@Marvin I'm on prefect 2. My database is exploding with data despite only performing around 35,000 flow runs but there are over 1 billion records for state. Why is this?

Your observation regarding the excessive number of state records in your database despite a relatively smaller number of flow runs is unusual and warrants further investigation. Here are a few potential reasons and considerations: 1. State Transitions: Each task and flow run in Prefect may go through multiple state transitions (e.g., Pending, Running, Success, Failed) during its lifecycle. If your tasks or flows are transitioning states excessively (e.g., due to retries or errors), this can lead to a large number of records in the state table. 2. Retries and Failures: If your flows are frequently retrying tasks or encountering failures, this would increase state transition counts, contributing to more records in the state database. 3. State Storage Configuration: Check how states are being logged and persisted. There might be verbose logging or persistence settings, either in Prefect's configuration or due to custom logging within tasks, that are contributing to the large number of state records. 4. Prefect Version-specific Behavior: Some versions of Prefect may have specific behaviors or bugs related to state management, as indicated by past user experiences. Unfortunately, there appear to be no specific GitHub issues directly relating to your situation. I recommend reviewing your flow and task configurations related to retries, and examining the Prefect documentation on result persistence to optimize how and when states are logged. If your issue persists, consider reaching out to the Prefect community or support channels for more specific guidance tailored to your setup.

@Marvin Can you tell me more about State Storage Configuration

In Prefect 2.x, state storage configuration involves managing how Prefect's logs and state transitions are recorded and stored. Here's a summary of relevant concepts: 1. Storage Concepts: - Prefect uses storage blocks to manage how flow code and logs are handled. This can include local storage or cloud-based storage solutions depending on deployment needs. Though primarily for flow code, understanding storage is crucial as it impacts how logs and states are handled and retrieved by workers. 2. Logging Configuration: - Prefect allows you to configure logging levels and choose how much detail you store in your logs. You can adjust this through environment variables, such as setting

PREFECT_LOGGING_LEVEL

for different verbosity levels (e.g., DEBUG, INFO, WARNING). Proper logging configuration can prevent excessive storage of unnecessary state transition logs. 3. Flow Storage: - If you don’t explicitly configure specific storage for your flows, Prefect defaults to a local filesystem configuration for convenience. However, depending on your infrastructure, you might want to configure remote or cloud storage to handle state and log data more efficiently. 4. State Management Best Practices: - Ensure your tasks and flows are optimized to minimize unnecessary state transitions, as each transition can be logged and stored. - Consider if all state information is necessary for your operations, or if some can be dropped without losing critical tracking information. For more detailed information and configuration options, you can explore Prefect's storage concepts documentation: Prefect Storage Documentation.

@Marvin Are all state transitions logged to the database regardless of specifying the persistent storage options in the @flow decorator

The management of state transitions and their logging in Prefect primarily hinges on the orchestration rules set for tasks and flows. Here's a summary based on current documentation: 1. State Transition Logging: Prefect by default logs state transitions to the database. This logging is essential for tracking the workflow execution and determining retry mechanisms, retries, or any orchestration rules applied during task and flow transitions. 2. Persistent Storage: The

@flow

decorator and any persistent storage options specified there relate to where results, artifacts, or certain flow-specific data are stored. This does not necessarily control how state transitions themselves are logged; the transitions are managed by Prefect's internal orchestration mechanism. 3. Impact of Persistent Storage on State Logging: While specifying persistent storage options influences how results from tasks are stored (e.g., in memory, local, or in specific storage blocks), it doesn't change the behavior of state transitions logging. The state transitions remain logged to Prefect's core database for tracking the lifecycle of each task and flow run. If your database is filling up with state transitions, it's crucial to explore optimization strategies such as managing how often states transition due to retries, orchestrating states more efficiently, or considering Prefect's guidelines on state orchestration types. More information about Prefect's orchestration and storage can be found at: - Prefect States Documentation

@Marvin why does prefect add a new record for state transition instead of updating the original record?

Prefect creates a new record for each state transition rather than updating the original record to maintain a complete history and ensure accurate orchestration. Here are the key reasons for this approach: 1. Historical Tracking: By recording each state transition as a new record, Prefect allows you to audit and review the exact lifecycle of each task or flow run. This historical data is crucial for debugging, analytics, and optimizing workflow efficiencies. 2. Rich State Objects: Prefect states encapsulate detailed information about the status of task or flow runs, such as transition reasons, retry attempts, and output data. Storing each of these transitions supports complex workflows by preserving all contextual information for each state change. 3. Orchestration Logic: Prefect relies on state transitions to manage the orchestration rules for flows and tasks. Each state carries different semantics and potential orchestration paths, which are crucial for determining what should happen next in the workflow. 4. Terminal States: Some states are terminal (e.g., COMPLETED, FAILED), while others are not. Recording transitions helps define when a task or flow is done, or if further action is required. This design facilitates sophisticated task orchestration and lifecycle management, enabling Prefect users to exploit the full potential of complex workflows. For more detailed insights, you can explore Prefect's documentation on states: Prefect States Documentation.

@Marvin We examined a single flow that had over 600,000 state records, with nearly all of them being the cancelling state for a month

The issue of having a large number of "cancelling" state records, particularly when they persist over an extended time, can be due to several potential pitfalls in the cancellation process: 1. Infrastructure Issues: If the infrastructure block for the flow run (e.g., Docker containers, Kubernetes jobs) has been removed, altered, or doesn't support cancellation, it might not correctly transition from the cancelling state to cancelled, leading to repeated attempts to cancel. 2. Identifier Scope Mismatch: If the scope or identifier used when attempting to cancel doesn't match the running infrastructure, the cancelling state may persist indefinitely. This could cause multiple, redundant attempts to change the state, resulting in numerous records. 3. Missing or Invalid Infrastructure PID: If the

infrastructure_pid

is missing or invalid, Prefect may log the effort to cancel but cannot actually enforce it, causing repeated attempts. 4. Unexpected Errors: If an unexpected error occurs during cancellation (e.g., network issues, permission errors), the cancellation may only partially complete, leading to multiple attempts that increase record counts. These issues suggest that the infrastructure or cancellation process is repeatedly encountering failure points, thus accumulating cancelling state records without resolution. To address this: - Verify Infrastructure Setup: Check that infrastructure components support and are correctly configured for cancellation. - Ensure Proper Identifiers: Validate that correct identifiers are used for the cancellation process. - Examine Error Logs: Investigate Prefect logs for any recurrent errors or failed attempts during cancellation processes. Enhanced cancellation support and analysis might be necessary to identify and resolve deeper issues in the workflow configuration or infrastructure environment.