Fix: Recent Execution History Not Showing

by Editorial Team 42 views
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Hey everyone, let's dive into a frustrating issue: recent execution history not showing up. We're talking about a gap between January 14th and January 18th. We need to figure out why recent observation executions aren't appearing in the history. This is super important because it impacts our ability to track and analyze what's going on. Let's get down to business and ensure that the recent executions are visible. We'll follow a systematic approach to pinpoint the cause and implement a fix. This document outlines the investigation, troubleshooting, and resolution steps for this issue. Our goal is to ensure that the execution history accurately reflects all recent runs, allowing for reliable monitoring and analysis. This issue has the potential to impact the accuracy of our reports, analytics, and overall system visibility. So, let's get started!

Understanding the Problem: Missing Executions

Our initial observation is that the execution history seems to have a gap, with the most recent visible execution dated January 14, 2026. However, we know that many executions have run since then. This discrepancy raises concerns about data integrity and the accuracy of our monitoring tools. The primary goal is to identify why recent execution data is missing and implement a solution to ensure that all executions are properly recorded and displayed in the history.

  • Key Issue: Missing execution history between January 14th and 18th.
  • Impact: Affects data integrity and monitoring accuracy.
  • Goal: Restore visibility of all recent executions.

We need to investigate the issue thoroughly to uncover the root cause and ensure it's resolved. This includes analyzing data flows, examining API endpoints, reviewing code, and verifying timestamp parsing. Let's work on getting those executions back on display!

Data Flow Investigation

First, we'll start by tracing the data flow. We'll check if the recent responses are even being written to BigQuery, which is our primary data store. If the data is not present in BigQuery, it indicates a problem with the data ingestion process. The data flow starts with the observation runner (scheduler), which triggers the execution. The results of the execution are then saved, usually into BigQuery. It can be caused by various issues, such as code errors or service failures. To start, we need to inspect the data in BigQuery.

  • BigQuery Check: Verify if recent responses are being written.
    • SELECT collected_at, prompt FROM responses ORDER BY collected_at DESC LIMIT 20 to query the 20 most recent responses and check the timestamps.
    • Make sure observation_id is correctly set. This helps associate responses with their respective observations.

If the data exists in BigQuery, but not showing, the problem might be elsewhere, such as with API endpoints, query filters, or frontend display issues. If the data is missing from BigQuery, then the problem is either with the observation runner or with the BigQuery insertion process.

API Endpoint Review

If the data does exist in BigQuery but isn't showing up in the execution history, we'll need to review the /api/observations/:id/responses endpoint. It's the API endpoint responsible for fetching the responses and displaying them. Specifically, we'll look for any filters that might be excluding the recent executions. The query filters might include date ranges or observation_id matching. Also, we'll check the front end code for any issues with timestamp parsing. If the frontend is misinterpreting the timestamps, the executions may not show up correctly.

  • Review /api/observations/:id/responses: Check query filters and timestamp parsing.
    • Verify the date range in the API query.
    • Ensure the observation_id is correct.
    • Check for any errors during timestamp conversion.

We need to make sure that the API endpoint is correctly fetching and displaying the recent execution history. We will check the queries and the data retrieval to pinpoint the issue.

Observation Runner Code

If the data is not in BigQuery, then the problem lies in the observation runner code, which is responsible for saving the responses. This could be due to insertion errors or silent failures. We'll start by reviewing the observation runner code to understand how it saves the responses. We will check the code to see where the data is being saved, and then check whether the BigQuery insertion is successful.

  • Review observation runner code: Focus on the saving process.
    • Check for silent failures in BigQuery insertion.
    • Add logging to track data loss.

We will add logs to pinpoint exactly where the data is lost. We need to implement proper logging to identify any errors that might be occurring during the data insertion process. The logging will include the data being saved, the timestamp, and any error messages. This will help us pinpoint exactly where the data is lost.

Implementing the Fix

After we've identified the root cause, it's time to implement a fix. This might involve updating the observation runner code, adjusting the API queries, or correcting the frontend display logic. Our primary goal is to make sure all the executions are visible again.

Steps for Implementation

  1. Implement Fix: Apply the necessary code changes based on our findings.
  2. Test Manually: Run a test observation via the UI and verify results.
  3. Verify Execution: Check if the new execution shows up immediately in the history.
  4. Confirm Timestamp: Ensure the timestamp is accurate.
  5. Commit Changes: If the fix works, commit changes with a descriptive message.
  6. Create PR: Push and create a pull request.

We'll go through these steps in order to ensure that the fix is applied correctly and that the execution history is working as expected. If the fix is not working, we will continue our investigation.

Key Files and Tools

Here are some of the key files that we'll be looking into:

  • src/routes/observations.ts: API endpoints for responses.
  • src/services/bigquery.ts: BigQuery insert/query logic.
  • src/services/scheduler.ts or observation runner: Where responses are saved.
  • frontend/src/App.tsx: The place in the frontend that fetches the data.

Make sure to use diagnostic queries like these to help:

  • D1: SELECT id, last_run_at FROM observations ORDER BY last_run_at DESC LIMIT 10
  • BigQuery: SELECT observation_id, collected_at FROM responses WHERE collected_at > '2026-01-14' LIMIT 50

Analyzing the Data and Explaining the Gap

Once we have our fix in place and verified, we need to analyze what went wrong, and document what happened. This is super important so that we can prevent the same issue from happening again. We'll need to figure out why the data wasn't showing up between January 14th and 18th. We should document the root cause, and if there was data loss, we need to explain it in detail. If the data was lost, it's important to understand the cause and the extent of the loss. If we can recover the data, we will attempt to do so.

Documentation and Recovery

  • Document findings: Explain the root cause of the issue.
  • Assess data loss: Determine if any data was lost and why.
  • Attempt recovery: Try to recover any missing data.

If we can recover the data, we will take the steps necessary to do so. If we can't recover the data, we will explain why, so that we can improve our systems. We should focus on preventing the issue from happening again. This will ensure that our system is reliable and the data is accurate.

Acceptance Criteria and Outcome

Our fix will be considered successful when:

  • New executions show up in the history immediately.
  • The historical gap is clearly explained (whether it was data loss or a display issue).
  • If data was lost, we document the details.
  • If recoverable, we've recovered the missing data.

Once these criteria are met, we'll output <done>HISTORY_FIXED</done>.

We'll push the changes, and create a PR. This whole process will help us improve our execution history and make it more reliable and accurate. Let's make sure that our data is always up-to-date and that we can easily track our execution history. Our goal is to ensure that the execution history accurately reflects all recent runs, allowing for reliable monitoring and analysis. This issue has the potential to impact the accuracy of our reports, analytics, and overall system visibility. So, let's get started!