Update hello-wal.py

Author: suyashcjoshi

hello-wal.py

@@ -1 +1,57 @@
+import datetime
 
+"""
+Entry point for WAL flush triggers that does basic data processing and it to a new table.
+Arguments: influxdb3_local (API object), table_batches (data written), args (optional trigger arguments).
+"""
+def process_writes(influxdb3_local, table_batches, args=None):
+    # Log that the plugin was triggered
+    influxdb3_local.info("Processing data with enhanced WAL plugin!")
+    
+    # Process each table's data
+    for table_batch in table_batches:
+        table_name = table_batch["table_name"]
+        rows = table_batch["rows"]
+        
+        # Skip processing our own output table to avoid recursion
+        if table_name == "data_insights":
+            continue
+            
+        # Log information about the data
+        influxdb3_local.info(f"Processing {len(rows)} rows from table {table_name}")
+        
+        # Calculate some basic statistics if we have numeric fields
+        total_values = 0
+        max_value = float('-inf')
+        min_value = float('inf')
+        has_numeric = False
+        
+        for row in rows:
+            # Look for numeric fields we can analyze
+            for field_name, value in row.items():
+                if isinstance(value, (int, float)) and field_name != "time":
+                    has_numeric = True
+                    total_values += value
+                    max_value = max(max_value, value)
+                    min_value = min(min_value, value)
+        
+        # Write insights to a dedicated table
+        line = LineBuilder("data_insights")
+        line.tag("source_table", table_name)
+        line.int64_field("row_count", len(rows))
+        
+        # Add statistics if we found numeric values
+        if has_numeric:
+            line.float64_field("max_value", max_value)
+            line.float64_field("min_value", min_value)
+            if len(rows) > 0:
+                line.float64_field("avg_value", total_values / len(rows))
+        
+        # Add a timestamp field showing when this processing occurred
+        line.string_field("processed_at", datetime.datetime.utcnow().isoformat())
+        
+        # Write the insights back to the database
+        influxdb3_local.write(line)
+        
+        # Log completion
+        influxdb3_local.info(f"Generated insights for {table_name}")