This document introduces Object-Relational Mapping (ORM) using Doctrine 2. It discusses different approaches to dealing with data from relational databases and their tradeoffs. It then explains how Doctrine 2 achieves low coupling and high cohesion through its use of entities, repositories, and query builders to efficiently retrieve and map data to objects.
With help of this small Proof of Concept, I have tried to demonstrate the usage of Neo4J (Graph DB) as a metastore for a Data Lake or a DW. Graph DBs can store highly relational data and help us in doing data discovery and impact analysis, which bit more complex to bee done in an RDBMS.
Apply hibernate to model and persist associations mappings in document versio...csandit
To implement any system using hibernate, a XML (Extensible Markup Language) mapping is
defined that specifies how the various objects will persist in the database. The XML specifies
mapping between classes and tables, properties and columns, association and foreign keys. In
this paper we primarily focus on how different association among classes should be mapped in
XML configuration file for persistence. A particular type of association should be specified in
XML configuration file using valid elements and tags provided by hibernate. Such a defined
XML document then create the valid schema, and the constraints between respective tables in
the database to implement any system. Here we present how the different types of associations
should be implemented in hibernate to have desired effect on the database. We also tested the
use cases those may violate the constraints specified by XML configuration and studied the
results
Java Persistence API is a collection of classes and methods to persistently store the vast amounts of data into a database which is provided by the Oracle Corporation.
Generally, Java developers use lots of code, or use the proprietary framework to interact with the database, whereas using JPA, the burden of interacting with the database reduces significantly. It forms a bridge between object models (Java program) and relational models (database program).
Easier building complex applications in Joomla! by adding a mapping layer between the domain model and the database. Some of this might be useful to improve Joomla!'s core.
While building custom Joomla!-extensions I often encountered difficulties implementing one-to-many and many-to-many relations.
Joomla! uses lists and items, where a list relates to a database table and an item to a record in that table. Because the underlying relational database is normalised, fields are atomic: contain only one value. In reality (in the domain) a person can have multiple addresses, an order can have multiple orderlines, a posting can have several comments, a product can have several images etc. Often, fields in our entities are not atomic, but can have collections of values. If we stay close to the one-to-one mapping of lists/items and database tables, as Joomla! natively does, it soon becomes messy when modelling more complex relationships. But if we use a mapping between the objects in our domain model and the database, we can easily use non-atomic fields. The same holds for inheritance mapping: a parent class and child classes can be mapped to a relational database in several ways. The Unified Content Model (UCM) is an example of class table inheritance mapping, where the superclass (core_content) has its own table, as do the subclasses. This opposed to the currently used concrete table inheritance, where every content-type maps to one table.
Fortunately this Object Relational Mapping has allready been extensively elaborated. No need to reinvent this wheel. In this presentation I'll show how I used Doctrine ORM for it. And I'll show how this could be used to improve Joomla!'s core.
How Pony ORM translates Python generators to SQL queriesponyorm
Pony ORM is an Object-Relational Mapper implemented in Python. It uses an unusual approach for writing database queries using Python generators. Pony analyzes the abstract syntax tree of a generator and translates it to its SQL equivalent. The translation process consists of several non-trivial stages.
This talk was given at EuroPython 2014 and reveals the internal details of the translation process.
With help of this small Proof of Concept, I have tried to demonstrate the usage of Neo4J (Graph DB) as a metastore for a Data Lake or a DW. Graph DBs can store highly relational data and help us in doing data discovery and impact analysis, which bit more complex to bee done in an RDBMS.
Apply hibernate to model and persist associations mappings in document versio...csandit
To implement any system using hibernate, a XML (Extensible Markup Language) mapping is
defined that specifies how the various objects will persist in the database. The XML specifies
mapping between classes and tables, properties and columns, association and foreign keys. In
this paper we primarily focus on how different association among classes should be mapped in
XML configuration file for persistence. A particular type of association should be specified in
XML configuration file using valid elements and tags provided by hibernate. Such a defined
XML document then create the valid schema, and the constraints between respective tables in
the database to implement any system. Here we present how the different types of associations
should be implemented in hibernate to have desired effect on the database. We also tested the
use cases those may violate the constraints specified by XML configuration and studied the
results
Java Persistence API is a collection of classes and methods to persistently store the vast amounts of data into a database which is provided by the Oracle Corporation.
Generally, Java developers use lots of code, or use the proprietary framework to interact with the database, whereas using JPA, the burden of interacting with the database reduces significantly. It forms a bridge between object models (Java program) and relational models (database program).
Easier building complex applications in Joomla! by adding a mapping layer between the domain model and the database. Some of this might be useful to improve Joomla!'s core.
While building custom Joomla!-extensions I often encountered difficulties implementing one-to-many and many-to-many relations.
Joomla! uses lists and items, where a list relates to a database table and an item to a record in that table. Because the underlying relational database is normalised, fields are atomic: contain only one value. In reality (in the domain) a person can have multiple addresses, an order can have multiple orderlines, a posting can have several comments, a product can have several images etc. Often, fields in our entities are not atomic, but can have collections of values. If we stay close to the one-to-one mapping of lists/items and database tables, as Joomla! natively does, it soon becomes messy when modelling more complex relationships. But if we use a mapping between the objects in our domain model and the database, we can easily use non-atomic fields. The same holds for inheritance mapping: a parent class and child classes can be mapped to a relational database in several ways. The Unified Content Model (UCM) is an example of class table inheritance mapping, where the superclass (core_content) has its own table, as do the subclasses. This opposed to the currently used concrete table inheritance, where every content-type maps to one table.
Fortunately this Object Relational Mapping has allready been extensively elaborated. No need to reinvent this wheel. In this presentation I'll show how I used Doctrine ORM for it. And I'll show how this could be used to improve Joomla!'s core.
How Pony ORM translates Python generators to SQL queriesponyorm
Pony ORM is an Object-Relational Mapper implemented in Python. It uses an unusual approach for writing database queries using Python generators. Pony analyzes the abstract syntax tree of a generator and translates it to its SQL equivalent. The translation process consists of several non-trivial stages.
This talk was given at EuroPython 2014 and reveals the internal details of the translation process.
The Agenda for the Webinar:
1. Introduction to Python.
2. Python and Big Data.
3. Python and Data Science.
4. Key features of Python and their usage in Business Analytics.
5. Business Analytics with Python – Real world Use Cases.
Agile Data Science 2.0 covers the theory and practice of applying agile methods to the practice of applied analytics research called data science. The book takes the stance that data products are the preferred output format for data science teams to effect change in an organization. Accordingly, we show how to "get meta" to enable agility in building applications describing the applied research process itself. Then we show how to use 'big data' tools to iteratively build, deploy and refine analytics applications. Tracking data-product development through the five stages of the "data value pyramid", we show you how to build applications from conception through development through deployment and then through iterative improvement. Application development is a fundamental skill for a data scientist, and by publishing your data science work as a web application, we show you how to effect maximal change within your organization.
Technologies covered include Python, Apache Spark (Spark MLlib, Spark Streaming), Apache Kafka, MongoDB, ElasticSearch and Apache Airflow.
Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
Agile Data Science 2.0 (O'Reilly 2017) defines a methodology and a software stack with which to apply the methods. *The methodology* seeks to deliver data products in short sprints by going meta and putting the focus on the applied research process itself. *The stack* is but an example of one meeting the requirements that it be utterly scalable and utterly efficient in use by application developers as well as data engineers. It includes everything needed to build a full-blown predictive system: Apache Spark, Apache Kafka, Apache Incubating Airflow, MongoDB, ElasticSearch, Apache Parquet, Python/Flask, JQuery. This talk will cover the full lifecycle of large data application development and will show how to use lessons from agile software engineering to apply data science using this full-stack to build better analytics applications. The entire lifecycle of big data application development is discussed. The system starts with plumbing, moving on to data tables, charts and search, through interactive reports, and building towards predictions in both batch and realtime (and defining the role for both), the deployment of predictive systems and how to iteratively improve predictions that prove valuable.
ADO.NET by ASP.NET Development Company in india
ADO.NET is a data access technology from the Microsoft .NET Framework that provides communication between relational and non-relational systems through a common set of components.
Video :
Courtesy:
http://www.ifourtechnolab.com
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. Object-relational mapping (ORM, O/RM, and O/R mapping) in computer software is a programming technique for converting data between incompatible type systems in object-oriented programming languages. This creates, in effect, a "virtual object database" that can be used from within the programming language. There are both free and commercial packages available that perform object-relational mapping, although some programmers opt to create their own ORM tools.
3. Loose coupling: Given two lines of code, A and B, they are coupled when B must change behavior only because A changed. Content coupling (high) is when one module modifies or relies on the internal workings of another module (e.g. accessing local data of another module). Therefore changing the way the second module produces data (location, type, timing) will lead to changing the dependent module. High cohesion: They are cohesive when a change to A allows B to change so that both add new value. Functional cohesion (best) is when parts of a module are grouped because they all contribute to a single well-defined task of the module Loose coupling and High Coupling
4.
5. Ways to achive “Low Cohesion” and “High coupling” Classes.... first approach class Orders { public function __construct($orderId) { $sql = “select o.order_id,.... from order o inner join o.order_product op on... Inner join op.product p on... inner join o.customers c on. where o.order_id = $orderId. } } class Customer { public function getFullName(){} public function getRating(){} } Class Product { public function getPrice(){} }
6.
7. class Order { public function __construct($orderId) { $sql = “select o.order_id,.... from order o where o.order_id = $orderId” } public function getOrderProducts() { $sql = “select op.order_product_id,.... from order_product op where order_id = $this->orderId” } public function getCustomer() { $sql = “select c.customer_id,.... from customer c where c.customer_id = $this->customerId” } } Ways to achive “Low Cohesion” and “High coupling”
8. class Order { public function __construct($orderId) { $sql = “select o.order_id,.... from order o where o.order_id = $orderId” } public function getOrderProducts() { $this->_orderProducts = new OrderProductCollection($this->orderId); } public function getCustomer() { $this->_customer = new Customer($this->customerId); } } Or even better... Ways to achive “Low Cohesion” and “High coupling”
9.
10. $queryBuilder = $em ->createQueryBuilder(); ->select('o') ->from('Entitiesrders', 'o') ->innerJoin('o.ordersProducts', 'op') ->innerJoin('o.customers', 'c') ->where($queryBuilder->expr()->in('o.ordersId', '?1')) ->setMaxResults(1) ->setParameter(1, '5030492') ->getQuery(); $order = $query->getSingleResult(); This will generate an efficent code: SELECT ..... FROM orders o0_ INNER JOIN orders_products o1_ ON o0_.orders_id = o1_.orders_id INNER JOIN customers c2_ ON o0_.customers_id = c2_.customers_id WHERE o0_.orders_id IN (?) LIMIT 1 Ways to achive “Low Cohesion” and “High coupling” Welcome to Doctrine 2: DQL language
11.
12. How to access the data: echo $order->getCustomer()->getFirstName(); echo $order->getCustomer()->getFullName(); echo $order->getAddressDeliveryStreet(); foreach( $order->getOrdersProducts() as $orderProduct ) { echo $orderProduct->getOrdersProductsId(); echo $orderProduct->getProductName(); } Doctrine create and inject (Hydrate) the data into $order, $customer $orderProducts (collection). We still have decouple and choesive objects Doctrine 2
13. We sill can do: $queryBuilder = $em ->createQueryBuilder(); ->select('o') ->from('Entitiesrders', 'o'); And access, but sql no as efficent...: “ SELECT o0.... FROM orders o0_ WHERE o0_.orders_id IN (?) LIMIT 1” echo $order->getCustomer()->getFirstName(); “ SELECT t0.... FROM customers t0 WHERE t0.customers_id = ?” echo $order->getCustomer()->getFullName(); echo $order->getAddressDeliveryStreet(); $orderProducts = $order->getOrdersProducts(); “ SELECT t0... FROM orders_products t0 WHERE orders_id = ?” foreach( $orderProducts as $orderProduct) { echo $orderProduct->getOrdersProductsId(); echo $orderProduct->getProductName(); } Doctrine 2
14. Also we have magic finder methods throw the repositories: $em->getRepository(“Entitiesrder”)->find() Or ->findByCustomerId() Doctrine 2
15. We meet the Doctrin Entities... <?php namespace Entities; /** * @Entity @Table(name="orders") */ class Orders { /** * @Id @Column(type="integer", name="orders_id") * @GeneratedValue(strategy="AUTO") */ private $ordersId; /** * @Column(type="integer", name="customers_id") */ private $customersId; /** * @ManyToOne(targetEntity="Customers") * @JoinColumn(name="customers_id", referencedColumnName="customers_id") */ private $customers; public function getOrdersId() { return $this->ordersId; } public function setOrderId($orderId) { $this->ordersId = $ordersId; } Doctrine 2
16. They don't follow the Active Record pattern like Zend_Table or Doctrine: “ Active record is an approach to accessing data in a database. A database table or view is wrapped into a class. Thus, an object instance is tied to a single row in the table. After creation of an object, a new row is added to the table upon save. Any object loaded gets its information from the database. When an object is updated the corresponding row in the table is also updated. The wrapper class implements accessor methods or properties for each column in the table or view.”
17. Data Mapper The Data Mapper is a layer of software that separates the in-memory objects from the database. Its responsibility is to transfer data between the two and also to isolate them from each other. With Data Mapper the in-memory objects needn't know even that there's a database present; they need no SQL interface code, and certainly no knowledge of the database schema. (The database schema is always ignorant of the objects that use it.) Since it's a form of Mapper (473), Data Mapper itself is even unknown to the domain layer $order = new Order; $order->setCustomerId(3); $order->setDeliveryAddress('....'); $em->persist($order); $em->flush();