SpringOne 2021
Session Title:The Making of the Oracle R2DBC Driver and How to Take Your Code from Synchronous to Reactive
Speakers: Kuassi Mensah, Director of Product Management at Oracle; Michael McMahon, Principal Member of Technical Staff at Oracle
The Loom project has been under work for many years, and just delivered Virtual Threads as a preview feature in the JDK 19. We now have a very precise idea of what they are and what you can do with them. Our good old Threads, created more than 25 years ago, will see a new kind of lightweight threads. This presentation shows you that creating a thread is easier and much cheaper, allowing the creation of millions of them in a single JVM. These virtual threads can be block at almost no cost. These new virtual threads bring with them new notions that will be covered in this talk. Loom threads are coming, and they will change the landscape of concurrent programming in Java.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Spring Framework Petclinic sample applicationAntoine Rey
Spring Petclinic is a sample application that has been designed to show how the Spring Framework can be used to build simple but powerful database-oriented applications.
The fork named Spring Framework Petclinic maintains a version both with a plain old Spring Framework configuration and a 3-layer architecture (i.e. presentation --> service --> repository).
Reactive Card Magic: Understanding Spring WebFlux and Project ReactorVMware Tanzu
Spring Framework 5.0 and Spring Boot 2.0 contain groundbreaking technologies known as reactive streams, which enable applications to utilize computing resources efficiently.
In this session, James Weaver will discuss the reactive capabilities of Spring, including WebFlux, WebClient, Project Reactor, and functional reactive programming. The session will be centered around a fun demonstration application that illustrates reactive operations in the context of manipulating playing cards.
Presenter : James Weaver, Pivotal
Découvrez le framework web Spring Boot qui a la cote !
Apprenez comment son système d'auto-configuration fonctionne.
Live coding et exemple de migration vers Spring Boot sont de la partie.
Introduction to KSQL: Streaming SQL for Apache Kafka®confluent
Join Tom Green, Solution Engineer at Confluent for this Lunch and Learn talk covering KSQL. Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka®. It provides an easy-to-use, yet powerful interactive SQL interface for stream processing on Kafka, without the need to write code in a programming language such as Java or Python. KSQL is scalable, elastic, fault-tolerant, and it supports a wide range of streaming operations, including data filtering, transformations, aggregations, joins, windowing, and sessionization.
By attending one of these sessions, you will learn:
-How to query streams, using SQL, without writing code.
-How KSQL provides automated scalability and out-of-the-box high availability for streaming queries
-How KSQL can be used to join streams of data from different sources
-The differences between Streams and Tables in Apache Kafka
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
The Loom project has been under work for many years, and just delivered Virtual Threads as a preview feature in the JDK 19. We now have a very precise idea of what they are and what you can do with them. Our good old Threads, created more than 25 years ago, will see a new kind of lightweight threads. This presentation shows you that creating a thread is easier and much cheaper, allowing the creation of millions of them in a single JVM. These virtual threads can be block at almost no cost. These new virtual threads bring with them new notions that will be covered in this talk. Loom threads are coming, and they will change the landscape of concurrent programming in Java.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Spring Framework Petclinic sample applicationAntoine Rey
Spring Petclinic is a sample application that has been designed to show how the Spring Framework can be used to build simple but powerful database-oriented applications.
The fork named Spring Framework Petclinic maintains a version both with a plain old Spring Framework configuration and a 3-layer architecture (i.e. presentation --> service --> repository).
Reactive Card Magic: Understanding Spring WebFlux and Project ReactorVMware Tanzu
Spring Framework 5.0 and Spring Boot 2.0 contain groundbreaking technologies known as reactive streams, which enable applications to utilize computing resources efficiently.
In this session, James Weaver will discuss the reactive capabilities of Spring, including WebFlux, WebClient, Project Reactor, and functional reactive programming. The session will be centered around a fun demonstration application that illustrates reactive operations in the context of manipulating playing cards.
Presenter : James Weaver, Pivotal
Découvrez le framework web Spring Boot qui a la cote !
Apprenez comment son système d'auto-configuration fonctionne.
Live coding et exemple de migration vers Spring Boot sont de la partie.
Introduction to KSQL: Streaming SQL for Apache Kafka®confluent
Join Tom Green, Solution Engineer at Confluent for this Lunch and Learn talk covering KSQL. Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka®. It provides an easy-to-use, yet powerful interactive SQL interface for stream processing on Kafka, without the need to write code in a programming language such as Java or Python. KSQL is scalable, elastic, fault-tolerant, and it supports a wide range of streaming operations, including data filtering, transformations, aggregations, joins, windowing, and sessionization.
By attending one of these sessions, you will learn:
-How to query streams, using SQL, without writing code.
-How KSQL provides automated scalability and out-of-the-box high availability for streaming queries
-How KSQL can be used to join streams of data from different sources
-The differences between Streams and Tables in Apache Kafka
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
Building a REST Service in minutes with Spring BootOmri Spector
A walk through building a micro service using Spring Boot.
Deck presented at Java 2016
Source accompanying presentation can be found at https://github.com/ospector/sbdemo
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Transactions and Concurrency Control PatternsJ On The Beach
Transactions and Concurrency Control Patterns by Vlad Mihalcea
Transactions and Concurrency Control are very of paramount importance when it comes to enterprise systems data integrity. However, this topic is very tough since you have to understand the inner workings of the database system, its concurrency control design choices (e.g. 2PL, MVCC), transaction isolation levels and locking schemes.
In this presentation, I’m going to explain what data anomalies can happen depending on the transaction isolation level, with references to Oracle, SQL Server, PostgreSQL, and MySQL.
I will also demonstrate that database transactions are not enough, especially for multi-request web flows. For this reason, I’m going to present multiple application-level transaction patterns based on both optimistic and pessimistic locking mechanisms.
Last, I’m going to talk about Concurrency Control strategies used in the Hibernate second-level caching mechanism, which can boost performance without compromising strong consistency.
Java REST API Comparison: Micronaut, Quarkus, and Spring Boot - jconf.dev 2020Matt Raible
"Use Spring Boot! No, use Micronaut!! Nooooo, Quarkus is the best!!!"
There's a lot of developers praising the hottest, and fastest, Java REST frameworks: Micronaut, Quarkus, and Spring Boot. In this session, you'll learn how to do the following with each framework:
✅ Build a REST API
✅ Secure your API with OAuth 2.0
✅ Optimize for production with Docker and GraalVM
I'll also share some performance numbers and pretty graphs to compare community metrics.
Related blog post: https://developer.okta.com/blog/2020/01/09/java-rest-api-showdown
GitHub repo: https://github.com/oktadeveloper/okta-java-rest-api-comparison-example
Microservice With Spring Boot and Spring CloudEberhard Wolff
Spring Boot and Spring Cloud are an ideal foundation for creating Microservices based on Java. This presentation explains basic concepts of these libraries.
Quarkus offers a great development experience. In this session, I’ll introduce you to the power of Quarkus Live Coding and tools that are useful to developers for debugging, deploying, and testing Quarkus applications.
Percona Live 2022 - The Evolution of a MySQL Database SystemFrederic Descamps
From a single MySQL instance to multi-site high availability, this is what you will find out in this presentation. You will learn how to make this transition and which solutions best suit changing business requirements (RPO, RTO). Recently, MySQL has extended the possibilities for easy deployment of architecture with integrated tools. Come and discover these open source solutions that are part of MySQL.
Youtube Link: https://youtu.be/CXTiwkZVoZI
( Microservices Architecture Training: https://www.edureka.co/microservices-architecture-training )
This Edureka's PPT on Spring Boot Interview Questions talks about the top questions asked related to Spring Boot.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Building a REST Service in minutes with Spring BootOmri Spector
A walk through building a micro service using Spring Boot.
Deck presented at Java 2016
Source accompanying presentation can be found at https://github.com/ospector/sbdemo
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Transactions and Concurrency Control PatternsJ On The Beach
Transactions and Concurrency Control Patterns by Vlad Mihalcea
Transactions and Concurrency Control are very of paramount importance when it comes to enterprise systems data integrity. However, this topic is very tough since you have to understand the inner workings of the database system, its concurrency control design choices (e.g. 2PL, MVCC), transaction isolation levels and locking schemes.
In this presentation, I’m going to explain what data anomalies can happen depending on the transaction isolation level, with references to Oracle, SQL Server, PostgreSQL, and MySQL.
I will also demonstrate that database transactions are not enough, especially for multi-request web flows. For this reason, I’m going to present multiple application-level transaction patterns based on both optimistic and pessimistic locking mechanisms.
Last, I’m going to talk about Concurrency Control strategies used in the Hibernate second-level caching mechanism, which can boost performance without compromising strong consistency.
Java REST API Comparison: Micronaut, Quarkus, and Spring Boot - jconf.dev 2020Matt Raible
"Use Spring Boot! No, use Micronaut!! Nooooo, Quarkus is the best!!!"
There's a lot of developers praising the hottest, and fastest, Java REST frameworks: Micronaut, Quarkus, and Spring Boot. In this session, you'll learn how to do the following with each framework:
✅ Build a REST API
✅ Secure your API with OAuth 2.0
✅ Optimize for production with Docker and GraalVM
I'll also share some performance numbers and pretty graphs to compare community metrics.
Related blog post: https://developer.okta.com/blog/2020/01/09/java-rest-api-showdown
GitHub repo: https://github.com/oktadeveloper/okta-java-rest-api-comparison-example
Microservice With Spring Boot and Spring CloudEberhard Wolff
Spring Boot and Spring Cloud are an ideal foundation for creating Microservices based on Java. This presentation explains basic concepts of these libraries.
Quarkus offers a great development experience. In this session, I’ll introduce you to the power of Quarkus Live Coding and tools that are useful to developers for debugging, deploying, and testing Quarkus applications.
Percona Live 2022 - The Evolution of a MySQL Database SystemFrederic Descamps
From a single MySQL instance to multi-site high availability, this is what you will find out in this presentation. You will learn how to make this transition and which solutions best suit changing business requirements (RPO, RTO). Recently, MySQL has extended the possibilities for easy deployment of architecture with integrated tools. Come and discover these open source solutions that are part of MySQL.
Youtube Link: https://youtu.be/CXTiwkZVoZI
( Microservices Architecture Training: https://www.edureka.co/microservices-architecture-training )
This Edureka's PPT on Spring Boot Interview Questions talks about the top questions asked related to Spring Boot.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Java DataBase Connectivity API (JDBC API)Luzan Baral
JDBC is a Java-based data access technology (Java Standard Edition platform) from Oracle Corporation. This technology is an API for the Java programming language that defines how a client may access a database. It provides methods for querying and updating data in a database. JDBC is oriented towards relational databases. A JDBC-to-ODBC bridge enables connections to any ODBC-accessible data source in the JVM host environment.
Introduction to JDBC and database access in web applicationsFulvio Corno
Introduction to the JDBC standard and best practices for database access from Web Applications.
Materiale realizzato per il corso di Sistemi Informativi Aziendali del Politecnico di Torino - http://bit.ly/sistinfo
Lecture 13 from the IAG0040 Java course in TTÜ.
See the accompanying source code written during the lectures: https://github.com/angryziber/java-course
The Tanzu Developer Connect is a hands-on workshop that dives deep into TAP. Attendees receive a hands on experience. This is a great program to leverage accounts with current TAP opportunities.
The Tanzu Developer Connect is a hands-on workshop that dives deep into TAP. Attendees receive a hands on experience. This is a great program to leverage accounts with current TAP opportunities.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
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.
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.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
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.
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).
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
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.
Dominate Social Media with TubeTrivia AI’s Addictive Quiz Videos.pdf
The Making of the Oracle R2DBC Driver and How to Take Your Code from Synchronous to Reactive
1. The Making of the Oracle R2DBC Driver
And How to Take Your Code from Synchronous to Reactive
Kuassi Mensah Michael McMahon
@kmensah
2. Agenda
ü Once Upon a Time
• The Making of Oracle R2DBC driver
• Taking Your Code from Synchronous to Reactive
Oracle Confidential
3. Once Upon a Time …
The Asynchronous Database Access API (ADBA)
• Started in 2017
• Goal
– A standard Java database access API that never blocks user threads
– No compatibility or complementarity with JDBC
• Motivations
– Asynchronous apps have better throughput
– Simultaneous access to multiple databases, e.g. map/reduce, sharded databases
– Fire and forget, e.g. DML, stored procedures
• The API is available from OpenJDK at https://bit.ly/3t3nkAd
3
4. 1. Per the Java SE team: “the future of Java scalability is Virtual
Threads”
–ADBA would not be accepted as a Java standard as it was an
async solution to a problem that would be addressed by virtual
threads.
– Unless it became a standard ADBA would have very little
impact, certainly not enough to justify the effort.
2. There was very little support within the Java community.
-- Gravitating towards R2DBC
4
Why Did We Stop Working on ADBA?
5. We changed our focus towards
• JDBC Reactive Extensions
• An Oracle R2DBC Driver
• Instrumenting the JDBC driver to support Virtual Threads
5
Then What?
7. User
Java
Code
JDBC
Reactive
Extension Standard
JDBC
API
3rd party
Reactive
Streams
Libraries
Async call with non-blocking
backpressure
operators (map, reduce, filters),
concurrency modeling,
monitoring, tracing
Implements Java SE
reactive stream
interface (Flow)
Full Reactive
Streams
Sync/blocking JDBC calls
Java
Business
Logic
User Java code
Oracle
Database
Oracle JDBC
driver
Summary of Oracle DB Access with Java – Part 1
8. Agenda
• Once Upon a Time
ü The Making of Oracle R2DBC driver
• Taking Your Code from Synchronous to Reactive
Oracle Confidential
9. public boolean execute() throws SQLException {
// Check if this statement is closed
if (isClosed)
throw new SQLException("Closed Statement");
// Check if all bind values are set
for (Object bindValue : bindValues) {
if (bindValue == null)
throw new SQLException("Bind value not set");
}
// Close the current ResultSet
if (resultSet != null)
resultSet.close();
// Reset the current update count
updateCount = -1;
// Blocking database call
SqlResult sqlResult = jdbcConnection.executeSql(sql, bindValues);
// Handle the result
resultSet = sqlResult.getResultSet();
updateCount = sqlResult.getUpdateCount();
return resultSet != null;
}
public Publisher<Boolean> executeAsyncOracle() throws SQLException {
// Check if this statement is closed
if (isClosed)
throw new SQLException("Closed Statement");
// Check if all bind values are set
for (Object bindValue : bindValues) {
if (bindValue == null)
throw new SQLException("Bind value not set");
}
// Close the current ResultSet
if (resultSet != null)
resultSet.close();
// Reset the current update count
updateCount = -1;
// Non-blocking database call
return Flux.from(jdbcConnection.executeSqlAsync(sql, bindValues))
.map(sqlResult -> {
// Handle the result
resultSet = sqlResult.getResultSet();
updateCount = sqlResult.getUpdateCount();
return resultSet != null;
});
}
Developing JDBC Reactive Extensions
10. public boolean execute() throws SQLException {
// Check if this statement is closed
if (isClosed)
throw new SQLException("Closed Statement");
// Check if all bind values are set
for (Object bindValue : bindValues) {
if (bindValue == null)
throw new SQLException("Bind value not set");
}
// Close the current ResultSet
if (resultSet != null)
resultSet.close();
// Reset the current update count
updateCount = -1;
// Blocking database call
SqlResult sqlResult = jdbcConnection.executeSql(sql, bindValues);
// Handle the result
resultSet = sqlResult.getResultSet();
updateCount = sqlResult.getUpdateCount();
return resultSet != null;
}
public Publisher<Boolean> executeAsyncOracle() throws SQLException {
// Check if this statement is closed
if (isClosed)
throw new SQLException("Closed Statement");
// Check if all bind values are set
for (Object bindValue : bindValues) {
if (bindValue == null)
throw new SQLException("Bind value not set");
}
// Close the current ResultSet
if (resultSet != null)
resultSet.close();
// Reset the current update count
updateCount = -1;
// Non-blocking database call
return Flux.from(jdbcConnection.executeSqlAsync(sql, bindValues))
.map(sqlResult -> {
// Handle the result
resultSet = sqlResult.getResultSet();
updateCount = sqlResult.getUpdateCount();
return resultSet != null;
});
}
Developing JDBC Reactive Extensions
11. public boolean execute() throws SQLException {
prepareForExecute();
SqlResult result = jdbcConnection.executeSql(sql, bindValues);
return handleResult(result);
}
public Publisher<Boolean> executeAsyncOracle() throws SQLException {
prepareForExecute();
return Flux.from(jdbcConnection.executeSqlAsync(sql, bindValues))
.map(sqlResult -> handleResult(sqlResult));
}
private void prepareForExecute() throws SQLException {
// Check if this statement is closed
if (isClosed)
throw new SQLException("Closed Statement");
// Check if all bind values are set
for (Object bindValue : bindValues) {
if (bindValue == null)
throw new SQLException("Bind value not set");
}
// Close the current ResultSet
if (resultSet != null)
resultSet.close();
// Reset the current update count
updateCount = -1;
}
private boolean handleResult(SqlResult result) {
resultSet = result.getResultSet();
updateCount = result.getUpdateCount();
return resultSet != null;
}
Developing JDBC Reactive Extensions
12. Developing JDBC Reactive Extensions
Setup
Blocking
Handle Result
Setup
Non-Blocking
Handle Result
Synchronous JDBC Setup
Blocking
Handle Result
Setup
Non-Blocking
Handle Result
Setup
Non-Blocking
Handle Result
Reactive JDBC
Database
Setup
Blocking
Handle Result
Database
13. Adapting JDBC Reactive Extensions for Oracle
R2DBC
R2DBC SPI Reactive Extensions API
ConnectionFactory
create()
OracleConnectionBuilder
buildConnectionPublisherOracle()
Statement
execute()
OraclePreparedStatement
executeAsyncOracle()
Result
map(Function)
OracleResultSet
publisherOracle(Function)
https://github.com/oracle/oracle-r2dbc
19. From Synchronous to Reactive: Update
static int updateJdbc(java.sql.Connection connection) throws SQLException {
try (PreparedStatement preparedStatement = connection.prepareStatement(
"UPDATE JdbcToR2dbcTable SET value = ? WHERE id = ?")) {
preparedStatement.setString(1, "JDBC");
preparedStatement.setInt(2, 0);
return preparedStatement.executeUpdate();
}
}
static Publisher<Integer> updateR2dbc(io.r2dbc.spi.Connection connection) {
return Flux.from(connection.createStatement(
"UPDATE JdbcToR2dbcTable SET value = ? WHERE id = ?")
.bind(0, "R2DBC")
.bind(1, 0)
.execute())
.flatMap(Result::getRowsUpdated);
}
20. From Synchronous to Reactive: Conditional
Branch
static int tryUpdateJdbc(java.sql.Connection connection) throws SQLException
{
// Try to update the row
int updateCount = updateJdbc(connection);
// If the row does not exist, then insert it.
if (updateCount == 0)
return insertJdbc(connection);
else
return updateCount;
}
static Publisher<Integer> tryUpdateR2dbc(io.r2dbc.spi.Connection connection)
{
// Try to update the row
return Flux.from(updateR2dbc(connection))
.flatMap(updateCount -> {
// If the row does not exist, then insert it.
if (updateCount == 0)
return insertR2dbc(connection);
else
return Flux.just(updateCount);
});
}
21. From Synchronous to Reactive: Error Recovery
static int tryInsertJdbc(java.sql.Connection connection) throws SQLException
{
try {
// Try to insert the row
return insertJdbc(connection);
}
catch (SQLException sqlException) {
// If the row already exists, then update it.
if (sqlException.getErrorCode() == UNIQUE_CONSTRAINT_VIOLATION)
return updateJdbc(connection);
else
throw sqlException;
}
}
static Publisher<Integer> tryInsertR2dbc(io.r2dbc.spi.Connection connection)
{
// Try to insert the row
return Flux.from(insertR2dbc(connection))
.onErrorResume(R2dbcException.class, r2dbcException -> {
// If the row already exists, then update it.
if (r2dbcException.getErrorCode() == UNIQUE_CONSTRAINT_VIOLATION)
return updateR2dbc(connection);
else
return Flux.error(r2dbcException);
});
}
22. From Synchronous to Reactive: Loop
static int loopJdbc(java.sql.Connection connection) throws SQLException {
do {
try {
// Try to update the row, or insert it if it does not exist
return tryUpdateJdbc(connection);
}
catch (SQLException sqlException) {
// If another database session has inserted the row before this
// one did, then recover from failure by continuing the loop.
if (sqlException.getErrorCode() != UNIQUE_CONSTRAINT_VIOLATION)
throw sqlException;
}
} while (true);
}
static Publisher<Integer> loopR2dbc(io.r2dbc.spi.Connection connection) {
// Try to update the row, or insert it if it does not exist
return Flux.from(tryUpdateR2dbc(connection))
.onErrorResume(R2dbcException.class, r2dbcException -> {
// If another database session has inserted the row before this
// one did, then recover from failure by recursively invoking this
// method.
if (r2dbcException.getErrorCode() != UNIQUE_CONSTRAINT_VIOLATION)
return Flux.error(r2dbcException);
else
return loopR2dbc(connection);
});
}
23. User
Java
Code
JDBC
Reactive
Extension Standard
JDBC API
R2DBC
+
3rd party
Reactive
Streams
Libraries
Async call with non-blocking
backpressure
operators (map, reduce, filters),
concurrency modeling,
monitoring, tracing
Implements Java SE
reactive stream
interface (Flow)
Full Reactive
Streams
Sync/blocking JDBC calls
Java
Business
Logic
User Java code
Oracle
Database
Oracle JDBC
driver
Summary of Oracle DB Access with Java – Part 2