prompter: "
Add error handling and logging to handle exceptions and log events during the change feed processing.
Use configuration files or environment variables to store the database, collection, endpoint, and primary key information.
Implement batch processing for the changes, to reduce the number of requests to the database.
Implement a retry mechanism for failed operations.
Add a monitoring and reporting system to track the progress and status of the change feed processing.
Use multithreading or parallel processing to increase performance.
Use a database connection pool to reuse database connections.
Implement a checkpoint mechanism to persist the processing progress in case of failures.
Implement a partitioned change feed processing for horizontal scaling.
Implement a load balancing mechanism to distribute the processing load among multiple instances.
Implement a caching mechanism to reduce the number of database queries.
Use a message queue to handle the changes before processing them.
Use a logging database to persist the change history and audit trail.
Implement security measures such as encryption, authentication, and authorization.
Use a content delivery network (CDN) to cache the data closer to the user.
Use a search engine to index the changes and make them searchable.
Use a reverse proxy to cache and distribute the incoming requests.
Use a load balancer to distribute the incoming requests to multiple instances.
Use a reverse proxy to cache and distribute the outgoing responses.
Use a load balancer to distribute the outgoing responses to multiple instances.
Use a middleware layer to perform additional operations on the changes before processing them.
Use a service mesh to handle service-to-service communication and routing.
Use a service discovery mechanism to dynamically discover the service instances.
Use a circuit breaker pattern to handle failures gracefully.
Use a health check mechanism to monitor the health of the service instances.
Use a service mesh to perform mutual TLS authentication and encryption between services.
Use a service mesh to handle service-to-service load balancing.
Use a service mesh to handle service-to-service retries and timeouts.
Use a service mesh to handle service-to-service rate limiting and quotas.
Use a service mesh to perform observability, monitoring, and tracing of service-to-service communication.
Use a service mesh to perform service-to-service traffic shifting and canary releases.
Use a service mesh to perform service-to-service policy enforcement.
Use a service mesh to perform service-to-service telemetry and analytics.
Use a service mesh to perform service-to-service security and compliance.
Use a service mesh to perform service-to-service infrastructure integration and orchestration.
Use a service mesh to perform service-to-service infrastructure provisioning and configuration.
Use a service mesh to perform service-to-service infrastructure maintenance and upgrades.
Use a service mesh to perform service-to-service infrastructure scaling and resiliency.
Use a service mesh to perform service-to-service infrastructure monitoring and debugging.
Use a service mesh to perform service-to-service infrastructure management and reporting.
Use a service mesh to perform service-to-service infrastructure logging and auditing.
Use a service mesh to perform service-to-service infrastructure security and compliance.
Add logging to log errors and other important events that occur during the execution of the program.
Use a configuration file to store the connection details, database and collection IDs, and other parameters, to make the code more modular and easily configurable.
Implement a mechanism to handle exceptions and errors, such as retrying failed operations, logging errors, and providing detailed error messages to the user.
Add unit tests to validate the functionality of the code, especially the handleChanges method, to ensure that it works as expected.
Use a message broker, such as Apache Kafka, to buffer and distribute the changes, to provide resiliency and scalability.
Use a load balancer, such as Apache Nginx, to distribute the load across multiple instances of the Change Feed Processor, to provide high availability and performance.
Use a caching layer, such as Redis, to cache frequently accessed data, to reduce the latency and improve the performance of the program.
Use a monitoring solution, such as Prometheus, to monitor the performance and health of the program, to quickly detect and respond to issues.
Use a security solution, such as OAuth2, to secure the access to the Azure Cosmos DB and the program, to prevent unauthorized access.
Use a containerization solution, such as Docker, to package the program and its dependencies into a portable and reproducible environment, to simplify deployment and management.
Use a continuous integration/continuous delivery (CI/CD) pipeline, such as Jenkins, to automate the build, test, and deployment of the program, to increase the efficiency and reliability of the development process.
Use a version control system, such as Git, to manage the source code and collaborate with other developers, to keep track of changes and maintain a history of the development process.
Use a static code analysis tool, such as SonarQube, to detect and prevent security vulnerabilities, bugs, and code smells, to improve the quality of the code.
Use a dynamic code analysis tool, such as JUnit, to validate the functionality of the code and detect issues early in the development process, to improve the reliability of the code.
Use a performance analysis tool, such as JProfiler, to analyze the performance of the program and identify performance bottlenecks, to optimize the performance of the program.
Use a memory analysis tool, such as JConsole, to monitor the memory usage of the program and identify memory leaks, to optimize the memory usage of the program.
Use a load testing tool, such as Apache JMeter, to validate the performance and scalability of the program under heavy load, to ensure that it meets the required performance and scalability criteria.
Use a cloud-based solution, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP), to host the program and its dependencies, to simplify deployment and management.
Use a database management solution, such as Apache Cassandra or Amazon DynamoDB, to store the data, to provide high availability and performance.
Use a data processing solution, such as Apache Spark or Apache Flink, to process the changes in parallel and at scale, to improve the performance of the program.
Use a data visualization solution, such as Tableau or PowerBI, to visualize the changes and monitor the performance and health of the program, to provide insights into the behavior of the program.
Use a machine learning solution etc. 
"

assistant: It looks like you have used a different service to generate possible actions here, which is a powerful creative solution. Make sure to find time to understand software design yourself.
Code that met all these changes would be a complete and full-featured framework. Let’s begin by describing a folder structure containing subprojects.