Not too long ago, a reactive variant of the JDBC driver was released, known as Reactive Relational Database Connectivity (R2DBC for short). While R2DBC started as an experiment to enable integration of SQL databases into systems that use reactive programming models, it now specifies a full-fledged service-provider interface that can be used to retrieve data from a target data source.
In this session, we’ll take a look at the new MariaDB R2DBC connector and examine the advantages of fully reactive, non-blocking development with MariaDB. And, of course, we’ll dive in and get a first-hand look at what it’s like to use the new connector with some live coding!
gRPC is a modern open source RPC framework that enables client and server applications to communicate transparently. It is based on HTTP/2 for its transport mechanism and Protocol Buffers as its interface definition language. Some benefits of gRPC include being fast due to its use of HTTP/2, supporting multiple programming languages, and enabling server push capabilities. However, it also has some downsides such as potential issues with load balancing of persistent connections and requiring external services for service discovery.
This document discusses upgrading an Openstack network to SDN with Tungsten Fabric. It evaluates three solutions: 1) using the same database across regions, 2) hot-swapping Open vSwitch and virtual routers, and 3) using an ML2 plugin. The recommended solution is #3 as it provides minimum downtime. Key steps include installing the OpenContrail driver, synchronizing network resources between Openstack and Tungsten, and live migrating VMs. Topology 2 is also recommended as it requires minimum changes. The upgrade migrated 80 VMs and 16 compute nodes to the SDN network without downtime. Issues discussed include synchronizing resources and migrating VMs between Open vSwitch and virtual routers.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
This document provides an overview of Postgresql, including its history, capabilities, advantages over other databases, best practices, and references for further learning. Postgresql is an open source relational database management system that has been in development for over 30 years. It offers rich SQL support, high performance, ACID transactions, and extensive extensibility through features like JSON, XML, and programming languages.
Taking Security Groups to Ludicrous Speed with OVS (OpenStack Summit 2015)Thomas Graf
Open vSwitch (OVS) has long been a critical component of the Neutron's reference implementation, offering reliable and flexible virtual switching for cloud environments.
Being an early adopter of the OVS technology, Neutron's reference implementation made some compromises to stay within the early, stable featureset OVS exposed. In particular, Security Groups (SG) have been so far implemented by leveraging hybrid Linux Bridging and IPTables, which come at a significant performance overhead. However, thanks to recent developments and ongoing improvements within the OVS community, we are now able to implement feature-complete security groups directly within OVS.
In this talk we will summarize the existing Security Groups implementation in Neutron and compare its performance with the Open vSwitch-only approach. We hope this analysis will form the foundation of future improvements to the Neutron Open vSwitch reference design.
A 20 minute talk about how WePay runs airflow. Discusses usage and operations. Also covers running Airflow in Google cloud.
Video of the talk is available here:
https://wepayinc.box.com/s/hf1chwmthuet29ux2a83f5quc8o5q18k
gRPC is an open source RPC framework that makes it easy to build a distributed system across multiple languages. It uses HTTP/2 for transport, has features like streaming, load balancing and authentication built-in. It is used widely at Google and is now available open source with implementations in 10 languages. gRPC benefits from being layered on HTTP/2 for interoperability and has a pluggable architecture for advanced features like monitoring and proxies.
Type of DDoS attacks with hping3 exampleHimani Singh
This document summarizes common DDoS attack types and how to execute them using hping3 or other tools. It describes application layer attacks like HTTP floods, protocol attacks like SYN floods, volumetric attacks like ICMP floods, and reflection attacks. It then provides commands to execute various TCP, UDP, ICMP floods and other DDoS attacks using hping3 by spoofing addresses, modifying flags, and targeting ports. Layer 7 attacks exploiting HTTP requests are also summarized.
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.
Présentation du talk de Fabien Arcellier- OCTO Technology
Avant 10 JVMs, maintenant 300 microservices avec des runtimes
transients. Le nombre de service à assembler pour construire
nos applications augmente.
Le SI se modernise, le monitoring peine à suivre. Que faut-il
repenser ?
Presentation by Lorenzo Mangani of QXIP at the October 26 SF Bay Area ClickHouse meetup
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
https://qxip.net/
This document discusses running MySQL on Kubernetes with Percona Kubernetes Operators. It provides an introduction to cloud native applications and Kubernetes. It then discusses the benefits and challenges of running MySQL on Kubernetes compared to database-as-a-service options. It introduces Percona Kubernetes Operators for MySQL, which help manage and configure MySQL deployments on Kubernetes. Finally, it discusses how to deploy MySQL with the Percona Kubernetes Operators, including prerequisites, connectivity, architecture, high availability, and monitoring.
HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...Altinity Ltd
This document summarizes Cloudflare's use of ClickHouse to analyze over 6 million HTTP requests per second. Some key points:
- Cloudflare previously used PostgreSQL, Citus, and Flink but these did not scale sufficiently.
- ClickHouse was chosen as it is fast, scalable, fault tolerant, and Cloudflare had existing expertise in it.
- Cloudflare designed ClickHouse schemas to aggregate HTTP data into totals, breakdowns by category, and unique counts into two tables using different engines.
- Tuning ClickHouse index granularity improved query latency by 50% and throughput by 3x.
- The new ClickHouse pipeline is more scalable, fault tolerant
This version of "Oracle Real Application Clusters (RAC) 19c & Later – Best Practices" was first presented in Oracle Open World (OOW) London 2020 and includes content from the OOW 2019 version of the deck. The deck has been updated with the latest information regarding ORAchk as well as upgrade tips & tricks.
A look at what HA is and what PostgreSQL has to offer for building an open source HA solution. Covers various aspects in terms of Recovery Point Objective and Recovery Time Objective. Includes backup and restore, PITR (point in time recovery) and streaming replication concepts.
R2DBC started as an experiment to enable integration of SQL databases into systems that use reactive programming models. Now it specifies a robust specification that can be implemented to manage data in a fully-reactive and completely non-blocking fashion.
This document discusses building reactive database drivers with R2DBC. It covers how Spring supports reactive database access, an introduction to R2DBC including its design principles and SPI. It then discusses how R2DBC drivers work internally and provides examples of using R2DBC with Spring Data repositories. While R2DBC brings reactivity to database access, it is still immature and has limitations compared to blocking approaches like JPA.
This document discusses API management for GraphQL APIs. It begins with an introduction to GraphQL and then covers how API management platforms can support GraphQL APIs, including developer portals, enforcing security and policies at the gateway, and analytics. Key capabilities for GraphQL API management include importing GraphQL schemas, analyzing query complexity, enforcing rate limits on operations, and providing insights into usage patterns and latency from analytics. The document emphasizes that GraphQL is a good fit for some problems but API management is still needed to maximize benefits for both GraphQL API providers and consumers.
Presentation on Presto (http://prestodb.io) basics, design and Teradata's open source involvement. Presented on Sept 24th 2015 by Wojciech Biela and Łukasz Osipiuk at the #20 Warsaw Hadoop User Group meetup http://www.meetup.com/warsaw-hug/events/224872317
GraphQL is an application layer query language from Facebook. With GraphQL, you can define your backend as a well-defined graph-based schema. Then client applications can query your dataset as they are needed. GraphQL’s power comes from a simple idea — instead of defining the structure of responses on the server, the flexibility is given to the client. Will GraphQL do to REST what REST did to SOAP?
Nowadays application implementation requires a lot of different competencies that make it impossible for a single person to build an entire application. This led to the need for a big multi-competencies team and increasing in communication issues during the project. But what if we could implement an application without the need of implementing a backend? CrudIt does exactly that. CrudiIt is an open-source framework that enables a small team or a single front-end developer to create small SaaS applications without any knowledge of backend, cloud and sysadmin needs.
During the event, we will see how to implement a simple SaaS service using Crudit and how to use the framework to accelerate application development.
Moreover, we will discuss how to host a CrudIt application without any sysadmin competencies in the cloud, using free services.
This talk will unveil how, thanks to this powerful low-code framework, it is possible to save data without writing a line of code, even if you are working in complex, multitenant scenarios.
The talk will explain also how the framework is born, how it works internally and promote the opensource philosophy that brings to this library.
From a monolith to microservices + REST: The evolution of LinkedIn's architec...Karan Parikh
This is the story of LinkedIn's journey from a monolithic web application to a microservice based architecture, some of the challenges they faced along the way, and the tools they built to make this transition possible, including the Rest.li and Deco frameworks.
The document discusses building simple web services with PHP. It begins by describing problems with monolithic architectures, such as being tightly coupled and not flexible. It then defines what a web service is and characteristics like being loosely coupled. The remainder of the document discusses REST APIs as a common approach to building web services, including using HTTP methods on endpoints to perform CRUD operations and common status codes. Requirements for building a sample books API using these concepts are also provided.
The document summarizes the multi-purpose NoSQL database ArangoDB. It describes ArangoDB as a second generation database that is open source, free, and supports multiple data models including documents, graphs, and key-value. It highlights main features such as being extensible through JavaScript, having high performance, and being easy to use through its web interface and query language AQL.
- Dynomite is a framework that makes non-distributed databases distributed by adding a proxy layer, auto-sharding, replication across datacenters, and more.
- Netflix uses Dynomite to provide high availability and scalability for several internal microservices and tools like Conductor, an orchestration engine that uses Redis.
- Conductor allows defining workflows as code and executing them in a distributed and scalable way using Dynomite and Dyno Queues.
RedisConf17 - Dynomite - Making Non-distributed Databases DistributedRedis Labs
Dynomite is a framework that makes non-distributed databases distributed by adding a proxy layer, auto-sharding, replication across datacenters, and more. It is used at Netflix to power various services by sitting on top of Redis and providing high availability, scalability, and tunable consistency. Conductor is a workflow orchestration engine used by Netflix that stores workflow definitions and state in Dynomite to allow reusable and controllable workflow processes.
The presentation which I was using during my talk at EPAM Lviv JS community about offline-first applications. Contains high-level review of tools and web platform to submerge folks in a world of offline-first thinking.
Angular (v2 and up) - Morning to understand - LinagoraLINAGORA
Slides of the talk about Angular, at the "Matinée Pour Comprendre" organized by Linagora the 22/03/17.
Discover what's new in Angular, why is it more than just a framework (platform) and how to manage your data with RxJs and Redux.
This document summarizes a presentation about reactive relational database connectivity (R2DBC). R2DBC provides an API designed from the ground up for reactive programming with relational databases. It utilizes reactive streams types and patterns to be completely non-blocking. The R2DBC driver SPI defines a minimal set of operations for drivers to implement. On top of the SPI, "humane" client APIs like Spring Data R2DBC provide easier usage. Current R2DBC supports drivers for several databases and can be used today for features like transactions, batching, and leveraging database-specific functionality. The future of R2DBC includes additional drivers, clients, and potential improvements to the SPI.
This document provides an overview and introduction to SpringBatch presented by Slobodan Lohja in 2021. The presentation covers why companies should open up their data, what Spring and SpringBatch are, demonstrates building a sample project to import data from a CSV file to a MongoDB database using SpringBatch, and discusses some closing thoughts and taking questions. The sample project walkthrough provides steps to set up the necessary configurations, components, and classes to run a batch job that reads from a CSV and writes to a MongoDB database using SpringBatch.
A Separation of Concerns: Clean Architecture on AndroidOutware Mobile
Presented at YOW! Connected 2015 by Kamal Kamal Mohamed & Ryan Hodgman
As an Android developer, I want to deliver features without making compromises on code quality.
Scenario 1 - Given I am dealing with 1000+ line activities, When I have to develop a complicated feature, Then I waste time orienting myself and fixing bugs.
Scenario 2 - Given I have integrated a backend API directly into my app logic, When that API changes, Then I have to refactor large segments of unrelated logic in order to utilise the new API.
Scenario 3 - Given I have cleanly architected my application, When business/presentation/backend logic changes, Then I can easily update the relevant code without breaking unrelated features!
In this talk, two Android developers will present their take on what a cleanly architected app looks like and why it makes our lives easier. A well-defined separation of concerns has benefits not just for our sanity as developers, but also for the project workflow as it allows multiple developers to collaborate on a single feature with ease. We will be exploring how the domain-driven approach can improve code clarity, allow you to easily write tests, and provide a scalable infrastructure for you to quickly iterate on. Join us on our path of discovery as we discuss the advantages, drawbacks and implementation specifics in the context of a small sample project.
This document provides an introduction and overview of Spark's Dataset API. It discusses how Dataset combines the best aspects of RDDs and DataFrames into a single API, providing strongly typed transformations on structured data. The document also covers how Dataset moves Spark away from RDDs towards a more SQL-like programming model and optimized data handling. Key topics include the Spark Session entry point, differences between DataFrames and Datasets, and examples of Dataset operations like word count.
RESTful applications: The why and how by Maikel MardjanJexia
REST: What, Why, How? RESTful design patterns have advantages in every programming language and more and more companies and application providers are shifting towards RESTful interfaces.
Includes:
- Essence of REST
- How does REST work?
- Designing RESTful APIs
- Advantages and Disadvantages of REST
- Tools to work with REST
Maikel Mardjan: Professional IT architect and involved designing large IT systems since we survived the y2k disaster.
Similar to Introducing the R2DBC async Java connector (20)
MariaDB Paris Workshop 2023 - NewpharmaMariaDB plc
This document summarizes Newpharma's transition from a standalone database server to an enterprise MariaDB Galera cluster configuration between 2018-2023. It discusses the business needs that drove the change, including increased traffic and access to multiple data sources. Key benefits of the Galera cluster are highlighted like synchronous replication, read/write access from any node, and automatic node joining. Challenges of migrating like converting table types and splitting large transactions are also outlined. The transition has supported Newpharma's growth to over 100 million euro in turnover.
MariaDB Paris Workshop 2023 - Performance OptimizationMariaDB plc
MariaDB is an open-source database that is highly tunable and modular. It allows for various storage engines, plugins, and configurations to optimize performance depending on usage. Key aspects that impact performance include memory allocation, disk access, query optimization, and architecture choices like replication, sharding, or using ColumnStore for analytics. Solutions like MyRocks, Spider, MaxScale can improve performance for transactional or large scale workloads by optimizing resources, adding high availability, and distributing load.
MariaDB Paris Workshop 2023 - MaxScale MariaDB plc
The document outlines requirements and criteria for a database solution involving two buildings 30km apart with a WAN link. The chosen solution was MariaDB with Galera cluster for high availability and synchronous replication across sites, along with Maxscale for read/write splitting and failover. Maxscale instances on each site allow for zero downtime database patching and upgrades per site, while the Galera cluster provides structure-independent synchronous replication between sites.
MariaDB Tech und Business Update Hamburg 2023 - MariaDB Enterprise Server MariaDB plc
MariaDB Enterprise Server 10.6 includes the following key features:
- New JSON functions and data types like UUID and INET4.
- Improved Oracle compatibility with function parameters.
- Enhanced partitioning capabilities like converting partitions.
- Optimistic ALTER TABLE for replicas to reduce downtime.
- Online schema changes without locking tables for improved performance.
- Security enhancements including password policies and privilege changes.
MariaDB SkySQL is a cloud database service that provides autonomous scaling, observability, and cloud backup capabilities. It offers multi-cloud and hybrid operations across AWS, Google Cloud, and on-premises databases. The service includes features like the Remote Observability Service (ROS) for monitoring across environments, and a Cloud Backup Service. It aims to provide a simple yet advanced service for scaling databases from small to extreme sizes with tools for automation, self-service, and unified operations.
The document discusses high availability solutions for MariaDB databases. It begins by defining high availability and concepts like Recovery Time Objective (RTO) and Recovery Point Objective (RPO). It then presents different MariaDB and MaxScale architectures that provide high availability, including single node, primary-replica, Galera cluster, and SkySQL solutions. Key aspects covered are automatic failover, load balancing, data filtering, and service level agreements.
Die Neuheiten in MariaDB Enterprise ServerMariaDB plc
This document summarizes new features in MariaDB Enterprise Server. Key points include:
- MariaDB Enterprise Server is geared toward enterprise customers and focuses on stability, robustness, and predictability.
- It has a longer release cycle than Community Server, with new versions every 2 years and long maintenance cycles. New features from Community Server are backported.
- Recent additions include analytics functions, JSON support, bi-temporal modeling, schema changes, database compatibility features, and security enhancements.
- The upcoming 23.x release will include new JSON functions, data types like UUID and INET4, Oracle compatibility features, partitioning improvements, and Galera enhancements.
Global Data Replication with Galera for Ansell Guardian®MariaDB plc
Ansell Guardian® faced challenges with their previous database replication solution as their data and usage grew globally. They evaluated MariaDB/Galera and implemented it to replace their legacy solution. The implementation was smooth using automation scripts. MariaDB/Galera provided increased performance, faster deployment times, and more reliable data synchronization across their 3 data centers compared to their previous solution. It helped resolve a critical data divergence issue and improved the user experience. They plan to further enhance their database infrastructure using MaxScale in the future.
SkySQL is the first and only database-as-a-service (DBaaS) to perform workload analysis with advanced deep learning models, identifying and classifying discrete workload patterns so DBAs can better understand database workloads, identify anomalies and predict changes.
In this session, we’ll explain the concepts behind workload analysis and show how it can be used in the real world (and with sample real-world data) to improve database performance and efficiency by identifying key metrics and changes to cyclical patterns.
SkySQL uses best-of-breed software, and when it comes to metrics and monitoring that means Prometheus and Grafana. SkySQL Monitor is built on both, and provides customers with interactive dashboards for both real-time and historic metrics monitoring. In addition, it meets the same high availability and security requirements as other SkySQL components, ensuring metrics are always available and always secure.
In this session, we’ll explain how SkySQL Monitor works, walk through its dashboards and show how to monitor key metrics for performance and replication.
The capabilities and features of MariaDB Platform continue to expand, resulting in larger and more sophisticated production deployments – and the need for better tools. To provide DBAs with comprehensive, consolidating tooling, we created MariaDB Enterprise Tools: an easy-to-use, modular command-line interface for interacting with any part of MariaDB Platform.
In this session, we will provide a preview of the MariaDB Enterprise Client, walk through current and planned modules and discuss future plans for MariaDB Enterprise Tools – including SkySQL modules and the ability to create custom modules.
Faster, better, stronger: The new InnoDBMariaDB plc
For MariaDB Enterprise Server 10.5, the default transactional storage engine, InnoDB, has been significantly rewritten to improve the performance of writes and backups. Next, we removed a number of parameters to reduce unnecessary complexity, not only in terms of configuration but of the code itself. And finally, we improved crash recovery thanks to better consistency checks and we reduced memory consumption and file I/O thanks to an all new log record format.
In this session, we’ll walk through all of the improvements to InnoDB, and dive deep into the implementation to explain how these improvements help everything from configuration and performance to reliability and recovery.
SkySQL implements a groundbreaking, state-of-the-art architecture based on Kubernetes and ServiceNow, and with a strong emphasis on cloud security – using compartmentalization and indirect access to secure and protect customer databases.
In this session, we’ll walk through the architecture of SkySQL and discuss how MariaDB leverages an advanced Kubernetes operator and powerful ServiceNow configuration/workflow management to deploy and manage databases on cloud infrastructure.
What to expect from MariaDB Platform X5, part 1MariaDB plc
MariaDB Platform X5 will be based on MariaDB Enterprise Server 10.5. This release includes Xpand, a fully distributed storage engine for scaling out, as well as many new features and improvements for DBAs and developers alike, including enhancements to temporal tables, additional JSON functions, a new performance schema, non-blocking schema changes with clustering and a Hashicorp Vault plugin for key management.
In this session, we’ll walk through all of the new features and enhancements available in MariaDB Enterprise Server 10.5. In addition, we will highlight those being backported to maintenance releases of MariaDB Enterprise Server 10.2, 10.3 and 10.4.
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Getting Started with Interactive Brokers API and Python.pdfRiya Sen
In the fast-paced world of finance, automation is key to staying ahead of the curve. Traders and investors are increasingly turning to programming languages like Python to streamline their strategies and enhance their decision-making processes. In this blog post, we will delve into the integration of Python with Interactive Brokers, one of the leading brokerage platforms, and explore how this dynamic duo can revolutionize your trading experience.
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
Annex K RBF's The World Game pdf documentSteven McGee
Signals & Telemetry Annex K for RBF's The World Game / Trade Federations / USPTO 13/573,002 Heart Beacon Cycle Time - Space Time Chain meters, metrics, standards. Adaptive Procedural template framework structured data derived from DoD / NATO's system of systems engineering tech framework
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...rightmanforbloodline
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
2. Who is this guy?
Rob Hedgpeth
Developer Evangelist
robh@mariadb.com
@probablyrealrob
rhedgpeth
3. Agenda
● Reactive Programming
○ What is it?
○ Why is it important?
● R2DBC
○ What is it?
○ How can you use it?
● Demo
Non-blocking
Database Euphoria
8. What is a stream?
@mariadb
Variables
changes in data
Starting event
initiates stream of data
Error
disruption in data
Completion
all data is processed
Time
9. Reactive Streams
@mariadb
● An initiative started in 2013 by several companies (e.g. Netflix, Pivotal,
Lightbend and many others)
● A specification
○ API types
○ Technology Compatibility Kit (TCK)
● The goal was to provide a standard for asynchronous stream processing with
non-blocking back pressure.
14. Reactive Streams API
@mariadb
public interface Publisher<T> {
public void subscribe(Subscriber<? super T> s);
}
public interface Subscriber<T> {
public void onSubscribe(Subscription s);
public void onNext(T t);
public void onError(Throwable t);
public void onComplete();
}
public interface Subscription {
public void request(long n);
public void cancel();
}
public interface Processor<T, R> extends Subscriber<T>, Publisher<R> {
}
16. Reactive Programming
@mariadb
● The next frontier in Java (and the JVM) for high-efficiency applications
● Fundamentally non-blocking
○ Often paired with asynchronous behaviors
○ ...but is completely agnostic to sync/async
● The key takeaway - back pressure
Async != Reactive
21. Design principles*
@mariadb
1. Be completely non-blocking, all the way to the database
2. Utilize Reactive Streams Types and Patterns
3. Provide a minimal set of operations that are implementation specific
4. Enable “humane” APIs to be built on top of the driver
* Ben Hale, Creator of R2DBC
22. Goals
@mariadb
✓ Fit seamlessly into Reactive JVM platforms
✓ Offer vendor-neutral access to standard features
✓ Embrace vendor-specific features
✓ Keep the focus on SQL
✓ Keep it simple
○ Provide a foundation for tools and higher-level API’s
○ Compliance should be unambiguous and easy to identify
24. Why a service-provider interface (SPI)?
@mariadb
● One of JDBC’s biggest failings was that the same API had to serve as both
humane API for users as well as an inhumane API for alternative clients like JPA,
Jdbi, etc.
○ API that users didn’t like using
○ Driver authors didn’t like implementing
● It also lead to drivers duplicating effort
○ ? binding
○ URL parsing