Presentation by Jan Verkade, Deltares, at the Delft-FEWS International User Days 2020, during Delft Software Days - Edition 2020. Monday, 2 November 2020.
Building large scale, job processing systems with Scala Akka Actor frameworkVignesh Sukumar
The document discusses building massive scale, fault tolerant job processing systems using the Scala Akka framework. It describes implementing a master-slave architecture with actors where an agent runs on each storage node to process jobs locally, achieving high throughput. It also covers controlling system load by dynamically adjusting parallelism, and implementing fine-grained fault tolerance through actor supervision strategies.
Oren Eini discusses the next major version of RavenDB 4.0, running on the CoreCLR, and skim over topics of performance (much higher), flexibility and ease of use.
Getting Deep on Orchestration - Nickoloff - DockerCon16allingeek
Orchestration platforms let us work with higher level ideas like services and jobs; but there is more to a platform than scheduling and service discovery. A platform is a collection of actors and APIs that work together and provide those higher level abstractions on a distributed system. In this session we'll go deep on the architecture of open source orchestration platforms, consider scaling pains, reveal extension points, and reflect on an orchestration platform at Amazon. We'll finish with a demo of a homemade abstraction for failure injection by policy.
This document discusses techniques for achieving sub-second iteration times when developing games, including optimizing pipeline latency through fast recompilation and reloading of resources. Individual files can be replaced and compiled quickly, and resources are separated from baking processes to allow fast iterations. Live reloading of Lua, C++ code, and shaders is enabled by treating them as normal resources and reloading the executable while maintaining game state. Fast compiles are achieved through profiling and incremental compiling.
Compilers can have a huge effect on software efficiency and performance by changing what user experiences are possible and reducing CPU and resource usage. They work by parsing code, generating machine-friendly representations, and emitting optimized machine code. As web programming grew in complexity, developers started building more efficient compilers for dynamic languages to preserve rapid development workflows while improving performance. There are various approaches to building compilers like interpreters, transpilers, using backends like LLVM, and fully custom solutions. The best approach depends on goals, constraints, and tradeoffs around control, performance, and development effort. Optimization focuses should include memory usage, caching, and runtime layout. Future areas may include database query compilation for real-time analytics on large datasets.
1) RDI uses RavenDB embedded in over 36,000 restaurants with around 500,000 individual machines processing $50,000 per second in payments.
2) RavenDB allows for unit testing without mocking the database, advanced statistics on data persistence, and transparent replication with high availability.
3) The challenges of using RavenDB on specialized older hardware with low memory and ESENT include fine-tuning memory usage, disabling caching, and automating recovery from unclean shutdowns with ESENTUTL.EXE.
Zapping ever faster: how Zap sped up by two orders of magnitude using RavenDBOren Eini
Join a real uplift experience with Hagay Albo, the CTO of the Zap/Yellow Page Group in Israel, in which he explains how his team was able to take a legacy (slow and hard to modify) group of sites and make them easier to work with, MUCH faster and greatly simplified the operational environment.
By prioritizing high availability, flexible data modeling and focusing on raw speed Zap was able to reduce its load times by Two Orders of Magnitudes. Using RavenDB as the core engine behind Zap's new sites had improved site traffic, reduced time to market and made it possible to implement the next-gen features that were previously beyond reach.
This presentation is dedicated to Design Patterns. The highlighted subtopics are Subsystem and Component Architecture Patterns, Concurrency Patterns, Memory Patterns, Safety and Reliability Patterns. The slides provide repeatable solutions to common software engineering problems.
This presentation by Oleh Medvedyev, Engineering Management Expert, GlobalLogic, was delivered at a GlobalLogic Embedded TechTalk in Lviv on March 29, 2017.
Session presented at the 6th IndicThreads.com Conference on Java held in Pune, India on 2-3 Dec. 2011.
http://Java.IndicThreads.com
This talk will explore how we REBOOTED our Project Design. After a decade of production usage, the RavenDB team addressed a lot of ongoing concerns & changed some of RavenDB's core architecture.
We'll investigate the driving forces behind it, the reasoning process & look at how it all turned out.
Know thy cost (or where performance problems lurk)Oren Eini
Performance happens. Whether you're designed for it or not it doesn’t matter, she is always invited to the party (and you better find her in a good mood). Knowing the cost of every operation, and how it distributes on every subsystem will ensure that when you are building that proof-of-concept (that always ends up in production) or designing the latest’s enterprise-grade application; you will know where those pesky performance bugs like to inhabit. In this session, we will go deep into the inner working of every performance sensitive subsystem. From the relative safety of the client to the binary world of Voron.
Functional reactive programming combines functional programming and reactive programming. Functional programming avoids mutable state and side effects through referential transparency and reusable functions. Reactive programming builds reactive systems through asynchronous data streams and treating everything as a stream. Observables are the core component, representing a stream of data over time, and operators allow querying, transforming, and combining observables. RxJS is a popular library for implementing functional reactive programming.
This document discusses using microservices with Varnish caching for improved performance and scalability. It describes moving from a monolithic Java application to many small, stateless services with Varnish caching all data. Key-value caching is implemented using X-Key to invalidate cache dynamically based on HTTP headers. This allows related cached objects to be automatically invalidated on a cache purge. The architecture has proven effective over 10 years in production by reducing complexity, improving debugging and scaling through statelessness and caching with Varnish and X-Key.
Training on iOS app development - Samesh Swongamikha & Neetin SharmaMobileNepal
This document summarizes key concepts for iOS app development including design patterns like MVVM, communication patterns, databases like CoreData, networking using REST APIs and libraries like AFNetworking, memory management with ARC, and an introduction to the Swift programming language. Useful resources for iOS development are also provided.
7 steps to simplifying your AI workflowsWisecube AI
1) Nephos is a hybrid cloud-enabled AI workflow service that allows users to visually build and run AI workflows in the cloud. It provides a visual workflow editor to drag and drop datasets, executables, and draw connecting edges to create workflows.
2) The 7 steps to using Nephos include: setting up a cloud cluster, registering resources like datasets and executables, creating the first AI workflow, running the workflow, experimenting on workflows, collaborating with others, and scheduling recurring workflow runs as jobs.
3) Key features of Nephos include supporting hybrid clouds, using open source technologies like Pegasus and Docker, and being built by experienced AI practitioners to simplify complex AI workflows.
The document discusses Cincom's Smalltalk source code control system called Store. It provides an overview of Store's concepts and current work, including tools, atomic loading, and database back-end improvements. Future plans include further enhancing tools, schema changes, optimizations, and configuration management capabilities.
- Steve Storey is a hobby coder and professional application architect at MOO, a London-based online printing company.
- MOO's technology stack currently uses a LAMP stack on Debian, but CouchDB is being evaluated as a potential NoSQL option due to its document-based data model and RESTful HTTP interface.
- CouchDB is a document store that allows developer-defined views on documents using map-reduce functions. It has built-in fault tolerant replication and a REST API, making it suitable for storing hierarchical and schema-less data like MOO uses.
AtlasCamp 2014: Preparing Your Plugin for JIRA Data CenterAtlassian
JIRA and Confluence are introducing new product editions with support for high availability and clustering. Depending on your add-on, supporting enterprise-grade deployments of JIRA and Confluence could require updates to your plugin. Get a sneak preview of the application architecture for HA and clustering, and learn how you can ensure your plugin is ready to run in these mission critical installations.
Developing in the cloud should not be different than on-premise development and AWS services aim to facilitate developers and reduce costs. The main AWS developer services that a developer should know include IAM for managing users and credentials, CloudWatch for monitoring and alarms, EC2 for renting compute resources, VPC for network management, S3 for object storage, DynamoDB for NoSQL database, RDS for relational databases, Elastic Beanstalk for deploying applications, and CloudFront for low-latency content delivery. Additional services are CloudFormation for provisioning via templates and Elasticache for caching.
.Net Garbage Collector is an important factor in improving development efficiency by eliminating the need to manually manage memory.
But sometimes, it's overhead may cause very long pauses in application execution. In this talk, I will show why this happens and how we can deal with it.
SQLite is an open source, public domain embedded SQL database that is zero-configuration, self-contained, and supports transactions that remain intact after system crashes or power failures. It implements most of SQL92 and stores the entire database in a single disk file, supporting databases up to terabytes in size. SQLite is faster than popular client/server databases for most common operations and has a simple API with bindings available for dozens of languages.
For the last 40 years or so, we used relational databases successfully in nearly all business contexts and systems of nearly all sizes. Therefore, if you feel no pain using a RDBMS, you can stay with it. But, if you always have to work around your RDBMS to get your job done, a document oriented database might be worth a look.
RavenDB is a 2nd generation document database that allows you to write a data-access layer with much more freedom and many less constraints. If you have to work with large volumes of data, thousands of queries per second, unstructured/semi-structured data or event sourcing, you will find RavenDB particulary rewarding.
In this talk we will explore some document database usage scenarios. I will share some data modeling techniques and many architectural criteria to help you to decide where safely adopt RavenDB as a right choice.
Meson: Heterogeneous Workflows with Spark at NetflixAntony Arokiasamy
Netflix uses Meson, an in-house workflow system, to orchestrate and schedule machine learning pipelines using Spark. Meson allows for heterogeneous environments, multi-tenancy, and optimized workflows for machine learning tasks like parameter sweeping with over 30,000 docker containers. It delegates execution to resource managers like Mesos and supports standard and custom steps, parameter passing, structured constructs, and two-way communication between steps.
1. The document discusses various options for implementing disaster recovery and high availability with Kafka across multiple data centers, including MirrorMaker, MirrorMaker 2, and stretch clusters.
2. MirrorMaker provides basic asynchronous replication between data centers but has limitations around failover and latency. MirrorMaker 2 and replicators support active-active production in both DCs but with more complexity.
3. Stretch clusters treat the multiple DCs as a single Kafka cluster with synchronous replication and no producer latency, but require more resources and rely on low WAN latency. The best option depends on requirements for developer ease, latency, consistency, and budget.
Reactive: Programming -> Systems -> ArchitectureAleksey Izmailov
A summary of Reactive topics.
Mostly based on whitepaper:
"Reactive Programming versus Reactive Systems
Landing on a set of simple Reactive design principles in a sea of constant confusion and overloaded expectations". (By Jonas Bonér and Viktor Klang, Lightbend Inc)
This presentation, given at the Fort Worth .NET User Group on 19 Sept. 2017, talks about serverless technology: What it is, when it's best to use, its features and limitations. It specifically focuses on Azure Functions and Azure Logic Apps.
Abstract:
A apresentação centra-se na temática de ter forma de controlar, versionar e actualizar toda a parte de Base de Dados de um projecto. Estamos a falar, desde a produção de modelos ER, a versionamento de scripts, passando pelo deploy dos mesmos e terminado na documentação. A apresentação conta ainda com uma breve demonstração do uso da ferramenta Flyway para versionar e controlar a execução de scripts nos diversos ambientes de um projecto.
Sobre o Nuno Alves:
Chamo-me Nuno Alves nascido em Coimbra, Portugal e vivi maioritariamente em Leiria. Licenciado em Engenharia Informática na ESTG-IPLeiria (Escola Superior de Tecnologia e Gestão) onde o gosto por dados e bases de dados se começou a desenvolver. Daí, profissionalmente a minha área de actuação ser em torno de bases de dados e infra-estruturas. Tenho cerca de 10 anos de experiência repartidos pelas áreas Financeira, Seguros, Governo, Militar em tecnologias que vão desde Oracle, PostgreSQL, MSSQLServer a DB2.
This presentation is dedicated to Design Patterns. The highlighted subtopics are Subsystem and Component Architecture Patterns, Concurrency Patterns, Memory Patterns, Safety and Reliability Patterns. The slides provide repeatable solutions to common software engineering problems.
This presentation by Oleh Medvedyev, Engineering Management Expert, GlobalLogic, was delivered at a GlobalLogic Embedded TechTalk in Lviv on March 29, 2017.
Session presented at the 6th IndicThreads.com Conference on Java held in Pune, India on 2-3 Dec. 2011.
http://Java.IndicThreads.com
This talk will explore how we REBOOTED our Project Design. After a decade of production usage, the RavenDB team addressed a lot of ongoing concerns & changed some of RavenDB's core architecture.
We'll investigate the driving forces behind it, the reasoning process & look at how it all turned out.
Know thy cost (or where performance problems lurk)Oren Eini
Performance happens. Whether you're designed for it or not it doesn’t matter, she is always invited to the party (and you better find her in a good mood). Knowing the cost of every operation, and how it distributes on every subsystem will ensure that when you are building that proof-of-concept (that always ends up in production) or designing the latest’s enterprise-grade application; you will know where those pesky performance bugs like to inhabit. In this session, we will go deep into the inner working of every performance sensitive subsystem. From the relative safety of the client to the binary world of Voron.
Functional reactive programming combines functional programming and reactive programming. Functional programming avoids mutable state and side effects through referential transparency and reusable functions. Reactive programming builds reactive systems through asynchronous data streams and treating everything as a stream. Observables are the core component, representing a stream of data over time, and operators allow querying, transforming, and combining observables. RxJS is a popular library for implementing functional reactive programming.
This document discusses using microservices with Varnish caching for improved performance and scalability. It describes moving from a monolithic Java application to many small, stateless services with Varnish caching all data. Key-value caching is implemented using X-Key to invalidate cache dynamically based on HTTP headers. This allows related cached objects to be automatically invalidated on a cache purge. The architecture has proven effective over 10 years in production by reducing complexity, improving debugging and scaling through statelessness and caching with Varnish and X-Key.
Training on iOS app development - Samesh Swongamikha & Neetin SharmaMobileNepal
This document summarizes key concepts for iOS app development including design patterns like MVVM, communication patterns, databases like CoreData, networking using REST APIs and libraries like AFNetworking, memory management with ARC, and an introduction to the Swift programming language. Useful resources for iOS development are also provided.
7 steps to simplifying your AI workflowsWisecube AI
1) Nephos is a hybrid cloud-enabled AI workflow service that allows users to visually build and run AI workflows in the cloud. It provides a visual workflow editor to drag and drop datasets, executables, and draw connecting edges to create workflows.
2) The 7 steps to using Nephos include: setting up a cloud cluster, registering resources like datasets and executables, creating the first AI workflow, running the workflow, experimenting on workflows, collaborating with others, and scheduling recurring workflow runs as jobs.
3) Key features of Nephos include supporting hybrid clouds, using open source technologies like Pegasus and Docker, and being built by experienced AI practitioners to simplify complex AI workflows.
The document discusses Cincom's Smalltalk source code control system called Store. It provides an overview of Store's concepts and current work, including tools, atomic loading, and database back-end improvements. Future plans include further enhancing tools, schema changes, optimizations, and configuration management capabilities.
- Steve Storey is a hobby coder and professional application architect at MOO, a London-based online printing company.
- MOO's technology stack currently uses a LAMP stack on Debian, but CouchDB is being evaluated as a potential NoSQL option due to its document-based data model and RESTful HTTP interface.
- CouchDB is a document store that allows developer-defined views on documents using map-reduce functions. It has built-in fault tolerant replication and a REST API, making it suitable for storing hierarchical and schema-less data like MOO uses.
AtlasCamp 2014: Preparing Your Plugin for JIRA Data CenterAtlassian
JIRA and Confluence are introducing new product editions with support for high availability and clustering. Depending on your add-on, supporting enterprise-grade deployments of JIRA and Confluence could require updates to your plugin. Get a sneak preview of the application architecture for HA and clustering, and learn how you can ensure your plugin is ready to run in these mission critical installations.
Developing in the cloud should not be different than on-premise development and AWS services aim to facilitate developers and reduce costs. The main AWS developer services that a developer should know include IAM for managing users and credentials, CloudWatch for monitoring and alarms, EC2 for renting compute resources, VPC for network management, S3 for object storage, DynamoDB for NoSQL database, RDS for relational databases, Elastic Beanstalk for deploying applications, and CloudFront for low-latency content delivery. Additional services are CloudFormation for provisioning via templates and Elasticache for caching.
.Net Garbage Collector is an important factor in improving development efficiency by eliminating the need to manually manage memory.
But sometimes, it's overhead may cause very long pauses in application execution. In this talk, I will show why this happens and how we can deal with it.
SQLite is an open source, public domain embedded SQL database that is zero-configuration, self-contained, and supports transactions that remain intact after system crashes or power failures. It implements most of SQL92 and stores the entire database in a single disk file, supporting databases up to terabytes in size. SQLite is faster than popular client/server databases for most common operations and has a simple API with bindings available for dozens of languages.
For the last 40 years or so, we used relational databases successfully in nearly all business contexts and systems of nearly all sizes. Therefore, if you feel no pain using a RDBMS, you can stay with it. But, if you always have to work around your RDBMS to get your job done, a document oriented database might be worth a look.
RavenDB is a 2nd generation document database that allows you to write a data-access layer with much more freedom and many less constraints. If you have to work with large volumes of data, thousands of queries per second, unstructured/semi-structured data or event sourcing, you will find RavenDB particulary rewarding.
In this talk we will explore some document database usage scenarios. I will share some data modeling techniques and many architectural criteria to help you to decide where safely adopt RavenDB as a right choice.
Meson: Heterogeneous Workflows with Spark at NetflixAntony Arokiasamy
Netflix uses Meson, an in-house workflow system, to orchestrate and schedule machine learning pipelines using Spark. Meson allows for heterogeneous environments, multi-tenancy, and optimized workflows for machine learning tasks like parameter sweeping with over 30,000 docker containers. It delegates execution to resource managers like Mesos and supports standard and custom steps, parameter passing, structured constructs, and two-way communication between steps.
1. The document discusses various options for implementing disaster recovery and high availability with Kafka across multiple data centers, including MirrorMaker, MirrorMaker 2, and stretch clusters.
2. MirrorMaker provides basic asynchronous replication between data centers but has limitations around failover and latency. MirrorMaker 2 and replicators support active-active production in both DCs but with more complexity.
3. Stretch clusters treat the multiple DCs as a single Kafka cluster with synchronous replication and no producer latency, but require more resources and rely on low WAN latency. The best option depends on requirements for developer ease, latency, consistency, and budget.
Reactive: Programming -> Systems -> ArchitectureAleksey Izmailov
A summary of Reactive topics.
Mostly based on whitepaper:
"Reactive Programming versus Reactive Systems
Landing on a set of simple Reactive design principles in a sea of constant confusion and overloaded expectations". (By Jonas Bonér and Viktor Klang, Lightbend Inc)
This presentation, given at the Fort Worth .NET User Group on 19 Sept. 2017, talks about serverless technology: What it is, when it's best to use, its features and limitations. It specifically focuses on Azure Functions and Azure Logic Apps.
Abstract:
A apresentação centra-se na temática de ter forma de controlar, versionar e actualizar toda a parte de Base de Dados de um projecto. Estamos a falar, desde a produção de modelos ER, a versionamento de scripts, passando pelo deploy dos mesmos e terminado na documentação. A apresentação conta ainda com uma breve demonstração do uso da ferramenta Flyway para versionar e controlar a execução de scripts nos diversos ambientes de um projecto.
Sobre o Nuno Alves:
Chamo-me Nuno Alves nascido em Coimbra, Portugal e vivi maioritariamente em Leiria. Licenciado em Engenharia Informática na ESTG-IPLeiria (Escola Superior de Tecnologia e Gestão) onde o gosto por dados e bases de dados se começou a desenvolver. Daí, profissionalmente a minha área de actuação ser em torno de bases de dados e infra-estruturas. Tenho cerca de 10 anos de experiência repartidos pelas áreas Financeira, Seguros, Governo, Militar em tecnologias que vão desde Oracle, PostgreSQL, MSSQLServer a DB2.
Data Lake and the rise of the microservicesBigstep
By simply looking at structured and unstructured data, Data Lakes enable companies to understand correlations between existing and new external data - such as social media - in ways traditional Business Intelligence tools cannot.
For this you need to find out the most efficient way to store and access structured or unstructured petabyte-sized data across your entire infrastructure.
In this meetup we’ll give answers on the next questions:
1. Why would someone use a Data Lake?
2. Is it hard to build a Data Lake?
3. What are the main features that a Data Lake should bring in?
4. What’s the role of the microservices in the big data world?
ContainerCon EU 2016 - Software-Defined Storage and Container SchedulersDavid vonThenen
This document discusses how software-defined storage (SDS) and container schedulers can work together in a game-changing way. It first covers container schedulers like Mesos and how they schedule tasks but lack persistent storage. It then discusses SDS solutions like ScaleIO that provide scale-out block storage across infrastructure. Finally, it proposes that combining SDS and schedulers by running a ScaleIO framework on Mesos provides numerous benefits like globally accessible persistent storage, high availability, and the ability to deploy applications anywhere. A demo is planned to illustrate configuring and using such an integrated SDS and scheduler solution.
IBM Integration Bus provides tools and features to help with integration development and administration. This presentation discusses tools for developers like the Integration Toolkit and API, as well as best practices for administrators around tasks like deployment, monitoring, and disaster recovery. It also covers how applications, libraries, and patterns can aid management of integration solutions.
Michael stack -the state of apache h basehdhappy001
The document provides an overview of Apache HBase, an open source, distributed, scalable, big data non-relational database. It discusses that HBase is modeled after Google's Bigtable and built on Hadoop for storage. It also summarizes that HBase is used by many large companies for applications such as messaging, real-time analytics, and search indexing. The project is led by an active community of committers and sees steady improvements and new features with each monthly release.
DevOps is changing today's software development world by helping us build better software, faster. However most of the knowledge and experience with DevOps is based around application software and ignores the database. We will examine how the concepts and principles of DevOps can be applied to database development by looking at both automated comparison analysis as well as migration script management. Automated building, testing, and deployment of database changes will be shown.
About the Presenter
Steve Jones is a Microsoft SQL Server MVP and has been working with SQL Server since version 4.2 on OS/2. After working as a DBA and developer for a variety of companies, Steve co-founded the community website SQLServerCentral.com in 2001. Since 2004, Steve has been the full-time editor of the site, ensuring it continues to be a great resource for SQL Server professionals. Over the last decade, Steve has written hundreds of articles about SQL Server for SQLServerCentral.com, SQL Server Standard magazine, SQL Server Magazine, and Database Journal.
SCaLE 15x - How Container Schedulers and Software-Defined Storage will Change...David vonThenen
Presentation at Southern California Linux Expo (SCaLE 15x). Discussion on what happens when you take a Software-based Storage platform integrated with Container Schedulers and moving that into the Cloud.
How Container Schedulers and Software-based Storage will Change the CloudDavid vonThenen
Persistent applications can be complex to manage and operate at scale but tend to be perfect for modern schedulers like Apache Mesos. The current trend in Containers is taking their ephemeral nature and turning it upside-down by running databases, key/value stores, WordPress, and etc within them. Internal direct attached storage and external storage are both options in running your long-running, persistent applications. The problem is how do you run these applications and containers in production environments?
This talk outlines how 2 Layer Scheduling, as known as the Offer-Accept model, found in Mesos and Software-based Storage enables deployment of managed frameworks and tasks while maintaining high availability, scale-out growth, and automation. This combination of technology will help build a "Skynet" like architecture for persistent applications and containers in the cloud.
This document summarizes Nicolas De Loof's talk about patterns for developing applications in the cloud. The talk discusses scaling applications horizontally and vertically, keeping stateless designs, using standards, and designing for failure. It also emphasizes continuous integration, deployment, and delivery practices like managing infrastructure as code and enabling zero downtime deployments.
Strata SC 2014: Apache Mesos as an SDK for Building Distributed FrameworksPaco Nathan
O'Reilly Media - Strata SC 2014
Apache Mesos is an open source cluster manager that provides efficient resource isolation for distributed frameworks—similar to Google’s “Borg” and “Omega” projects for warehouse scale computing. It is based on isolation features in the modern kernel: “cgroups” in Linux, “zones” in Solaris.
Google’s “Omega” research paper shows that while 80% of the jobs on a given cluster may be batch (e.g., MapReduce), 55-60% of cluster resources go toward services. The batch jobs on a cluster are the easy part—services are much more complex to schedule efficiently. However by mixing workloads, the overall problem of scheduling resources can be greatly improved.
Given the use of Mesos as the kernel for a “data center OS”, two additional open source components Chronos (like Unix “cron”) and Marathon (like Unix “init.d”) serve as the building blocks for creating distributed, fault-tolerant, highly-available apps at scale.
This talk will examine case studies of Mesos uses in production at scale: ranging from Twitter (100% on prem) to Airbnb (100% cloud), plus MediaCrossing, Categorize, HubSpot, etc. How have these organizations leveraged Mesos to build better, more scalable and efficient distributed apps? Lessons from the Mesos developer community show that one can port an existing framework with a wrapper in approximately 100 line of code. Moreover, an important lesson from Spark is that based on “data center OS” building blocks one can rewrite a distributed system much like Hadoop to be 100x faster within a relatively small amount of source code.
These case studies illustrate the obvious benefits over prior approaches based on virtualization: scalability, elasticity, fault-tolerance, high availability, improved utilization rates, etc. Less obvious outcomes also include: reduced time for engineers to ramp-up new services at scale; reduced latency between batch and services, enabling new high-ROI use cases; and enabling dev/test apps to run on a production cluster without disrupting operations.
Extended Flexagon FlexDeploy® Technical Overview presentation with product screenshots. Presentation extended with their permission. Slides demonstrating connection and deployment to Oracle Service Bus.
The document introduces Microsoft's Windows Azure cloud platform. It summarizes that Windows Azure provides an operating system for the cloud that abstracts away hardware and provides services for automated management, scalable computing and storage. It allows developers to build applications and services that can easily scale across large, connected data centers. The talk demonstrates how Windows Azure allows building complex service architectures from simple components like web and worker roles that interact through a durable storage system. It emphasizes that the platform aims to provide a familiar development experience while handling all the complexities of highly scalable cloud services.
DevOps with Elastic Beanstalk - TCCC-2014scolestock
This document discusses using AWS Elastic Beanstalk for deploying applications. It describes Elastic Beanstalk as a platform as a service that handles provisioning infrastructure and managing application deployments. It covers how to deploy application versions through the AWS console, command line, IDE plugins, or a CI/CD tool like Jenkins. It also discusses how Elastic Beanstalk uses applications, environments, and versions to model deployments and provides configuration, monitoring, logging and scaling capabilities.
Big SQL 3.0 is a SQL-on-Hadoop solution that provides SQL access to data stored in Hadoop. It uses the same table definitions and metadata as Hive, accessing data already stored in Hadoop without requiring a proprietary format. Big SQL extends Hive's syntax with features like primary keys and foreign keys. Tables in Big SQL and Hive represent views of data stored in Hadoop rather than separate storage structures.
Methodologies and flows for chip designDerek Pappas
This document discusses the benefits of using a Chip Specification Language (CSL) to automatically generate shared infrastructure elements across hardware, software, simulation, and verification domains for chip design. Key points include:
- CSL allows for a single specification of infrastructure that can be compiled to generate equivalent code for all domains, avoiding errors from manual generation and maintenance.
- Automatically generating infrastructure can reduce engineering time spent on repetitive tasks by 10-100x, allowing teams to focus on design.
- Shared infrastructure specified in CSL maintains consistency when changes are made, rather than requiring updates across different files and teams manually.
- CSL can generate test benches, vectors, and other elements to dramatically increase verification points
This chapter discusses software development security. It covers topics like programming concepts, compilers and interpreters, procedural vs object-oriented languages, application development methods like waterfall vs agile models, databases, object-oriented design, assessing software vulnerabilities, and artificial intelligence techniques. The key aspects are securing the entire software development lifecycle from initial planning through operation and disposal, using secure coding practices, testing for vulnerabilities, and continually improving processes.
This document discusses Azure Resource Manager templates, which provide a declarative and automated way to deploy resources in Azure. Some key points:
- ARM templates define the deployment of Azure resources through a JSON file, allowing deployments to be automated, repeatable, and easy to manage.
- Templates use parameters for user input, variables for reuse, and outputs to capture deployment results. Expressions allow dynamic values.
- Template execution establishes dependencies between resources through functions like dependsOn and reference.
- Templates can be linked to decompose deployments and allow reuse of common configurations. State can be passed between templates through parameters, variables, and outputs.
DSD-INT 2024 Delft3D FM Suite 2025.01 2D3D - New features + Improvements - Ge...Deltares
Presentation by Tjitske Geertsema (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 Delft3D FM Suite 2025.01 1D2D - Beta testing programme - HuttenDeltares
Presentation by Rinske Hutten (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Monday, 11 November 2024, Delft.
DSD-INT 2024 MeshKernel and Grid Editor - New mesh generation tools - CarniatoDeltares
Presentation by Luca Carniato (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 Quantifying wind wake effects around offshore wind farms in the ...Deltares
Presentation by Firmijn Zijl (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 Salinity intrusion in the Rhine-Meuse Delta - GeraedsDeltares
Presentation by Marlein Geraeds (Delft University of Technology, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 El-Nakheel beach swimmer safety study - DobrochinskiDeltares
Presentation by João Dobrochinski (Deltares, Netherlands) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 Development of a Delft3D FM Scheldt Estuary Model - VanledeDeltares
Presentation by Joris Vanlede (Flanders Hydraulics Research, Belgium) at the Delft3D User Days, during the Delft Software Days - Edition 2024 (DSD-INT 2024). Tuesday, 12 November 2024, Delft.
DSD-INT 2024 Modeling the effects of dredging operations on salt transport in...Deltares
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6. “Scripting a Delft-FEWS configuration
1. Why at all relevant?
2. What do we mean by “scripting”?
3. Why script?
4. Approaches
− what do we (not) script?
− where do we script from?
− what tools do we use?
5. Example: the Deltares global fluvial flow forecasting system
6. Where/ how to start?
7. Some notes
• Scripting is very much ‘a field in progress’
• Not all questions have been asked
• No definitive answers have been given
• Slowly, some ‘best practices’are forming
This Photo by Unknown Author is licensed under CC BY-NC
8. Why spend time talking about scripting?
• Delft-FEWS developments in place that allow for scripting to be done
− templating of General Adapters, Transformations
− storage of metadata in .csv, .dbf/.shp files
− increased use of ‘functions’ in location set definition, threshold definitions, etc.
• Solves potential problems
− efficient configuration building
− separation of responsibilities: metadata management v. Delft-FEWS config management
− near immediate uptake of metadata changes in forecasting applications
− availability of configuration skills
• Growing number of applications where scripting is used to build configurations
9. What do we mean by “scripting a Delft-FEWS config”?
“Scripting is the automated creation of parts of a Delft-FEWS configuration, based on
metadata.”
• Gauge locations
• Basin models
• Rating curves
• Thresholds
• …
• Workflow files
• Map layer files
• Topology files
• ModuleDataSetFiles
• …
10. Why script?
1. Ensure compliance between metadata and configurations
2. Quickly include metadata changes in a forecasting application
3. Impose uniformity on configurations
4. Cost efficiency
5. Allow for separation of tasks: management of metadata v management of configurations
6. …
This Photo by Unknown Author is licensed under CC BY-SA-NC
11. What do we (not) script?
Do script:
• config elements that are subject to frequent updates
• elements that are repeated throughout the configuration
Do not script:
• elements that are highly custom (explorer.xml, global.properties, …)
• elements that appear few times in your configuration only
• elements that don’t change very often
→ this varies from application to application
12. Approaches: where to script from?
• file based storage (e.g., .xlsx or .csv files)
• local database (e.g., MS Access Database)
• database on a server (e.g., SQL)
• …
Considerations include…
• What’s currently available?
• Who manages the metadata v. who builds the configuration
• Degree of referential data integrity required
• Costs
• …
This Photo by Unknown Author is licensed under CC BY-SA
13. Approaches: tools for scripting
• Any scripting language. So far we’ve seen scripts in Visual Basic, Python and R.
• Export of data to .csv files is almost trivial
• Helpful if there is a good xml library available
− Matlab: XML toolbox
− Python: xml.etree.ElementTree library
− R: xml2 package
20. Scripts
• Python based; written by Martijn Kwant
• Produce over 5,100 configuration files
− ~2,100 workflow files
− ~1,800 ModuleDataSetFiles
− ~ 80 mapLayerFiles jointly containing ~380,000 lines of metadata
• In future, outputs will automatically be included in the operational client-server system (!)
22. gloffis: the Deltares global fluvial flow forecasting system
• patchwork of multiple models that can jointly cover the earth’s land
• firmly based on wflow models, made possible by ‘three clicks to a model’ principle
• models vary in
− coverage (and may, and indeed do, overlap)
− temporal and spatial resolution
− model type (sbm, hbv, …)
− required wflow.exe version (2019.1, 2020.1 …)
• workflows vary in
− model that is called
− weather forecast product that is used
(currently, choice between various DWD ICON products: regional/global, ensemble/deterministic)
→ this is where the scripting rationale is: NWPforecasts, wflow models and workflows
24. gloffis metadata repository: MS Access database
• Enforces referential data integrity
• Allows for user-friendly access and editing of data using forms
• Not centrally hosted → pros and cons
28. Changes that are made, are conveniently highlightedby version control software
30. Scripting: where and how to start?
• Does it make sense for me to invest in ‘scripting’?
• How ‘dynamic’ is my configuration? Where, in the config, are the dynamics?
• How is my metadata organized?
• Do I have expertise in scripting available?
• …
→ feel free to ask for help!
This Photo by Unknown Author is licensed under CC BY-SA
32. Jan Verkade
[email protected], +31 6 5161 6107
Questions to the audience
Have you considered moving to a ‘scripted configuration’?
If so, what were/are your considerations?