Performance Evaluation of Jpa Based Orm Techniques
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An Empirical Study on the Practice of Maintaining Object-Relational Mapping Code in Java Systems
An Empirical Study on the Practice of Maintaining Object-Relational Mapping Code in Java Systems Tse-Hsun Chen Weiyi Shang Jinqiu Yang Queen’s University Concordia University University of Waterloo Ontario, Canada Quebec, Canada Ontario, Canada [email protected] [email protected] [email protected] Ahmed E. Hassan Michael W. Godfrey Queen’s University University of Waterloo Ontario, Canada Ontario, Canada [email protected] [email protected] Mohamed Nasser Parminder Flora BlackBerry BlackBerry Ontario, Canada Ontario, Canada ABSTRACT code. Future studies should carefully examine ORM code, Databases have become one of the most important compo- in particular given the rising use of ORM in modern software nents in modern software systems. For example, web ser- systems. vices, cloud computing systems, and online transaction pro- cessing systems all rely heavily on databases. To abstract 1. INTRODUCTION the complexity of accessing a database, developers make use Managing data consistency between source code and database of Object-Relational Mapping (ORM) frameworks. ORM is a difficult task, especially for complex large-scale systems. frameworks provide an abstraction layer between the appli- As more systems become heavily dependent on databases, it cation logic and the underlying database. Such abstraction is important to abstract the database accesses from devel- layer automatically maps objects in Object-Oriented Lan- opers. Hence, developers nowadays commonly make use of guages to database records, which significantly reduces the Object-Relation Mapping (ORM) frameworks to provide a amount of boilerplate code that needs to be written. conceptual abstraction between objects in Object-Oriented Despite the advantages of using ORM frameworks, we ob- Languages and data records in the underlying database. -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version} -
JDO Getting Started Guide (V5.0) Table of Contents
JDO Getting Started Guide (v5.0) Table of Contents Key Points. 2 Understanding the JARs . 3 JDO Tutorial (v5.0) . 4 Background . 4 Step 0 : Download DataNucleus AccessPlatform . 4 Step 1 : Take your model classes and mark which are persistable . 4 Step 2 : Define the 'persistence-unit' . 7 Step 3 : Enhance your classes . 8 Step 4 : Write the code to persist objects of your classes . 10 Step 5 : Run your application . 12 Step 6 : Controlling the schema . 14 Step 7 : Generate any schema required for your domain classes . 17 Any questions? . 18 Developing applications is, in general, a complicated task, involving many components. Developing all of these components can be very time consuming. The Java Data Objects API (JDO) was designed to alleviate some of this time spent, providing an API to allow java developers to persist object-oriented data into any database, and providing a query language using the same Java syntax as the developer is already familiar with. DataNucleus JDO provides an implementation of this JDO standard, allowing you, the user, to persist your object-oriented data to not only the RDBMS datastores the standard was intended for, but also to a wide range of other datastores. These include popular map stores such as Cassandra and HBase, the Neo4j graph store, spreadsheets in Excel or OpenDocument formats, JSON formatted Amazon and Google Storage options, the popular MongoDB JSON-like document store, as well as ubiquitous LDAP and more besides. DataNucleus doesn’t purport to be the best solution to every problem. For example, where you want to bulk persist large amounts of data then other solutions that get closer to the datastore API would be more appropriate. -
Big Data - an Overview
Faculty of Computer University Automatic Science and Politehnica Control and Engineering of Bucharest Computers Department Big Data - An Overview - Ciprian-Octavian Truică [email protected] Overview • What is Big Data? • Why Big Data? • Types of Big Data • Techniques • Distributed architecture • Cloud Computing • Storage and tools 03.10.2018 2 What is Big Data? • Big Data is high volume, high velocity and high variety of data that require new forms of processing to enable knowledge extraction, insight discovery, decision making, and process optimization 03.10.2018 3 What is Big Data? 03.10.2018 4 What is Big Data? • The 4 V’s of Big Data: 1. Volume – The main characteristic of Big Data is the volume – The volume of data impacts its analysis – Historical data is important especially for Business Intelligence – Data generated from different sources are stored together to create correlations and extract knowledge. 03.10.2018 5 What is Big Data? • The 4 V’s of Big Data: 2. Variety – Variety refers to the types of data available for analysis – Multiple types of data: numbers, dates, text, images, video, etc. – Multiple sources for data: companies databases, social media, blogs, etc. – Structured, unstructured and hybrid types of data. 03.10.2018 6 What is Big Data? • The 4 V’s of Big Data: 3. Veracity – Veracity refers to the trustworthiness of the data. – The quality of the data can affect the analysis process – The data must be representative, relevant, consistent, accurate and current to discover patterns – The data must be preprocessed to extract relevant knowledge 03.10.2018 7 What is Big Data? • The 4 V’s of Big Data: 4. -
Java LDAP Persistence with Datanucleus
Java LDAP Persistence with DataNucleus Stefan Seelmann [email protected] Java LDAP Persistence with DataNucleus • Stefan Seelmann • Freelancer – Software Development with Java – LDAP, Identity- and Access-Management • Open Source Developer – Apache Directory Project – DataNucleus LDAP Store Java LDAP Persistence with DataNucleus Agenda • Motivation • Java Persistence, JDO and DataNucleus • Basic Demo • DataNucleus LDAP Store • Advanced Demo • Status and Conclusion Java LDAP Persistence with DataNucleus Java LDAP Development • Java APIs for LDAP – Mature: Netscape LDAP SDK, JLDAP (Novell/OL) – Modern: Unbound ID, Apache Directory, OpenDS • Hopefully a common Java LDAP API soon? – JNDI, Spring-LDAP • Drawback: – Developer has to deal with LDAP • DN, RDN, filters, modification items, error codes – Boiler-Plate code, exception handling Java LDAP Persistence with DataNucleus Java Persistence • Standards – JPA (Java Persistence API): JSR 220, RDBMS only – SDO (Service Data Objects): JSR 235 – JDO: (Java Data Object): JSR-12 and JSR-243 • Products – O/R Mapper: Hibernate, TopLink/EclipseLink, ... – Apache iBATIS, Cayenne, OpenJPA, Tuscany, ... – DataNucleus – ... Java LDAP Persistence with DataNucleus JDO • Java-centric API to access persistent data • Datastore independent • Started by Sun, now driven by Apache JDO • Versions 1.0, 2.0, 2.1, 2.2, 2.3 in progress • Three main parts – Persistence definition (metadata) – Persistence API – Query language/API Java LDAP Persistence with DataNucleus DataNucleus • Reference implementation of JDO • Apache -
Tema 4: Implementación: Frameworks 4.3
Ingeniería del Software II Tema 4: Implementación: Frameworks 4.3. Hibernate A. Goñi, J. Iturrioz Índice • 1) Motivación: Por qué utilizar un Framework como Hibernate • 2) Hibernate • 2.1) Introducción • 2.2) Arquitectura de Hibernate • 2.3) Clases persistentes (Ficheros de configuración: .hbm.xml) • 2.4) Asociaciones entre clases (Ficheros de configuración: .hbm.xml) • 2.5) Session Factory (Fichero de configuración: hibernate.cfg.xml) • 2.6) Session y Transaction • 2.7) Lenguajes de interrogación: HBL (y Criteria) • 2.8) Aspectos de interés: ciclo de vida, asociaciones 1:N, 1:1, N:M, null values, borrado, actualización, recuperación de datos,… 2 1) Motivación • En las vistas se necesita tener disponibles datos (almacenados en el nivel de datos) a los que se accede por medio de la lógica de negocio (objetos bean) • Programación: con un lenguaje OO • Persistencia de datos: con BDOO o con BD relacionales Las BD relacionales están más extendidas que las BDOO: buena opción para la persistencia • Si se usan BD relacionales, entonces hay que “mapear” tuplas relacionales en objetos • En ese caso, una herramienta de mapeo objeto-relacional como Hibernate es una buena elección 3 1) Motivación ¿Cómo se cargan estos objetos en el componente h:selectOneMenu? Lógica de Negocio Acceso a Datos Si DataAccess usa un SGBDOO como objectDB ¿Y si se usara un Sistema de Gestión de Bases de Datos Relacional (SGBDR)? Se puede implementar usando Java JDBC 4 1) Motivación Sin embargo, JSF permite hacer cosas más potentes… Expresión escrita en EL (Expression Language) de JSF. Permite más posibilidades de acceso a propiedades. En este caso, invocar al método getDescription()… JSF (basado en Java, que es OO) permite trabajar con objetos. -
Martin Ledvinka Winter Term 2020
JPA Martin Ledvinka [email protected] Winter Term 2020 Martin Ledvinka ([email protected]) JPA Winter Term 2020 1 / 15 Contents 1 Introduction 2 Tasks Martin Ledvinka ([email protected]) JPA Winter Term 2020 2 / 15 Introduction Introduction Martin Ledvinka ([email protected]) JPA Winter Term 2020 3 / 15 Introduction Object-relational mapping Mapping between the database (declarative) schema and the data structures in an object oriented language. Martin Ledvinka ([email protected]) JPA Winter Term 2020 4 / 15 Introduction JPA Basics The idea: \map Java classes to database records" Object-relational mapping in Java @Entity CREATE TABLE PERSON ( public Person f ID bigint PRIMARY KEY NOT NULL, @Id HASNAME varchar(255) private Long id; ); private String hasName; // setters+getters g Martin Ledvinka ([email protected]) JPA Winter Term 2020 5 / 15 Introduction JPA Main Concepts Entity class: a Java class representing a set of persistent objects mapped onto a relational table Persistence Unit: the set of all entity classes that are persistently mapped to one database Persistence Context: the set of all entities defined in the persistence unit being used at a given time Entity manager: the interface for interacting with a Persistence Context Martin Ledvinka ([email protected]) JPA Winter Term 2020 6 / 15 Introduction JPA Main Concepts { Visual Figure: Main JPA concepts and their relationship. Martin Ledvinka ([email protected]) JPA Winter Term 2020 7 / 15 Introduction JPA { EntityManager EntityManager (EM) instance is in fact a generic DAO, while entities can be understood as DTO's. -
JPA Query Guide (V5.2) Table of Contents
JPA Query Guide (v5.2) Table of Contents Query API . 2 setFirstResult(), setMaxResults(). 2 setHint() . 2 setParameter(). 3 getResultList() . 3 getSingleResult(). 3 executeUpdate() . 4 setFlushMode() . 4 setLockMode(). 4 Large Result Sets : Loading Results at Commit(). 4 Result Set : Caching of Results. 5 Large Result Sets : Size . 5 RDBMS : Result Set Type. 5 RDBMS : Result Set Control . 6 JPQL . 7 SELECT Syntax . 7 FROM Clause . 7 Fetched Fields . 11 WHERE clause (filter) . 11 GROUP BY/HAVING clauses . 11 ORDER BY clause . 12 Fields/Properties . 13 Operators . 13 Literals . 13 Parameters. 14 CASE expressions . 15 JPQL Functions . 15 Collection Fields . 30 Map Fields . 30 Subqueries. 30 Specify candidates to query over . 31 Range of Results . 32 Query Result . 32 Query Execution. 34 Named Query . 35 JPQL : SQL Generation for RDBMS. 36 JPQL DELETE Queries . 37 JPQL UPDATE Queries . 37 JPQL Syntax Strictness . 38 JPQL Syntax BNF Notation. 38 Criteria . 43 Creating a Criteria query . 43 JPQL equivalent of the Criteria query. 43 Criteria API : Result clause. 44 Criteria API : FROM clause joins. 44 Criteria API : WHERE clause . 44 Criteria API : Ordering . 46 Criteria API : Parameters . 47 Criteria API : Subqueries . .. -
JPA Persistence Guide (V5.2) Table of Contents
JPA Persistence Guide (v5.2) Table of Contents EntityManagerFactory. 2 Create an EMF in JavaSE . 2 Create an EMF in JavaEE . 2 Persistence Unit . 3 EntityManagerFactory Properties . 6 Closing EntityManagerFactory . 27 Level 2 Cache. 27 Datastore Schema. 34 Schema Generation for persistence-unit . 34 Schema Auto-Generation at runtime . 35 Schema Generation : Validation . 36 Schema Generation : Naming Issues . 36 Schema Generation : Column Ordering . 37 Schema : Read-Only. 37 SchemaTool . 38 Schema Adaption . 44 RDBMS : Datastore Schema SPI . 44 EntityManager. 48 Opening/Closing an EntityManager. 48 Persisting an Object. 49 Persisting multiple Objects in one call . 49 Finding an object by its identity . 50 Finding an object by its class and unique key field value(s) . 50 Deleting an Object . 51 Deleting multiple Objects. 51 Modifying a persisted Object. 52 Modifying multiple persisted Objects . 52 Refreshing a persisted Object . 52 Getting EntityManager for an object. 53 Cascading Operations . 53 Orphans . 54 Managing Relationships . 54 Level 1 Cache. 56 Object Lifecycle. 58 Transaction PersistenceContext . 58 Extended PersistenceContext . 58 Detachment . 58 Helper Methods . 59 Transactions . 60 Locally-Managed Transactions. 60 JTA Transactions. 61 Container-Managed Transactions . 63 Spring-Managed Transactions . 63 No Transactions . 63 Transaction Isolation . 64 Read-Only Transactions . 64 Flushing . 65 Transactions with lots of data. 66 Transaction Savepoints . 67 Locking . 68 Optimistic Locking. 68 Pessimistic (Datastore) Locking . 69 Datastore. -
Espresso: Brewing Java for More Non-Volatility with Non-Volatile Memory
Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory Mingyu Wu, Ziming Zhao, Haoyu Li, Heting Li, Haibo Chen, Binyu Zang, Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University Abstract straction brought by the language virtual machine (e.g., JVM) Fast, byte-addressable non-volatile memory (NVM) em- complicates the persistence management. braces both near-DRAM latency and disk-like persistence, The mainstream persistent programming model leverages a which has generated considerable interests to revolutionize coarse-grained abstraction like Java Persistence API (JPA)[9] system software stack and programming models. However, it to provide easy-to-use transactional APIs for programmers to is less understood how NVM can be combined with managed persist their data. However, it does not consider the emer- runtime like Java virtual machine (JVM) to ease persistence gence of NVM, and creates unnecessary transformation over- management. This paper proposes Espresso1, a holistic ex- head between Java objects and native serialized data. In tension to Java and its runtime, to enable Java programmers contrast, the recent proposed Persistent Collections for Java to exploit NVM for persistence management with high perfor- (PCJ) [14] provides a fine-grained programming model to en- mance. Espresso first provides a general persistent heap de- able users to manipulate persistent data in object level. How- sign called Persistent Java Heap (PJH) to manage persistent ever, it has built an independent type system against the orig- data as normal Java objects. The heap is then strengthened inal one in Java, which makes it hard to be compatible with with a recoverable mechanism to provide crash consistency existing Java programs since it mandates the use of the collec- for heap metadata. -
JDO Getting Started Guide (V5.1) Table of Contents
JDO Getting Started Guide (v5.1) Table of Contents Key Points. 2 Understanding the JARs . 3 JDO Tutorial (v5.1) . 4 Background . 4 Step 0 : Download DataNucleus AccessPlatform . 4 Step 1 : Take your model classes and mark which are persistable . 4 Step 2 : Define the 'persistence-unit' . 7 Step 3 : Enhance your classes . 8 Step 4 : Write the code to persist objects of your classes . 10 Step 5 : Run your application . 12 Step 6 : Controlling the schema . 14 Step 7 : Generate any schema required for your domain classes . 17 Any questions? . 18 Developing applications is, in general, a complicated task, involving many components. Developing all of these components can be very time consuming. The Java Data Objects API (JDO) was designed to alleviate some of this time spent, providing an API to allow java developers to persist object-oriented data into any database, and providing a query language using the same Java syntax as the developer is already familiar with. DataNucleus JDO provides an implementation of this JDO standard, allowing you, the user, to persist your object-oriented data to not only the RDBMS datastores the standard was intended for, but also to a wide range of other datastores. These include popular map stores such as Cassandra and HBase, the Neo4j graph store, spreadsheets in Excel or OpenDocument formats, JSON formatted Amazon and Google Storage options, the popular MongoDB JSON-like document store, as well as ubiquitous LDAP and more besides. DataNucleus doesn’t purport to be the best solution to every problem. For example, where you want to bulk persist large amounts of data then other solutions that get closer to the datastore API would be more appropriate. -
Towards Standard-Based (Healthcare) Ecosystems Of
Department of Computer Science Overskrift her Towards Standard-Based (Healthcare) Ecosystems of Systems Navn på oplægsholder Navn på KU- enhed Konstantinos Manikas 1st Scandinavian Workshop on the Engineering of Systems-of-Systems (SWESoS 2015) For at ændre ”Enhedens”Enhedens navn”navn” og ”Sted”Sted og dato”:dato”: (Work presented in collaboration with Klaus Marius Hansen, Henrik Bærbak Christensen, Morten Kyng, Jens Knodel) Klik i menulinjen, vælg ”Indsæt””Indsæt” >> ”Sidehoved”Sidehoved / SidefodSidefod”.”. Indføj ”Sted”Sted og datodato”” i feltet for dato og ”Enhedens”Enhedens Slide 1 navnnavn”” i Sidefod Department of Computer Science Overskrift her Overview Tekst starter uden Investigate intersection of software ecosystems and punktopstilling systems of systems For at få punkt- opstilling på teksten, brug Propose: Standard-based ecosystem of systems forøg indrykning Facilitated by For at få venstre- stillet tekst uden Reference architecture punktopstilling, brug formindsk indrykning Explicit orchestration (governance, certification) Standards only means to value creation For at ændre ”Enhedens”Enhedens navn”navn” og ”Sted”Sted og dato”:dato”: Klik i menulinjen, vælg ”Indsæt””Indsæt” >> ”Sidehoved”Sidehoved / SidefodSidefod”.”. Indføj ”Sted”Sted og datodato”” i feltet for dato og ”Enhedens”Enhedens Slide 2 navnnavn”” i Sidefod Department of Computer Science Overskrift her Background Tekst starter uden punktopstilling PhD thesis on software ecosystems for telemedicine services For at få punkt- opstilling på teksten, brug Problem: forøg indrykning Low incentives for TM development For at få venstre- although potential benefits stillet tekst uden High requirements on interoperability punktopstilling, brug formindsk Data sharing indrykning “Silo” solution For at ændre Telemedicine domain: ” ” ”EnhedensEnhedens navn”navn Mission-critical nature og ”Sted”Sted og dato”:dato”: regulated Klik i menulinjen, vælg ”Indsæt””Indsæt” >> requirements on safety, security, privacy ”Sidehoved”Sidehoved / SidefodSidefod”.”.