@Marvin Can you mention Prefect's ability to interact with different third-party systems and native connections ex. Kubernetes, Databricks, Airbyte etc

Prefect's ecosystem is designed to interact with various third-party systems and native connections, enabling seamless integration with a variety of services. Here's a brief overview of how Prefect can work with some specific systems: 1. Kubernetes: Prefect offers Kubernetes as a deployment option for running and orchestrating workloads. It allows you to deploy your flows to Kubernetes clusters, managing the execution within that environment. Detailed information can be found in the Prefect documentation on Kubernetes deployment. - Kubernetes Deployment Guide 2. Databricks: Prefect can integrate with Databricks, facilitating the provisioning of clusters and running data workflows within the Databricks environment. This allows users familiar with Databricks to leverage Prefect without the need to learn about managing infrastructure on platforms like Azure. - Rec Room Case Study 3. Airbyte: While the provided documentation excerpts do not directly detail Prefect's integration with Airbyte, Prefect typically allows for integration with various ETL tools and data ingestion services. Hence, it's reasonable to expect that there might be ways to orchestrate Airbyte workflows or connectors using Prefect's framework. For the most accurate and specific information on Prefect's integration capabilities with these and other systems, refer to Prefect's official documentation or their community forums where users share their integration strategies and best practices.

Thanks @Marvin Another question - How can I kick off flows from a task that is present in a different flow?

To kick off flows from a task within a different flow in Prefect, you can utilize the orchestration pattern. This involves treating deployed flows much like external services where one flow can call another flow. Here's a general approach on how you could implement this: 1. Ensure both the calling flow and the called flow are deployed. 2. Within the task from where you want to trigger the other flow, you would use a method to start the deployed flow. This could be through an API call to the Prefect server/cloud or by using Prefect's client library within the task code. 3. The calling task can be configured to either wait for the called flow to complete or to continue with other tasks in its own flow while the called flow runs independently. This pattern is beneficial when you want conceptual and execution separation, like executing tasks on different infrastructure that might be required by specific parts of your flow. For detailed instructions and code, refer to Prefect's documentation or guides that discuss inter-flow communication or advanced workflow patterns. You can also consult Prefect Community forums or the official documentation on how to work with such patterns. - Second Generation Workflow Engine - Workflow Design Patterns

@Marvin - Is the above integrations supported in the open source version or just in the cloud version?

Both Prefect open-source and Prefect Cloud support integrations with various third-party systems, each with its own level of functionality and features: Prefect Open Source: Known as Prefect Core, it provides the essential features needed for workflow semantics and the development of repeatable data pipelines. It allows for the creation of complex pipelines and offers various integrations with third-party systems, enabling you to work with a variety of user infrastructures, services, and tools. Prefect Cloud: On top of the features available in Prefect Core, Prefect Cloud provides additional capabilities such as an advanced UI for monitoring, out-of-the-box logging, and notifications. It uses a Hybrid Execution Model, which alleviates certain security concerns. Prefect Cloud can accelerate the deployment and management of jobs and is particularly beneficial for teams requiring enhanced observability, control, and orchestration capabilities. Therefore, both the open-source and the cloud versions support various integrations; however, the Cloud version may offer a more comprehensive and managed experience. For additional insights and specifics on feature support for different integrations, you should check the official Prefect documentation or inquire with their support community. - Prefect with Modern Health - Prefect with Nationals

@Marvin Does Prefect core support Dynamic & parameterized tasks creation?

Yes, Prefect Core supports both dynamic and parameterized task creation: Dynamic Tasks: Prefect allows for the creation of workflows where the number of tasks can change based on runtime information. For example, you might have a task that queries a database for a list of items, and then processes each item with another task. In traditional workflow systems, this might require a fixed number of tasks or complex workarounds. In Prefect, you can dynamically generate tasks based on runtime data, ensuring that your workflow can adapt to the data it processes. Parameterized Tasks: Prefect workflows can be designed to accept parameters, allowing you to pass different values at runtime. This is particularly useful when you want to execute the same task or flow with varying inputs, without having to duplicate your workflow definition every time the input changes. Dynamic and parameterized task creation in Prefect Core provides the flexibility to handle a wide range of workflow patterns, including tasks that depend on data that is only available during the flow's execution. For more detailed examples and guidelines on implementing these patterns, you should refer to the Prefect documentation or seek support from the Prefect community. - Dynamic Workflows in Prefect

@Marvin Does prefect core support event driven workflows?

Prefect Core is designed to support event-driven workflows to some extent. While it is fundamentally an orchestration tool where workflows are typically scheduled or triggered manually, it has the capacity to be reactive to external events. Here's how: - Orchestration: Prefect's orchestration capabilities allow workflows to be reactive to contextual factors that may be external triggers or events. That means workflows can be configured to respond to certain conditions or state changes. - Integration with Serverless Platforms: By integrating Prefect with serverless platforms (e.g., AWS Lambda via services like Pulumi), you can create event-driven workflows that trigger Prefect flows based on specific events in your data environment. This allows for dynamic execution and cost-efficient data processing, as resources are allocated on-demand in response to events. Prefect Core provides a certain level of support for event-driven workflows, considering its robust orchestration features combined with reactive execution models. The design aims to facilitate workflow management responsive to external events without direct manipulation of the workflow's implementation. - Orchestrating Event-Driven Serverless Data Pipelines - Workflow Coordination Spectrum If you need to set up event-driven data workflows with specific triggers, such as file uploads, webhooks, or other signals, Prefect Core can be configured to work with these, potentially in conjunction with external services that manage the events.

@Marvin does prefect cloud offer these event driven options?

Prefect Cloud does indeed offer support for event-driven workflows. Here are some features and options available in Prefect Cloud that facilitate event-driven orchestration: 1. Versatile Scheduling: Prefect Cloud allows for both time-based and event-based scheduling. This means that flows can be triggered not just at a predetermined time but also in response to specific events or conditions. 2. Automations: Prefect Cloud supports automations that can trigger notifications or the execution of flows based on certain events, making your workflows responsive and dynamic. 3. Webhooks: With the support for webhooks, Prefect Cloud can accept events from internal or external sources. This allows external systems to trigger flows in the cloud based on various events like Git commits, data uploads, system alerts, or any action that can send a webhook. These features enrich Prefect Cloud's capabilities to streamline operations in an event-driven manner, catering to workflows where timing or event occurrence are critical triggers for the execution. For further details on how to implement event-driven workflows or automation in Prefect Cloud, you should explore Prefect's official documentation or reach out to their support team. - Getting Started with Prefect Cloud - Prefect Cloud vs Open Source

@Marvin Can you talk about the prefect worklow definition languages?

Prefect's workflow definition language is inherently Python-based, which means that users define their workflows using Python code. This leverages Python's readability and flexibility, allowing developers familiar with the language to easily create, maintain, and reason about their workflows. Here are some key aspects of Prefect's workflow definition language: 1. Pythonic Syntax: Workflow definitions are just Python scripts, making use of functions and decorators provided by the Prefect library. This allows for readably expressing workflow logic with Python's familiar constructs. 2. Tasks and Flows: In Prefect, workflows are composed of tasks, which are the smallest units of logic. Tasks are defined as Python functions and are annotated using the

@task

decorator. Flows are collections of tasks and define the workflow's structure, including task dependencies. 3. Composability: Prefect workflows are highly composable, meaning that tasks and flows can be modular, reused, and combined in various ways to build complex workflows. 4. Infrastructure Abstraction: Prefect offers infrastructure abstraction to various degrees depending on whether you're using the open-source or cloud version. Platform teams can customize interfaces and access to infrastructure without significant overhead. Prefect's approach to workflow orchestration seeks to reduce mental overhead for developers and provide robustness in the presence of failures. To create workflows, developers need to handle errors gracefully, anticipate infrastructure issues, and design their workflows to be as fault-tolerant as possible. Overall, the Prefect workflow definition language is designed to be highly readable and Python-driven, oriented towards simplicity, and maximizing developer productivity. - A Platform Approach to Workflow Orchestration For more detailed information about Prefect's workflow definition language and examples, you can explore Prefect's official documentation or engage with their community forums.

@Marvin Does Prefect support kubernetes executor

Yes, Prefect does support the use of Kubernetes as an executor for running flows. Here's some information on how Prefect interacts with Kubernetes: Kubernetes Executor: Prefect allows you to deploy and run your flows using Kubernetes, which is called a Kubernetes executor in this context. A Kubernetes executor in Prefect is responsible for running each task in a flow in potentially separate Kubernetes Pods. This makes use of Kubernetes' ability to dynamically manage the life cycle of containers based on the computational needs of a Prefect flow. Deployment: Prefect provides documentation on how to deploy your flows to a Kubernetes cluster. The deployment can be done via the Prefect CLI, and you can deploy all the flows or individual ones by their name (

prefect deploy

). Running Flows in Kubernetes: Once the deployments are successfully created, you can run them through the Prefect UI or the CLI (

prefect deployment run

). The documentation specifically notes that while much of the deployment guidance is general for any Kubernetes cluster, some specifics may differ across cloud providers, particularly with container registries and access management, and provides a focus on Amazon EKS as an example. Prerequisites: For setting up flows on Kubernetes using Prefect, you will need a Prefect Cloud account, a cloud provider account (AWS, GCP, or Azure), Python and Prefect installations, Helm, and the Kubernetes CLI (

kubectl

). Integration with Cloud Providers: Prefect's interaction with Kubernetes can be tied with various cloud providers, ensuring that you can use their managed Kubernetes services, such as EKS on AWS, GKE on Google Cloud, or AKS on Azure. To get started and for comprehensive instructions on how to set up and use the Kubernetes executor with Prefect, you should refer to the official Prefect documentation regarding Kubernetes.

@Marvin Is this for both Core and cloud setup?

The ability to use Kubernetes as an executor and to deploy flows to a Kubernetes cluster is available for both Prefect Core and Prefect Cloud. Here's how they work together: Prefect Core: This is the open-source engine that executes your workflows. Prefect Core allows you to define flows (workflows) and tasks within those flows. Even in its open-source form, this could include the ability to run tasks on Kubernetes, as long as you have access to a Kubernetes cluster where you can deploy your workflow containers. Prefect Cloud: Prefect Cloud is the managed service that provides a control plane over your Prefect Core workflows. So, when you deploy Prefect on Kubernetes, Prefect Core is running within your Kubernetes cluster, executing your tasks, while Prefect Cloud provides additional features such as a UI, observability, and advanced orchestration capabilities. Regardless of whether you're using Prefect Core or Prefect Cloud, you can use Kubernetes to run your Prefect flows. The instructions on setting up Kubernetes with Prefect typically apply to both, except for the additional features and integrations that Prefect Cloud offers. Deploying and running Prefect flows on Kubernetes provides dynamic scaling and management of execution resources, taking advantage of Kubernetes' orchestration capabilities regardless of whether you're using the open-source or the hosted cloud solution.

@Marvin How can I simulate and test flow execution in a Kubernetes pod while running in local where I'm using a docker setup?

To simulate and test flow execution in a Kubernetes pod while using Docker on your local machine, you can follow a process like this: 1. Local Docker Setup: - Ensure that your Docker environment is properly set up and that the Docker daemon is running. 2. Docker Image for Flow: - Package your Prefect flow in a Docker image. This usually involves creating a Dockerfile that specifies your Python environment, installs Prefect, and copies your flow code into the image. 3. Prefect Configuration: - Make sure to set the

PREFECT_API_URL

environment variable so that your flow container can communicate with the Prefect API. If you're using Docker on Linux, you may need to use the

--network="host"

option and set the

PREFECT_API_URL

to

<http://localhost:4200/api>

. - Command example for running Docker:

docker run --network="host" -e PREFECT_API_URL=<http://localhost:4200/api> your-prefect-image

4. Kubernetes Emulation: - Utilize tools like Minikube, kind (Kubernetes IN Docker), or Docker Desktop's built-in Kubernetes cluster to run a local Kubernetes instance that can mimic the behavior of a managed Kubernetes service. - Deploy the Prefect agent to your local Kubernetes cluster. Ensure it is configured correctly to pick up jobs from your Prefect Cloud or Server instance. - Use

kubectl

to interact with your local Kubernetes instance as you would with a cloud-hosted one. 5. Test and Debug: - Use the Prefect CLI or UI to register and execute your flow. - Monitor logs and output from the Kubernetes pods to inspect flow execution and debug as necessary. This local setup will allow you to test how your flow would behave in a real Kubernetes environment, without having to deploy to a cloud service. For further guidance, consult the Prefect documentation on deploying flows using Kubernetes and working with Docker containers. Please note that this synopsis provides a sketch of the process. Specific commands and configuration may vary depending on your environment and setup.

@Marvin What are the security/user access options while running the core version? Can I integrate with okta or any oauth providers?

Prefect Core, as the open-source version, does not include built-in user management or access controls that are present in Prefect Cloud. Security and access control in Prefect Core are generally implemented at the infrastructure level by the user or administrator managing the Prefect deployments. Although there isn't a built-in feature in Prefect Core for integration with Okta or OAuth providers directly, you can implement authentication and authorization using several approaches: 1. Proxy Servers: Place a reverse proxy server in front of your services (like the Prefect UI or API), which can integrate with OAuth providers, including Okta, to manage access. Popular proxy servers such as Nginx or Apache can be configured with modules to handle OAuth. 2. API Gateway: Use an API gateway that supports OAuth to secure access to the Prefect API. The gateway can handle access tokens, client credentials, and other aspects of OAuth. 3. Custom Services: Build custom services or middleware for user authentication and authorization that integrates with Prefect's services and your chosen identity providers. 4. Container Orchestration Controls: When deploying Prefect Core within a container orchestration system like Kubernetes, leverage the built-in access control mechanisms, such as role-based access control (RBAC), to secure access to the resources. 5. Infrastructure Security: Ensure that the underlying infrastructure where Prefect Core runs has proper security measures, such as firewall rules, security groups, and access policies. It's important to note that while you can secure Prefect Core using these methods, you will need to manually set them up and maintain them since Prefect Core itself does not manage user access. For built-in authentication and authorization features, you would need to use Prefect Cloud or Prefect Server (the self-hosted version of Prefect Cloud). - Prefect Security For specific instructions on how to set up security and access control in your Prefect Core deployment, consult the relevant security and infrastructure documentation applicable to your environment.

Hey @Marvin can you give me a doc where there is an example of deploying workflows in prefect using jenkins?

It seems that there is no direct excerpt available from the Prefect documentation that specifically details deploying workflows in Prefect using Jenkins. However, here are some general steps you can take to achieve this integration: 1. Install Prefect: Ensure both Jenkins and Prefect are installed and properly configured in your environment. 2. Create Jenkins Pipeline: Define a Jenkins pipeline that includes the necessary steps to deploy a Prefect workflow. This pipeline can be defined in a

Jenkinsfile

or configured through the Jenkins web interface. 3. Prefect CLI: In the Jenkins pipeline steps, use Prefect's command-line interface (CLI) to register and deploy your flows. For example:

prefect register flow --file my_flow.py --project my_project
   prefect deployment create my-flow-deployment.yaml

4. Trigger Jenkins Job: Configure a job in Jenkins that can be either manually triggered or set up with webhook triggers for automating flow deployments upon certain conditions, such as a push to a Git repository. 5. Monitoring: Monitor the output of the Jenkins job for any deployment issues and configure notifications for success or failure states. To implement this, you must craft a Jenkins job that uses Prefect's CLI commands for deploying flows, similar to how you would use Prefect Cloud or Prefect Server. As the details can vary based on your specific setup and requirements, I recommend looking at both Prefect's CLI documentation and Jenkins' pipeline documentation to create a workflow that suits your needs. Given that there's no direct example in the provided excerpts, for more specialized guidance on integrating Jenkins and Prefect, it may be helpful to consult the Prefect community or support forums, where other users might have shared similar experiences or to check the Prefect documentation for more on deploying flows, then adapt those instructions for a Jenkins environment.

@Marvin - Understood, Are there other best practices that are available on how to setup a CICD process when there are multiple teams developing on different repo's but deploying to the same prefect server?

When setting up a CI/CD process for multiple teams working on different repositories but deploying to the same Prefect server, adopting best practices is essential for maintaining a smooth and structured workflow. While the provided excerpts do not directly address CI/CD best practices, here are some general best practices that can apply to such a setup: 1. Standardize Workflow Definitions: Establish conventions for defining and structuring Prefect flows across teams. Consistent structure helps in managing and deploying flows across different repositories and teams. 2. Dedicated Infrastructure for CI/CD: Use separate infrastructure or environments for CI/CD processes to ensure that the deployment pipeline is isolated from production workloads. 3. Version Control and Branch Policies: Implement strict version control practices with branch protection rules. Require peer reviews and automated tests before merging to ensure code quality. 4. Automated Testing: Have automated tests for Prefect flows as part of your CI pipeline. This will catch issues early and reduce the likelihood of deployment failures. 5. Deployment Artifacts: Generate versioned deployment artifacts for flows during the CI process. This might include Docker images or configuration files. 6. Access Control: Ensure that CI/CD pipelines have appropriate access to deploy to the Prefect server, typically managed through service accounts or deployment tokens with limited permissions. 7. Monitoring and Notifications: Integrate monitoring and notification systems to alert relevant team members about the status of CI/CD pipelines, especially when it comes to failures. 8. Centralized Logging: Implement centralized logging for all CI/CD activity to easily trace issues across pipelines. 9. Rollback Procedures: Prepare automated rollback procedures in case a deployment introduces issues in the shared Prefect server environment. 10. Documentation: Maintain up-to-date documentation regarding the CI/CD setup, including instructions on how to manage deployments and troubleshoot issues. While this is not an exhaustive list, it provides a solid foundation of best practices to consider. To further refine these practices, you may want to engage with the Prefect community, explore Prefect's official documentation for any additional CI/CD recommendations, and look for case studies or blog posts about how other organizations have successfully managed similar setups.

@Marvin Where can I see the logs of a flow? Is it possible to natively push this log to S3 or a logstash?

Prefect captures logs for your flow and task runs, providing useful information for monitoring, troubleshooting, and auditing. Here's how you can access and utilize Prefect logs: Viewing Logs: - You can view and filter logs directly in the Prefect UI or Prefect Cloud. - Logs can also be accessed via the Prefect API for additional processing or querying. Pushing Logs to External Services: - While Prefect doesn't natively push logs to external services such as S3 or Logstash, you can implement a custom logging handler in your flows that forwards logs to these services. - For S3, you could write a custom logger that uploads the log files to an S3 bucket after the execution of a flow. - With Logstash, you can create a logging handler that sends log messages to a Logstash instance, possibly via a Logging pipeline where Prefect logs are pushed to Logstash and then filtered or transformed as needed. To set up these custom logging handlers, you will need to configure your Prefect environment to use Python's logging module to attach a handler that performs the desired data forwarding or storage. For more specific details and guides on how to configure logging in Prefect and integrate with services such as S3 or Logstash, you should consult the Prefect documentation, particularly sections related to logging and how to customize logging in your Prefect setup. You may also engage with the Prefect community to learn about best practices or seek advice on implementing these solutions.

@Marvin does the prefect core or cloud offer metrics around resource utilization? Like memory used, CPU usage etc?

Prefect does not provide direct metrics on resource utilization such as memory used or CPU usage natively either in Prefect Core or Prefect Cloud. Prefect focuses on orchestration and workflow management rather than monitoring the underlying execution environment at the resource level. However, Prefect does offer an "Observability API" referred to as Sens-O-Matic in Prefect 2.0, which introduces the concepts of resources and events: - Resources: Represent objects or systems that interact with your data flow, including but not limited to Kubernetes clusters, databases, and S3 buckets. - Events: Can be emitted by any resource and captured by Prefect. They describe actions, operations, heartbeats, or relationships. This new observability API allows Prefect to extend its functionality throughout the stack, capturing events related to the data flow. It enables useful integrations such as correlating Kubernetes cluster events to workflow failures, or discovering how recently database tables were updated. For actual resource utilization metrics like memory and CPU, you would typically rely on the underlying execution environment's monitoring tools. For example, when running flows in Kubernetes clusters, you would use Kubernetes monitoring tools such as Prometheus and Grafana to track CPU and memory usage. Similarly, cloud providers offer their own monitoring solutions (such as Amazon CloudWatch for AWS) that you can configure to monitor your Prefect workloads. If you need to integrate these resource metrics within the Prefect environment, you might need to implement custom code that interacts with these monitoring tools and forwards relevant data to Prefect or another observability platform. Refer to the following links for more detailed insights: - Sens-O-Matic: The Observability API For more detailed guidance, you should consult with Prefect's official documentation or reach out to their community support for assistance on setting up such monitoring capabilities.

@Marvin Does Prefect integrate with alation?

The provided excerpts do not specifically mention an integration between Prefect and Alation. Alation is a data catalog platform used for organizing data assets within an enterprise, and Prefect is focused on workflow and data pipeline orchestration. However, even if there isn't a native integration available out-of-the-box, you could potentially create a connection between Prefect and Alation using custom Python scripts or tasks within your Prefect flows. This would likely involve using Alation's API or SDK to interact with the Alation data catalog from within a Prefect flow. For example, you could create a task that updates metadata in Alation when certain conditions are met in the flow, or that checks for certain metadata before starting a flow. To create such an integration, you would need to: 1. Understand Alation's API or SDK and identify the endpoints and functions you need to interact with. 2. Develop custom tasks in your Prefect flows that make API calls to Alation using these endpoints. 3. Test the integration to ensure that your Prefect flows can successfully communicate with the Alation data catalog and perform the intended actions. For more information on how you can integrate Prefect with other tools and platforms, it's recommended to check Prefect's official documentation and forums, or contact their support for expert guidance. If there is a popular demand for Alation integration, it might also be something that the Prefect team would consider adding in the future, so keeping an eye on Prefect's updates or contributing to their community could be beneficial.

@Marvin does the prefect core have the ability to restart pipeline from a certain stage?

Prefect Core's workflow semantics allow for a clean and easy-to-use feature set for building data pipelines, which include tasks, flows, and the management of their states. While the provided excerpts do not specify the ability to restart a pipeline from a certain stage, Prefect does feature a robust state system that tracks the success, failure, and other statuses of tasks within a flow. If a flow in Prefect is interrupted or fails at a certain point, Prefect's design allows tasks to be retried. This functionality can be implemented using the following concepts: 1. Retry Mechanisms: Tasks in Prefect can be configured with retry settings, which automatically retry a task if it fails. 2. Resuming Flows: With the right setup, including the use of result handlers and checkpointing in Prefect 1.0 or result persistence mechanisms in Prefect 2.0 (Orion), it may be possible to resume a flow from where it left off if an error occurred. 3. Conditional Logic: With conditional logic, you can design your flow to handle errors and potentially skip certain tasks or jump to different points in the workflow. While Prefect Core does not have a direct "restart from a failed task" feature, you can design your flows to be robust and potentially resume processes handling intermediate results and states wisely. The Prefect UI and Prefect Cloud may provide more advanced features in terms of managing the state of flows and inspecting past runs for failure points. For detailed guidance on retries, result persistence, and error handling in Prefect Core, please refer to the official documentation. If precise functionality for restarting a pipeline from a certain stage is required, you may also look into Prefect's newer features in Prefect 2.0 (Orion) or consider contributing to the Prefect Core's open-source codebase to add such a feature.

@Marvin does the prefect cloud support the ability to restart pipeline from a certain stage?

As with Prefect Core, Prefect Cloud allows for the orchestration of data imports and pipelines, providing visibility into successes, failures, and performance issues via cloud logs. Although the provided excerpts do not directly indicate a feature that allows the restarting of pipelines from a certain stage, Prefect Cloud generally offers advanced orchestration and monitoring capabilities. With Prefect Cloud, you have the ability to: 1. Retry Failed Tasks: Tasks can be automatically retried when they fail. This is part of Prefect's robust error handling mechanism. 2. Monitor Pipeline Execution: Using Prefect Cloud’s UI, you can closely monitor the execution of pipelines and identify at which stage a task has failed. 3. Restart Flows: If a flow fails, you can trigger it to restart. While there is not a one-click solution to restart a flow from the failed task itself, the granular visibility into the flow's execution may help you decide how best to resume or restart the process. 4. Use Result Handlers: Result handlers (in Prefect 1.0) or result persistence (in Prefect 2.0 Orion) can be used to persist intermediate outputs, which can aid in resuming execution from a certain point without redoing previous work. To enable a kind of selective restart, you would typically set up your flows to make judicious use of checkpoints or design the flow with idempotency in mind. This helps to ensure that rerunning a flow does not unnecessarily repeat successfully completed tasks. For details about restarting flows and handling failed executions, you can refer to the Prefect documentation and explore the functionalities available when orchestrating flows using Prefect Cloud. It's also recommended to review their forum or reach out to support for detailed guidance tailored to your use case, as features continue to evolve.

@MarvinIf I'm self hosting prefect, can I add a visualization layer to view the dags and their dependencies?

Yes, when self-hosting Prefect, you get access to the Prefect UI, which includes a visualization layer for viewing flows and their dependencies. The Prefect UI is an integral part of the platform, designed to give users a comprehensive view of their workflows, including the ability to see how tasks are related and the current state of each task within a flow. Here's how you can access and make use of the visualization features in the Prefect UI for a self-hosted server: - After installing Prefect and hosting your own Prefect server, start the server using the Prefect CLI with the command:

prefect server start

. - Once the server is up, open a web browser and navigate to the Prefect server UI by going to the address

<http://127.0.0.1:4200>

by default, or whatever URL is produced by the start command. - In the UI, you’ll have an overview of all your Prefect flows. You can inspect individual flows to view their tasks and dependencies in a graphical format, often displayed as a Directed Acyclic Graph (DAG). - The visual representation helps troubleshoot and understand the sequence and concurrency of task execution, as well as the current status of each task. - Errors and logs can be checked for each task within the UI, aiding in debugging and validating the flow execution. Prefect ensures that the experience of developing locally and viewing flows in the UI is closely aligned with how workflows are executed in production, thereby streamlining the management and monitoring processes. To access more advanced visualization customizations, you might consider integrating Prefect with other visualization tools, building your own UI components on top of Prefect’s API, or contributing to Prefect’s UI if your visualization needs go beyond the existing functionalities. For instructions and guides on the Prefect UI and server setup, consult the official Prefect documentation on hosting a server.

@Marvin If I'm self hosting prefect, can I use the helm chart for deployment or do I need to have a cloud account?

You can definitely use the Prefect Helm chart for deployment without needing a Prefect Cloud account when self-hosting. Prefect provides a Helm chart specifically designed to simplify the deployment of Prefect on Kubernetes clusters. Here are the steps outlined in the documentation to deploy Prefect using Helm: 1. Add the Prefect Helm Repository:

shell
   helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update

2. Create a Namespace:

shell
   kubectl create namespace prefect

3. Kubernetes Secret for API Key: If you are using Prefect Cloud as part of your self-hosted solution for additional features, you would create a Kubernetes secret with your Prefect Cloud API key:

shell
   kubectl create secret generic prefect-api-key --namespace=prefect --from-literal=key=YOUR-PREFECT-CLOUD-API-KEY

4. Configure Chart Values: Customize the deployment by creating a

values.yaml

file where you can specify configurations for the Prefect agent, UI, and other components as needed. 5. Deploy the Helm Chart: Use the Helm command to deploy Prefect into your Kubernetes cluster using the values from your `values.yaml`:

shell
   helm install prefect prefect/prefect --namespace=prefect -f values.yaml

If you're not using Prefect Cloud and are completely self-hosting, you can ignore steps that involve the Prefect Cloud API key, and focus on setting up the necessary components for your Prefect installation on Kubernetes. For detailed information on the Helm chart and deployment processes, please refer to the official Prefect documentation on deployment with Kubernetes.

@Marvin What is the equivalent of airflow's connections in prefect?

The equivalent of Airflow's connections in Prefect would be Prefect Secrets for storing sensitive connection strings or tokens, and Prefect Blocks for more structured and re-usable resources. Prefect Secrets: These are used to securely store and access sensitive information which can include database credentials, API keys, etc. Similar to Airflow's connections, they are stored in a secure manner and can be accessed within flows to create connections to external systems. In Prefect 2.0 (Orion), Blocks system can be used for a similar purpose, further offering the ability to manage and use these configurations for different tasks and flows. If you need to set up and use connections or credentials akin to Airflow's within Prefect, you will use Secrets and Blocks to manage those configurations securely. For detailed instruction and guidance on using Secrets and setting up connections in Prefect, refer to the official documentation on Prefect Secrets and Blocks (in Prefect 2.0 Orion).

@Marvin can you point me to an example of creating a pipeline using the API?

The provided excerpts do not include a direct example of creating a pipeline using the Prefect API. However, interacting with Prefect programmatically via the API generally involves making HTTP requests to Prefect's backend services to perform various actions, such as creating flows, registering tasks, triggering flow runs, and managing deployments. While I cannot provide specific code within these excerpt results due to the lack of direct examples in the documentation, you can follow these high-level steps to create a pipeline using the Prefect API: 1. Authentication: Securely authenticate to the Prefect API with the necessary credentials, which may require obtaining an API key or token if you are using Prefect Cloud. 2. Define a Flow: Programmatically define your pipeline by specifying tasks and their dependencies. This can be done in Python by using Prefect's client libraries. 3. Register the Flow: Use an HTTP POST request to register the flow with Prefect's backend services. You would typically include the flow definition in your request body. 4. Triggering a Flow Run: After registration, you can trigger a flow run through an HTTP POST request to Prefect's API endpoint designated for flow runs. If you need to work directly with the Prefect API to create and manage pipelines, the most comprehensive resource would be Prefect's official documentation, which should contain guides, API references, and potentially Python code examples for interacting with Prefect programmatically. Additionally, you can explore Prefect's GitHub repository or their community forums for community-contributed examples and assistance.

@Marvin can we atleast create a flow or deployment using API and is there an example?

Creating flows and deployments using the Prefect API is indeed possible, and while the provided excerpts do not include a direct code example for this, I can guide you through the general process on how to use Prefect's CLI and API for these operations. Creating Flows: In Prefect 2.0 (Orion), flows are defined in Python, and the creation process generally happens within your Python code. To define a flow in Python, you would use the provided Prefect constructs, such as

Flow

and

Task

. Once defined, you can register the flow with Prefect using the CLI:

python
from prefect import flow

@flow
def my_flow():
    # Define flow's tasks and logic here

# Register the flow
my_flow.register(project_name="my_project")

Creating Deployments: Deployments are specifications for running flows. After defining a flow, you can use the Prefect CLI to create a deployment YAML file:

shell
prefect deployment build my_flow.py:my_flow -n my-deployment -q my-queue -a

This will generate a descriptive YAML file for the deployment. You can then apply this YAML file to create or update the deployment:

shell
prefect deployment apply my-deployment.yaml

Using the API: To use the API for these operations, you would need to make HTTP POST requests to the corresponding Prefect API endpoints while including necessary details such as flow definitions in JSON format. The Prefect client libraries abstract much of the complexity involved in these API interactions. For the exact API endpoints, request formats, and more detailed examples, please refer to Prefect’s API documentation and the GitHub repository which may include examples and further instructions on creating and managing flows and deployments via the API.

@Marvin If I'm self hosting prefect, can I do a highly available and fault tolerant setup?

While the provided excerpts do not detail the specifics of setting up a highly available and fault-tolerant Prefect environment, self-hosting Prefect does allow for the creation of such a system. It would involve appropriately configuring and managing the underlying infrastructure to achieve high availability (HA) and fault tolerance. Here are some strategies you could use for setting up a self-hosted Prefect server with high availability and fault tolerance: 1. Database Clustering: Use a clustered database setup with replication to ensure that the backend database is fault-tolerant and can handle the failure of individual nodes without losing data or suffering downtime. 2. Load Balancers: Employ load balancers to distribute traffic across multiple instances of the Prefect API server and UI, which can help manage and reroute traffic in case one of the instances fails. 3. Redundancy and Scalability: Deploy multiple instances of your Prefect components across different nodes or availability zones to provide redundancy. Use container orchestration platforms like Kubernetes to manage these instances and automatically replace failed ones. 4. Persistent Storage: Ensure that your flows and task results are using persistent and resilient storage services. 5. Monitoring and Alerts: Implement a comprehensive monitoring solution to detect and alert on any issues as soon as they occur, which is crucial for maintaining an HA system. 6. Backup and Recovery Plans: Have backups and a recovery plan in place for Prefect's metadata database so that you can recover quickly from failures. Keep in mind that setting up a self-hosted HA and fault-tolerant system can be complex and typically requires a good understanding of the infrastructure components you're working with. For more specialized guidance on how to achieve this setup, you might want to explore Prefect's community channels or inquire directly with Prefect support. Additionally, consider consulting with your infrastructure team or a DevOps specialist who can help design and implement a solution appropriate for your needs.

@Marvin can I setup CICD on self hosted prefect where I have multiple teams with their different repos?

Setting up a CI/CD pipeline for a self-hosted Prefect instance with multiple teams and different repositories is certainly possible. While the provided excerpts do not offer a direct guideline for such a setup, here's a high-level approach based on common best practices: 1. Standardize CI/CD Processes: Establish agreed-upon CI/CD procedures and configurations across the teams. This promotes a consistent pipeline setup, even though the teams may work on separate repositories. 2. Version Control Strategy: Each team should use a version control system for their code (such as Git) and follow a branching strategy that accommodates their development workflows. 3. Automated Testing and Linting: Implement automatic code testing and linting to ensure that code adheres to quality standards before being deployed or merged. 4. Prefect Flow Registration: Automate the registration of Prefect flows within the CI/CD pipelines. When a team merges changes to their main branch, the CI/CD pipeline should handle flow testing, building, and registration. 5. Docker and Kubernetes: For teams deploying flows to Kubernetes, use Docker to containerize flows and Kubernetes manifests or Helm charts to manage deployments. Integrate these tools into your CI/CD pipeline for seamless deployment. 6. Prefect Deployment Automations: Use Prefect CLI commands or the Prefect API within your CI/CD pipeline to automate the deployment of flows to your self-hosted Prefect server. 7. Monitoring and Notifications: Integrate monitoring tools to track CI/CD pipeline performance and set up notifications for success or failure of deployment steps. 8. Isolated Environments: Create isolated environments (dev, staging, prod) for testing and rolling out changes safely. This might involve different Prefect workspaces or server instances. 9. Access Control: Manage access to the CI/CD system and the Prefect server securely, ensuring that only authorized personnel can deploy changes to production flows. 10. Documentation and Onboarding: Maintain clear documentation of the CI/CD processes, tooling, and any Prefect-specific steps. This is especially important when onboarding new team members or for cross-team collaboration. For technical instructions on integrating Prefect components into your CI/CD pipeline, you will likely need to refer to Prefect's official documentation, as well as the documentation of the CI/CD tools you're using, such as Jenkins, GitLab CI, GitHub Actions, etc. If needed, tailor the setup to accommodate the specifics of your infrastructure and organizational policies.

@Marvin can you help me understand the differences between prefect cloud and prefect core in terms of hosting challenges?

Prefect Core and Prefect Cloud are designed to handle workflow orchestration differently, which naturally leads to different hosting challenges: Prefect Core: - Self-Hosting: Prefect Core is an open-source engine that you can run locally or on any infrastructure that supports Python. You're responsible for hosting, which includes setting up the execution environment, managing upgrades, configuring scaling, and ensuring fault tolerance and high availability. - Infrastructure Management: You manage all aspects of the infrastructure including servers, databases, and any third-party integrations you use for monitoring or logging. - Monitoring and Alerting: Out-of-the-box Prefect Core doesn't include a monitoring UI, so you will need to either use the included UI that comes with Prefect Server (the self-hosted API and UI for Prefect Core) or integrate with third-party tools for detailed monitoring and alerting. Prefect Cloud: - Managed Service: With Prefect Cloud, Prefect hosts the orchestration engine. This eliminates many of the hosting challenges related to infrastructure management, scaling, and availability. - Security: Prefect Cloud provides a secure cloud environment where you don't need to worry about a lot of the security considerations of hosting your own server. Prefect uses a "hybrid execution model" which means your workflow code runs in your own infrastructure, but the orchestration is cloud-based, providing additional security. - Out-of-the-Box Features: Prefect Cloud provides additional features such as a detailed UI for monitoring, alerting, and managing your workflows. - Zero-Maintenance: Since it's a managed service, upgrading, scaling, and general system maintenance are handled by Prefect, significantly reducing the operational overhead. Overall, Prefect Core offers greater control over the orchestration environment but comes with the overhead of self-management, while Prefect Cloud abstracts away these hosting challenges and offers additional features at the cost of less direct control over the orchestration environment. For more information on hosting a self-hosted Prefect: - Hosting a Prefect Server To get a comprehensive comparison and understanding of the specific hosting challenges, reviewing the official Prefect documentation or engaging with the Prefect community and support can be beneficial.

@Marvin can you help me understand the differences between prefect cloud and prefect core in terms of impact on user experience?

The differences between Prefect Cloud and Prefect Core can have a significant impact on user experience in several areas: Ease of Deployment: - Prefect Cloud: Offers accelerated deployment capabilities without the need for users to handle underlying infrastructure or security concerns. - Prefect Core: Users are responsible for coordinating the deployment of their workflows, which may require additional configurations and the setup of infrastructure. User Interface and Monitoring: - Prefect Cloud: Includes an intuitive, out-of-the-box Cloud UI that provides robust monitoring capabilities, allowing users to see failures, successes, and outages clearly. - Prefect Core: While Prefect Core can be run with a UI, it might not offer the same level of detail and ease-of-use as Prefect Cloud without additional setup and integration work. Logging and Notification: - Prefect Cloud: Provides built-in wrappers for logging and notification, saving users time from having to implement these features themselves. - Prefect Core: Users may need to set up their logging and notification mechanisms, adding a layer of complexity to the workflow development process. Security: - Prefect Cloud: Eliminates many security concerns through the Hybrid Execution Model, which separates workflow execution from orchestration. - Prefect Core: Users must ensure the security of their data and infrastructure as they are hosting Prefect themselves. Feedback Loop: - Prefect Cloud: The monitoring and reporting features in Prefect Cloud can help develop a positive feedback loop between data capture, reporting, and analysis. - Prefect Core: Users must build and maintain their own feedback loop mechanisms to continuously improve their pipelines. Overall User Experience: - Prefect Cloud: Aims to streamline operations, allowing teams to move faster and more efficiently. The managed service aspect means less time spent on maintenance and infrastructure management. - Prefect Core: Gives users complete control but comes with the overhead of configuring and managing the orchestration environment. The selection between Prefect Cloud and Prefect Core depends on the team's size, resources, and specific needs, including ease of use, the desire for control over infrastructure, and the capacity to manage security. For more detailed information on the user experience differences, you can check out: - The Nationals using Prefect - Intro to Workflow Orchestration Reading case studies and user testimonials can also be helpful in understanding how different organizations are impacted by these differences.

Hey @Marvin, is there an easy way to create flows/deployments using declarative syntax like yaml or json in prefect?

Yes, Prefect does support the creation of flows and deployments using a declarative YAML syntax. This is particularly useful for teams working within Kubernetes environments or for those who prefer a declarative approach to infrastructure as code. Here's how Prefect incorporates declarative syntax for flows and deployments: - Kubernetes: Since Kubernetes objects are defined in YAML, Prefect supports creating deployments with YAML, especially when working with Kubernetes work pools. - prefect.yaml: This YAML file is used by the

prefect deploy

command to deploy flows. As part of the deployment process, the command can also build and push Docker images if necessary. - Multiple Deployments: You can define multiple deployment declarations within a single YAML file, each with a unique

name

field. These can be deployed individually by using the

--name

flag with the

prefect deploy

command. For example, a simple

prefect.yaml

might look like this:

yaml
# prefect.yaml

deployment-1:
  name: my-first-deployment
  flow_location: flows/hello.py
  work_queue_name: primary-queue

deployment-2:
  name: my-second-deployment
  flow_location: flows/goodbye.py
  work_queue_name: secondary-queue

You can deploy a specific deployment by running a command like:

shell
prefect deploy --name my-first-deployment

For detailed information on creating flows and deployments with declarative syntax, refer to the Prefect documentation on Kubernetes integration and deployment, where you can find guidelines and examples of YAML configurations for Prefect deployments.

@Marvin How will above work when I have a multi repo setup and flows are deployed to a central server? Users should only be creating the yaml and should already have flows in their github repo that gets pushed via CICD.

In a multi-repo setup where each team has their flows in separate GitHub repositories and all deployments are pushed to a central Prefect server, a CI/CD pipeline can be set up to manage the process efficiently. Here’s how it generally works: 1. CI/CD Pipeline: - Teams integrate their flow repository with a CI/CD tool (e.g., GitHub Actions, Jenkins, GitLab CI/CD). - The CI/CD pipeline watches for changes to the flow definitions or the

prefect.yaml

files and triggers corresponding actions. 2. Flow Registration: - When developers push updates to their flow definitions, the CI/CD pipeline uses Prefect's CLI to register the flow with the central Prefect server. - This could involve pulling the latest code from the repository, packaging it up, building a Docker image if applicable, and using the Prefect CLI or API to register the flow. 3. YAML Deployment Files: - Developers create or update

prefect.yaml

configuration files in their repositories stipulating how and where the flows should be deployed. - The

deployment.yaml

can declare multiple deployments, each with its own configurations, and developers can trigger deployments individually or altogether using the CI/CD pipeline. 4. CI/CD with Prefect Guide: - Precise steps can be learned from Prefect's guidance on integrating with CI/CD processes. This guide provides a general template for using CI/CD to manage Prefect deployments and is adaptable to accommodate the specifics of a multi-repo setup. 5. Triggering Deployments: - The CI/CD pipeline uses Prefect's deployment features (like the

prefect deploy

command) to send these

prefect.yaml

declarations to the Prefect server for deployment. - A common pattern is to trigger this process upon merging a pull request into the

main

branch, ensuring that only tested and reviewed code is deployed. Using the concepts from Prefect’s deployment features with CI/CD provides an efficient method for managing updates to flows, changes in schedules, and container builds. This works well in a multi-repo setup by allowing individual teams to manage their deployments independently while everything converges on the central server for execution. For more detailed instructions on setting up CI/CD with Prefect, refer to the Prefect CI/CD guide and read about the latest features such as deploying many flows at once from the Prefect release notes.

Hey @Marvin can you explain how to run backfills and catchup's in prefect

In Prefect, running backfills and catchup jobs typically involves scheduling your flows to run for past dates. Prefect doesn't have "backfill" or "catchup" operators like Airflow, but you can achieve similar functionality through a few different methods. However, the provided excerpts do not reveal a direct method for running backfills or catchups. Here's how you can handle backfills and catchup logic in Prefect: 1. Schedule Retrospective Runs: - You can manually trigger flow runs for past scheduled runs that were missed or need to be re-executed. - This can be done through the Prefect UI or CLI by specifying the desired start time corresponding to the past execution period. 2. Automated Catchup Logic: - If a flow has missed scheduled runs and you prefer them to run automatically, you can implement custom logic within your flow code to check for missed runs and execute the code corresponding to those time periods. - This might involve checking timestamps, last successful run dates, or leveraging any other mechanism your data pipeline uses to track progress. 3. Parameterized Execution: - You can also use parameters to pass specific execution dates to your flows, allowing you to rerun the flow for any desired time range, effectively manually creating catchup behavior. 4. Adjusting Schedules: - When defining your schedule in Prefect, you may adjust it to include past dates for which you want to run the flow, effectively creating a catchup. Please note that the specific techniques might require some development work to align with your use case and that you will need to be aware of idempotency and data consistency when running flows for past dates. For detailed guidance on how to implement backfills or catchup schedules in Prefect, consult Prefect's official documentation or reach out to the community in their Slack channels for more hands-on support.