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Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

MyDtt-Plus is a starter of spring-boot, It is an object-oriented Java framework that helps developers increase productivity, "Domain Driven Table" is the concept of DTT, It makes you focus more on domain objects rather than tables. Aims to make it easy to automatically create DB tables based on your Java model with annotation driven. It's also support the ability of create table automatically for MyBatis what Hibernate can do and optionally export SQL to local file，Each table can be added database name concat with table name and fully comments, It can work with ORM frameworks such as MyBatis-Plus and MyBatis with little learning and usage costs. It's worth mentioning that DTT can be MyBatis-Plus is integrated in a 0-code way, just like JPA. However, MyDtt-Plus and MyBatis-Plus may be easier to expand and use than JPA. In addition to supporting the functions of JPA, DDT provides multi-table associated SQL DDL based on MyBatis operation, DTT support databases server for MYSQL, ORACLE, DB2, SQLSERVER, MARIADB, POSTGRESQL and embedded database for H2, HSQL, DERBY.

Balea creates a proxy of javax.sql.DataSource, providing dynamic routing to multiple container based databases following a On Demand Data Source Access architecture. Docker is used as manager to create, start or stop database containers on demand and link correspondent data volumes. Thanks to this approach is possible to distribute data across multiple databases keeping active only the necessary ones in each moment maximizing the use of system resources. The distribution and management of data is hidden under the hood, any process that uses this data source interface (i.e. Hibernate) will perceive it as a single source of data. It makes possible for standard SQL database like Postgresql or MySQL to be maintained, backed up or versioned with simple file system operations as the data volumes are attached dynamically to the managed Docker containers. Docker integration allows also to manage database containers in remote hosts.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The actions performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz and http://sourceforge.net/p/dbvolution/blog/

JCQL is a tool allowing to generate boilerplate java code from existing cassandra schema. It is intended to be used with Cassandra 2.1+ due to support of UDTs (User Defined Types)/Tuples/Collections. Cassandra's UDTs/Tuples/Collections and an ability to introspect schema through java driver make it possible to automatically generate POJOs and corresponding mappers between database and java models. Properly generated java code saves development efforts and is less error-prone compared to hand coding. Accompanied with proper CI and deployment it can also guarantee consistency between database and java models at any point of application lifecycle from development to production rollout. JCQL does not rely on java reflection or annotations which means all discrepancies between actual cassandra schema and what client code expects it to be will be identified during compilation not at runtime in the middle of the night right after production release. No need to worry about Cassandra client code performance implications due to use of reflection.

ASV Toolbox is a modular collection of tools for the exploration of written language data. They work either on word lists or text and solve several linguistic classification and clustering tasks. The topics covered contain language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction. On a more abstract level, the algorithms deal with various kinds of word similarity, using pattern based and statistical approaches. The collection can be used to work on large real world data sets as well as for studying the underlying algorithms. The ASV Toolbox can work on plain text files and connect to a MySQL database. While it is especially designed to work with corpora of the Leipzig Corpora Collection, it can easily be adapted to other sources.

Java Prolog Interface (JPI) is an Application Provider Interface (API) for interaction between Java and Prolog programming languages. Is a bidirectional interface that communicate Java applications with Prolog program or database and Prolog procedures with Java class and methods. JPI is an abstraction layer over concrete prolog drivers over Prolog Engines. This API define all mechanism to interact with any Prolog Engine and maintain the application independent to a specific underlying engine. JPI have several connectors to open source prolog engines like SWI, YAP, XSB native engines and tuProlog, jTrolog, jLog Java based prolog engines. JPI study all related Java-Prolog integration libraries and take the betters features from each solution with the propose to achieve a common integration interface. The last feature allows switch the under laying Prolog Engine driver and the application code still be the same. JPI run over any Java Virtual Machine that support Java SE 5 or above. The project was tested over HotSpot, Open J9 and JRockit Virtual Machines over Operating Systems like Windows (7,8,10), Linux (Debian, Ubuntu) and Mac OS X. Can be deployed on Servlets Containers like Jetty, Tomcat or Glassfish Application Server. JPI can be include in any Java Project using the commonest Java Integration Development Enviroment (IDE) like Eclipse, Netbeans, IntelijIDEA and so on. JPI is developed and maintained by Prolobjectlink Project an open source initiative for build logic based applications using Prolog like fundamental Logic Programming Language in the persistence layer and application programming.

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph

# Pact Spring/JUnit5 Support This module extends the base [Pact JUnit5 module](../pact-jvm-provider-junit5). See that for more details. For writing Spring Pact verification tests with JUnit 5, there is an JUnit 5 Invocation Context Provider that you can use with the `@TestTemplate` annotation. This will generate a test for each interaction found for the pact files for the provider. To use it, add the `@Provider` and `@ExtendWith(SpringExtension.class)` and one of the pact source annotations to your test class (as per a JUnit 5 test), then add a method annotated with `@TestTemplate` and `@ExtendWith(PactVerificationSpringProvider.class)` that takes a `PactVerificationContext` parameter. You will need to call `verifyInteraction()` on the context parameter in your test template method. For example: ```java @ExtendWith(SpringExtension.class) @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT) @Provider("Animal Profile Service") @PactBroker public class ContractVerificationTest { @TestTemplate @ExtendWith(PactVerificationSpringProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } } ``` You will now be able to setup all the required properties using the Spring context, e.g. creating an application YAML file in the test resources: ```yaml pactbroker: host: your.broker.host auth: username: broker-user password: broker.password ``` You can also run pact tests against `MockMvc` without need to spin up the whole application context which takes time and often requires more additional setup (e.g. database). In order to run lightweight tests just use `@WebMvcTest` from Spring and `MockMvcTestTarget` as a test target before each test. For example: ```java @WebMvcTest @Provider("myAwesomeService") @PactBroker class ContractVerificationTest { @Autowired private MockMvc mockMvc; @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { context.setTarget(new MockMvcTestTarget(mockMvc)); } } ``` You can also use `MockMvcTestTarget` for tests without spring context by providing the controllers manually. For example: ```java @Provider("myAwesomeService") @PactFolder("pacts") class MockMvcTestTargetStandaloneMockMvcTestJava { @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { MockMvcTestTarget testTarget = new MockMvcTestTarget(); testTarget.setControllers(new DataResource()); context.setTarget(testTarget); } @RestController static class DataResource { @GetMapping("/data") @ResponseStatus(HttpStatus.NO_CONTENT) void getData(@RequestParam("ticketId") String ticketId) { } } } ``` **Important:** Since `@WebMvcTest` starts only Spring MVC components you can't use `PactVerificationSpringProvider` and need to fallback to `PactVerificationInvocationContextProvider`