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meelahme 2025-05-05 14:16:43 -07:00
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@ -275,7 +275,7 @@ To get the most out of the in-memory cache, follow these guidelines:
##### Use the trigger-specific namespace
The cache is designed to support stateful operations while maintaining isolation between different triggers. Use the trigger-specific namespace for most operations and the global namespace only when data sharing across triggers is necessary.
The Processing engine provides a cache that supports stateful operations while maintaining isolation between different triggers. Use the trigger-specific namespace for most operations and the global namespace only when data sharing across triggers is necessary.
##### Use TTL appropriately
@ -288,7 +288,7 @@ influxdb3_local.cache.put("weather_data", api_response, ttl=300)
##### Cache computation results
Store the results of expensive calculations that need to be utilized frequently:
Store the results of expensive calculations that you frequently utilize:
```python
# Cache aggregated statistics
@ -307,12 +307,11 @@ if not influxdb3_local.cache.get("lookup_table"):
##### Consider cache limitations
- **Memory Usage**: Since cache contents are stored in memory, monitor your memory usage when caching large datasets.
- **Server Restarts**: Because the cache is cleared when the server restarts, design your plugins to handle cache initialization (as noted above).
- **Memory Usage**: Since the system stores cache contents in memory, monitor your memory usage when caching large datasets.
- **Server Restarts**: Because the server clears the cache on restart, design your plugins to handle cache initialization (as noted above).
- **Concurrency**: Be cautious of accessing inaccurate or out-of-date data when multiple trigger instances might simultaneously update the same cache key.
### Next Steps
With an understanding of the InfluxDB 3 Shared Plugin API, you're ready to build data processing workflows that can transform, ana
lyze, and respond to your time series data.
With an understanding of the InfluxDB 3 Shared Plugin API, you're ready to build data processing workflows that can transform, analyze, and respond to your time series data.
To find example plugins you can extend, visit the [plugin repo](https://github.com/influxdata/influxdb3_plugins) on GitHub.