Most Active Speaker

Vadym Kazulkin

Vadym Kazulkin

Head of Development at ip.labs in Bonn, Germany

Bonn, Germany

Actions

Vadym Kazulkin is Head of Development at ip.labs GmbH, a 100% subsidiary of the FUJIFLM Group, based in Bonn. ip.labs is the world's leading white label e-commerce software imaging company. Vadym has been involved with the Java ecosystem for over twenty years. His focus and interests currently include the design and implementation of highly scalable and available applications, Serverless and AWS Cloud. Vadym is the co-organizer of the Java User Group Bonn meetup and AWS Community Builder in the Serverless category and a frequent speaker at various Meetups and conferences.

Awards

  • Most Active Speaker 2023

Area of Expertise

  • Information & Communications Technology
  • Business & Management

Topics

  • Backend Developer
  • Cloud & DevOps
  • Serverless
  • aws
  • Java & JVM
  • Spring Boot
  • cloud
  • DevOps
  • Serverless computing
  • AWS Community Day
  • AWS DevOps
  • Serverless Day
  • Java Conference

Revolutionize DevOps with ML capabilities. Deep dive into Amazon CodeGuru and DevOps Guru

AWS is on a journey to revolutionize DevOps using the latest technologies. AWS thinks of it this way: code, logs, and application metrics are all data that we can optimize with machine learning (ML).
In this talk I will deep dive into two AWS completely managed Serverless services: CodeGuru and DevOps Guru and explore their capabilities on the concrete practical examples.
Amazon CodeGuru Reviewer uses ML and automated reasoning to automatically identify critical issues, security vulnerabilities, and hard-to-find bugs during application development. I also provides recommendations to developers on how to fix issues to improve code quality and dramatically reduce the time it takes to fix bugs before they reach customer-facing applications and result in a bad experience
Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

AWS is on a journey to revolutionize DevOps using the latest technologies. AWS thinks of it this way: code, logs, and application metrics are all data that we can optimize with machine learning (ML).
In this talk I will deep dive into two AWS completely managed Serverless services: CodeGuru and DevOps Guru and explore their capabilities on the concrete practical examples.

Amazon CodeGuru vs SonarQube for Java Developers on AWS

In this talk I will compare 2 services which aim at automatically identifing critical issues, security vulnerabilities, and hard-to-find bugs during application development: Amazon CodeGuru and SonarQube from the perspective of the Java developer on AWS. Amazon CodeGuru Reviewer uses ML and automated reasoning to provide recommendations to developers on how to fix issues to improve code quality and dramatically reduce the time it takes to fix bugs before they reach customer-facing applications and result in a bad experience. SonarQube is an open-source platform for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs, code smells, and security vulnerabilities on 20+ programming languages. SonarQube offers reports on duplicated code, coding standards, unit tests, code coverage, code complexity, comments, bugs, and security vulnerabilities

In this talk I will compare 2 services which aim at automatically identifing critical issues, security vulnerabilities, and hard-to-find bugs during application development: Amazon CodeGuru and SonarQube from the perspective of the Java developer on AWS.

Detect operational anomalies in Serverless applications with Amazon DevOps Guru

In this talk we’ll use a standard serverless application that uses API Gateway, Lambda, DynamoDB, SQS, SNS, Kinesis, Step Functions, Aurora (Serverless) (and other AWS-managed services). We'll explore how Amazon DevOps Guru recognizes operational issues and anomalies like increased latency and error rates (timeouts, throttling and increased latency). We will also explore DevOps Guru "Proactive Insights" which recognize configurational anti-patterns like missing failure destination on Kinesis Data Streams or DLQ on SQS or over-provisioning of AWS services like DynamoDB tables. We'll also integrate DevOps Guru with PagerDuty to provide even better incident management. We'll also investigate current shortcomings of the DevOps Guru service.

Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

In this talk we’ll use a standard serverless application that uses API Gateway, Lambda, DynamoDB, SQS, SNS, Kinesis, Step Functions, Aurora (Serverless) (and other AWS-managed services). We'll explore how Amazon DevOps Guru recognizes operational issues and anomalies like increased latency and error rates (timeouts, throttling and increased latency). We will also explore DevOps Guru "Proactive Insights" which recognize configurational anti-patterns like missing failure destination on Kinesis Data Streams or DLQ on SQS or over-provisioning of AWS services like DynamoDB tables. We'll also integrate DevOps Guru with PagerDuty to provide even better incident management.

Github Copilot vs Amazon CodeWhisperer for Java developers

In this talk I will compare 2 services Github Copilot (including Copilot X) and Amazon CodeWhisperer from the perspective of the Java developers in terms of the quality of the given recommendations for simple tasks, complex algorithms, Spring Boot and AWS development, IDE integration and pricing.

Both services are the machine learning-powered services that help improve developer productivity by generating code recommendations based on developers’ comments in natural language and their code. Based on natural language comments, these services also automatically recommend unit test code that matches your implementation code.

In this talk I will compare 2 services Github Copilot (including Copilot X) and Amazon CodeWhisperer from the perspective of the Java developers in terms of the quality of the given recommendations for simple tasks, complex algorithms, Spring Boot and AWS development, IDE integration and pricing.

Both services are the machine learning-powered services that help improve developer productivity by generating code recommendations based on developers’ comments in natural language and their code.

Making sense of service quotas of AWS Serverless services and how to deal with them

There is a misunderstanding, that everything is possible with the Serverless Services in AWS, for example that your Lambda function may scale without limitations .
But each AWS service (not only Serverless) has a big list of quotas that everybody needs to be aware of, understand and take into account during the development.

In this talk I'll explain the most important quotas (from the hyper scalability point of view, but not only) of the Serverless Services like API Gateway, Lambda, DynamoDB, SQS and Aurora Serverless and how to architect your solution with these quotas in mind.

In this talk I'll explain the most important quotas of Serverless Services like API Gateway, Lambda, DynamoDB, SQS and Aurora Serverless and how to architect your solution with these quotas in mind.

How to reduce cold starts for Java Serverless applications in AWS: GraalVM, AWS SnapStart and Co

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud provider improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java developers to reduce the cold start times like GraalVM Native Image and AWS Lambda SnapStart based on on FirecrackerVM snapshot and CRaC (Coordinated Restore at Checkpoint) project.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud providers improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java developers to reduce the cold start times like GraalVM Native Image and AWS Lambda SnapStart based on CRaC (Coordinated Restore at Checkpoint) project.

How to reduce cold starts for Java Serverless applications with Sping Boot on AWS

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud provider improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java Serverless developers using Spring Boot to reduce the cold start times like GraalVM Native Image and AWS Lambda SnapStart based on CRaC (Coordinated Restore at Checkpoint) project

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud provider improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java Serverless developers using Spring Boot to reduce the cold start times like GraalVM Native Image and AWS SnapStart based on CRaC (Coordinated Restore at Checkpoint) Project.

Amazon CodeGuru and CodeWhisperer for Java Developers on AWS

In this talk I will talk about 2 AWS Services which aim at increasing the productivity of Java developers on AWS: Amazon CodeGuru and CodeWhisperer. We'll go through some practical examples how to analyze the Java application with the help of these services.

Amazon CodeGuru Reviewer uses ML and automated reasoning to provide recommendations to developers on how to fix issues to improve code quality and dramatically reduce the time it takes to fix bugs before they reach customer-facing applications and result in a bad experience.

Amazon CodeWhisperer is the machine learning-powered services that help improve developer productivity by generating code recommendations based on developers’ comments in natural language and their code. Based on natural language comments, these services also automatically recommend unit test code that matches your implementation code.

In this talk I will talk about 2 AWS Services which aim at increasing the productivity of Java developers on AWS: Amazon CodeGuru and CodeWhisperer. We'll go through some practical examples how to analyze the Java application with the help of these services.

From starting the experiment to the DevOps Guru insight in less than 10 minutes

In this lightning talk a will design the experiment (e.g. throttling, time out or latency) on the Serverless service
(Lambda, API Gateway, DynamoDB, SQS, Kinesis Data Streams or Step Functions) a let DevOps Guru Service recognize the incident
and alert me. The goal is to show the community the functionality of the amazing DevOps
Guru service. I can let the community decide, what experiment to run.

In this lightning talk a will design the experiment (e.g. throttling, time out or latency) on the Serverless service
(Lambda, API Gateway, DynamoDB, SQS, Kinesis Data Streams or Step Functions) a let DevOps Guru Service recognize the incident
and alert me. The goal is to show the community the functionality of the amazing DevOps
Guru service.

Hey ChatGPT, help me build Serverless application on AWS

In this talk we'll explore the capabilities of ChatGPT to help us build Serverless application which uses of API Gateway, Lambda, DynamoDB, DynamoDB Streams, SQS and other AWS managed services. We'll look not only at the quality of code suggestions but also at quality of the suggested Infrastructure as a Code with AWS SAM and other tools.

In this talk we'll explore the capabilities of ChatGPT to help us build Serverless application which uses of API Gateway, Lambda, DynamoDB, DynamoDB Streams, SQS and other AWS managed services. We'll look not only at the quality of code suggestions but also at quality of the suggested Infrastructure as a Code with AWS SAM and other tools.

AWS Lambda SnapStart: Why, How and What

In this talk I will cover the following topics:

- Challenges of AWS Serverless applications written in Java
- Challenges and limitations of existing solutions like Graal VM Native Image
- What is AWS SnapStart and how it addresses those challenges
- Benchmarking AWS Lambda SnapStart using plain Java and also frameworks like Quarkus, Micronaut and SpringBoot
- Optimization techniques like Priming
- Current challenges and limitations of AWS Lambda SnapStart

In this talk I will cover the following topics:

- Challenges of AWS Serverless applications written in Java
- Challenges and limitations of existing solutions like Graal VM Native Image
- What is AWS SnapStart and how it addresses those challenges
- Benchmarking AWS Lambda SnapStart using plain Java and also frameworks like Quarkus, Micronaut and SpringBoot
- Optimization techniques like Priming
- Current challenges and limitations of AWS Lambda SnapStart

Workshop ML-based Amazon DevOps Guru for the Serverless applications

In this workshop, we’ll use a standard serverless application that uses API Gateway, Lambda, DynamoDB, S3, SQS, Kinesis, Step Functions (and other AWS-managed services). We'll explore how Amazon DevOps Guru recognizes operational issues and anomalies like increased latency and error rates (timeouts, throttling and increased latency). We will also explore DevOps Guru "Proactive Insights" which recognize configurational anti-patterns like missing failure destination on Kinesis Data Streams or DLQ on SQS or over-provisioning of AWS services like DynamoDB tables. We'll also integrate DevOps Guru with PagerDuty to provide even better incident management.

Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

In this workshop, we’ll use a standard serverless application that uses API Gateway, Lambda, DynamoDB, S3, SQS, Kinesis, Step Functions (and other AWS-managed services). We'll explore how Amazon DevOps Guru recognizes operational issues and anomalies like increased latency and error rates (timeouts, throttling and increased latency). We will also explore DevOps Guru "Proactive Insights" which recognize configurational anti-patterns like missing failure destination on Kinesis Data Streams or DLQ on SQS or over-provisioning of AWS services like DynamoDB tables. We'll also integrate DevOps Guru with PagerDuty to provide even better incident management.

Revolutionizing DevOps lifecycle with Amazon CodeCatalyst and DevOps Guru

AWS is on a journey to revolutionize DevOps using the latest technologies. In this talk I'll introduce 2 Amazon services which cover different stages of the DevOps lifecycle: CodeCatalyst and DevOps Guru.

Amazon CodeCatalyst is an integrated service for software development teams adopting continuous integration and deployment practices into their software development process. CodeCatalyst puts the tools you need all in one place. You can plan work, collaborate on code, and build, test, and deploy applications with continuous integration/continuous delivery (CI/CD) tools. You can also integrate AWS resources with your projects by connecting your AWS accounts to your CodeCatalyst space. By managing all of the stages and aspects of your application lifecycle in one tool, you can deliver software quickly and confidently.

Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

High performance Serverless Java on AWS

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

Java meets Generative AI on AWS

In this talk we'll explore capabilities of the Generative AI Services for Java developers on AWS like Amazon Bedrock and Amazon Q Code Transformation.

Amazon Bedrock is a fully managed service for building and scaling generative AI applications that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities you need to build generative AI applications, simplifying development while maintaining privacy and security.

Amazon Q Code Transformation simplifies upgrading and modernizing existing application code using Amazon Q, a new type of assistant powered by generative artificial intelligence (AI). Amazon Q Code Transformation can perform Java application upgrades which we'll explore on our example of upgrading sample Java application to the newer version.

In this talk we'll explore capabilities of the Generative AI Services for Java developers on AWS like Amazon Bedrock and Amazon Q Code Transformation.

Exploring Data API for Amazon Aurora Serverless v2

In this talk I briefly explain the benefits of the Amazon Aurora Serverless v2 in general and the features behind the Data API for Amazon Aurora Serverless v2. I will use AWS SDK for Java to explore the different flavours of Data API: (batch) statement execution and working with database transactions. I will also provide Data API performance metrics (for example making Data API call to Aurora Serverless v2 from AWS Lambda) and compare it with the metrics of other database session management solutions like RDS Proxy. I'll also discuss different performance optimization techniques, Data API current constraints and make some comparisons between Data APIs for Amazon Aurora Serverless for v1 and v2.

In this talk I briefly explain the benefits of the Amazon Aurora Serverless v2 in general and the features behind the Data API for Amazon Aurora Serverless v2. I will use AWS SDK for Java to explore the different flavours of Data API: (batch) statement execution and working with database transactions. I will also provide Data API performance metrics (for example making Data API call to Aurora Serverless v2 from AWS Lambda) and compare it with the metrics of other database session management solutions like RDS Proxy . I'll also discuss different performance optimization techniques, Data API current constraints and make some comparisons between Data APIs for Amazon Aurora Serverless for v1 and v2.

How to develop, run and optimize Spring Boot 3 application on AWS Lambda

In this talk I will present and compare several options of how to run Spring Boot 3 application on AWS Lambda using AWS Serverless Java Container, Spring Cloud Function, AWS Lambda Web Adapter and Custom Docker Image. I'll also discuss strategies how to optimize cold start of such Lambda function with AWS Custom Lambda Runtime based on GraalVM Native Image and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. Is Spring Boot native support of CRaC also a potential optimization? I'll also present various (SnapStart) priming techniques to reduce the cold start even further.

In this talk I will present and compare several options of how to run Spring Boot 3 application on AWS Lambda using AWS Serverless Java Container, Spring Cloud Function, AWS Lambda Web Adapter and Custom Docker Image. I'll also discuss strategies how to optimize cold start of such Lambda function with AWS Custom Lambda Runtime based on GraalVM Native Image and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. Is Spring Boot native support of CRaC also a potential optimization? I'll also present various (SnapStart) priming techniques to reduce the cold start even further.

Developing highly scalable image storage solution with AWS Serverless at ip.labs

ip.labs is the world's leading white label e-commerce software imaging company and processes millions of images every day. The workflows of our users consist of designing, saving, loading, ordering and delivering to the printing facilities the photo products like prints, photobooks, calendars, gift products among others. In this talk we'll explore the challenges of our previous solution based on ALB, EC2, EBS and EFS, our motivation and architecture behind the reimplementation of our image storage solution based on AWS Serverless services like API Gateway, Lambda, DynamoDB, SQS, SNS, EventBridge and others, benefits that we've got with this new solution but also challenges we needed to overcome and trade-offs we had to make.

ip.labs is the world's leading white label e-commerce software imaging company and processes millions of images every day. The workflows of our users consist of designing, saving, loading, ordering and delivering to the printing facilities the photo products like prints, photobooks, calendars, gift products among others. In this talk we'll explore the challenges of our previous solution based on ALB, EC2, EBS and EFS, our motivation and architecture behind the reimplementation of our image storage solution based on AWS Serverless services like API Gateway, Lambda, DynamoDB, SQS, SNS, EventBridge and others, benefits that we've got with this new solution but also challenges we needed to overcome and trade-offs we had to make.

How to run and optimize Spring Boot 3 applications on AWS Lambda

In this talk I will present and compare several options of how to run Spring Boot 3 application on AWS Lambda using AWS Serverless Java Container, Spring Cloud Function, AWS Lambda Web Adapter and Custom Docker Image. I'll also discuss strategies how to optimize cold start of such Lambda function with AWS Custom Lambda Runtime based on GraalVM Native Image and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. Is Spring Boot native support of CRaC also a potential optimization? I'll also present various (SnapStart) priming techniques to reduce the cold start even further.

In this talk I will present and compare several options of how to run Spring Boot 3 application on AWS Lambda using AWS Serverless Java Container, Spring Cloud Function, AWS Lambda Web Adapter and Custom Docker Image. I'll also discuss strategies how to optimize cold start of such Lambda function with AWS Custom Lambda Runtime based on GraalVM Native Image and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. Is Spring Boot native support of CRaC also a potential optimization? I'll also present various (SnapStart) priming techniques to reduce the cold start even further.

Exploring relational database access over HTTP with Data API and AWS SDK for Java

In this talk I'll explore the AWS feature to access the relational Amazon Aurora Serverless v2 (PostgreSQL) database over HTTP with Data API. I will use AWS SDK for Java to explore the different flavours of Data API: (batch) statement execution and working with database transactions. I will also provide Data API performance metrics (for example making Data API call to Aurora Serverless v2 from AWS Lambda) and compare it with the metrics of other database session management solutions like RDS Proxy instead. I'll also discuss different performance optimization techniques, Data API current constraints and make some comparisons between Data APIs for Amazon Aurora Serverless for v1 and v2.

In this talk I'll explore the AWS feature to access the relational Amazon Aurora Serverless v2 (PostgreSQL) database over HTTP with Data API. I will use AWS SDK for Java to explore the different flavours of Data API: (batch) statement execution and working with database transactions. I will also provide Data API performance metrics (for example making Data API call to Aurora Serverless v2 from AWS Lambda) and compare it with the metrics of other database session management solutions like RDS Proxy instead. I'll also discuss different performance optimization techniques, Data API current constraints and make some comparisons between Data APIs for Amazon Aurora Serverless for v1 and v2.

Measure developer productivity by counting the Lines of Code again. And how Serverless minimizes it

The goal of Serverless is to focus on writing the code that delivers business value and offload everything else to your trusted partners (like Cloud providers or SaaS vendors). You want to iterate quickly and today's code quickly becomes tomorrow's technical debt. In this talk we will go through AWS Serverless architectures where you only glue together different Serverless managed services relying solely on configuration, minimizing the amount of the code we have to write. Renaissance of measuring developer productivity by counting the Lines of Code?

FaaS or not to FaaS? Visible and invisible benefits of the Serverless paradigm

When we talk about prices, we often only talk about Lambda costs. In our applications, however, we rarely use only Lambda. Usually we have other building blocks like API Gateway, data sources like SNS, SQS or Kinesis. We also store our data either in S3 or in serverless databases like DynamoDB or recently in Aurora Serverless. All of these AWS services have their own pricing models to look out for. In this talk, we will draw a complete picture of the total cost of ownership in serverless applications and present a decision-making list for determining whether to rely on serverless paradigm in your project. In doing so, we look at the cost aspects as well as other aspects such as understanding application lifecycle, software architecture, platform limitations, organizational knowledge and plattform and tooling maturity. We will also discuss current challenges adopting serverless such as lack of high latency ephemeral storage, lack of durable storage, unsufficient network performance and missing security features.

Adapting Java for the Serverless world

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times and high memory footprint. For both you have to pay to the cloud providers of your choice. That's why  most developers tried to avoid using Java for such use cases. But the times change: Community and cloud providers improve things steadily for Java developers. In this talk we look at the features and possibilities AWS cloud provider offers for the Java developers and look the most popular Java frameworks, like Micronaut, Quarkus and Spring (Boot) and look how (AOT compiler and GraalVM Native Image play a huge role) they address Serverless challenges and enable Java for broad usage in the Serverless world. We'll also look into AWS Lambda SnapStart feature based on CRaC (Coordinated Restore at Checkpoint) project which also reduces the cold start time of Java Serverless application on AWS. We also look into the tools which help us figure out the optimal balance between Lambda memory footprint, invocation time and execution cost.

In this talk we look at the features and possibilities AWS cloud provider offers for the Java developers and look the most popular Java frameworks, like Micronaut, Quarkus and Spring (Boot) and look how they address Serverless challenges and enable Java for broad usage in the Serverless world.

Projects Valhalla and Loom

In diesem Vortrag erläutern wir die Motivation, Mehrwerte, Herausforderungen und aktuellen Stand folgender Projekte Valhalla und Loom.

Im Projekt Valhalla werden Inline Type in Java eingeführt. Inline Type ist ein unveränderlicher Typ, der sich nur durch den Zustand seiner Eigenschaften unterscheidet. Der Zweck ist es, für solche Datentypen den Speicherverbrauch und Zugriffszeiten zu reduzieren.
Im Projekt Loom gilt es leichtgewichtige Threads in Java zu implementieren. Der Zweck ist, es keinen Trade Off mehr zwischen Einfachheit und Skalierbarkeit des Quellcodes einzugehen und beides unter ein Hut zu bringen.

In diesem Vortrag erläutern wir die Motivation, Mehrwerte, Herausforderungen und aktuellen Stand folgender Projekte Valhalla und Loom.

Im Projekt Valhalla werden Inline Type in Java eingeführt. Inline Type ist ein unveränderlicher Typ, der sich nur durch den Zustand seiner Eigenschaften unterscheidet. Der Zweck ist es, für solche Datentypen den Speicherverbrauch und Zugriffszeiten zu reduzieren.
Im Projekt Loom gilt es leichtgewichtige Threads in Java zu implementieren. Der Zweck ist es, keinen Trade Off mehr zwischen Einfachheit und Skalierbarkeit des Quellcodes einzugehen und beides unter ein Hut zu bringen.

Convince your boss to go Serverless

Serverless value proposition is huge. I draw a complete picture of the total cost of ownership in serverless applications and present a decision-making list for determining if and whether to rely on serverless paradigm in your project. I also discuss current challenges adopting serverless

Serverless value proposition is huge. In this talk, I draw a complete picture of the total cost of ownership in serverless applications consisting of no infrastructure maintenance , autoscaling and fault tolerance build in, focus on business value and innovation, faster time to market and so on. I also and present a decision-making list for determining if and whether to rely on serverless paradigm in your project. In doing so, I look at the cost aspects as well as other aspects such as understanding application lifecycle, software architecture, platform limitations, organizational knowledge and plattform and tooling maturity. I also discuss current challenges adopting serverless.

FaaS or not to FaaS – Sichtbare und nicht sichtbarbare Vorteile von Serverless Paradigma

Wenn wir über Preise sprechen, sprechen wir oft nur über Lambdakosten. In unseren Anwendungen verwenden wir jedoch selten nur Lambda. Normalerweise haben wir andere Bausteine wie API Gateway, Datenquellen wie SNS, SQS oder Kinesis. Außerdem speichern wir unsere Daten entweder in S3 oder in Serverless-Datenbanken wie DynamoDB oder kürzlich in Aurora Serverless. Alle diese AWS Services haben ihre eigenen Preismodelle, auf die wir achten müssen. In diesem Vortrag werden wir ein vollständiges Bild der Total Costs of Ownership in Serverless-Anwendungen zeichnen und eine Entscheidungsliste für die Fragestellung präsentieren, ob und wann es sich lohnt, im Projekt auf das Serverless-Paradigma zu setzen. Dabei betrachten wir sowohl die Kosten- wie auch andere Aspekte, z. B. das Verständnis von Applikationslebenszyklus, Architektur, Plattformlimitierungen und organisatorischem Wissen.

Wenn wir über Preise sprechen, sprechen wir oft nur über Lambdakosten. In unseren Anwendungen verwenden wir jedoch selten nur Lambda. Normalerweise haben wir andere Bausteine wie API Gateway, Datenquellen wie SNS, SQS oder Kinesis. Außerdem speichern wir unsere Daten entweder in S3 oder in Serverless-Datenbanken wie DynamoDB oder kürzlich in Aurora Serverless. Alle diese AWS Services haben ihre eigenen Preismodelle, auf die wir achten müssen. In diesem Vortrag werden wir ein vollständiges Bild der Total Costs of Ownership in Serverless-Anwendungen zeichnen und eine Entscheidungsliste für die Fragestellung präsentieren, ob und wann es sich lohnt, im Projekt auf das Serverless-Paradigma zu setzen. Dabei betrachten wir sowohl die Kosten- wie auch andere Aspekte, z. B. das Verständnis von Applikationslebenszyklus, Architektur, Plattformlimitierungen und organisatorischem Wissen.

Vadym Kazulkin

Head of Development at ip.labs in Bonn, Germany

Bonn, Germany

Actions

Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.

Jump to top