Easy ORM-ness with Objectify-Appengine - Indicthreads cloud computing confere...IndicThreads
Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011.
http://CloudComputing.IndicThreads.com
Abstract: There are many persistence frameworks on Google App Engine. Google has its low-level datastore API, then there are standards based frameworks like JPA and there are more frameworks like Objectify-Appengine specifically for Google App Engine. Managing persistence is still a challenge. With so many options, we face the dilemma of choosing right framework for the application we are building.
Objectify-Appengine extends the convenience of NoSql by providing a Hibernate-style mapping layer between your application and GAE datastore. In this session I will cover handling persistence on Google App Engine with Objectify-Appengine. At one hand Google datastore API is low level and at the other there are standard based frameworks like JPA/JDO. Objectify-Appengine tries to hit the sweet spot between the two.
Takeaways for the Audience
Audience will see the capabilities of Objectify-Appengine and how can they leverage it to manage persistence on Google App Engine. The best practises to be followed when using Objectify, and demo of an application built on Objectify-Appengine.
Speaker:Meetu Maltiar is a Senior Consultant at Inphina. In his six year experience he has worked as Software developer, Knowledge Manager and as a Mentor. He is passionate about technology and has experience in product development as well as services. He has experience on Google App Engine and has migrated existing applications to cloud and he has also worked on Green-field projects. He likes to invest his time in exploring upcoming languages like Scala and technology frameworks like GridGain, Esper and Hadoop and extensively blogs about them.
Data access patterns and technologies are discussed. The Data Access Object (DAO) pattern separates data access from business logic. Spring JDBC and myBatis provide APIs for SQL queries and object mapping. Object-relational mapping (ORM) tools like Hibernate reduce code by mapping objects to relational databases but can reduce performance. JDBC template provides basic data access while frameworks offer additional features.
A powerful ORM tool to design data base access layer.
Fills gaps of mismatches between OOPs and RDBMS paradigm. Also maintains adequate ultra performance of database access.
An automated, configurable persistence of java objects with tables in data base.
May not be a good solution for data-centric application which uses only stored procedures to implement business logic in the database.
Hibernate, created by Gavin King, known as the best and dominated object/relational persistence (ORM) tool for Java developers (Now is support .NET). It provides many elegant and innovative ways to simplifies the relational database handling task in Java.
Hibernate is great at a lot of things, but its something that needs to be used appropriately. In this tutorials, it providing many step by step examples and explanations on using Hibernate3
we recap the basic and advanced notions regarding different types of Object types used when working in UiPath environment. We are going to go through the basics (e.g.: concatenation, conversion between different types of data) and deep dive into more advanced topics like linqs or selectors, recap how to work with datatables, from initialization to filtering and touch upon the subject of invoking code inside an UiPath project.
Certification preparation - Net classses and functions.pptxRohit Radhakrishnan
we recap the basic and advanced notions regarding different types of Object types used when working in UiPath environment. We are going to go through the basics (e.g.: concatenation, conversion between different types of data) and deep dive into more advanced topics like linqs or selectors, recap how to work with datatables, from initialization to filtering and touch upon the subject of invoking code inside an UiPath project.
Easy ORM-ness with Objectify-Appengine - Indicthreads cloud computing confere...IndicThreads
Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011.
http://CloudComputing.IndicThreads.com
Abstract: There are many persistence frameworks on Google App Engine. Google has its low-level datastore API, then there are standards based frameworks like JPA and there are more frameworks like Objectify-Appengine specifically for Google App Engine. Managing persistence is still a challenge. With so many options, we face the dilemma of choosing right framework for the application we are building.
Objectify-Appengine extends the convenience of NoSql by providing a Hibernate-style mapping layer between your application and GAE datastore. In this session I will cover handling persistence on Google App Engine with Objectify-Appengine. At one hand Google datastore API is low level and at the other there are standard based frameworks like JPA/JDO. Objectify-Appengine tries to hit the sweet spot between the two.
Takeaways for the Audience
Audience will see the capabilities of Objectify-Appengine and how can they leverage it to manage persistence on Google App Engine. The best practises to be followed when using Objectify, and demo of an application built on Objectify-Appengine.
Speaker:Meetu Maltiar is a Senior Consultant at Inphina. In his six year experience he has worked as Software developer, Knowledge Manager and as a Mentor. He is passionate about technology and has experience in product development as well as services. He has experience on Google App Engine and has migrated existing applications to cloud and he has also worked on Green-field projects. He likes to invest his time in exploring upcoming languages like Scala and technology frameworks like GridGain, Esper and Hadoop and extensively blogs about them.
Data access patterns and technologies are discussed. The Data Access Object (DAO) pattern separates data access from business logic. Spring JDBC and myBatis provide APIs for SQL queries and object mapping. Object-relational mapping (ORM) tools like Hibernate reduce code by mapping objects to relational databases but can reduce performance. JDBC template provides basic data access while frameworks offer additional features.
A powerful ORM tool to design data base access layer.
Fills gaps of mismatches between OOPs and RDBMS paradigm. Also maintains adequate ultra performance of database access.
An automated, configurable persistence of java objects with tables in data base.
May not be a good solution for data-centric application which uses only stored procedures to implement business logic in the database.
Hibernate, created by Gavin King, known as the best and dominated object/relational persistence (ORM) tool for Java developers (Now is support .NET). It provides many elegant and innovative ways to simplifies the relational database handling task in Java.
Hibernate is great at a lot of things, but its something that needs to be used appropriately. In this tutorials, it providing many step by step examples and explanations on using Hibernate3
we recap the basic and advanced notions regarding different types of Object types used when working in UiPath environment. We are going to go through the basics (e.g.: concatenation, conversion between different types of data) and deep dive into more advanced topics like linqs or selectors, recap how to work with datatables, from initialization to filtering and touch upon the subject of invoking code inside an UiPath project.
Certification preparation - Net classses and functions.pptxRohit Radhakrishnan
we recap the basic and advanced notions regarding different types of Object types used when working in UiPath environment. We are going to go through the basics (e.g.: concatenation, conversion between different types of data) and deep dive into more advanced topics like linqs or selectors, recap how to work with datatables, from initialization to filtering and touch upon the subject of invoking code inside an UiPath project.
Certification preparation - Error Handling and Troubleshooting recap.pptxRohit Radhakrishnan
The Error Handling and Troubleshooting session focuses on the understanding of system and business exceptions and on how to use the error handling activities in UiPath Studio. We also discuss about debugging methods and logging best practices.
The Orchestrator Functions presentation offers a brief introduction and recap of the main UiPath Orchestrator features that are covered in the certification objectives. It focuses on understanding the general components and working with assets, queues and triggers.
Testing database content with DBUnit. My experience.Serhii Kartashov
The document discusses testing the database layer in a smarter way than usual. It proposes using multiple databases for development, QA, and production environments. The best practice is to initialize test data, call the API, compare the actual database data to the expected data from a file. This can be done using the DBUnit framework which provides components for connecting to a database, representing test data sets, and performing database operations for testing purposes.
The document discusses using Hibernate, an object-relational mapping framework, to provide object persistence and retrieval by mapping Java objects to database tables. It explains what Hibernate does, its features like object-relational mapping and transaction management, and provides examples of persisting and retrieving objects using Hibernate's API. The document also shows how to configure a Hibernate project and map classes and associations between objects and database tables.
The document provides an introduction and overview of Hibernate, including its architecture, configuration, object-relational mapping capabilities, querying functionality, caching, and transactions. Hibernate allows developers to work with Java objects in Java code while it handles the conversion between objects and relational data. The document outlines how to set up Hibernate and map objects and relationships to database tables.
JPA is a specification that defines a standard for object/relational persistence and object queries with Java. It allows developers to work with objects in Java code while the JPA implementation handles persistence to a relational database. Key aspects include entity objects that represent data, the EntityManager for CRUD operations, and JPQL for object-oriented queries. JPA supports both container-managed and resource-local transactions.
Object Identification and its management is heart of any test automation. this document explains different ways for object identification in selenium, and also importance of its maintenance.
Introduction to JPA and Hibernate including examplesecosio GmbH
In this talk, held as part of the Web Engineering lecture series at Vienna University of Technology, we introduce the main concepts of Java Persistence API (JPA) and Hibernate.
The first part of the presentation introduces the main principles of JDBC and outlines the major drawbacks of JDBC-based implementations. We then further outline the fundamental principles behind the concept of object relation mapping (ORM) and finally introduce JPA and Hibernate.
The lecture is accompanied by practical examples, which are available on GitHub.
Learn what's new in Project Wonder's ERRest framework. Also, see some tips about security and versioning for your REST services, and learn how you can use HTML routing to build Web apps with ERRest.
- Hibernate is an ORM tool that uses POJO objects and allows working with objects instead of directly with the database.
- It uses a SessionFactory to provide Sessions which represent a single unit of work and handles dirty checking. Mapping is defined in XML files.
- Hibernate supports complex queries using the Criteria interface to create restrictions and conditions equivalent to SQL queries.
The document provides an overview of Java Persistence API (JPA) terminology and concepts. It defines key JPA terms like entity, persistence unit, entity manager, and persistence context. It describes entity characteristics and the different types of entity managers, persistence contexts, and transaction managers supported by JPA. The document also covers JPQL, query parameters, named queries, inheritance, collections, caching and more advanced JPA features.
- get() throws an ObjectNotFoundException if no data is found, while load() returns a proxy object without hitting the database
- When accessing properties of the proxy object returned by load(), it will trigger a SELECT to the database and throw an ObjectNotFoundException if no data exists
- get() immediately hits the database to check for data, while load() defers the database hit until property access if a proxy is returned
- It is generally better to use get() when you expect data to exist, and check for its return value being null, rather than using load()
The document discusses best practices for using the Java Persistence API (JPA). It covers:
- The entity manager and persistence context manage entities within a scope like a transaction.
- Entities transition between managed, detached, and removed states that impact database synchronization.
- Queries retrieve and manage entities from the persistence context and database.
- Transactions and locking strategies like optimistic and pessimistic concurrency control how changes are made.
- Extended and transaction scoped persistence contexts determine entity lifetime and caching.
- The second level cache can improve performance by caching entities across contexts.
ERRest - Designing a good REST serviceWO Community
Learn how to design your REST service with the correct HTTP verbs and codes. We also talk on how to manage optimistic locking and caching for your REST services.
Hibernate framework simplifies the development of java application to interact with the database. Hibernate is an open source, lightweight, ORM (Object Relational Mapping) tool.
An ORM tool simplifies the data creation, data manipulation and data access. It is a programming technique that maps the object to the data stored in the database.
Hibernate, created by Gavin King, known as the best and dominated object/relational persistence (ORM) tool for Java developers (Now is support .NET). It provides many elegant and innovative ways to simplifies the relational database handling task in Java.
Hibernate is great at a lot of things, but its something that needs to be used appropriately. In this tutorials, it providing many step by step examples and explanations on using Hibernate3.
Spring Data is a high level SpringSource project whose purpose is to unify and ease the access to different kinds of persistence stores, both relational database systems and NoSQL data stores.
This document provides an overview of the Hibernate Query Language (HQL) including:
- HQL queries are translated into SQL queries and allow object-oriented queries rather than operating on tables and columns.
- It describes the FROM, SELECT, WHERE, ORDER BY, and GROUP BY clauses of HQL and provides examples of each.
- It also includes instructions on configuring Hibernate and Java and an overview of how Hibernate works independently of code.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
This presentation was presented at OSS camp in New Delhi. It deals with the basics of Scala language and how we can use it to build scalable Applications
- FitNesse is an open-source automated testing framework that allows developers, testers, and customers to collaborate on software testing by writing tests in a wiki format.
- Tests in FitNesse wiki format call custom fixtures that act as a bridge between the wiki pages and the system under test. Fixtures can be written in many programming languages including Scala.
- To write a FitNesse wiki test, tables are used with inputs in even columns and method names in odd columns to call methods defined in fixtures, allowing tests to be written in a domain-specific language format.
Certification preparation - Error Handling and Troubleshooting recap.pptxRohit Radhakrishnan
The Error Handling and Troubleshooting session focuses on the understanding of system and business exceptions and on how to use the error handling activities in UiPath Studio. We also discuss about debugging methods and logging best practices.
The Orchestrator Functions presentation offers a brief introduction and recap of the main UiPath Orchestrator features that are covered in the certification objectives. It focuses on understanding the general components and working with assets, queues and triggers.
Testing database content with DBUnit. My experience.Serhii Kartashov
The document discusses testing the database layer in a smarter way than usual. It proposes using multiple databases for development, QA, and production environments. The best practice is to initialize test data, call the API, compare the actual database data to the expected data from a file. This can be done using the DBUnit framework which provides components for connecting to a database, representing test data sets, and performing database operations for testing purposes.
The document discusses using Hibernate, an object-relational mapping framework, to provide object persistence and retrieval by mapping Java objects to database tables. It explains what Hibernate does, its features like object-relational mapping and transaction management, and provides examples of persisting and retrieving objects using Hibernate's API. The document also shows how to configure a Hibernate project and map classes and associations between objects and database tables.
The document provides an introduction and overview of Hibernate, including its architecture, configuration, object-relational mapping capabilities, querying functionality, caching, and transactions. Hibernate allows developers to work with Java objects in Java code while it handles the conversion between objects and relational data. The document outlines how to set up Hibernate and map objects and relationships to database tables.
JPA is a specification that defines a standard for object/relational persistence and object queries with Java. It allows developers to work with objects in Java code while the JPA implementation handles persistence to a relational database. Key aspects include entity objects that represent data, the EntityManager for CRUD operations, and JPQL for object-oriented queries. JPA supports both container-managed and resource-local transactions.
Object Identification and its management is heart of any test automation. this document explains different ways for object identification in selenium, and also importance of its maintenance.
Introduction to JPA and Hibernate including examplesecosio GmbH
In this talk, held as part of the Web Engineering lecture series at Vienna University of Technology, we introduce the main concepts of Java Persistence API (JPA) and Hibernate.
The first part of the presentation introduces the main principles of JDBC and outlines the major drawbacks of JDBC-based implementations. We then further outline the fundamental principles behind the concept of object relation mapping (ORM) and finally introduce JPA and Hibernate.
The lecture is accompanied by practical examples, which are available on GitHub.
Learn what's new in Project Wonder's ERRest framework. Also, see some tips about security and versioning for your REST services, and learn how you can use HTML routing to build Web apps with ERRest.
- Hibernate is an ORM tool that uses POJO objects and allows working with objects instead of directly with the database.
- It uses a SessionFactory to provide Sessions which represent a single unit of work and handles dirty checking. Mapping is defined in XML files.
- Hibernate supports complex queries using the Criteria interface to create restrictions and conditions equivalent to SQL queries.
The document provides an overview of Java Persistence API (JPA) terminology and concepts. It defines key JPA terms like entity, persistence unit, entity manager, and persistence context. It describes entity characteristics and the different types of entity managers, persistence contexts, and transaction managers supported by JPA. The document also covers JPQL, query parameters, named queries, inheritance, collections, caching and more advanced JPA features.
- get() throws an ObjectNotFoundException if no data is found, while load() returns a proxy object without hitting the database
- When accessing properties of the proxy object returned by load(), it will trigger a SELECT to the database and throw an ObjectNotFoundException if no data exists
- get() immediately hits the database to check for data, while load() defers the database hit until property access if a proxy is returned
- It is generally better to use get() when you expect data to exist, and check for its return value being null, rather than using load()
The document discusses best practices for using the Java Persistence API (JPA). It covers:
- The entity manager and persistence context manage entities within a scope like a transaction.
- Entities transition between managed, detached, and removed states that impact database synchronization.
- Queries retrieve and manage entities from the persistence context and database.
- Transactions and locking strategies like optimistic and pessimistic concurrency control how changes are made.
- Extended and transaction scoped persistence contexts determine entity lifetime and caching.
- The second level cache can improve performance by caching entities across contexts.
ERRest - Designing a good REST serviceWO Community
Learn how to design your REST service with the correct HTTP verbs and codes. We also talk on how to manage optimistic locking and caching for your REST services.
Hibernate framework simplifies the development of java application to interact with the database. Hibernate is an open source, lightweight, ORM (Object Relational Mapping) tool.
An ORM tool simplifies the data creation, data manipulation and data access. It is a programming technique that maps the object to the data stored in the database.
Hibernate, created by Gavin King, known as the best and dominated object/relational persistence (ORM) tool for Java developers (Now is support .NET). It provides many elegant and innovative ways to simplifies the relational database handling task in Java.
Hibernate is great at a lot of things, but its something that needs to be used appropriately. In this tutorials, it providing many step by step examples and explanations on using Hibernate3.
Spring Data is a high level SpringSource project whose purpose is to unify and ease the access to different kinds of persistence stores, both relational database systems and NoSQL data stores.
This document provides an overview of the Hibernate Query Language (HQL) including:
- HQL queries are translated into SQL queries and allow object-oriented queries rather than operating on tables and columns.
- It describes the FROM, SELECT, WHERE, ORDER BY, and GROUP BY clauses of HQL and provides examples of each.
- It also includes instructions on configuring Hibernate and Java and an overview of how Hibernate works independently of code.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
This presentation was presented at OSS camp in New Delhi. It deals with the basics of Scala language and how we can use it to build scalable Applications
- FitNesse is an open-source automated testing framework that allows developers, testers, and customers to collaborate on software testing by writing tests in a wiki format.
- Tests in FitNesse wiki format call custom fixtures that act as a bridge between the wiki pages and the system under test. Fixtures can be written in many programming languages including Scala.
- To write a FitNesse wiki test, tables are used with inputs in even columns and method names in odd columns to call methods defined in fixtures, allowing tests to be written in a domain-specific language format.
Akka 2.0 is a toolkit for building highly concurrent, distributed, and fault-tolerant applications on the JVM. It provides actors as the core abstraction for concurrency and distribution. Actors encapsulate state and behavior and communicate asynchronously by message passing. Akka provides elasticity so that new messages can be processed while an actor is busy. It also includes features for fault tolerance using a "let it crash" model and transparent distribution through routing.
The document discusses various data structures in Scala including queues and binary search trees. It describes functional queues in Scala as immutable data structures with head, tail, and enqueue operations. It also covers different implementations of queues and optimizations. For binary search trees, it explains the binary search tree property, provides a Tree class representation in Scala, and algorithms for in-order, pre-order, and post-order tree traversals along with their Scala implementations.
Category theory concepts such as objects, arrows, and composition map nicely to structures in Scala. Functions in Scala represent arrows between types. Composition allows combining functions. Category theory diagrams illustrate relationships between types and functions through commutative diagrams. For example, product types in category theory correspond to tuples in Scala, with projection functions representing the arrows. Learning category theory provides insights into abstraction and mathematical properties underlying programming concepts.
Scala collections provide a uniform approach to working with data structures. The core abstractions are Traversable and Iterable, which define common operations like map and foreach. Concrete implementations include lists, sets, and maps. Collections aim to be object-oriented, generic, and optionally persistent or immutable. The uniform return type principle ensures operations return collections of the same type. Key features are higher-order functions, pattern matching, and treating all data types like collections.
My talk at Bangalore Java Users Group. It was meant developers who want to get them started on Scala. This talk objectives was to get started on creating a project in Scala, write some code using collections and test it using ScalaTest.
Akka is a toolkit for building highly concurrent, distributed, and fault-tolerant applications on the JVM. It provides actors as the fundamental unit of concurrency. Actors receive messages asynchronously and process them one at a time by applying behaviors. Akka uses a supervision hierarchy where actors monitor child actors and handle failures through configurable strategies like restart or stop. This provides clean separation of processing and error handling compared to traditional approaches.
Java Web Programming on Google Cloud Platform [2/3] : DatastoreIMC Institute
This document provides an introduction to Google App Engine Datastore and using JPA with Datastore. It describes Datastore as a schema-less database that stores entities composed of properties. It discusses Datastore operations, the storage model, and compares Datastore to relational databases. It also covers setting up JPA, example entity and query code, and transaction management. Unsupported JPA features on Datastore are also listed.
This document provides information on using the Google App Engine Datastore and ORM tools to manage data in a cloud environment. It describes the key concepts of entities, properties, queries and transactions using both the low-level Datastore API and higher-level ORM such as DataNucleus. Code examples are given for common operations like entity creation, retrieval, updating and deletion in both APIs.
The document discusses tips, tricks, and design patterns for implementing Java EE applications using Enterprise JavaBeans (EJB) 3 technology. It covers pitfalls and best practices for session beans, entity beans, and message-driven beans. It also discusses refactoring techniques and both old and new design patterns like the session facade and interceptors.
Looking at performance may quickly become a tuning exercise, the hunt for the last 2% algorithmic improvement or JVM parameter readjustment. This presentation takes a somewhat more removed look at Enterprise Java performance - taking in the broader picture of the entire multi-tier architecture and applying common sense performance concepts from every day life an applying those to enterprise Java architectures. To serve as an eye opener.
(originally created for the Oracle Technology Day on Java Enterprise Performance, May 2011, The Netherlands)
The 90-Day Startup with Google AppEngine for JavaDavid Chandler
The document discusses Google App Engine, a platform for developing and hosting web applications on Google's infrastructure. It provides an overview of App Engine and how to get started, discusses some limitations and tradeoffs compared to traditional web hosting, and recommends frameworks and techniques for building scalable applications on App Engine, including Objectify, Guice, and gwt-dispatch. It also notes that while App Engine is still relatively new, it has significant potential for developing scalable applications with minimal upfront costs.
Spring Data Requery is alternatives of Spring Data JPA
Requery is lightweight ORM for DBMS (MySQL, PostgreSQL, H2, SQLite, Oracle, SQL Server)
Spring Data Requery provide Query By Native Query, Query By Example and Query By Property like Spring Data JPA
Spring Data Requery is better performance than JPA
Spring Data JPA in detail with spring bootrinky1234
Spring Data JPA provides features for pagination, sorting, and querying data from databases. It allows querying data through method names or annotations. For complex queries, the @Query annotation can be used to define JPQL or native SQL queries. Named queries define reusable queries through annotations. Transactions in Spring manage multiple database operations together to maintain data integrity. Declarative and programmatic transaction management approaches are supported through annotations and programming.
The document provides an overview and agenda for a presentation on Struts 2, covering key topics such as configuration, actions, interceptors, results, tag libraries, and validation. Struts 2 is an open source MVC web framework that aims to improve developer productivity through features like convention over configuration, plug-in architecture, and reusable tag libraries. The presentation discusses Struts 2 concepts like the request pipeline, value stack, OGNL expressions, and type conversions that allow dynamic data handling.
PHP Barcelona 2010 - Architecture and testabilityGiorgio Sironi
This document discusses techniques for improving code testability and maintainability. It recommends:
1. Applying SOLID principles like the single responsibility principle and dependency injection to reduce coupling and improve cohesion.
2. Avoiding direct static method calls and singleton patterns in favor of dependency injection as it allows dependencies to be mocked/stubbed for testing.
3. Following the law of Demeter to avoid excessive coupling between classes.
4. Using factories instead of singletons to allow injecting fakes/mocks for testing. Testing first and listening to tests results in more maintainable code.
PyCon India 2010 Building Scalable apps using appenginePranav Prakash
This document discusses building scalable apps using Google App Engine. It introduces Google App Engine and shows a simple "Hello World" app written in Python. It describes how App Engine uses the Datastore for scalable data storage and modeling data with entities and properties. It also covers caching with Memcache and techniques for scaling apps, including using entity groups, batches, sharding, and caching.
SPCA2013 - SharePoint Nightmares - Coding Patterns and PracticesNCCOMMS
Donald Hessing presented on SharePoint coding patterns and practices. He discussed various anti-patterns to avoid such as directly accessing the SPList[] array and SPList.Items collection. He also covered the service locator pattern and how it can reduce tight coupling. Finally, he demonstrated how to properly dispose of objects in PowerShell scripts using Start-SPAssignment and Stop-SPAssignment.
The document provides an overview and guidelines for using design patterns with Django. It discusses common patterns for organizing URLs, models, views, and generic views. Some key patterns covered include using multiple managers and custom manager methods for models, handling GET and POST requests in the same view function, and creating chainable and lazy querysets. The document acts as documentation for best practices when building Django applications and sites.
This document discusses performance tuning of .NET applications. It begins by defining performance tuning as improving system performance. It then discusses identifying bottlenecks through profiling, such as by using dotTrace and dotMemory tools. Various ways to optimize performance are covered, such as choosing appropriate collections, making applications parallel as needed, and managing memory efficiently by reducing allocation and using value types and IDisposable patterns. The document aims to provide guidance on optimizing .NET applications through measurement and profiling.
The document discusses object-relational mapping (ORM) using Java Persistence API (JPA). It provides an overview of modern ORM solutions and annotations used in JPA like @Entity, @Id, @ManyToOne. It explains how JPA allows object-oriented programming while taking advantage of capabilities of relational databases like large data storage and transactions. Key goals of ORM are a natural programming model and minimizing database access.
WCF Data Services (formerly known as "ADO.NET Data Services") is a component of the .NET Framework that enables you to create services that use the Open Data Protocol (OData) to expose and consume data over the Web or intranet by using the semantics of representational state transfer (REST). OData exposes data as resources that are addressable by URIs. Data is accessed and changed by using standard HTTP verbs of GET, PUT, POST, and DELETE. OData uses the entity-relationship conventions of the Entity Data Model to expose resources as sets of entities that are related by associations.
Data Seeding via Parameterized API RequestsRapidValue
A quick guide on how to data seed via parameterized API requests. Parameterization is very important for automation testing. It helps you to iterate on input data with multiple data sets that make your scripts reusable and maintainable. In few scenarios, you can still manage with hard coded request but the same approach will not work out where sheer count of combinations is to be validated. By implementing the right solution, you can keep your code base and test data size at ideal range and still savor the benefits of optimal coverage.
Spring Data JPA provides features for pagination, sorting, and querying data from a database. It supports defining queries declaratively through method names, named queries annotated with @NamedQuery, and custom queries annotated with @Query. Transactions in Spring manage a sequence of database operations atomically through programmatic or declarative transaction management using annotations or configuration. Custom repositories may also be implemented to define additional queries beyond standard Spring Data JPA features.
Deferred Processing in Ruby - Philly rb - August 2011rob_dimarco
The document discusses various options for deferred processing and queuing in Ruby, including Delayed::Job, Resque, Amazon SQS, and AMQP. It provides an overview of how each works, how to install and use them, their advantages and disadvantages, and when each may or may not be a good fit for different needs.
Dapper caches query information like SQL statements and parameters to improve performance when materializing objects from query results. The cache is stored in a ConcurrentDictionary that is never flushed, so it could cause memory issues with dynamically-generated SQL. Queries using parameters are preferred since the cache key depends on the SQL and parameters, allowing caching of the execution plan. Buffering determines if all rows are loaded into memory before iterating. QueryMultiple is used for queries returning multiple result sets. Dirty tracking with interfaces allows Dapper to detect whether updates actually changed data to skip unnecessary SQL generation.
Struts 2 is an MVC framework that is the successor to Struts and WebWork 2, providing a simple architecture based around interceptors, actions, and results with conventions over configuration and support for technologies like Spring, Velocity, and Ajax. It aims to bring the best of Struts 1 and WebWork 2 together while being easier to test and use through defaults and annotations. The framework can integrate with many open source libraries and supports features like localization, type conversion, and configuration through XML files and annotations.
Similar to Easy ORMness with Objectify-Appengine (20)
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
SAP S/4 HANA sourcing and procurement to Public cloud
Easy ORMness with Objectify-Appengine
1. Easy ORM-ness with Objectify-Appengine Meetu Maltiar Inphina Technologies
2. Overall Presentation Goal Google Datastore Basics Options available for managing persistence Objectify-Appengine Demo of an application using Objectify
3. Enough About Me Senior Software Engineer at Inphina Technologies that interest me: Cloud Computing Scala Hadoop
5. Entities An Entity is an object’s worth of data in the datastore In datastore an Entity is like HashMap-like object of type Entity Datastore is conceptually a HashMap of keys to entities, and an Entity is conceptually a HashMap of name/value pairs
6. Operations Get() an entity as a whole from datastore Put() an entity as whole in datastore Delete() an entity from datastore Query() for entities matching criteria you define
7. Keys In the datastore, entities are identified by the id (or name) and a kind, which corresponds to the type of Object you are storing. So, to get Employee #111 from datastore, we need to call something like get_from_datastore (“Employee”, 111)
8. Keys Continued There is actually a third parameter required to identify an entity and it is called parent Parent places the child in the same entity group as the parent Parent (which can be null for the un-parented, root entity) is also required to uniquely identify an Entity.
9. Keys Continued So, to get Employee #111 from datastore we need to call something equivalent to: get_from_datastore (“Employee”, 111, null) or, get_from_datastore (“Employee”, 111, the_parent). Instead of passing three parameters datastore wraps it in a single Object called Key.
10. Transactions Data gets stored in gigantic form of thousands of machines In order to perform an atomic transaction datastore requires that entities lie on same servers. To give us more control over where our data is stored, the datastore has a concept of an entity group To give us more control over where our data is stored, the datastore has a concept of an entity group
11. Transactions Continued Within a Transaction we can access data from a single entity group Choose entity groups carefully Why not have everything in a single entity group? Google restricts number of requests per second per entity group
12. Executing Transactions When we execute get(), put(), delete() or query() in transaction We must operate it on single entity group All operations will either fail or succeed completely If another process modifies the data before commit datastore operation will fail
18. Objectify It lets you persist, retrieve, delete and query typed objects All native datastore features are supported It provides type safe key and query classes
19. Objectify Design Considerations Minimally impacts cold start time. It is light weight No external dependencies apart from GAE SDK
20. Working With Datastore Entity ent = new Entity(“car”); ent.setProperty(“color”, “red”); ent.setProperty(“doors”, 2); service.put(ent);
25. delete() Operation Objectify ofy = Objectifyservice.begin(); ofy.delete(Employee.class, 123); Employee emp = // get Employee from some where ofy.delete(emp);
28. Objectify Best Practices Use a DAO to register entities Automatic Scanning not advised adds to initialization time will require dependency jars apart from GAE will require changes in web.xml GAE spins down the instance when not in use. When it comes up the request is slow because of added initialization time. This is called cold start.
29. Objectify Best Practices … Use Batch Gets Instead of Queries Use Indexes sparingly By default every field of object will be indexed. It comes with heavy computational price. Use @Unindexed on entity level and @Indexed at fields required for query Avoid @Parent In JPA “owned” entity relationship provides referential integrity checking and cascading deletes and saves. Not so here.
31. Conclusion JDO/JPA learning curve is steep due to App Engine’s non-relational nature and unique concepts such as entity groups, owned and un-owned relationships. Google Datastore is low level. It makes no pretense of being relational but also does not allow working with objects. It just stores the objects of type com.google.appengine.api.datastore.Entity Objectify is light weight and it preserves the simplicity and transparency of low level API and does all work converting to and from POJOS to Entity Objects.
33. References Objectify-Appengine http://code.google.com/p/objectify-appengine/ Google IO 2011 Session on highly productive gwt rapid development with app-engine objectify-requestfactory and gwt-platform Twitter mining with Objectify-Appengine http://www.ibm.com/developerworks/java/library/j-javadev2-14/?ca=drs-