Update README.md

Author: idatsy

README.md

@@ -1,15 +1,17 @@
 # QuestDB Python Library
 
-A robust, strongly-typed Python library for interacting with QuestDB, providing an easy-to-use interface for querying, writing data, and managing logs.
+A robust, strongly-typed Python library for interacting with QuestDB, providing an easy-to-use interface for querying and writing data, with additional features such as easy integration with the built in python logging library automatically saving all logs, including tracebacks and stack traces to DB. Easy set-up with docker-compose and a grafana dashboard is included.
 
-## Features
+## Key Features
 
 - Asynchronous and synchronous query support
-- Strongly-typed data structures
+- Strongly-typed data structures using `msgspec`
 - Easy data insertion with automatic flushing
 - Pandas DataFrame integration
-- Built-in logging handler for QuestDB
+- Seamless logging integration with Python's built-in logging system
+- Docker Compose setup for QuestDB with Prometheus and Grafana monitoring
 - Comprehensive test coverage
+- Optional error handling callback
 
 ## Installation
 
@@ -23,6 +25,7 @@ pip install py-questdb
 
 ```python
 from py_questdb import QuestDB
+from datetime import datetime
 
 # Initialize QuestDB client
 db = QuestDB(host="localhost", port=9000)
@@ -62,11 +65,43 @@ db = QuestDB()
 db = QuestDB(host="questdb.example.com", port=9000, username="user", password="pass")
 ```
 
+### Querying Data
+
+```python
+# Synchronous query
+for row in db.query_sync("SELECT * FROM sensors WHERE location = 'NYC'"):
+    print(f"Temperature: {row['temperature']}, Humidity: {row['humidity']}")
+
+# Asynchronous query
+async for row in db.query("SELECT * FROM sensors WHERE location = 'LA'"):
+    print(f"Temperature: {row['temperature']}, Humidity: {row['humidity']}")
+
+# Query into Pandas DataFrame
+df = db.query_df_sync("SELECT * FROM sensors")
+print(df.describe())
+
+# Query with type hinting
+class SensorReading(msgspec.Struct):
+    location: str
+    temperature: float
+    humidity: int
+
+async for reading in db.query("SELECT * FROM sensors", into_type=SensorReading):
+    print(f"Location: {reading.location}, Temp: {reading.temperature}°C, Humidity: {reading.humidity}%")
+
+# Query with error handling
+def error_handler(response: bytes):
+    print(f"Error in query: {response.decode()}")
+
+results = db.query_sync("SELECT * FROM non_existent_table", error_handler=error_handler)
+```
+
 ### Writing Data
 
 ```python
 from datetime import datetime
 from py_questdb.db_types import QuestDBFields
+import msgspec
 
 # Single write with auto-flush
 db.write({
@@ -106,51 +141,60 @@ sensor_data = SensorData(location="SF", temperature=22.5, humidity=65, timestamp
 db.write(sensor_data)
 ```
 
-### Querying Data
-
-```python
-# Synchronous query
-for row in db.query_sync("SELECT * FROM sensors WHERE location = 'NYC'"):
-    print(f"Temperature: {row['temperature']}, Humidity: {row['humidity']}")
-
-# Asynchronous query
-async for row in db.query("SELECT * FROM sensors WHERE location = 'LA'"):
-    print(f"Temperature: {row['temperature']}, Humidity: {row['humidity']}")
+### Logging Integration
 
-# Query into Pandas DataFrame
-df = db.query_df_sync("SELECT * FROM sensors")
-print(df.describe())
-
-# Query with type hinting
-class SensorReading(msgspec.Struct):
-    location: str
-    temperature: float
-    humidity: int
-
-async for reading in db.query("SELECT * FROM sensors", into_type=SensorReading):
-    print(f"Location: {reading.location}, Temp: {reading.temperature}°C, Humidity: {reading.humidity}%")
-```
-
-### Logging
+The library provides seamless integration with Python's built-in logging system, allowing you to easily write logs directly to QuestDB:
 
 ```python
 import logging
 from py_questdb import QuestDB
 from py_questdb.log_handler import QuestDBLogHandler
 
-# Set up QuestDB logging
+# Initialize QuestDB client
 questdb_client = QuestDB()
+
+# Create QuestDB log handler
 handler = QuestDBLogHandler(questdb_client=questdb_client)
+
+# Get logger and add handler
 logger = logging.getLogger()
 logger.addHandler(handler)
 logger.setLevel(logging.INFO)
 
-# Log messages
+# Now all your logs will be written to QuestDB
 logger.info("System started")
 logger.warning("Low disk space")
 logger.error("Connection failed", exc_info=True)
+
+# You can log exceptions with full stack traces
+try:
+    1 / 0
+except Exception as e:
+    logger.exception("An error occurred")
+
+# The logs are written to QuestDB in real-time, allowing for immediate analysis and monitoring
+```
+
+This integration allows you to leverage QuestDB's powerful querying capabilities for log analysis, making it easy to search, filter, and analyze your application logs.
+
+## Docker Compose Setup
+
+The project includes a Docker Compose configuration to run QuestDB along with Prometheus for metrics collection and Grafana for visualization. To use this setup:
+
+1. Navigate to the `/etc` directory in the project.
+2. Run the following command:
+
+```bash
+docker-compose up -d
 ```
 
+This will start QuestDB, Prometheus, and Grafana containers. You can access:
+- QuestDB Web Console: http://localhost:9000
+- Prometheus: http://localhost:9090
+- Grafana: http://localhost:3000 (default credentials: admin/admin)
+
+> A pre-configured Grafana dashboard for QuestDB metrics is included in the `questdb_dashboard.json` file, allowing for immediate visualization of your QuestDB instance's performance.
+
 ## Testing
 
 Run the test suite using pytest:
@@ -165,4 +209,4 @@ Contributions are welcome! Please feel free to submit a Pull Request.
 
 ## License
 
-This project is licensed under the [MIT License](LICENSE).
+This project is licensed under the [MIT License](LICENSE).