Using Spring Data and MongoDB with Cloud Foundry
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- The document discusses using Spring and MongoDB with Cloud Foundry. It covers challenges with data access like scaling horizontally and heterogeneous data needs. - Spring Framework provides data access support for MongoDB through Spring Data. It includes APIs, object mapping, and generic repositories that improve productivity. - Spring Data for MongoDB includes MongoTemplate for direct access, converters for mapping documents to POJOs, and MongoRepository for common CRUD operations. Examples demonstrate basic usage. - The document shows how to integrate MongoDB documents with JPA entities for cross-store domain models and provides an example of saving to MongoDB via Spring on Cloud Foundry.
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The document summarizes the key concepts of Java Persistence API (JPA) and EntityManager for managing object-relational mapping and persistence of data. It discusses how JPA simplifies data access using plain old Java objects (POJOs), standardizes annotations and configuration, and supports pluggable persistence providers. Entities, relationships, inheritance mapping strategies, identity, and the entity lifecycle involving the entity manager are described at a high level.
Testdrive AngularJS with Spring 4
A simple application called cvdb is used to illustrate best practices in combining AngularJS as a client browser technology with a Spring based Java server.
The server architecture utilizes the new Spring Boot module that was introduced with Spring 4 together with other Spring modules like Spring Data, Spring Security, Spring MVC.
QueryDSL is used to access a H2 in memory database.
JPA Best Practices
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.
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Tobias Trelle gave a presentation on Java persistence frameworks for MongoDB. He discussed the MongoDB Java driver, Spring Data MongoDB, Morphia, and Hibernate OGM. For each framework, he covered key features, configuration, object mapping, queries, and examples. He concluded by comparing the frameworks and suggesting which may be best based on the level of abstraction and standards needed.
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SparkR allows users to perform large-scale data analysis from R. It provides a unified approach for working with big data using DataFrames. The current release of SparkR includes DataFrame support released in Spark 1.4 for improved performance. Future developments will expand SparkR's capabilities for large-scale machine learning, simple parallel programming APIs, and tighter integration with existing R packages. The open source project has an growing community of over 20 contributors working to advance SparkR.
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Spring Data provides a unified model for data access and management across different data access technologies such as relational, non-relational and cloud data stores. It includes utilities such as repository support, object mapping and templating to simplify data access layers. Spring Data MongoDB provides specific support for MongoDB including configuration, mapping, querying and integration with Spring MVC. It simplifies MongoDB access through MongoTemplate and provides a repository abstraction layer.
Apache Persistence Layers
The document summarizes and compares several Apache persistence frameworks: Cayenne, OpenJPA, and iBatis. It discusses their object-relational mapping capabilities, supported databases, licensing, communities, and when each framework may be most suitable to use. Example code is provided for typical data access operations like retrieving and updating objects in each framework.
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Spring Data provides a common data access layer for various data stores like MongoDB, JPA, Neo4J, and Redis. It simplifies polyglot persistence through repositories that provide common CRUD methods. Developers define entities specific to the data store and a repository interface with finder methods. Spring Data generates an implementation that interacts with the underlying data store transparently. This allows writing data access code that is portable across data sources.
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Rapid, Scalable Web Development with MongoDB, Ming, and Python
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Grails is a web application framework built on Groovy that aims to eliminate much of the boilerplate code required by traditional Java/Spring/JPA applications. It utilizes conventions over configuration, embedded Tomcat, and the Groovy Object Relational Mapper (GORM) to provide a full stack for rapid development of web applications. The document argues that Grails allows developing applications with substantially less code while maintaining the benefits of technologies like Spring and Hibernate, and provides examples of how simple domain classes and basic CRUD operations are in Grails compared to traditional Java approaches.
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👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
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👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
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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.
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HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
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Using Spring Data and MongoDB with Cloud Foundry
- 1. Using Spring and MongoDB with Cloud Foundry Chris Harris, Architect
- 2. Spring Data
- 3. Data Access Challenge #1: Scale Horizontally
- 4. Data Access Challenge #2: Heterogeneous data access needs
- 6. NoSQL offers several data store categories Column Key-Value Document Graph
- 10. Spring MongoDB
- 13. Mongo Template Direct Usage of the Mongo Template:
- 14. Generic Repository Interface for generic CRUD operations on a repository for a specific type
- 15. Paging and Sorting Repository Extends “ CrudRepository ” Usage: Paging and Sorting Repository:
- 16. Custom Repository Custom Repository: Keywords : Keyword Sample Logical result GreaterThan findByAgeGreaterThan(int age) {"age" : { "$gt" : age}} LessThan findByAgeLessThan(int age) {"age" : { "$lt" : age}} Between findByAgeBetween(int from, int to) {"age" : { "$gt" : from, "$lt" : to}} NotNull findByFirstnameNotNull() { ” firstname" : {" $ne" : null}} Null findByFirstnameNull() { ” firstname" : null} Like findByFirstnameLike(String name) " firstname " : firstname } ( regex ) etc….
- 17. Cross Store
- 18. JPA and MongoDB JPA “Customer” with a “ SurveyInfo ” Document
- 19. Using a Cross-Store Saving a Customer with a SurveryInfo Mongo Document:
- 20. Cloud Foundry
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Editor's Notes
- Oracle RAC clustering is very expensive. $70K/CPU according to list. Even at ½ the cost, it’s still really expensive to build out a horizontal data access grid.
- The amount of log data amassed in Facebook’s operations is staggering. Rothschild said Facebook manages more than 25 terabytes of data per day in logging data, which he said was the equivalent of about 1,000 times the volume of mail delivered daily by the U.S. Postal Service. BASE (basically available, soft state, eventually consistent)