Microservices
Kafka APIs for TxEventQ
Transactional Event Queues (TxEventQ) now support the KafkaProducer and KafkaConsumer classes from Apache Kafka.
Oracle Database can now be used as a source or target for applications using the Kafka APIs.
ODP.NET: Advanced Queuing and Transactional Event Queues
ODP.NET Core and managed ODP.NET now support Advanced Queuing (AQ) and Transactional Event Queues (TxEventQ) application programming interfaces (APIs) that can be used in modern applications, such as microservices. TxEventQ's highly optimized and partitioned implementation leverages the functions of Oracle database so that producers and consumers can exchange messages at high throughput, by storing messages persistently, and propagate messages between queues on different databases. TxEventQ are a high performance partitioned implementation with multiple event streams per queue, while AQ is a disk-based implementation for simpler workflow use cases.
ODP.NET developers can leverage the same APIs no matter if they use TxEventQ or AQ. The APIs provide access to a robust and feature-rich message queuing systems integrated with Oracle database. It can be used with web, mobile, IoT, and other data-driven and event-driven applications to stream events or communicate with each other as part of a workflow.
Prometheus/Grafana for Oracle
Prometheus/Grafana for Oracle will provide database metrics for developers running in a Kubernetes/Docker (K8S) environment. Database metrics are stored in Prometheus, a time-series database and metrics tailored for developers are displayed using Grafana dashboards. A database metrics exporter aids the metrics exports from database views into Prometheus time series database.
Developers of modern applications like microservices use observability at the app tier and often overlook the data tier. Data-driven applications don't get a full picture of the execution and performance of the application. Traditional database metrics are seen through AWR reports and Enterprise Manager, which are more targeted to the DBAs and less to the developers. For developers and architects, Prometheus and Grafana have become the tools for configuring metrics dashboards, setting alerts and taking remedial action. Developers can now tie in the app-tier metrics, Kubernetes container metrics, and Oracle database metrics on behalf of the application together in a single dashboard. In addition to metrics, logs and tracing is also enabled to truly get unified observability on a single pane of glass.
Python and REST Drivers for Transactional Event Queues (TxEventQ)
Database 23ai introduces support in new languages for Transactional Event Queues (TxEventQ). TxEventQ can now be used in Python and with REST APIs (REST APIs implemented to be like Kafka's Confluent REST APIs). TxEventQ already has support for PL/SQL, C/C++, and Java using JMS or JDBC.
This feature increases developer productivity by allowing REST APIs applications to take advantage of Transactional Event Queues (TxEventQ) to handle application and data events. With increasing popularity of Python for Machine Learning applications, TxEventQ in the Oracle Database can now be part of the Machine Learning application data and events infrastructure.
Saga APIs using Oracle Saga Framework
Oracle Saga APIs are implemented in the database and provide a framework to implement transactional semantics for microservices built with the Oracle Database. The orchestrator Saga framework provides a way to maintain atomic data consistency across microservices.
Sagas are concurrent and execute local transactions in each participant database making it more efficient than distributed ACID transactions, thereby simplifying application code and increasing developer productivity.
Transactional Event Queues (TxEventQ) Propagation
Queues are used widely to send and receive events and messages between applications, increasingly being built as microservices. Transactional Event Queues (TxEventQ) are queues built into the Oracle Database. Queue propagation allows multiple databases to act as producers and consumers of events and messages. Producer applications can send events in queues in one database, set up queue propagation to a remote database, and Consumer applications can consume events in queues in the remote database.
Queue propagation is used to consolidate critical events and data from remote locations to a central location for consolidated processing. Propagation is used to operate Transactional Event Queues (TxEventQ) as a reliable and secure Event Mesh, with multiple queues across multiple databases participating to send events and messages across the enterprise reliably and to remote subscribers with permissions. TxEventQ supports exactly-once messaging which makes it simpler to build and test applications.