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
Alternatives of JPA
Requery provide simple Object Mapping & Generate SQL to execute without reflection and session, so fast than JPA, simple and easy to learn.
Presented at BJUG, 6/12/2012 by Roger Brinkley
This talk is on 55 new features in Java 7 you (probably) didn't hear about in an ignite format of one per minute. No stopping, no going back....Questions, sure but only if time remains (otherwise save for later).
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
Alternatives of JPA
Requery provide simple Object Mapping & Generate SQL to execute without reflection and session, so fast than JPA, simple and easy to learn.
Presented at BJUG, 6/12/2012 by Roger Brinkley
This talk is on 55 new features in Java 7 you (probably) didn't hear about in an ignite format of one per minute. No stopping, no going back....Questions, sure but only if time remains (otherwise save for later).
Im zweiten Teil seiner OData Session zeigt Rainer Stropek, wie man eigene OData-Provider entwickelt. In einem durchgängigen Beispiel demonstriert er, wie man erst einen LINQ-Provider und darauf aufbauend einen OData-konformen REST Service erstellt und von verschiedenen Programmiersprachen und Tools darauf zugreift. In der Session werden Grundkenntnisse von OData und LINQ vorausgesetzt.
자프링(자바 + 스프링) 외길 12년차 서버 개발자가 코프링(코틀린 + 스프링)을 만난 후 코틀린의 특징과 스프링의 코틀린 지원을 알아가며 코프링 월드에서 살아남은 이야기…
코드 저장소: https://github.com/arawn/kotlin-support-in-spring
StxNext Lightning Talks - Feb 12, 2016
Kotlin - one of the popular programming languages built on top of Java that runs on JVM. Thanks to JetBrains support and excellent IDE integration, it’s an ideal choice when it comes to Android development. 100% Java compatibility, interoperability and no runtime overhead is just the beginning of a long list of strengths. Kotlin is supposed to be a subset of SCALA, on one hand covering major advantages for developers and on the other - keeping short compile times.
This presentation is a Developer Starter - a set of hand-picked information allowing a person with no knowledge of Kotlin to start writing basic Android activities and set up an Android-kotlin project. It starts with language background, reasons for its creation and advantages. Then presents basic use cases, syntax, structures and patterns. Later on Kotlin is presented in Android context. Simple project structure, imports and Kotlin usage with Android SDK is explained. In the end cost of Kotlin usage is presented and the language is compared to SCALA and SWIFT.
Spring Day | Spring and Scala | Eberhard WolffJAX London
2011-10-31 | 09:45 AM - 10:30 AM
Spring is widely used in the Java world - but does it make any sense to combine it with Scala? This talk gives an answer and shows how and why Spring is useful in the Scala world. All areas of Spring such as Dependency Injection, Aspect-Oriented Programming and the Portable Service Abstraction as well as Spring MVC are covered.
Im zweiten Teil seiner OData Session zeigt Rainer Stropek, wie man eigene OData-Provider entwickelt. In einem durchgängigen Beispiel demonstriert er, wie man erst einen LINQ-Provider und darauf aufbauend einen OData-konformen REST Service erstellt und von verschiedenen Programmiersprachen und Tools darauf zugreift. In der Session werden Grundkenntnisse von OData und LINQ vorausgesetzt.
자프링(자바 + 스프링) 외길 12년차 서버 개발자가 코프링(코틀린 + 스프링)을 만난 후 코틀린의 특징과 스프링의 코틀린 지원을 알아가며 코프링 월드에서 살아남은 이야기…
코드 저장소: https://github.com/arawn/kotlin-support-in-spring
StxNext Lightning Talks - Feb 12, 2016
Kotlin - one of the popular programming languages built on top of Java that runs on JVM. Thanks to JetBrains support and excellent IDE integration, it’s an ideal choice when it comes to Android development. 100% Java compatibility, interoperability and no runtime overhead is just the beginning of a long list of strengths. Kotlin is supposed to be a subset of SCALA, on one hand covering major advantages for developers and on the other - keeping short compile times.
This presentation is a Developer Starter - a set of hand-picked information allowing a person with no knowledge of Kotlin to start writing basic Android activities and set up an Android-kotlin project. It starts with language background, reasons for its creation and advantages. Then presents basic use cases, syntax, structures and patterns. Later on Kotlin is presented in Android context. Simple project structure, imports and Kotlin usage with Android SDK is explained. In the end cost of Kotlin usage is presented and the language is compared to SCALA and SWIFT.
Spring Day | Spring and Scala | Eberhard WolffJAX London
2011-10-31 | 09:45 AM - 10:30 AM
Spring is widely used in the Java world - but does it make any sense to combine it with Scala? This talk gives an answer and shows how and why Spring is useful in the Scala world. All areas of Spring such as Dependency Injection, Aspect-Oriented Programming and the Portable Service Abstraction as well as Spring MVC are covered.
20 minute lightning talk I gave at the May 2014 NEJUG meeting on what's new in Java 8. Citations for the code examples and images are available in the downloadable presentation and are linked indirectly via the References page.
In a world where users have ever higher expectations from the apps they use, having data always available, even when the device is offline has become increasingly important.
In this talk we will go through different ways of saving data on the phone and introduce Realm as a replacement for SQLite and ORM's.
Through an example app it will be demonstrated that thinking "Offline first" not only affects your apps architecture for the better, but also results in happier users.
Java and Spring Data JPA: Easy SQL Data Access
Abstract
Presenter: Miya W. Longwe, MBA, MSE, Tech Lead, Staples, Inc, Framingham MA 01702
Accessing data repositories in various applications programming languages typically involves writing of tedious boilerplate lines of code. Some application development frameworks such as Spring have tried to make the experience more succinct by providing abstraction layers such as HibernateTemplate and JdbcTemplate, etc. Despite these APIs, the developers still spend a lot time writing repetitive code than concentrating on implementing business requirements. Developers at Spring, led by Oliver Gierke, introduced Spring Data JPA which “aims to significantly improve the implementation of data access layers by reducing the effort to the amount that's actually needed. As a developer you write your repository interfaces, including custom finder methods, and Spring will provide the implementation automatically”.
Spring Data JPA provides a powerful, out-of-the-box alternative to creating your own DAO framework. You declare custom repository operations on an interface, and the framework generates dynamic implementations (not code generation) automatically, based on conventions around method names. As part of the presentation, we'll also review a demo to look at Spring Java configuration (as opposed to XML configuration), and investigate the @Profile annotation – configuration details which may make life a bit easier in various ways when setting up unit testing of our repository classes, using out-of-the-box alternative to creating DAO framework, how to create custom repositories, pagination and support for custom queries among other features.
Presenter's Bio
Miya W. Longwe is a Senior Software Engineer and Tech Lead at Staples, Inc. where he is currently working on an initiative to re-platform the company’s ecommerce architecture to offer platform-driven, modular products that can be quickly customized, enhanced, and branded as needed.
Miya has been a software professional since 1997. His 16 years software development career includes working for large companies to small startups, building solutions for enterprises and consumers, working with a broad range of technologies.
Miya Longwe is a hands-on java developer. He believes that in order to be a relevant and effective software developer one needs to remain a deeply knowledgeable, up-to-date, and productive software developer. His research interests include model-driven engineering, domain specific languages, test driven development and project risk management.
Miya graduated from the University of Malawi (Lilongwe, Malawi) and has an MBA from the University of Wales Cardiff Business School (Wales, UK) and a Masters in Software Engineering from Brandeis University (MA, USA).
Occasionally, Miya can be spotted fishing the banks of the south shore (MA) with his two boys, William and Daniel.
Postgres vs Mongo / Олег Бартунов (Postgres Professional)Ontico
РИТ++ 2017, Backend Conf
Зал Конгресс-холл, 6 июня, 17:00
Тезисы:
http://backendconf.ru/2017/abstracts/2781.html
Я хочу немного порушить стереотипы, что Postgres - это чисто реляционная СУБД из прошлого века, плохо приспособленная под реалии современных проектов. Недавно мы прогнали YCSB для последних версий Postgres и Mongodb и увидели их плюсы и минусы на разных типах нагрузки, о которых я буду рассказывать. ...
Welcome to the wonderful world of Java Streams ported for the CFML world!The beauty of streams is that the elements in a stream are processed and passed across the processing pipeline. Unlike traditional CFML functions like map(), reduce() and filter() which create completely new collections until all items in the pipeline are processed. With streams, the elements are streamed across the pipeline to increase efficiency and performance.
ITB2019 CBStreams : Accelerate your Functional Programming with the power of ...Ortus Solutions, Corp
This session will introduce the cbStreams module. It will discuss what Java streams are, each of the available methods and options, and how to implement cbStreams into their applications. With real-world examples of stream implementation, this session will also show how using streams can enhance the performance of your application and reduce latency. Target Audience: Anyone wishing to learn about Java streams.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
2. Agenda
• What is ORM
• Pros / Cons of JPA
• Current status in Coupang
• Alternatives of JPA
• Slick
• jOOQ
• Exposed
• Requery
3. ORM (Object Relational Mapping)
• OOP’s object graph vs Relational Database
• Focus OOP, not Relational Database
• No matter of RDBMS vendor - Same code
• Hibernate coverage is 95% over traditional SQL statements
• ORM not suitable for data centric application in performance
• Why should you use an ORM?
4. Pros of JPA
• Focus to Java Object graph & OOP
• No need to know relations and constraints of entities
• No need to know specific DB features by various vendor
• No need to know SQL, just use Java API
• Supplement by HQL or JPQL or QueryDSL
• All support for Stateful, Stateless (Default is Stateful)
5. Cons of JPA
• If you knew SQL already, JPA is wired
• Hard to learning (exponential)
• Low performance by stateful and fetch by id
• No suitable for Bulk or Set operations
• Massive Insert, Statistical Summary (Cube …)
• Non-Threadsafe Session - Low throughput
• Need to learn specific JPAVendor (Hibernate, Eclipse Link)
• HQL, @DynamicInsert, @LazyCollection
• 2nd Cache (recommend JCache (JSR-305))
6. Current Status in Coupang
• No Deep Dive
• Missing override (hashCode, equals, toString)
• No using @NatualId
• Bad Policy / Bad Design
• Every entity has Own identifier (Some case no need)
• Poor performance -> Mislead “JPA is bad”
• Apply not suitable case
• Bulk operations, Statistical operations
• No use supplement features
• StatelessSession, 2nd Cache …
7. Features in Alternatives of JPA
• Design Principle
• OOP based, Support Multi DBVendor
• No need stateful for Reference object
• Support association, inheritance, converter in JPA
• Performance
• Speed up like Plain SQL
• Stateless
• Support Asynchronous or Reactive
• Support Bulk or Batch operations
16. jOOQ
• Reflect Database Schema to generate Entity Class
• Typesfe SQL (akaTypesafe MyBatis)
• Database First (Not ORM)
• Stateless
• Need DBMS Owner Authority
• jOOQ vs Hibernate :When to choose which
20. requery
• No reflection (apt code generation) - Fast instancing
• Fast startup & performance
• Schema generation
• Blocking / Non-blocking API (Reactive with RxJava)
• Support partial object / refresh / upsert
• Custom type converter like JPA
• Compile time entity validation
• Support almost JPA annotations
21. @Entity
abstract class AbstractPerson {
@Key @Generated int id;
@Index("name_index") // table specification
String name;
@OneToMany // relationships 1:1, 1:many, many to many
Set<Phone> phoneNumbers;
@Converter(EmailToStringConverter.class)
Email email;
@PostLoad // lifecycle callbacks
void afterLoad() { updatePeopleList(); }
// getter, setters, equals & hashCode automatically generated into Person.java
}
requery - define entity
Identifier
Entity class
Converter
Listeners
22. Result<Person> query = data
.select(Person.class)
.where(Person.NAME.lower().like("b%"))
.and(Person.AGE.gt(20))
.orderBy(Person.AGE.desc())
.limit(5)
.get();
Observable<Person> observable = data
.select(Person.class)
.orderBy(Person.AGE.desc())
.get()
.observable();
requery - query
Query by Fluent API
Reactive Programming
Cold Observable
Non blocking
23. @Entity(model = "tree")
interface TreeNode {
@get:Key
@get:Generated
val id: Long
@get:Column
var name: String
@get:ManyToOne(cascade = [DELETE])
var parent: TreeNode?
@get:OneToMany(mappedBy = "parent", cascade = [SAVE, DELETE])
val children: MutableSet<TreeNode>
}
requery - self refence by Kotlin
Identifier
Entity class
1:N, N:1
Cascade
24. requery - Blob/Clob usage
class ByteArrayBlobConverter : Converter<ByteArray, Blob> {
override fun getPersistedSize(): Int? = null
override fun getPersistedType(): Class<Blob> = Blob::class.java
override fun getMappedType(): Class<ByteArray> = ByteArray::class.java
override fun convertToMapped(type: Class<out ByteArray>?, value: Blob?): ByteArray? {
return value?.binaryStream?.readBytes()
}
override fun convertToPersisted(value: ByteArray?): Blob? {
return value?.let { SerialBlob(it) }
}
}
25. requery - Blob property
@Entity(model = "kt")
interface BigModel {
@get:Key
@get:Generated
@get:Column(name = "model_id")
val id: Int
@get:Column(name = "model_name")
var name: String?
@get:Convert(value = ByteArrayBlobConverter::class)
@get:Column(name = "model_picture")
var picture: ByteArray?
}
Blob column
27. Exposed - Kotlin SQL Framework
• Lightweight SQL Library
• Provide two layers of data access
• Typesafe SQL wrapping DSL
• Lightweight Data Access Object
• See : First steps with Kotlin/Exposed
• Cons
• Not support parameterized SQL
28. Exposed - SQL DSL
object Users : Table() {
val id = varchar("id", 10).primaryKey() // Column<String>
val name = varchar("name", length = 50) // Column<String>
val cityId = (integer("city_id") references Cities.id).nullable() // Column<Int?>
}
object Cities : Table() {
val id = integer("id").autoIncrement().primaryKey() // Column<Int>
val name = varchar("name", 50) // Column<String>
}
30. Exposed - SQL DSL - Join
(Users innerJoin Cities)
.slice(Users.name, Cities.name)
.select {
(Users.id.eq("andrey") or Users.name.eq("Sergey")) and
Users.id.eq("sergey") and Users.cityId.eq(Cities.id)
}
.forEach {
println("${it[Users.name]} lives in ${it[Cities.name]}")
}
31. Exposed - SQL DSL - Join 2
((Cities innerJoin Users)
.slice(Cities.name, Users.id.count())
.selectAll()
.groupBy(Cities.name))
.forEach {
val cityName = it[Cities.name]
val userCount = it[Users.id.count()]
if (userCount > 0) {
println("$userCount user(s) live(s) in $cityName")
} else {
println("Nobody lives in $cityName")
}
}
32. Exposed - DAO
object Users : IntIdTable() {
val name = varchar("name", 50).index()
val city = reference("city", Cities)
val age = integer("age")
}
object Cities: IntIdTable() {
val name = varchar("name", 50)
}
Schema Definition
class User(id: EntityID<Int>) : IntEntity(id){
companion object : IntEntityClass<User>(Users)
var name by Users.name
var city by City referencedOn Users.city
var age by Users.age
}
class City(id: EntityID<Int>) : IntEntity(id) {
companion object : IntEntityClass<City>(Cities)
var name by Cities.name
val users by User referrersOn Users.city
}
Entity Definition
33. Exposed - DAO Usage
val munich = City.new {
name = "Munich"
}
User.new {
name = "a"
city = munich
age = 5
}
User.new {
name = "b"
city = munich
age = 27
}
munich.users.joinToString { it.name }
User.find { Users.age.between(18, 60) }
OneTo Many
All user’s name in Munich
34. Conclusion
• Already legacy database exists ? Use only Java
• jOOQ or requery
• Scala only ? -> Slick
• Kotlin only ? -> requery, Exposed
• No matter language? -> requery
• Need Reactive programming? ->
• requery with kotlinx-requery
• kotlinx-rxjava2-jdbc ( we will open March )