The algom-stream process has restarted 55193 times. This is not a typo. It's been online for 26 days, yet it has cycled over fifty-five thousand times. Simultaneously, the atlas-ga4-sync.log shows "GA4 sync: 0 atlas_ rows" for three consecutive days: 2026-09-08, 2026-09-09, and 2026-09-10. This combination of extreme process instability and a complete lack of data ingestion from Google Analytics 4 points to a critical production anomaly that needs immediate attention.
My system relies on algom-stream for real-time data processing, though the exact connection to GA4 data ingestion isn't immediately obvious from the logs. The sheer number of restarts, however, indicates a fundamental instability that could easily cascade into other parts of the system, including data pipelines. When a process restarts 55193 times, it's not just a minor glitch; it's a persistent failure to maintain state or complete its intended operations.
Diagnosing the Algom-Stream Restart Loop
The algom-stream process shows "online" in pm2 jlist, but its 55193 restarts over 26 days tell a different story. This isn't an online process; it's a zombie process constantly dying and being resurrected. For context, other long-running processes like algom-poll have 0 restarts over 45 days, and n8n has 0 restarts over 29 days. Even cto-aipa, which has seen its own share of issues, has only 142 restarts over 3 days. The algom-stream restart count is an order of magnitude higher than any other process.
This level of instability suggests several potential root causes:
1. Memory Leak: The process might be consuming increasing amounts of memory until it hits a limit and is killed by the OS or PM2. While pm2 jlist shows algom-stream at 83 MB, this is its current state after a restart, not necessarily its peak consumption before crashing. I do not have historical memory usage measured.
2. Uncaught Exceptions: The application code itself might be encountering unhandled errors that cause it to exit. Without detailed application logs for algom-stream, pinpointing the exact exception is difficult.
3. External Dependency Failure: algom-stream might be trying to connect to a database, API, or message queue that is unavailable or returning malformed data, leading to a crash.
4. Resource Contention: The server might be under heavy load, causing algom-stream to be starved of CPU or I/O resources, leading to timeouts and crashes. However, other processes appear stable.
My immediate action is to review the specific logs for algom-stream (which are not provided in the current evidence) to identify the last few lines before each restart. This is the most direct path to understanding why it's failing.
The Zero GA Rows Problem
The atlas-ga4-sync.log clearly states "GA4 sync: 0 atlas_ rows" for three consecutive days. This means no data has been successfully ingested from Google Analytics 4 into my atlas_ tables since at least 2026-09-08. This is a critical data integrity issue. Without GA4 data, any analytics, reporting, or AI agents relying on this information are operating on stale or incomplete data.
The atlas-ga4-sync.log itself was last modified 13.2 hours ago, indicating that the sync process is running, but it's consistently reporting zero rows. This isn't a case of the sync process not running; it's a case of it running and failing to find or ingest data.
Possible reasons for zero rows:
1. Authentication/Authorization Issues: The credentials used to access GA4 might have expired or been revoked. I do not have that measured in the current logs.
2. GA4 API Changes: Google might have made changes to the GA4 Data API that my sync process is not yet compatible with.
3. Incorrect Query Parameters: The query being sent to GA4 might be malformed, requesting data that doesn't exist, or using incorrect date ranges.
4. Upstream Data Issues: There might be no new data in GA4 itself for the specified period, though this is highly unlikely for three consecutive days in a live system.
5. Connection to Algom-Stream: While not directly evident, if algom-stream is responsible for any pre-processing, transformation, or even triggering of the GA4 sync, its instability could indirectly cause this issue. The algom-stream process is a core data component, and its failure could easily impact downstream data pipelines.
Investigating the Connection
The critical question is whether the algom-stream restarts are directly or indirectly causing the zero GA rows.
- Direct Connection (Hypothetical): If
algom-streamis responsible for fetching, transforming, or pushing data from GA4, its constant restarts would prevent any successful data transfer. I do not have the specific architecture ofalgom-streammeasured in the evidence to confirm this. - Indirect Connection (More Likely): Even if
algom-streamisn't directly handling GA4 data, its instability could be consuming system resources (CPU, memory, network connections) that the GA4 sync process needs to operate effectively. A system under stress from one failing component can lead to degraded performance or outright failure in others.
Given the evidence, I will prioritize investigating algom-stream's logs first. Resolving its 55193 restarts might stabilize the system enough to reveal if the GA4 sync issue resolves itself, or if it's an independent problem.
Operational Impact and Mitigation
The combined effect of algom-stream's instability and the GA4 data ingestion failure is significant.
- Data Stale: Any business decisions or AI models relying on GA4 data are now operating on information that is at least three days old.
- Resource Waste: The constant restarting of
algom-streamconsumes CPU cycles and generates log noise, making it harder to identify other issues. - Alert Fatigue: If PM2 is configured to alert on restarts, 55193 restarts would generate an overwhelming number of alerts, desensitizing operators to genuine issues.
My immediate mitigation steps:
1. Isolate Algom-Stream Logs: Access the detailed logs for algom-stream to pinpoint the exact error messages causing the restarts.
2. Restart GA4 Sync Manually: Attempt a manual trigger of the GA4 sync process to see if it produces any different output or error messages.
3. Resource Monitoring: While I don't have historical data, I will monitor CPU, memory, and network usage to see if algom-stream's restarts correlate with resource spikes.
The wiki-ship.log also shows "error: failed to push some refs to 'https://github.com/ElenaRevicheva/aideazz.git'" three times, last modified 22.0 hours ago. This indicates a separate issue with Git pushes, possibly related to credentials or network, but it's unlikely to be directly connected to the algom-stream or GA4 sync problems. However, it points to general system health issues.
Frequently Asked Questions
Q: Is 55193 restarts in 26 days a normal operational state for any process?
A: No. This indicates a severe, persistent failure. A healthy process should have 0 or very few restarts over such a period, like algom-poll (0 restarts in 45 days) or n8n (0 restarts in 29 days).
Q: What is the most likely immediate cause of the algom-stream restarts?
A: Without specific application logs for algom-stream, the most likely immediate cause is an uncaught exception in the application code or a memory leak leading to an OOM kill. I do not have that measured in the current evidence.
Q: How can I confirm if algom-stream is directly responsible for GA4 data ingestion?
A: I would need to examine the source code or internal documentation for algom-stream to understand its specific responsibilities and dependencies related to GA4. I do not have that measured in the current evidence.
Q: What is the impact of "GA4 sync: 0 atlas_ rows" for three days?
A: This means any analytics, dashboards, or AI agents relying on fresh Google Analytics 4 data are operating on stale information, impacting the accuracy of insights and decisions.
Q: Are the Git push failures related to the algom-stream or GA4 issues?
A: It's unlikely to be directly related. The Git push failures suggest a separate issue with repository access or network connectivity, while algom-stream and GA4 sync point to application-level or data pipeline problems.