This presentation gives an overview of SPEC Cloud (TM) IaaS 2016, the first industry standard benchmark that measures the performance of infrastructure as a service clouds. More details on benchmark at https://www.spec.org/cloud_iaas2016/ .
End-End Security with Confluent Platform confluent
(Vahid Fereydouny, Confluent) Kafka Summit SF 2018
Security and compliance are key concerns for many organizations today and it is very important that we can meet these requirements in our platform. This is also extremely critical for customers who are adopting Confluent cloud offerings, since moving the streaming platform to cloud exposes new security and governance issues.
In this session, we will discuss how Confluent is providing control and visibility to address these concerns and enable secure streaming platforms. We will cover the main pillars of IT security in access control (authentication, authorization), data confidentiality (encryption) and auditing.
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...confluent
Watch this talk here: https://www.confluent.io/online-talks/scaling-security-on-100s-of-millions-of-mobile-devices-using-kafka-and-scylla-on-demand
Join mobile cybersecurity leader Lookout as they talk through their data ingestion journey.
Lookout enables enterprises to protect their data by evaluating threats and risks at post-perimeter endpoint devices and providing access to corporate data after conditional security scans. Their continuous assessment of device health creates a massive amount of telemetry data, forcing new approaches to data ingestion. Learn how Lookout changed its approach in order to grow from 1.5 million devices to 100 million devices and beyond, by implementing Confluent Platform and switching to Scylla.
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
Mike Weber - Nagios and Group Deployment of Service ChecksNagios
This presentation will show how you can create groups of checks like CPU metrics, Oracle metrics or IIS metrics and push them to all of the hosts that require them. The presentation will provide a script that will allow you to select and implement hundreds of groups of checks that have been developed for NRPE, NCPA, WMI, NSClient++, NRDP and NRDS.
End-End Security with Confluent Platform confluent
(Vahid Fereydouny, Confluent) Kafka Summit SF 2018
Security and compliance are key concerns for many organizations today and it is very important that we can meet these requirements in our platform. This is also extremely critical for customers who are adopting Confluent cloud offerings, since moving the streaming platform to cloud exposes new security and governance issues.
In this session, we will discuss how Confluent is providing control and visibility to address these concerns and enable secure streaming platforms. We will cover the main pillars of IT security in access control (authentication, authorization), data confidentiality (encryption) and auditing.
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...confluent
Watch this talk here: https://www.confluent.io/online-talks/scaling-security-on-100s-of-millions-of-mobile-devices-using-kafka-and-scylla-on-demand
Join mobile cybersecurity leader Lookout as they talk through their data ingestion journey.
Lookout enables enterprises to protect their data by evaluating threats and risks at post-perimeter endpoint devices and providing access to corporate data after conditional security scans. Their continuous assessment of device health creates a massive amount of telemetry data, forcing new approaches to data ingestion. Learn how Lookout changed its approach in order to grow from 1.5 million devices to 100 million devices and beyond, by implementing Confluent Platform and switching to Scylla.
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
Mike Weber - Nagios and Group Deployment of Service ChecksNagios
This presentation will show how you can create groups of checks like CPU metrics, Oracle metrics or IIS metrics and push them to all of the hosts that require them. The presentation will provide a script that will allow you to select and implement hundreds of groups of checks that have been developed for NRPE, NCPA, WMI, NSClient++, NRDP and NRDS.
Design and Implementation of Incremental Cooperative Rebalancingconfluent
Watch this talk here: https://www.confluent.io/online-talks/design-and-implementation-of-incremental-cooperative-rebalancing-on-demand
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it’s widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself.
Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol.
However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, we’ll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs.
This talk will cover:
-The internals of Incremental Cooperative Rebalancing
-Uses cases that benefit from Incremental Cooperative Rebalancing
-Implementation in Kafka Connect
-Performance results in Kafka Connect clusters
Patterns and Pains of Migrating Legacy Applications to KubernetesJosef 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 German blue chip company onto a Kubernetes cluster within one year.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
OSMC 2021 | Use OpenSource monitoring for an Enterprise Grade PlatformNETWAYS
There are many tools and frameworks for monitoring. Usually when you think of an Open Source solution, you don’t think to implement it in a COTS product. Nevertheless, this session will tell you how you can implement tools such as Prometheus, Grafana and ELK into such an Enterprise application platform. Monitoring performance, throughput and error rate is important to be in control of your transactions. If you use a Service Bus or SOA/BPM suite product there are a lot out of the box diagnostics waiting for you. The puzzle here is how to get it out in a useful way. Besides of the many commercial solutions also Open Source tools can help you out with it. You can export runtime diagnostics out of the Diagnostics framework, monitor your SOA Composites and trace down Service Bus statistics using Prometheus and Grafana. The session will elaborate how to set up a proper monitoring using these tools, also in a proactive way where automated monitoring is a must for every application environment.
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!
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.
The Foundations of Multi-DC Kafka (Jakub Korab, Solutions Architect, Confluen...confluent
Kafka is notoriously tricky for multi-dc use cases. The log abstraction and client failover breaks down when you cannot at least guarantee offset consistency. In this talk, we define the current state of Kafka in terms of multi-dc usage, how different approaches provide different guarantees as well as examining the missing gaps, and how the community is addressing them.
(Stephane Maarek, DataCumulus) Kafka Summit SF 2018
Security in Kafka is a cornerstone of true enterprise production-ready deployment: It enables companies to control access to the cluster and limit risks in data corruption and unwanted operations. Understanding how to use security in Kafka and exploiting its capabilities can be complex, especially as the documentation that is available is aimed at people with substantial existing knowledge on the matter.
This talk will be delivered in a “hero journey” fashion, tracing the experience of an engineer with basic understanding of Kafka who is tasked with securing a Kafka cluster. Along the way, I will illustrate the benefits and implications of various mechanisms and provide some real-world tips on how users can simplify security management.
Attendees of this talk will learn about aspects of security in Kafka, including:
-Encryption: What is SSL, what problems it solves and how Kafka leverages it. We’ll discuss encryption in flight vs. encryption at rest.
-Authentication: Without authentication, anyone would be able to write to any topic in a Kafka cluster, do anything and remain anonymous. We’ll explore the available authentication mechanisms and their suitability for different types of deployment, including mutual SSL authentication, SASL/GSSAPI, SASL/SCRAM and SASL/PLAIN.
-Authorization: How ACLs work in Kafka, ZooKeeper security (risks and mitigations) and how to manage ACLs at scale
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...confluent
Ever wondered just how many CPU cores of KSQL Server you need to provision to handle your planned stream processing workload ? Or how many GBits of aggregate network bandwidth, spread across some number of processing threads, you'll need to deal with combined peak throughput of multiple queries ? In this talk we'll first explore the basic drivers of KSQL throughput and hardware requirements, building up to more advanced query plan analysis and capacity-planning techniques, and review some real-world testing results along the way. Finally we will recap how and what to monitor to know you got it right!
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Consistency and Completeness: Rethinking Distributed Stream Processing in Apa...Guozhang Wang
We present Apache Kafka’s core design for stream processing, which relies on its persistent log architecture as the storage and inter-processor communication layers to achieve correctness guarantees. Kafka Streams, a scalable stream processing client library in Apache Kafka, defines the processing logic as read process-write cycles in which all processing state updates and result outputs are captured as log appends. Idempotent and transactional write protocols are utilized to guarantee exactly once semantics. Furthermore, revision-based speculative processing is employed to emit results as soon as possible while handling out-of-order data. We also demonstrate how Kafka Streams behaves in practice with large-scale deployments and performance insights exhibiting its flexible and low-overhead trade-offs.
Building an Event-oriented Data Platform with Kafka, Eric Sammer confluent
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. Many organizations understand the use cases around their data – fraud detection, quality of service and technical operations, user behavior analysis, for example – but are not necessarily data infrastructure experts. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes an hour of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality.
Attendees will leave this session knowing not just which open source projects go into a system such as this, but how they work together, what tradeoffs and decisions need to be addressed, and how to present a single general purpose data platform to multiple applications. This session should be attended by data infrastructure engineers and architects planning, building, or maintaining similar systems.
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Stream Me Up, Scotty: Transitioning to the Cloud Using a Streaming Data Platformconfluent
Many enterprises have a large technical debt in legacy applications hosted in on-premises data centers. There is a strong desire to modernize and move to a cloud-based infrastructure, but the world won’t stop for you to transition. Existing applications need to be supported and enhanced; data from legacy platforms is required to make decisions that drive the business. On the other hand, data from cloud-based applications does not exist in a vacuum. Legacy applications need access to these cloud data sources and vice versa.
Can an enterprise have it both ways? Can new applications be built in the cloud while existing applications are maintained in a private data center?
Monsanto has adopted a cloud-first mentality—today most new development is focused on the cloud. However, this transition did not happen overnight.
Chrix Finne and Bob Lehmann share their experience building and implementing a Kafka-based cross-data-center streaming platform to facilitate the move to the cloud—in the process, kick-starting Monsanto’s transition from batch to stream processing. Details include an overview of the challenges involved in transitioning to the cloud and a deep dive into the cross-data-center stream platform architecture, including best practices for running this architecture in production and a summary of the benefits seen after deploying this architecture.
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.
Design and Implementation of Incremental Cooperative Rebalancingconfluent
Watch this talk here: https://www.confluent.io/online-talks/design-and-implementation-of-incremental-cooperative-rebalancing-on-demand
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it’s widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself.
Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol.
However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, we’ll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs.
This talk will cover:
-The internals of Incremental Cooperative Rebalancing
-Uses cases that benefit from Incremental Cooperative Rebalancing
-Implementation in Kafka Connect
-Performance results in Kafka Connect clusters
Patterns and Pains of Migrating Legacy Applications to KubernetesJosef 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 German blue chip company onto a Kubernetes cluster within one year.
The talk will be about the lessons we've learned - the best practices and pitfalls we've discovered along our way.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
OSMC 2021 | Use OpenSource monitoring for an Enterprise Grade PlatformNETWAYS
There are many tools and frameworks for monitoring. Usually when you think of an Open Source solution, you don’t think to implement it in a COTS product. Nevertheless, this session will tell you how you can implement tools such as Prometheus, Grafana and ELK into such an Enterprise application platform. Monitoring performance, throughput and error rate is important to be in control of your transactions. If you use a Service Bus or SOA/BPM suite product there are a lot out of the box diagnostics waiting for you. The puzzle here is how to get it out in a useful way. Besides of the many commercial solutions also Open Source tools can help you out with it. You can export runtime diagnostics out of the Diagnostics framework, monitor your SOA Composites and trace down Service Bus statistics using Prometheus and Grafana. The session will elaborate how to set up a proper monitoring using these tools, also in a proactive way where automated monitoring is a must for every application environment.
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!
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.
The Foundations of Multi-DC Kafka (Jakub Korab, Solutions Architect, Confluen...confluent
Kafka is notoriously tricky for multi-dc use cases. The log abstraction and client failover breaks down when you cannot at least guarantee offset consistency. In this talk, we define the current state of Kafka in terms of multi-dc usage, how different approaches provide different guarantees as well as examining the missing gaps, and how the community is addressing them.
(Stephane Maarek, DataCumulus) Kafka Summit SF 2018
Security in Kafka is a cornerstone of true enterprise production-ready deployment: It enables companies to control access to the cluster and limit risks in data corruption and unwanted operations. Understanding how to use security in Kafka and exploiting its capabilities can be complex, especially as the documentation that is available is aimed at people with substantial existing knowledge on the matter.
This talk will be delivered in a “hero journey” fashion, tracing the experience of an engineer with basic understanding of Kafka who is tasked with securing a Kafka cluster. Along the way, I will illustrate the benefits and implications of various mechanisms and provide some real-world tips on how users can simplify security management.
Attendees of this talk will learn about aspects of security in Kafka, including:
-Encryption: What is SSL, what problems it solves and how Kafka leverages it. We’ll discuss encryption in flight vs. encryption at rest.
-Authentication: Without authentication, anyone would be able to write to any topic in a Kafka cluster, do anything and remain anonymous. We’ll explore the available authentication mechanisms and their suitability for different types of deployment, including mutual SSL authentication, SASL/GSSAPI, SASL/SCRAM and SASL/PLAIN.
-Authorization: How ACLs work in Kafka, ZooKeeper security (risks and mitigations) and how to manage ACLs at scale
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...confluent
Ever wondered just how many CPU cores of KSQL Server you need to provision to handle your planned stream processing workload ? Or how many GBits of aggregate network bandwidth, spread across some number of processing threads, you'll need to deal with combined peak throughput of multiple queries ? In this talk we'll first explore the basic drivers of KSQL throughput and hardware requirements, building up to more advanced query plan analysis and capacity-planning techniques, and review some real-world testing results along the way. Finally we will recap how and what to monitor to know you got it right!
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Consistency and Completeness: Rethinking Distributed Stream Processing in Apa...Guozhang Wang
We present Apache Kafka’s core design for stream processing, which relies on its persistent log architecture as the storage and inter-processor communication layers to achieve correctness guarantees. Kafka Streams, a scalable stream processing client library in Apache Kafka, defines the processing logic as read process-write cycles in which all processing state updates and result outputs are captured as log appends. Idempotent and transactional write protocols are utilized to guarantee exactly once semantics. Furthermore, revision-based speculative processing is employed to emit results as soon as possible while handling out-of-order data. We also demonstrate how Kafka Streams behaves in practice with large-scale deployments and performance insights exhibiting its flexible and low-overhead trade-offs.
Building an Event-oriented Data Platform with Kafka, Eric Sammer confluent
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. Many organizations understand the use cases around their data – fraud detection, quality of service and technical operations, user behavior analysis, for example – but are not necessarily data infrastructure experts. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes an hour of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality.
Attendees will leave this session knowing not just which open source projects go into a system such as this, but how they work together, what tradeoffs and decisions need to be addressed, and how to present a single general purpose data platform to multiple applications. This session should be attended by data infrastructure engineers and architects planning, building, or maintaining similar systems.
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Stream Me Up, Scotty: Transitioning to the Cloud Using a Streaming Data Platformconfluent
Many enterprises have a large technical debt in legacy applications hosted in on-premises data centers. There is a strong desire to modernize and move to a cloud-based infrastructure, but the world won’t stop for you to transition. Existing applications need to be supported and enhanced; data from legacy platforms is required to make decisions that drive the business. On the other hand, data from cloud-based applications does not exist in a vacuum. Legacy applications need access to these cloud data sources and vice versa.
Can an enterprise have it both ways? Can new applications be built in the cloud while existing applications are maintained in a private data center?
Monsanto has adopted a cloud-first mentality—today most new development is focused on the cloud. However, this transition did not happen overnight.
Chrix Finne and Bob Lehmann share their experience building and implementing a Kafka-based cross-data-center streaming platform to facilitate the move to the cloud—in the process, kick-starting Monsanto’s transition from batch to stream processing. Details include an overview of the challenges involved in transitioning to the cloud and a deep dive into the cross-data-center stream platform architecture, including best practices for running this architecture in production and a summary of the benefits seen after deploying this architecture.
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.
A Survey of Container Security in 2016: A Security Update on Container PlatformsSalman Baset
This talk is an update of container security in 2016. It describes the security measures that containers provide, shows how containers provide security measures out of box that are prone to configuration errors when running applications directly on host, and finally lists the ongoing in container security in the community.
Open Source Tools for Container Security and Compliance @Docker LA Meetup 2/13Zach Hill
Data and policy driven approach for container security and compliance using open-source Anchore. Presented at Docker Meetup LA 2/13/2017 including demos
Docker containers & the Future of Drupal testing Ricardo Amaro
Story of an investigation to improve cloud
The sad VirtualMachine story
Containers and non-containers
DEMO - Drupal Docker
Drupal Testbots story in a Glance
Docker as a testing automation factor
DEMO - Docker Tesbot
Integration path
Originally presented at API Strat and Practice conference in Boston 2016 by me and Mandy Whaley, this presentation shows the multiple archetypes that you could encounter while trying to govern APIs at your company.
Introduction to Infrastructure as Code & Automation / Introduction to ChefNathen Harvey
Your customers expect you to continuously deliver delightful experiences. This means that you’ll need to continuously deliver application and infrastructure updates. Hand-crafted servers lovingly built and maintained by a system administrator are a thing of the past. Golden images are fine for initial provisioning but will quickly fail as your configuration requirements change over time.
It’s time for you to fully automate the provisioning and management of your infrastructure components. Welcome to the world of infrastructure as code! In this new world, you’ll be able to programmatically provision and configure the components of your infrastructure.
Disposable infrastructure whose provisioning, configuration, and on-going maintenance is fully automated allow you to change the way you build and deliver applications. Move your applications and infrastructure towards continuous delivery.
In this talk, we’ll explore the ideas behind “infrastructure as code” and, specifically, look at how Chef allows you to fully automate your infrastructure. If you’re brave enough, we’ll even let you get your hands on some Chef and experience the delight of using Chef to build and deploy some infrastructure components.
Priming Your Teams For Microservice Deployment to the CloudMatt Callanan
You think of a great idea for a microservice and want to ship it to production as quickly as possible. Of course you'll need to create a Git repo with a codebase that reuses libraries you share with other services. And you'll want a build and a basic test suite. You'll want to deploy it to immutable servers using infrastructure as code that dev and ops can maintain. Centralised logging, monitoring, and HipChat notifications would also be great. Of course you'll want a load balancer and a CNAME that your other microservices can hit. You'd love to have blue-green deploys and the ability to deploy updates at any time through a Continuous Delivery pipeline. Phew! How long will it take to set all this up? A couple of days? A week? A month?
What if you could do all of this within 30 minutes? And with a click of a button soon be receiving production traffic?
Matt introduces "Primer", Expedia's microservice generation and deployment platform that enables rapid experimentation in the cloud, how it's caused unprecedented rates of learning, and explain tips and tricks on how to build one yourself with practical takeaways for everyone from the startup to the enterprise.
Video: https://www.youtube.com/watch?v=Xy4EkaXyEs4
Meetup: http://www.meetup.com/Devops-Brisbane/events/225050723/
DOXLON November 2016 - Data Democratization Using SplunkOutlyer
In this session, Neil Roy Chowdhury - Lead Splunk Consultant @ Strft - looks at Splunk to foster collaboration between dev and ops teams in a safe and secure way. We focus on the need for semantic logging and what part data models can play in everyone speaking the same language, not just for dev and ops teams, but for information security and other business areas too.
S.R.E - create ultra-scalable and highly reliable systemsRicardo Amaro
Site Reliability Engineering enables agility and stability.
SREs use Software Engineering to automate themselves out of the Job.
My advice, if you want to implement this change in your company is to start with action items, alter your training and hiring, implement error budgets, do blameless postmortems and reduce toil.
https://events.drupal.org/dublin2016/sessions/sre-create-ultra-scalable-and-highly-reliable-systems
AskTom: How to Make and Test Your Application "Oracle RAC Ready"?Markus Michalewicz
Oracle Real Application Clusters (Oracle RAC) is the preferred availability and scalability solution for Oracle Databases, as most applications can benefit from its capabilities without making any changes. This mini session explains the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can test and ensure that your application is “Oracle RAC ready.”
This deck was first presented in OOW19 as an AskTom theater / mini session and will be presented as a full version in other conferences going forward at which time I will provide an updated version of the deck.
A survey-report-on-cloud-computing-testing-environmentshritosh kumar
Cloud computing not only changes the way of obtaining computing resources (such as computers, infrastructures, data storage, and application services), but also changes the way of managing and delivering computing services, technologies, and solutions. Cloud computing leads an opportunity in offering testing as a service (TaaS) for SaaS and clouds. Meanwhile, it causes new issues, challenges and needs in software testing, particular in testing clouds and cloud-based applications. This paper provides a comprehensive tutorial on cloud testing and cloud-based application testing. It answers the common questions raised by engineers and managers, and it provides clear concepts, discusses the special objectives, features, requirements, and needs in cloud testing. It offers a clear comparative view between web-based software testing and cloud-based application testing. In addition, it examines the major issues, challenges, and needs in testing cloud-based software applications. Furthermore, it also summarizes and compares different commercial products and solutions supporting cloud testing as services.
VMworld 2013: How to Replace Websphere Application Server (WAS) with TCserver VMworld
VMworld 2013
Kaushik Bhattacharya, Pivotal
Michel Bond, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
The Cisco Open SDN Controller is a commercial distribution of OpenDaylight that delivers business agility through automation of standards-based network infrastructure.
Built as a highly scalable software-defined networking (SDN) platform, the Open SDN Controller abstracts away the complexity of managing heterogeneous networks to improve service delivery and reduce operating costs.
The controller exposes REST APIs to allow other applications to take advantage capabilities of the controller and unlock the power of the underlying network infrastructure, and JAVA APIs to allow for the creation of new network services.
This session will present the basic constructs of the controller and the capabilities of the REST and JAVA APIs to demonstrate how the Open SDN Controller abstracts away the complexity of managing heterogeneous networks to improve service delivery and reduce operating costs.
Interop ITX: Moving applications: From Legacy to Cloud-to-CloudSusan Wu
Cloud computing provides an array of hosting and service options to fit your overall company strategy. Sometimes a public cloud is your best option and other times your data requirements demand a private cloud. As needs converge, a hybrid solution continues to gain popularity. Developers must consider if their applications might be run on either or both.
Hear about Midokura.com's journey going from the colos to cloud servers to AWS.
Hewlett Packard Entreprise | Stormrunner load | Game ChangerJeffrey Nunn
StormRunner Load is the newest innovative HPE Software Solution for Agile Cloud testing.
It is Simple, Smart and Scalable.
Simple: Because customer can now create and execute tests in less than 10 min. Very easy to create and simulate transactions, no need to create complex scripts
Smart: It provides real-time analytics, helping customers to find performance issues before moving apps to production.
Scalable: It allows the customer to deploy load-generators across the globe via multiple cloud providers (Amazon, Azure and HP Helion). Customers can simulate from 1 to millions of virtual users within minutes.
Cloud Readiness : CAST & Microsoft Azure Partnership OverviewCAST
Learn more about accelerating Cloud Migration: https://www.castsoftware.com/use-cases/cloud-readiness-and-migration
A joint team from CAST and Microsoft worked to define rules that assess the ability of an existing codebase to migrate to Microsoft Azure. The team then integrated the rules into CAST Highlight and moved the solution itself to Azure.
In this report, we describe the process and what we did before, during, and after the hackfest, including the following:
• How we produced the rules that assess the ability to migrate to Azure
• How we benchmarked the rules
• How we migrated the CAST Highlight service to Azure
• What the architecture looked like and future plans
• Learnings from the process
Our first objective was to define rules that assess the ability of applications to migrate to Azure and integrate those rules into CAST Highlight. This was the more-complex task for our team.
Our second objective was to move the existing application to Azure, thus profiting from App Service features such as auto-scaling and deployment slots. The existing application is a Java web app running on Apache Tomcat and using PostgreSQL as its database. This is a frequent scenario for web applications running in Azure, so we did not anticipate having any issues with this task.
Learn more about accelerating Cloud Migration: https://www.castsoftware.com/use-cases/cloud-readiness-and-migration
PaaS Lessons: Cisco IT Deploys OpenShift to Meet Developer DemandCisco IT
Cisco IT added OpenShift by Red Hat to its technology mix to rapidly expose development staff to a rich set of web-scale application frameworks and runtimes. Deploying Platform-as-a-Service (PaaS) architectures, like OpenShift, bring with it:
- A Focus on the Developer Experience
- Container Technology
- Network Security and User Isolation
- Acceleration of DevOps Models without Negatively Impacting Business
In this session, Cisco and Red Hat will take you through:
- The problems Cisco set out to solve with PaaS. - How OpenShift aligned with their needs.
- Key lessons learned during the process.
Business & IT Strategy Alignment: This track targets the juncture of business and IT considerations necessary to create competitive advantage. Example topics include: new architecture deployments, competitive differentiators, long-term and hidden costs, and security.
Attendees will learn how to align architecture and technology decisions with their specific business needs and how and when IT departments can provide competitive advantage.
OCCIware@POSS 2016 - an extensible, standard XaaS cloud consumer platformMarc Dutoo
OCCIware at Paris Open Source Summit 2016 - an extensible, standard XaaS cloud consumer platform - demos : Docker & Linked Data Studios, online playground
Similar to SPEC Cloud (TM) IaaS 2016 Benchmark (20)
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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
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
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/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Topics
• Standard Performance Corporation (SPEC)
• What are cloud characteristics?
• What is a cloud benchmark?
• SPEC Cloud IaaS 2016 benchmark, the first industry standard
benchmark
– Workloads
– Setup
– Metrics
– Compliant run
– Stopping conditions
– Published results
• What’s next in SPEC Cloud* benchmarks?
2
3. SPEC Background
• Standard Performance Evaluation Corporation (SPEC) is a non-profit corporation
formed to establish, maintain and endorse a standardized set of relevant
benchmarks that can be applied to the newest generation of high-performance
computers.
– http://www.spec.org/
• Well-known benchmarks such as SPEC CPU 2006, SPEC Virt, SPEC Power…
• Cloud Subcommittee released the first industry standard benchmark (SPEC Cloud
IaaS 2016) for infrastructure-as-a-service cloud in May 2016.
– http://spec.org/benchmarks.html#cloud
3
4. SPEC Cloud IaaS Benchmark and OpenStack
• Measuring cloud performance in general and OpenStack
performance in particular is hard.
– Too many configuration options
– Too many metrics
– Difficult to repeat experiments and compare results
• SPEC Cloud IaaS 2016 can be used to measure and publish
performance (and related configuration) of each OpenStack
release.
– Each release can be measured by the benchmark
– Such results can potentially be published before an OpenStack summit
or during key note, giving credence to the release.
– All results are peer-reviewed before publishing.
4
5. What is a cloud?
• Public cloud
• Private cloud
• Hybrid cloud
5
6. What is a cloud?
• SPEC OSG Cloud Subcommittee Glossary
• Blackbox Cloud
• A cloud-provider provides a general specification of the system
under test (SUT), usually in terms of how the cloud consumer
may be billed. The exact hardware details corresponding to these
compute units may not be known. This will typically be the case
if the entity benchmarking the cloud is different from a cloud
provider, e.g., public clouds or hosted private clouds.
• Whitebox Cloud
• The SUT’s exact engineering specifications including all hardware
and software are known and under the control of the tester. This
will typically be the case for private clouds.
Source: https://www.spec.org/cloud_iaas2016/docs/faq/html/glossary.html
6
7. What are cloud characteristics?
7
SPEC RESEARCH GROUP - CLOUD WORKING GROUP
https://research.spec.org/working-groups/rg-cloud-working-
group.html
READY FOR RAIN? A VIEW FROM SPEC RESEARCH ON
THE FUTURE OF CLOUD METRICS
https://research.spec.org/fileadmin/user_upload/documents/
rg_cloud/endorsed_publications/SPEC-RG-2016-
01_CloudMetrics.pdf
8. Elasticity
8
- THE DEGREE TO WHICH A SYSTEM IS ABLE TO ADAPT
TO WORKLOAD CHANGES BY PROVISIONING AND DE-
PROVISIONING RESOURCES IN AN AUTONOMIC
MANNER, SUCH THAT AT EACH POINT IN TIME THE
AVAILABLE RESOURCES MATCH THE CURRENT DEMAND
AS CLOSELY AS POSSIBLE
Source: READY FOR RAIN? A VIEW FROM SPEC RESEARCH ON THE FUTURE OF CLOUD METRICS, SPEC RG Cloud
Working Group
9. Scalability
9
- THE ABILITY OF THE SYSTEM TO SUSTAIN INCREASING
WORKLOADS BY MAKING USE OF ADDITIONAL
RESOURCES, AND THEREFORE, IN CONTRAST TO ELASTICITY,
IT IS NOT DIRECTLY RELATED TO HOW WELL THE
ACTUAL RESOURCE DEMANDS ARE MATCHED BY THE
PROVISIONED RESOURCES AT ANY POINT IN TIME.
Source: READY FOR RAIN? A VIEW FROM SPEC RESEARCH ON THE FUTURE OF CLOUD METRICS, SPEC RG Cloud Working Group
10. SPEC Cloud - Scalability and Elasticity Analogy
Climbing a mountain
10
c c{
{
{
{
{
Elasticity – time for each step
IDEAL
Scalability
• Mountain: Keep on climbing
• Cloud: keep on adding load without errors
Elasticity
• Mountain: Each step takes identical time
• Cloud: performance within limits as load increases
{
{
{
11. Cloud Infinitely Scalable and Elastic?
• "Public clouds can have ‘infinite’ scale.”
– In reality
• Resource limitation: Limited instances can be created within
availability zone
• Budget limitation: for testing
• Performance variation: due to multiple tenants
• “Private clouds can be fully elastic”
– In reality
• Performance variation: may have better performance under no
load, unless configured correctly
11
12. What is a good IaaS cloud benchmark?
• Measures meaningful metrics that are comparable are reproducible
• Measures cloud “elasticity” and “scalability”
• Benchmark IaaS clouds, not the workloads!
• Measures performance of public and private infrastructure-as-a-service (IaaS)
clouds
• Measure provisioning and run-time performance of a cloud
• Uses workloads that resemble “real” workloads
– No micro benchmarks
• Places no restriction on how a cloud is configured.
• Places no restriction on the type of instances a tester can use (e.g., no
restriction on VM/baremetal, CPU, memory, disk, network)
– However, once a tester selects instance types for the test, they cannot be changed
– Minimum machines part of cloud: 4 (with appropriate specs for these machines)
• Allows easy addition of new workloads
12
13. SPEC Cloud IaaS 2016 Benchmark
• The first industry standard benchmark that measures
the scalability, elasticity, mean instance provisioning
time of clouds, and more
• Uses real workloads in multi-instance configuration
13
14. SPEC Cloud IaaS 2016 - Workloads
14
YCSB / Cassandra
Framework used by a common set
of workloads for evaluating
performance of different key-value
and cloud serving stores.
KMeans
-Hadoop-based CPU intensive
workload
-Chose Intel HiBench
implementation
YCSB Application
Instance
comprising
7 instances
KMeans Application
Instance
comprising
6 instances
No restriction on instance configuration,
i.e., cpu, mem, disk, net
15. How does SPEC Cloud IaaS 2016 measure
Scalability and Elasticity?
• Run each application YCSB and Kmeans instance once
(baseline phase)
• Create multiple YCSB and Kmeans application instances over
time (elasticity + scalability phase) with statistically similar
work
– The more application instances are created and perform work, the
higher the scalability
– The low variability in performance during elasticity + scalability phase
relative to baseline phase, the higher the elasticity
15
16. Benchmark phases
• Baseline phase
– Determine the performance of a single application instance for
determining QoS criteria.
– The assumption is that only a single application instance is run
in the cloud (white-box) or in the user account (black-box)
• Elasticity + Scalability phase
– Determine cloud elasticity and scalability results when multiple
workloads are run
– Stops when QoS criteria as determined from baseline is not
met, or maximum AIs as set by a tester are reached
– Scalability results are normalized against a “reference platform”
16
17. Elasticity + Scalability phase – pictorial representation
c c
Time
AIindex
Stopping condition (QoS / user specified limits)
c c
c c
AI
Provisioned
2nd run
complete
1st run
complete
YCSB K-Means
17
• Each block indicates the time for a valid
run within an application instance
• Application instances (comprising
multiple instances are provisioning on
the left)
18. SPEC Cloud™ IaaS 2016 Benchmark - Setup
CloudBench
Baseline
driver
Elasticity +
Scalability
driver
Report
generator
Cloud SUT
Group of boxes represents an
application instance.
Different color within a group
indicates workload generators/
different VM sizes.
Benchmark harness
Benchmark harness. It comprises of Cloud Bench (cbtool)
and baseline/elasticity drivers, and report generators. cbtool
creates application instances in a cloud and collects metrics.
cbtool is driven by baseline and scalability driver to execute
the baseline and elasticity + scalability phases of the
benchmark, respectively.
For white-box clouds the benchmark harness is outside the
SUT. For black-box clouds, it can be in the same location or
campus.
18
19. Metrics Definitions for SPEC Cloud™ IaaS 2016
Benchmark (1/2) – Primary Metrics
• Scalability
– measures the total amount of work performed by
application instances running in a cloud. Scalability is
reported as a unit-less number.
– Example: Support through of two application instances is
5000 ops/s, then normalized to reference platform,
scalability is 2.0
• Elasticity
– measures whether the work performed by application
instances scales linearly in a cloud. It is expressed as a
percentage (out of 100).
– Example: self-referential. How bad a cloud performs when
the offered load by the benchmark increases
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20. Metrics Definitions for SPEC Cloud™ IaaS 2016
Benchmark (2/2) – Secondary Metrics
• Mean Instance Provisioning Time
– measures the interval between instance creation request sent by the
benchmark harness and the moment when the instance is reachable on
port 22 (SSH port).
• AI Run Success
– measures the percentage of runs across all application instances that
successfully completed.
• AI provisioning success
– Measures the percentage of application instances that provisioning
successfully.
• Phase start and end date/time and region (not a metric but
reported as part of fair usage)
– Date/time when the elasticity phase was started, and the region in which
the test was performed.
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21. Stopping Conditions
• 20% of the AIs fail to provision
• 10% of the AIs have any errors reported in any of the AI runs
• 50% of the AIs have QoS condition violated across any run
– QoS conditions are defined per workload and are relative to baseline
– YCSB: 50% of AIs have one or more run where
• Throughput is less than 33% of baseline throughput
• 99% Insert or Read latency for any run is 3.33 times greater than Insert and Read
latency in baseline
– K-Means: 50% of AIs have one or more run where
• Complete time is 3.33 times greater than completion time in baseline phase
• Maximum number of AIs as set by the cloud provider has
been provisioned.
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23. SPEC Cloud IaaS 2016 Update and Future Cloud
Benchmarks (get involved)
• Update for SPEC Cloud IaaS 2016 coming soon
– Updates for OpenStack Newton
– Easy writing of adapters for new clouds (lib cloud support)
– Miscellaneous bug fixes
• Gen Involved: Future SPEC Cloud* benchmarks for
– Object storage
– Serverless systems
– …
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https://www.spec.org/benchmarks.html#cloud
Editor's Notes
Scalability
measures the total amount of work performed by application instances running in a cloud. The aggregate work performed by one or more application instances should linearly scale in an ideal cloud.Scalability is reported as a unit-less number.
The number is an aggregate of workloads metrics across all application instances running in a cloud normalized by workload metrics in a reference cloud platform. The reference platform metrics are an average of workload metrics taken across multiple cloud platforms during the benchmark development.
Elasticity
measures whether the work performed by application instances scales linearly in a cloud. That is, for statistically similar work, the performance of N application instances in a cloud must be the same as the performance of application instances during baseline phase when no other load is introduced by the tester. Elasticity is expressed as a percentage (out of 100). The higher, the better.
Elasticity is self-referential.