernetesconfluent
(Gwen Shapira + Matthias J. Sax, Confluent) Kafka Summit SF 2018
Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Kafka Streams is not only scalable, but fully elastic allowing for dynamic scale-in and scale-out as the library handles state migration transparently in the background. By running Kafka Streams applications on Kubernetes, you will be able to use Kubernetes powerful control plane to standardize and simplify the application management—from deployment to dynamic scaling.
In this technical deep dive, we’ll explain the internals of dynamic scaling and state migration in Kafka Streams. We’ll then show, with a live demo, how a Kafka Streams application can run in a Docker container on Kubernetes and the dynamic scaling of an application running in Kubernetes.
Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...HostedbyConfluent
One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions.
Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions.
In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.
In the last two years, Netflix has seen a mass migration to Spark from Pig and other MR engines. This talk will focus on the challenges of that migration and the work that has made it possible. This will include contributions that Netflix has made to Spark to enable wider adoption and on-going projects to make Spark appeal to a broader range of analysts, beyond data and ML engineers.
Speaker Ryan Blue
dA Platform is a production-ready platform for stream processing with Apache Flink®. The Platform includes open source Apache Flink, a stateful stream processing and event-driven application framework, and dA Application Manager, a central deployment and management component. dA Platform schedules clusters on Kubernetes, deploys stateful Flink applications, and controls these applications and their state.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
The Good, the Bad and the Ugly of Migrating Hundreds of Legacy Applications ...Josef Adersberger
Running applications on Kubernetes can provide a lot of benefits: more dev speed, lower ops costs, and a higher elasticity & resiliency in production. Kubernetes is the place to be for cloud native apps. But what to do if you’ve no shiny new cloud native apps but a whole bunch of JEE legacy systems? No chance to leverage the advantages of Kubernetes? Yes you can!
We’re facing the challenge of migrating hundreds of JEE legacy applications of a major German insurance company onto a Kubernetes cluster within one year. We're now close to the finish line and it worked pretty well so far.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way. We'll provide our answers to life, the universe and a cloud native journey like:
- What technical constraints of Kubernetes can be obstacles for applications and how to tackle these?
- How to architect a landscape of hundreds of containerized applications with their surrounding infrastructure like DBs MQs and IAM and heavy requirements on security?
- How to industrialize and govern the migration process?
- How to leverage the possibilities of a cloud native platform like Kubernetes without challenging the tight timeline?
Migrating Hundreds of Legacy Applications to Kubernetes - The Good, the Bad, ...QAware GmbH
CloudNativeCon North America 2017, Austin (Texas, USA): Talk by Josef Adersberger (@adersberger, CTO at QAware)
Abstract:
Running applications on Kubernetes can provide a lot of benefits: more dev speed, lower ops costs, and a higher elasticity & resiliency in production. Kubernetes is the place to be for cloud native apps. But what to do if you’ve no shiny new cloud native apps but a whole bunch of JEE legacy systems? No chance to leverage the advantages of Kubernetes? Yes you can!
We’re facing the challenge of migrating hundreds of JEE legacy applications of a major German insurance company onto a Kubernetes cluster within one year. We're now close to the finish line and it worked pretty well so far.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way. We'll provide our answers to life, the universe and a cloud native journey like:
- What technical constraints of Kubernetes can be obstacles for applications and how to tackle these?
- How to architect a landscape of hundreds of containerized applications with their surrounding infrastructure like DBs MQs and IAM and heavy requirements on security?
- How to industrialize and govern the migration process?
- How to leverage the possibilities of a cloud native platform like Kubernetes without challenging the tight timeline?
2017 Microservices Practitioner Virtual Summit: Microservices at Squarespace ...Ambassador Labs
This talk covers the past, present, and future of Microservices at Squarespace. We begin with our journey to microservices, and describe the platform that made this possible. We introduce our idea of the “Pillars of Microservices”, everything a developer needs to have a successful production service. For each pillar we describe why we think it is important and discuss the implementation and how we utilize it in our environment. Next, we look to the future evolution of our microservices environment including how we are using containerization and Kubernetes to overcome some of the problems we’ve faced with more static infrastructure.
Kubernetes @ Squarespace: Kubernetes in the DatacenterKevin Lynch
This talk was presented at SRE NYC Meetup on August 16, 2017 at Squarespace HQ.
https://www.youtube.com/watch?v=UJ1QAKprVr4
As the engineering teams at Squarespace grow, we have been building more and more microservices. However, this has added operational strain as we try to shoehorn a growing, complex dynamic environment into our static data center infrastructure. We needed to rethink how we handle deployments, dependency management, resource allocation, monitoring, and alerting. Docker containerization and Kubernetes orchestration helps us tackle many of these problems, but the journey has been challenging. In this talk, we’ll discuss the challenges of running Kubernetes in a datacenter and how we switched to a more SLA-focused alert structure than per instance health with Prometheus and AlertManager.
Using Kubernetes to make cellular data plans cheaper for 50M usersMirantis
Use case of Kubernetes based NFV infrastructure used in production to run an open source evolved packet core. Presented by Facebook Connectivity and Mirantis at KubeCon + CloudNativeCon Europe 2020.
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at LyftChester Chen
Talk 1. Scaling Apache Spark on Kubernetes at Lyft
As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, We will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale. Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.
Speaker: Li Gao
Li Gao is the tech lead in the cloud native spark compute initiative at Lyft. Prior to Lyft, Li worked at Salesforce, Fitbit, Marin Software, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Kafka, Apache Airflow, Apache Hive, and Apache Cassandra.
Introduction to Kubernetes. Covers Kubernetes multicloud case studies with both AWS and Google Container Engine (GKE). Delves into challenges of implementing Kubernetes yourself in AWS when there is no dedicated ops team (devs only).
Microservices @ Work - A Practice Report of Developing MicroservicesQAware GmbH
Cloud Native Night October 2016, Mainz: Talk by Simon Bäumler (Technical Chief Designer at QAware).
Join our Meetup: www.meetup.com/cloud-native-night
Abstract: This talk takes a practice oriented approach to examine microservice oriented architecture. It will show two real systems, one build from scratch in a microservice architecture, the other migrated from a monolithic system to a microservice architecture.
With the example of these two systems the pittfalls, advantages and lessons learned using microservice oriented architectures will be discussed.
While both systems use the java stack, including spring boot and spring cloud many topics will be kept general and will be of interest for all developers.
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...Guglielmo Iozzia
Slides from my talk at the Hadoop User Group Ireland meetup on June 13th 2016: building a data pipeline to ingest data from sources of different nature into Hadoop in minutes (and no coding at all) using the Open Source Streamsets Data Collector tool.
Supporting Hadoop in containers takes much more than the very primitive support Docker provides using the Storage Plugin. A production scale Hadoop deployment inside containers needs to honor anti/affinity, fault-domain and data-locality policies. Kubernetes alone, with primitives such as StatefulSets and PersitentVolumeClaims, is not sufficient to support a complex data-heavy application such as Hadoop. One needs to think about this problem more holistically across containers, networking and storage stacks. Also, constructs around deployment, scaling, upgrade etc in traditional orchestration platforms is designed for applications that have adopted a microservices philosophy, which doesn't fit most Big Data applications across the ingest, store, process, serve and visualization stages of the pipeline. Come to this technical session to learn how to run and manage lifecycle of containerized Hadoop and other applications in the data analytics pipeline efficiently and effectively, far and beyond simple container orchestration. #BigData, #NoSQL, #Hortonworks, #Cloudera, #Kafka, #Tensorflow, #Cassandra, #MongoDB, #Kudu, #Hive, #HBase, PARTHA SEETALA, CTO, Robin Systems.
Get Lower Latency and Higher Throughput for Java ApplicationsScyllaDB
Getting the best performance out of your Java applications can often be a challenge due to the managed environment nature of the Java Virtual Machine and the non-deterministic behaviour that this introduces. Automatic garbage collection (GC) can seriously affect the ability to hit SLAs for the 99th percentile and above.
This session will start by looking at what we mean by speed and how the JVM, whilst extremely powerful, means we don’t always get the performance characteristics we want. We’ll then move on to discuss some critical features and tools that address these issues, i.e. garbage collection, JIT compilers, etc. At the end of the session, attendees will have a clear understanding of the challenges and solutions for low-latency Java.
In my presentation, I will summarize the applied and practical aspects of creating sustainable software products. What does it mean - "green" software for users and developers? I want to explain how creating “green” software can be driven by multiple organizational layers. And how building “green” software products can help the organization increase overall software product efficiency.
This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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
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.
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/
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.
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.
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/
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
5. 5
Challenge #1 : Communication Cost
Doing it the traditional way –
1. Communication lag – takes too long to formulate requirements from developers
2. XY problem – no idea what the real problem is
3. Validation and policy injection is manually done
6. 6
Challenge #1 : Communication Cost
Solution: Create an opionated Internal Developer Platform and form an API based contract with
users
Philosophy :
• When you have APIs and their documentation users rarely need to communicate with you
• Easier to explicitly define what you provide and what you don’t
• Standardization = low re-invention of wheel, less pets, easier to propagate tech culture
Implementation :
• In CaaS we make use of K8s APIs to expose features to users. Custom Resource Definitions (CRDs)
and Operators fits us well.
• Admission control webhooks, podSecurityPolicy and networkPolicy
7. 7
Challenge #1 : Communication Cost
Jiange : Validation without human communication
Jiange
etcd K8s API
9. 9
Challenge #2 : Day 2 Ops
Day 1 Ops :
• Provisioning
• Step 1
• Step 2
• Step 3… N
• Procedural – easy to automate
Day 2 Ops:
• Maintainence
• Not always the same
• Improvements – need to keep an eye on various components
• Metrics
• Logs
• Traces
10. 10
Challenge #2 : Day 2 Ops
Solution: Infrastructure as Data instead of Infrastructure as Code
Script
for X
Script
for Y
Script
for Z
IaC – run scripts one by one
Data
Store Infra
Infra
Control
Loop
Reconcile Spec
Reconcile Status
IaD – Store the state as Data and
reconcile until state is achieved
11. 11
Challenge #2 : Day 2 Ops
Solution: Infrastructure as Data instead of Infrastructure as Code
In CaaS we have written controllers based on same approach
• Klone – Binary that provisions master nodes and system components based on git configs (written in
Go)
• Node operator – used for creating worker nodes
• Namespace operator – used for creating user namespaces with correct permissions, good defaults,
jenkins repositories, harbor projects etc when user on boards.
• Gateway controller – For creating istio ingress gateways
• Wildcard instant domain controller – For instantly creating simple domains to test out services
• Cloud controlller manager – for creating load balancers
• Endpoints controller – for creating container native load balancers
12. 12
Challenge #3 : Day 2 Ops
Internet
Load Balancer
K8s API
Node
List
Cloud
Controller
Manager
K8s cluster nodes
14. 14
Challenge #3 : Container Networks
• Kubernetes network != Host Network
• Pods are not first class citizens (not flat network)
• Pods are ephemeral
• Fair Load balancing does not happen when using NodePorts
• Additional hops (through K8s node Iptables)
• Source IP is not preserved
• Network is difficult to use
15. 15
Challenge #3 : Container Networks
Solution: No one size fits all, provide all
solutions with good defaults and let users
choose
Shared Gateway +
Auto Assigned
Domain
Dedicated Gateway +
Custom Domain
Domain Auto Assigned Any Domain
Performance Not isolated Isolated
Maintainence (for
users)
Zero High
Customization Low Fully customizable
Cost Low High
16. 16
Challenge #3 : Container Networks
Solution: Container Native Load balancing
Legacy Load
Balancer
Container Native
Load Balancer
Number of hops 2 1
IP preservation Remote IP lost Remote IP
preserved
Load Balancing Across nodes Across containers
Health checks Only for Nodes Application level
health checks
17. 17
Future Challenges:
Multicluster CaaS -
Network
Deployments
IPv4 not enough (need IPv6 and/or VPCs)
Stateful apps -
Local persistence
Remote persistence
GPU
SRIOV
CPU pinning
Single Data proxy