Serverless applications increasingly involve distributed systems where errors and bottlenecks can have significant downstream impact. This can be compounded by the ephemeral nature of FaaS offerings in which errors can be difficult to diagnose retroactively. In this session we'll discuss instrumentation and "self-healing" architectural patterns that will improve resiliency of your application and drive improved observability and performance.
Serverless applications increasingly involve distributed systems where errors and bottlenecks can have significant downstream impact. This can be compounded by the ephemeral nature of FaaS offerings in which errors can be difficult to diagnose retroactively. In this session we'll discuss instrumentation and "self-healing" architectural patterns that will improve resiliency of your application and drive improved observability and performance.
Npm has modules for devops, like logging, metrics, service discovery. But when you arrive to production, you may find that these are already handled by old players. Avoid the same mistakes I did, when my first node app was on its way to the world.
Processing TeraBytes of data every day and sleeping at nightLuciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for assessment and threat hunting. Building this kind of stuff on AWS is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story. In this talk, we will tell you how we achieved this ambitious goal and how we missed a few good nights of sleep while trying to do that! Spoiler alert: contains AWS, serverless, elastic search, monitoring, alerting & more!
Processing TeraBytes of data every day and sleeping at nightLuciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for assessment and threat hunting.
Building this kind of stuff in the cloud is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story.
In this talk, we will tell you how we achieved this ambitious goal and how we missed a few good nights of sleep while trying to do that!
Spoiler alert: contains AWS, serverless, elastic search, monitoring, alerting & more!
Processing Terabytes of data every day … and sleeping at night (infiniteConf ...Luciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for security assessment and threat hunting.
Building this kind of stuff in the cloud is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story. In this talk, Luciano and Domagoj will tell you how they achieved this ambitious goal and how they missed a few good nights of sleep while trying to do that!
Spoiler alert: contains AWS, lambda, elastic search, monitoring, alerting & more!
What Crimean War gunboats teach us about the need for schema registriesAlexander Dean
In 1853 Britain’s workshops built 90 new gunboats for the Royal Navy in just 90 days: an astonishing feat of engineering. Industrial standardization made this possible - and in this talk, my first at Strata, I argued that data-sophisticated corporations need a new standardization of their own, in the form of schema registries like Confluent Schema Registry or Snowplow’s own Iglu.
Talk abstract:
At the start of the Crimean War in 1853, Britain's Royal Navy needed 90 new gunboats ready to fight in the Baltic in just 90 days. Assembling the boats was straightforward - the challenge was to build all of the engine sets in time. Marine engineer John Penn did an unusual thing: he took a pair of reference engines, disassembled them and distributed the pieces to the best machine shops across Britain. These workshops - latter-day micro-services - each built 90 sets of their allocated parts, which were then assembled into the engines for the new gunboats, ready for battle.
This was the nineteenth century - how could the Admiralty be certain that the parts from all these independent workshops would come together to form 90 high-powered engines? The answer lay in a crucial piece of standardization: the Whitworth thread, the world’s first national screw thread standard, devised by Sir Joseph Whitworth in 1841. By the time the Royal Navy came knocking, this standard had been adopted by workshops across Britain; John Penn could be confident that engine parts built by any workshop to the Whitworth standard would fit together.
In this talk, Snowplow co-founder Alexander Dean will draw on the story of the Crimean War gunboats to argue that our data processing architectures urgently require a standardization of their own, in the form of schema registries. Like the Whitworth screw thread, a schema registry, such as Confluent Schema Registry or Snowplow’s own Iglu, allows enterprises to standardise on a set of business entities which can be used throughout their batch and stream processing architectures. Like the artisanal workshops in 1850s Britain, micro-services can work on narrowly defined data processing tasks, confident that their inputs and outputs will be compatible with their peers.
This talk will start with the rationale for putting a schema registry at the heart of your business, before moving on to the practicalities of an implementation, including: a side-by-side comparison of the available registries; best practises about schema versioning; strategies around schema federation across different companies such as Snowplow’s own Iglu Central.
Open stack ocata summit enabling aws lambda-like functionality with openstac...Shaun Murakami
Presentation delivered at the OpenStack summit Barcelona 2016.
https://www.openstack.org/videos/video/enabling-aws-s3-lambda-like-functionality-with-openstack-swift-and-openwhisk
Does the concept of server-less architecture intrigue you? OpenWhisk (https://git.io/vKeu3) accelerates innovation through creative chaining of microservices into highly scalable applications. By abstracting away infrastructure, OpenWhisk frees small teams to rapidly work on independent pieces of code simultaneously, keeping development focused solely on creating essential business logic. OpenWhisk allows you to create rules to connect events with actions and compose microservices that get executed independently and in parallel.
With a bit of code, you can have OpenWhisk process events from your Swift Object Storage; similar to what you can do with Lambda functions and AWS S3 storage. As an example, we will demonstrate how you can create an OpenWhisk action to transform an image into a thumbnail whenever a new (larger) image is uploaded into a Swift Container.
(DVO205) Monitoring Evolution: Flying Blind to Flying by InstrumentAmazon Web Services
Today, AdRoll runs its infrastructure by instrumentation: constantly asking empirical questions, analyzing data for answers, and designing new features with instrumentation in mind to understand how functionality will work upon release. AdRoll’s development methodology did not start out this way, however. It took a cultural shift and many new tools and processes to adopt this approach. In this session, AdRoll and Datadog will discuss how to evolve your organization from a state of “flying blind” to a culture focused on monitoring and data-based decisions. Session sponsored by Datadog.
Serverless applications increasingly involve distributed systems where errors and bottlenecks can have significant downstream impact. This can be compounded by the ephemeral nature of FaaS offerings in which errors can be difficult to diagnose retroactively. In this session we'll discuss instrumentation and "self-healing" architectural patterns that will improve resiliency of your application and drive improved observability and performance.
Npm has modules for devops, like logging, metrics, service discovery. But when you arrive to production, you may find that these are already handled by old players. Avoid the same mistakes I did, when my first node app was on its way to the world.
Processing TeraBytes of data every day and sleeping at nightLuciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for assessment and threat hunting. Building this kind of stuff on AWS is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story. In this talk, we will tell you how we achieved this ambitious goal and how we missed a few good nights of sleep while trying to do that! Spoiler alert: contains AWS, serverless, elastic search, monitoring, alerting & more!
Processing TeraBytes of data every day and sleeping at nightLuciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for assessment and threat hunting.
Building this kind of stuff in the cloud is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story.
In this talk, we will tell you how we achieved this ambitious goal and how we missed a few good nights of sleep while trying to do that!
Spoiler alert: contains AWS, serverless, elastic search, monitoring, alerting & more!
Processing Terabytes of data every day … and sleeping at night (infiniteConf ...Luciano Mammino
This is the story of how we built a highly available data pipeline that processes terabytes of network data every day, making it available to security researchers for security assessment and threat hunting.
Building this kind of stuff in the cloud is not that complicated, but if you have to make it near real-time, fault tolerant and 24/7 available, well... that's another story. In this talk, Luciano and Domagoj will tell you how they achieved this ambitious goal and how they missed a few good nights of sleep while trying to do that!
Spoiler alert: contains AWS, lambda, elastic search, monitoring, alerting & more!
What Crimean War gunboats teach us about the need for schema registriesAlexander Dean
In 1853 Britain’s workshops built 90 new gunboats for the Royal Navy in just 90 days: an astonishing feat of engineering. Industrial standardization made this possible - and in this talk, my first at Strata, I argued that data-sophisticated corporations need a new standardization of their own, in the form of schema registries like Confluent Schema Registry or Snowplow’s own Iglu.
Talk abstract:
At the start of the Crimean War in 1853, Britain's Royal Navy needed 90 new gunboats ready to fight in the Baltic in just 90 days. Assembling the boats was straightforward - the challenge was to build all of the engine sets in time. Marine engineer John Penn did an unusual thing: he took a pair of reference engines, disassembled them and distributed the pieces to the best machine shops across Britain. These workshops - latter-day micro-services - each built 90 sets of their allocated parts, which were then assembled into the engines for the new gunboats, ready for battle.
This was the nineteenth century - how could the Admiralty be certain that the parts from all these independent workshops would come together to form 90 high-powered engines? The answer lay in a crucial piece of standardization: the Whitworth thread, the world’s first national screw thread standard, devised by Sir Joseph Whitworth in 1841. By the time the Royal Navy came knocking, this standard had been adopted by workshops across Britain; John Penn could be confident that engine parts built by any workshop to the Whitworth standard would fit together.
In this talk, Snowplow co-founder Alexander Dean will draw on the story of the Crimean War gunboats to argue that our data processing architectures urgently require a standardization of their own, in the form of schema registries. Like the Whitworth screw thread, a schema registry, such as Confluent Schema Registry or Snowplow’s own Iglu, allows enterprises to standardise on a set of business entities which can be used throughout their batch and stream processing architectures. Like the artisanal workshops in 1850s Britain, micro-services can work on narrowly defined data processing tasks, confident that their inputs and outputs will be compatible with their peers.
This talk will start with the rationale for putting a schema registry at the heart of your business, before moving on to the practicalities of an implementation, including: a side-by-side comparison of the available registries; best practises about schema versioning; strategies around schema federation across different companies such as Snowplow’s own Iglu Central.
Open stack ocata summit enabling aws lambda-like functionality with openstac...Shaun Murakami
Presentation delivered at the OpenStack summit Barcelona 2016.
https://www.openstack.org/videos/video/enabling-aws-s3-lambda-like-functionality-with-openstack-swift-and-openwhisk
Does the concept of server-less architecture intrigue you? OpenWhisk (https://git.io/vKeu3) accelerates innovation through creative chaining of microservices into highly scalable applications. By abstracting away infrastructure, OpenWhisk frees small teams to rapidly work on independent pieces of code simultaneously, keeping development focused solely on creating essential business logic. OpenWhisk allows you to create rules to connect events with actions and compose microservices that get executed independently and in parallel.
With a bit of code, you can have OpenWhisk process events from your Swift Object Storage; similar to what you can do with Lambda functions and AWS S3 storage. As an example, we will demonstrate how you can create an OpenWhisk action to transform an image into a thumbnail whenever a new (larger) image is uploaded into a Swift Container.
(DVO205) Monitoring Evolution: Flying Blind to Flying by InstrumentAmazon Web Services
Today, AdRoll runs its infrastructure by instrumentation: constantly asking empirical questions, analyzing data for answers, and designing new features with instrumentation in mind to understand how functionality will work upon release. AdRoll’s development methodology did not start out this way, however. It took a cultural shift and many new tools and processes to adopt this approach. In this session, AdRoll and Datadog will discuss how to evolve your organization from a state of “flying blind” to a culture focused on monitoring and data-based decisions. Session sponsored by Datadog.
Netflix changed its data pipeline architecture recently to use Kafka as the gateway for data collection for all applications which processes hundreds of billions of messages daily. This session will discuss the motivation of moving to Kafka, the architecture and improvements we have added to make Kafka work in AWS. We will also share the lessons learned and future plans.
Eine Client-Server-Architektur stellt besondere Anforderungen an die Client-Server-Kommunikation. Einerseits wird Sparsamkeit angestrebt, andererseits absolute Flexibilität, Wiederverwendbarkeit und Wartbarkeit. Gerade im GWT-Umfeld fehlen clientseitig eine vollwertige JVM und das Reflection-API. Hinzu kommt noch der teilweise ungewohnte Umgang mit den asynchronen Aufrufen. In diesem Vortrag wird das Command Pattern vorgestellt. Es werden konkrete Lösungsansätze für Batching, Caching, Security und Journaling vorgestellt.
(DVO204) Monitoring Strategies: Finding Signal in the NoiseAmazon Web Services
"You need to monitor only a few machines and applications before fixing issues in your environment becomes very complicated. Throw in the type of dynamic infrastructure provided by Amazon EC2, and your static monitoring strategies will most likely not scale. Knowing which metrics to watch and how to troubleshoot based on those metrics will help you solve problems more quickly. In this session, we will look at a framework for your metrics and how to use it to find solutions to the issues that come up. We will cover the three types of monitoring data; what to collect; what should trigger an alert (avoiding an alert storm); and how to follow the resources to find the root causes of problems. Session sponsored by Datadog.
"
From Zero to Hadoop: a tutorial for getting started writing Hadoop jobs on Am...Alexander Dean
Hadoop is everywhere these days, but it can seem like a complex, intimidating ecosystem to those who have yet to jump in.
In this hands-on workshop, Alex Dean, co-founder of Snowplow Analytics, will take you "from zero to Hadoop", showing you how to run a variety of simple (but powerful) Hadoop jobs on Elastic MapReduce, Amazon's hosted Hadoop service. Alex will start with a no-nonsense overview of what Hadoop is, explaining its strengths and weaknesses and why it's such a powerful platform for data warehouse practitioners. Then Alex will help get you setup with EMR and Amazon S3, before leading you through a very simple job in Pig, a simple language for writing Hadoop jobs. After this we will move onto writing a more advanced job in Scalding, Twitter's Scala API for writing Hadoop jobs. For our final job, we will consolidate everything we have learnt by building a more sophisticated job in Scalding.
DevOps Days Tel Aviv - Serverless ArchitectureAntons Kranga
Slides from Serverless Architecture with AWS workshop that has been delivered in Tel Aviv at December 2016 and XP Days in Kyiv at November. We go in details about AWS Lambda and give few implementation blueprints targeted to web applications
In this Meetup Yaar Reuveni – Team Leader & Nir Hedvat – Software Engineer from Liveperson Data Platform R&D team, will talk about the journey we made from early days of the data platform in production with high friction and low awareness to issues into a mature, measurable data platform that is visible and trustworthy.
Serverless is aiming to be the future of software development, but what does it really mean running without servers? In this session we will explain how to build a serverless application on top of AWS. We will understand how AWS Lambda functions work, how to use them properly and how can we debug and monitor serverless application.
Micro-Services and cloud-based applications demand robust and flexible CI/CD pipelines. Manually creating a Jenkins Job using the UI is just not an option any more, but which tool should we use? In this talk we will cover Jenkns Pipeline, Gitlab CI and Concourse. We will see the use case for every tool and when should we use it.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1MaB0Lv.
Evan Krall talks about Paasta, which is Yelp's platform for running services, built on Docker, Mesos, Marathon, SmartStack, git, and Jenkins. Filmed at qconnewyork.com.
Evan Krall is a Site Reliability Engineer at Yelp, and has been hacking on Docker at Yelp since 2013.
Keystone processes over 1 trillion events per day with at-least once processing semantics in the cloud. We will explore in detail how we have modified and leverage Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in the Amazon AWS cloud within a year.
Lambda is the next stage in the evolution of the AWS platform. It allows you to build reactive, event-driven systems that are easy to deploy, update and scale. Amazon manages all the undifferentiated heavy-lifting for you so you can focus on delivering value to your customers with even greater speed and cost efficiency.
Join Yan in this talk as we take a deep dive through AWS Lambda and the Serverless framework.
We'll see how to start building reactive systems using AWS Lambda, Kinesis and API Gateway, without having to manage any servers. And, you only pay for your services when they are used. We'll discuss lessons learned, best practices and current limitations with AWS Lambda.
We'll also get to know the Serverless framework, which helps automate both deployment and versioning so that you can better focus on the things that matter to your customers.
URP? Excuse You! The Three Kafka Metrics You Need to KnowTodd Palino
What do you really know about how to monitor a Kafka cluster for problems? Is your most reliable monitoring your users telling you there’s something broken? Are you capturing more metrics than the actual data being produced? Sure, we all know how to monitor disk and network, but when it comes to the state of the brokers, many of us are still unsure of which metrics we should be watching, and what their patterns mean for the state of the cluster. Kafka has hundreds of measurements, from the high-level numbers that are often meaningless to the per-partition metrics that stack up by the thousands as our data grows.
We will thoroughly explore three key monitoring concepts in the broker, that will leave you an expert in identifying problems with the least amount of pain:
Under-replicated Partitions: The mother of all metrics
Request Latencies: Why your users complain
Thread pool utilization: How could 80% be a problem?
We will also discuss the necessity of availability monitoring and how to use it to get a true picture of what your users see, before they come beating down your door!
Serverless design considerations for Cloud Native workloadsTensult
We have built a news website with more than a billion views per month and we are sharing the learnings from that experience covering Serverless architectures, Design considerations, and Gotchas.
Netflix changed its data pipeline architecture recently to use Kafka as the gateway for data collection for all applications which processes hundreds of billions of messages daily. This session will discuss the motivation of moving to Kafka, the architecture and improvements we have added to make Kafka work in AWS. We will also share the lessons learned and future plans.
Eine Client-Server-Architektur stellt besondere Anforderungen an die Client-Server-Kommunikation. Einerseits wird Sparsamkeit angestrebt, andererseits absolute Flexibilität, Wiederverwendbarkeit und Wartbarkeit. Gerade im GWT-Umfeld fehlen clientseitig eine vollwertige JVM und das Reflection-API. Hinzu kommt noch der teilweise ungewohnte Umgang mit den asynchronen Aufrufen. In diesem Vortrag wird das Command Pattern vorgestellt. Es werden konkrete Lösungsansätze für Batching, Caching, Security und Journaling vorgestellt.
(DVO204) Monitoring Strategies: Finding Signal in the NoiseAmazon Web Services
"You need to monitor only a few machines and applications before fixing issues in your environment becomes very complicated. Throw in the type of dynamic infrastructure provided by Amazon EC2, and your static monitoring strategies will most likely not scale. Knowing which metrics to watch and how to troubleshoot based on those metrics will help you solve problems more quickly. In this session, we will look at a framework for your metrics and how to use it to find solutions to the issues that come up. We will cover the three types of monitoring data; what to collect; what should trigger an alert (avoiding an alert storm); and how to follow the resources to find the root causes of problems. Session sponsored by Datadog.
"
From Zero to Hadoop: a tutorial for getting started writing Hadoop jobs on Am...Alexander Dean
Hadoop is everywhere these days, but it can seem like a complex, intimidating ecosystem to those who have yet to jump in.
In this hands-on workshop, Alex Dean, co-founder of Snowplow Analytics, will take you "from zero to Hadoop", showing you how to run a variety of simple (but powerful) Hadoop jobs on Elastic MapReduce, Amazon's hosted Hadoop service. Alex will start with a no-nonsense overview of what Hadoop is, explaining its strengths and weaknesses and why it's such a powerful platform for data warehouse practitioners. Then Alex will help get you setup with EMR and Amazon S3, before leading you through a very simple job in Pig, a simple language for writing Hadoop jobs. After this we will move onto writing a more advanced job in Scalding, Twitter's Scala API for writing Hadoop jobs. For our final job, we will consolidate everything we have learnt by building a more sophisticated job in Scalding.
DevOps Days Tel Aviv - Serverless ArchitectureAntons Kranga
Slides from Serverless Architecture with AWS workshop that has been delivered in Tel Aviv at December 2016 and XP Days in Kyiv at November. We go in details about AWS Lambda and give few implementation blueprints targeted to web applications
In this Meetup Yaar Reuveni – Team Leader & Nir Hedvat – Software Engineer from Liveperson Data Platform R&D team, will talk about the journey we made from early days of the data platform in production with high friction and low awareness to issues into a mature, measurable data platform that is visible and trustworthy.
Serverless is aiming to be the future of software development, but what does it really mean running without servers? In this session we will explain how to build a serverless application on top of AWS. We will understand how AWS Lambda functions work, how to use them properly and how can we debug and monitor serverless application.
Micro-Services and cloud-based applications demand robust and flexible CI/CD pipelines. Manually creating a Jenkins Job using the UI is just not an option any more, but which tool should we use? In this talk we will cover Jenkns Pipeline, Gitlab CI and Concourse. We will see the use case for every tool and when should we use it.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1MaB0Lv.
Evan Krall talks about Paasta, which is Yelp's platform for running services, built on Docker, Mesos, Marathon, SmartStack, git, and Jenkins. Filmed at qconnewyork.com.
Evan Krall is a Site Reliability Engineer at Yelp, and has been hacking on Docker at Yelp since 2013.
Keystone processes over 1 trillion events per day with at-least once processing semantics in the cloud. We will explore in detail how we have modified and leverage Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in the Amazon AWS cloud within a year.
Lambda is the next stage in the evolution of the AWS platform. It allows you to build reactive, event-driven systems that are easy to deploy, update and scale. Amazon manages all the undifferentiated heavy-lifting for you so you can focus on delivering value to your customers with even greater speed and cost efficiency.
Join Yan in this talk as we take a deep dive through AWS Lambda and the Serverless framework.
We'll see how to start building reactive systems using AWS Lambda, Kinesis and API Gateway, without having to manage any servers. And, you only pay for your services when they are used. We'll discuss lessons learned, best practices and current limitations with AWS Lambda.
We'll also get to know the Serverless framework, which helps automate both deployment and versioning so that you can better focus on the things that matter to your customers.
URP? Excuse You! The Three Kafka Metrics You Need to KnowTodd Palino
What do you really know about how to monitor a Kafka cluster for problems? Is your most reliable monitoring your users telling you there’s something broken? Are you capturing more metrics than the actual data being produced? Sure, we all know how to monitor disk and network, but when it comes to the state of the brokers, many of us are still unsure of which metrics we should be watching, and what their patterns mean for the state of the cluster. Kafka has hundreds of measurements, from the high-level numbers that are often meaningless to the per-partition metrics that stack up by the thousands as our data grows.
We will thoroughly explore three key monitoring concepts in the broker, that will leave you an expert in identifying problems with the least amount of pain:
Under-replicated Partitions: The mother of all metrics
Request Latencies: Why your users complain
Thread pool utilization: How could 80% be a problem?
We will also discuss the necessity of availability monitoring and how to use it to get a true picture of what your users see, before they come beating down your door!
Serverless design considerations for Cloud Native workloadsTensult
We have built a news website with more than a billion views per month and we are sharing the learnings from that experience covering Serverless architectures, Design considerations, and Gotchas.
Stephen Liedig: Building Serverless Backends with AWS Lambda and API GatewaySteve Androulakis
Stephen Liedig (Amazon Web Services) is a Public Sector Solutions Architect at AWS working closely with local and state governments, educational institutions, and non-profit organisations across Australia and New Zealand to design, and deliver, highly secure, scalable, reliable and fault-tolerant architectures in the AWS Cloud while sharing best practices and current trends, with a specific focus on DevOps, messaging, and serverless technologies.
Learn how to monitor and manage your serverless APIs in production. We show you how to set up Amazon CloudWatch alarms, interpret CloudWatch logs for Amazon API Gateway and AWS Lambda, and automate common maintenance and management tasks on your service.
Skillenza Build with Serverless Challenge - Advanced Serverless ConceptsDhaval Nagar
Skillenza is back with another game-changing virtual hackathon for you. Seize this amazing opportunity to create projects on serverless architecture. For those of you who are not acquainted with it, serverless architectures are system designs that use third-party services to build and run applications.
As developers, this helps you to gain better scalability and flexibility without needing any administration to manage infrastructure. So you can build quicker and at a reduced cost as well.
https://skillenza.com/challenge/build-with-serverless-online-hackathon-aws
As serverless architectures become more popular, AWS customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems.
As serverless architectures become more popular, AWS customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems.
Building Resilient Serverless Systems with Non-Serverless ComponentsJeremy Daly
Serverless functions (like AWS Lambda, Google Cloud Functions, and Azure Functions) have the ability to scale almost infinitely to handle massive workload spikes. While this is a great solution to compute, it can be a MAJOR PROBLEM for other downstream resources like RDBMS, third-party APIs, legacy systems, and even most managed services hosted by your cloud provider. Whether you’re maxing out database connections, exceeding API quotas, or simply flooding a system with too many requests at once, serverless functions can DDoS your components and potentially take down your application. In this talk, we’ll discuss strategies and architectural patterns to create highly resilient serverless applications that can mitigate and alleviate pressure on non-serverless downstream systems during peak load times.
Get the EDGE to scale: Using Cloudfront along with edge compute to scale your...Amazon Web Services
You could use Cloud Front to deliver pages faster, however, customized processing still required requests to be forwarded back to compute resources at centralized servers, which may slow down the end user experience. This session shows how a combination of Cloud Front, and edge compute can help you scale out your resources in a much more effective way than you think.
Speaker: Anil Nair
Solution Architect, Amazon India
Choosing the right messaging service for your serverless app [with lumigo]Dhaval Nagar
By their nature, serverless applications are highly-distributed and event-driven, relying heavily on relaying events from one service to another. With that in mind, selecting the right messaging service for routing events is critical for your serverless application's functionality and performance.
I reviewed the three major event-routing services on AWS -- SNS, SQS, and EventBridge. Also, examine their differences and which service is optimal for which use case.
Finally, looked at the best way to monitor and debug a serverless application that uses an event-routing messaging service
Serverless Architectures and Continuous DeliveryRobin Weston
Serverless architectures have been touted as the next evolution of cloud-hosted software. Indeed, the promise of resiliency and scalability without the need for infrastructure management sounds too good to be true!
But how well do serverless architectures play with the patterns and practises of continuous delivery? Do they help or hinder us in our goal of delivering frequent and low risk software changes to production? What are the trade-offs to weigh up when considering using a serverless architecture on your next project?
This presentation will give a brief initial introduction to the world of serverless architectures. We’ll then look at their benefits and drawbacks, with a particular focus on our recent experiences building and operating a production AWS Lambda and API Gateway system. We’ll also look at the ever-evolving tooling and service ecosystems, and make some suggestions regarding how to start safely dipping your toes in the serverless waters.
.NET Fest 2019. Stas Lebedenko. Practical serverless use cases in Azure with ...NETFest
Serverless technology is trending, but in-depth details are missing. How does it fit with non-serverless components? What are the practical use cases? Should you fight vendor lock-in? And what about limits and pitfalls with Azure? I will answer those questions, share a few tricks and short demo.
I'll cover serverless usage scenarios with Azure, what problems can be solved, and what is a viable adoption strategy. Then I'm going to talk about technology shortcomings, when to omit it and how to rip all benefits. There are circumstances when a cloud-agnostic approach is beneficial, so I discuss serverless frameworks too and why vendor lock is not that bad. Finally, we'll look at a short demo that illustrates why you have to use specific serverless patterns.
Similar to PDX Serverless Meetup - Self-Healing Serverless Applications (20)
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Unleash Unlimited Potential with One-Time Purchase
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A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
2. AWS | LAMBDA FEATURES PAGE
AWS Lambda invokes your code only when
needed and automatically scales to support the
rate of incoming requests without requiring you
to configure anything. There is no limit to the
number of requests your code can handle.
The Promise:
SELF-HEALING SERVERLESS APPLICATIONS | PG2
3. AWS | LAMBDA FEATURES PAGE
The Reality:
AWS Lambda invokes your code only when
needed and automatically scales to support the
rate of incoming requests without requiring you
to configure anything. There is no limit to the
number of requests your code can handle.
s
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SELF-HEALING SERVERLESS APPLICATIONS | PG3
4. What to expect
when you’re not expecting.
SELF-HEALING SERVERLESS APPLICATIONS | PG4
5. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
SELF-HEALING SERVERLESS APPLICATIONS | PG5
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
6. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
Synchronous invocations:
• Function fails
• Returns error to caller
• Logs timestamp, error message,
& stack trace to CloudWatch
Asynchronous invocations:
• Retries up to three times (or
more if reading from a stream)
• Caller is unaware of error
• Logs timestamp, error message,
& stack trace to CloudWatch
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
An event triggers your
Lambda to run, but raises
an unhandled exception
in your code.
SELF-HEALING SERVERLESS APPLICATIONS | PG6
7. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
Synchronous invocations:
• Lambda returns error to caller
(if client hasn’t timed out)
• Logs timestamp and error
message to CloudWatch
Asynchronous invocations:
• Retries up to three times (more
if reading from stream)
• Caller is unaware of error
• Logs timestamp & error
message to CloudWatch
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
An event triggers your
Lambda to run, but
execution does not
complete within the
configured maximum
execution time.
(Lambda’s default
configuration is a
3-second timeout.)
SELF-HEALING SERVERLESS APPLICATIONS | PG7
8. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
When noisy:
• Behaves as Uncaught
Exception
• Visible in CloudWatch, but may
be difficult to diagnose without
event visibility
When silent:
• Unexpected application
behavior
• Can be lost permanently
• Can tank performance and
dramatically spike costs
An event triggers your
Lambda to run, but the
message is malformed or
state is improperly
provided causing
unexpected behavior.
SELF-HEALING SERVERLESS APPLICATIONS | PG8
9. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
Unbuffered invocations:
• Fails to invoke
• No retry
• Visible in CloudWatch metrics,
but not in logs
Buffered invocations:
• Initially fails to invoke
• Will eventually continue
reading from stream as volume
drops
Your application becomes
throttled as more Lambda
instances are required
than are allowed to be
concurrently running by
AWS for your account.
Your compute can’t scale
high enough.
SELF-HEALING SERVERLESS APPLICATIONS | PG9
10. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
Unbuffered invocations:
• Fails to invoke
• No retry
• Visible in CloudWatch metrics,
nothing in logs
(but really non-obvious)
Buffered invocations:
• Initially fails to invoke
• Will eventually continue
reading from stream as volume
drops
Your application becomes
throttled as more new
Lambda instances are
required than are allowed
to spawn by AWS for your
account.
Your compute can’t scale
fast enough.
SELF-HEALING SERVERLESS APPLICATIONS | PG10
11. FAILURE TYPES DESCRIPTION
Common Serverless Failures
FOR LAMBDA-BASED ARCHITECTURES
DEFAULT BEHAVIOR
• Runtime Error:
• Uncaught Exception
• Timeout
• Bad State
• Scaling:
• Concurrency Limits
• Spawn Limits
• Bottlenecking
Upstream bottlenecks:
• Fails to invoke
• No retry
• Visible in CloudWatch, as long
as you know where to look
Downstream bottlenecks:
• Can throw error, timeout,
and/or distribute failures to
other functions.
• Can cause cascading failures
• Can tank performance and
dramatically spike costs
Your application is
throttled due to
throughput pressure
upstream or downstream
of your Lambda.
Your architecture can’t
scale enough.
SELF-HEALING SERVERLESS APPLICATIONS | PG11
13. Self-Healing Design Principles
LEADING PRACTICES FOR RESILIENT SYSTEMS
STANDARDIZE FAIL GRACEFULLY
• Reroute and unblock
• Automate known
solutions
• Notify a human
SELF-HEALING SERVERLESS APPLICATIONS | PG13
Learn to fail.
• Introduce universal
instrumentation
• Collect event-centric
diagnostics
• Give everyone visibility
PLAN FOR FAILURE
• Identify service limits
• Use self-throttling
• Consider alternative
resource types
15. Scenario: Uncaught Exceptions
WHEN THINGS BREAK AND YOU DON’T KNOW WHY
PROBLEM
Lambda periodically fails.
Error messages and stack
traces are visible in
CloudWatch logs. Failing
events are lost, making
reproduction difficult.
KEY PRINCIPLES
• Introduce universal
instrumentation
• Collect event-centric
diagnostics
• Give everyone visibility
SOLUTION
• Use function wrapper or
decorator pattern
• Capture and log events
which fail
SELF-HEALING SERVERLESS APPLICATIONS | PG15
Decrease time to resolution by capturing event data.
17. WHEN YOUR LAMBDAS AREN’T GETTING INVOKED
PROBLEM
API Gateway hits
throughput limits and fails
to invoke Lambda on
every request.
KEY PRINCIPLES
• Identify service limits
• Use self-throttling
• Notify a human
SOLUTION
• Implement retries with
exponential backoff
logic for 429 responses
• Raise alarm on:
4XXError
Scenario: Upstream bottleneck
SELF-HEALING SERVERLESS APPLICATIONS | PG17
Don’t overlook client-side solutions to backend failures.
19. WHEN EXECUTION TAKES TOO LONG
PROBLEM
Lambda is periodically
timing out.
KEY PRINCIPLES
• Introduce universal
instrumentation
• Use self-throttling
• Consider alternative
resource types
SOLUTION
• Use function wrapper or
decorator pattern
• Evaluate Fargate or
alternative long-running
resources
Scenario: Timeouts
SELF-HEALING SERVERLESS APPLICATIONS | PG19
Enforce your own limits.
21. WHEN FAILURES ARE BLOCKING THE REST OF THE STREAM
PROBLEM
Lambda exceptions and/or
timeouts are blocking
processing of a Kinesis
shard.
KEY PRINCIPLES
• Reroute and unblock
• Automate known
solutions
• Consider alternative
resource types
SOLUTION
• Introduce state machine-
type logic
• Move bad messages to
alternate stream
• Potentially architect with
Fargate or SNS
Scenario: Stream processing gets “stuck”
SELF-HEALING SERVERLESS APPLICATIONS | PG21
Small failures are preferable to large ones.
22. PROBLEM
Your Lambdas have scaled
up but are depleting your
RDS database connection
pools.
KEY PRINCIPLES
• Identify service limits
• Automate known
solutions
• Give everyone visibility
SOLUTION
• Always close database
connections
• Scale your database
• Map your dependencies
Scenario: Downstream bottleneck
WHEN LAMBDA IS OUT-SCALING YOUR DATABASE
SELF-HEALING SERVERLESS APPLICATIONS | PG22
Scale dependencies, too.