Migration to UV, introducing db_write, mqtt_ingestion, db_migrations, half working prototype.

This commit is contained in:
Andrej Mickov
2025-10-28 23:14:58 +01:00
parent 5028dae200
commit 0b96c72f45
47 changed files with 2641 additions and 81 deletions

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.gitignore vendored
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/iotDashboard/demo1.db
**/__pycache__/
iotDashboard/db_create.py
.venv/

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.python-version Normal file
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3.13

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CONNECTION_STRING=postgresql://user:password@localhost:5432/iotdashboard

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__pycache__/
*.py[cod]
*.so
.Python
*.egg-info/

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db_migrations/README.md Normal file
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# Database Migrations
This directory contains all database schema definitions and migrations for the IoT Dashboard project.
## Quick Start
### 1. Install Dependencies
```bash
pip install alembic sqlalchemy python-dotenv psycopg2-binary
```
### 2. Configure Database
Set `CONNECTION_STRING` or `DATABASE_URL` in the root `.env` file:
```bash
CONNECTION_STRING=postgresql://user:password@localhost:5432/iotdashboard
```
### 3. Create Initial Migration
```bash
chmod +x migrate.sh
./migrate.sh create "initial schema"
```
### 4. Review Migration
Check the generated file in `alembic/versions/`
### 5. Apply Migration
```bash
./migrate.sh upgrade
```
## Usage
### Create a New Migration
After editing `models.py`:
```bash
./migrate.sh create "add new column"
```
### Apply Migrations
```bash
./migrate.sh upgrade
```
### Check Current Version
```bash
./migrate.sh current
```
### View History
```bash
./migrate.sh history
```
### Rollback
```bash
./migrate.sh downgrade 1
```
## File Structure
```
db_migrations/
├── models.py # SQLAlchemy models (schema definition)
├── alembic.ini # Alembic configuration
├── alembic/
│ ├── env.py # Migration environment
│ ├── script.py.mako # Migration template
│ └── versions/ # Generated migrations
├── migrate.sh # Helper script
└── README.md # This file
```
## Modifying Schema
1. Edit `models.py` to define your changes
2. Run `./migrate.sh create "description"`
3. Review the generated migration
4. Run `./migrate.sh upgrade`
## Using Models in Services
Services can import models from here:
```python
# In services/db_write/db_writer.py or any other service
import sys
sys.path.insert(0, '/path/to/db_migrations')
from models import SensorReading
```
Or better, use relative imports if properly structured.
## Notes
- The `.env` file should be in the project root (`../.env`)
- Migrations are applied to whatever database is in `CONNECTION_STRING`
- Always review generated migrations before applying
- Keep `models.py` as the single source of truth for schema

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db_migrations/alembic.ini Normal file
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# Alembic Configuration
[alembic]
script_location = alembic
file_template = %%(year)d%%(month).2d%%(day).2d_%%(hour).2d%%(minute).2d_%%(rev)s_%%(slug)s
timezone = UTC
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARN
handlers = console
qualname =
[logger_sqlalchemy]
level = WARN
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S

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from logging.config import fileConfig
from sqlalchemy import engine_from_config, pool
from alembic import context
import os
import sys
# Add parent directory to path so we can import models
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
# Load environment variables
from dotenv import load_dotenv
load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
# Import your models
from models import Base
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# Set database URL from environment
database_url = os.getenv('CONNECTION_STRING') or os.getenv('DATABASE_URL')
if database_url:
config.set_main_option('sqlalchemy.url', database_url)
# Interpret the config file for Python logging.
# This line sets up loggers basically.
if config.config_file_name is not None:
fileConfig(config.config_file_name)
# add your model's MetaData object here
# for 'autogenerate' support
target_metadata = Base.metadata
# other values from the config, defined by the needs of env.py,
# can be acquired:
# my_important_option = config.get_main_option("my_important_option")
# ... etc.
def run_migrations_offline() -> None:
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(
url=url,
target_metadata=target_metadata,
literal_binds=True,
dialect_opts={"paramstyle": "named"},
)
with context.begin_transaction():
context.run_migrations()
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
with connectable.connect() as connection:
context.configure(
connection=connection, target_metadata=target_metadata
)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
run_migrations_offline()
else:
run_migrations_online()

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"""${message}
Revision ID: ${up_revision}
Revises: ${down_revision | comma,n}
Create Date: ${create_date}
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
${imports if imports else ""}
# revision identifiers, used by Alembic.
revision: str = ${repr(up_revision)}
down_revision: Union[str, Sequence[str], None] = ${repr(down_revision)}
branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
def upgrade() -> None:
"""Upgrade schema."""
${upgrades if upgrades else "pass"}
def downgrade() -> None:
"""Downgrade schema."""
${downgrades if downgrades else "pass"}

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"""Initial
Revision ID: dae12d7f4ddf
Revises:
Create Date: 2025-10-28 22:06:45.637836+00:00
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = 'dae12d7f4ddf'
down_revision: Union[str, Sequence[str], None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Upgrade schema."""
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('devices',
sa.Column('id', sa.Text(), nullable=False),
sa.Column('name', sa.Text(), nullable=False),
sa.Column('location', sa.Text(), nullable=True),
sa.Column('is_active', sa.Boolean(), nullable=True),
sa.PrimaryKeyConstraint('id')
)
op.create_table('telemetry',
sa.Column('time', sa.DateTime(timezone=True), nullable=False),
sa.Column('device_id', sa.Text(), nullable=False),
sa.Column('metric', sa.Text(), nullable=False),
sa.Column('value', sa.Float(), nullable=False),
sa.Column('unit', sa.Text(), nullable=True),
sa.ForeignKeyConstraint(['device_id'], ['devices.id'], ),
sa.PrimaryKeyConstraint('time', 'device_id', 'metric')
)
op.create_index('idx_telemetry_device_time', 'telemetry', ['device_id', 'time'], unique=False)
# ### end Alembic commands ###
def downgrade() -> None:
"""Downgrade schema."""
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index('idx_telemetry_device_time', table_name='telemetry')
op.drop_table('telemetry')
op.drop_table('devices')
# ### end Alembic commands ###

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"""
Database models for the IoT Dashboard.
To modify schema:
1. Edit models here
2. Run: alembic revision --autogenerate -m "description"
3. Review the generated migration in alembic/versions/
4. Run: alembic upgrade head
"""
from sqlalchemy import Boolean, Column, Float, ForeignKey, Index,Text, DateTime
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class Device(Base):
"""IoT devices registered in the system."""
__tablename__ = 'devices'
id = Column(Text, primary_key=True)
name = Column(Text, nullable=False)
location = Column(Text)
is_active = Column(Boolean, default=True)
def __repr__(self):
return f"<Device(id={self.id}, name={self.name})>"
class Telemetry(Base):
"""
Time-series telemetry data from devices.
This will be converted to a TimescaleDB hypertable.
"""
__tablename__ = 'telemetry'
time = Column(DateTime(timezone=True), primary_key=True, nullable=False)
device_id = Column(Text, ForeignKey('devices.id'), primary_key=True, nullable=False)
metric = Column(Text, primary_key=True, nullable=False) # e.g., 'light', 'temperature'
value = Column(Float, nullable=False)
unit = Column(Text)
__table_args__ = (
Index('idx_telemetry_device_time', 'device_id', 'time'),
)
def __repr__(self):
return f"<Telemetry(device={self.device_id}, metric={self.metric}, value={self.value})>"

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# Introduction
## Overview
This is a bachelor's thesis project combining IoT, AI and much more.
It presents a smart IoT system designed to ingset, store and visualize telemetry data from distributed devices.
The project leverages microservices for modularity and good scalability, Redis as a message queue / buffer, and TimescaleDB
for fast and efficient time-series data storage.

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services:
redis:
image: redis:8
ports:
- "6379:6379"
volumes:
- redis-data:/data
restart: unless-stopped
mqtt:
image: eclipse-mosquitto:2.0
ports:
- "1883:1883"
- "9001:9001"
volumes:
- ./mosquitto/mosquitto.conf:/mosquitto/config/mosquitto.conf
restart: unless-stopped
volumes:
redis-data:

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services:
redis:
image: redis:8
ports:
- "6379:6379"
volumes:
- redis-data:/data
restart: unless-stopped
mqtt:
image: eclipse-mosquitto:2.0
ports:
- "1883:1883"
- "9001:9001"
volumes:
- ./mosquitto/mosquitto.conf:/mosquitto/config/mosquitto.conf
restart: unless-stopped
timescaledb:
image: timescale/timescaledb:latest-pg17
environment:
POSTGRES_USER: ${POSTGRES_USER:-postgres}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-example}
POSTGRES_DB: ${POSTGRES_DB:-iot_data}
ports:
- "5432:5432"
volumes:
- timescaledb-data:/var/lib/postgresql/data
restart: unless-stopped
volumes:
redis-data:
timescaledb-data:

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allow_anonymous true
listener 1883 0.0.0.0

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# Generated by Django 5.1.7 on 2025-03-27 10:47
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Device',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('ip', models.CharField(max_length=20)),
('protocol', models.CharField(choices=[('mqtt', 'MQTT'), ('http', 'HTTP')], max_length=20)),
],
),
migrations.CreateModel(
name='SensorType',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50, unique=True)),
('unit', models.CharField(max_length=20)),
('protocol', models.CharField(choices=[('mqtt', 'MQTT'), ('http', 'HTTP')], max_length=20)),
('topic', models.CharField(blank=True, max_length=100, null=True)),
('endpoint', models.CharField(blank=True, max_length=100, null=True)),
],
),
migrations.CreateModel(
name='Sensor',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('enabled', models.BooleanField(default=True)),
('device', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='sensors', to='iotDashboard.device')),
('type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='iotDashboard.sensortype')),
],
),
]

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from dotenv import load_dotenv
from pathlib import Path
import os
from huey import SqliteHuey
# from huey import SqliteHuey
# Build paths inside the project like this: BASE_DIR / 'subdir'.
@@ -46,7 +46,7 @@ INSTALLED_APPS = [
'django.contrib.messages',
'django.contrib.staticfiles',
'iotDashboard',
'huey.contrib.djhuey',
# 'huey.contrib.djhuey',
]
@@ -142,11 +142,11 @@ STATIC_URL = 'static/'
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
HUEY = {
'huey_class': 'huey.SqliteHuey', # Or 'huey.RedisHuey' for Redis
'filename': 'demo.db', # SQLite file for task storage
'results': True,
'store_none': False,
'immediate': False,
'utc': True,
}
# HUEY = {
# 'huey_class': 'huey.SqliteHuey', # Or 'huey.RedisHuey' for Redis
# 'filename': 'demo.db', # SQLite file for task storage
# 'results': True,
# 'store_none': False,
# 'immediate': False,
# 'utc': True,
# }

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def main():
print("Hello from iotdashboard!")
if __name__ == "__main__":
main()

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[project]
name = "iotdashboard"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"alembic>=1.17.0",
"django>=5.2.7",
"openai>=2.6.1",
"paho-mqtt>=2.1.0",
"psycopg2-binary>=2.9.11",
"python-dotenv>=1.2.1",
"redis>=7.0.1",
"requests>=2.32.5",
]

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Django==4.2.5
redis==4.5.4
paho-mqtt==1.6.1
psycopg2-binary==2.9.7
huey==2.4.5
python-dotenv==1.0.0
requests==2.31.0
openai==1.3.0
Django
redis
paho-mqtt
psycopg2-binary
python-dotenv
requests
openai

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# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0
REDIS_PASSWORD=
# Database Configuration (PostgreSQL/TimescaleDB)
DATABASE_URL=postgresql://user:password@localhost:5432/iot_dashboard
# Consumer Configuration
CONSUMER_GROUP_NAME=db_writer
CONSUMER_NAME=worker-01
BATCH_SIZE=100
BATCH_TIMEOUT_SEC=5
PROCESSING_INTERVAL_SEC=1
# Stream Configuration
STREAM_PATTERN=mqtt_stream:*
DEAD_LETTER_STREAM=mqtt_stream:failed
# Database Table Configuration
TABLE_NAME=sensor_readings
ENABLE_TIMESCALE=false
# Logging Configuration
LOG_LEVEL=INFO

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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# Virtual environments
.venv/
venv/
ENV/
env/
# Environment variables
.env
.env.local
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# Logs
*.log
# Database
*.db
*.sqlite
*.sqlite3
# OS
.DS_Store
Thumbs.db
# uv
.uv/
uv.lock

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3.13

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# Database Writer Service
A robust, production-ready service that reads sensor data from Redis streams and writes it to PostgreSQL/TimescaleDB. Part of the IoT Dashboard project.
## Features
-**Reliable consumption** from Redis streams using consumer groups
-**Batch processing** for high throughput
-**At-least-once delivery** with message acknowledgments
-**Dead letter queue** for failed messages
-**Connection pooling** for database efficiency
-**Graceful shutdown** handling
-**Flexible schema** that adapts to changes
-**Structured logging** with JSON output
-**Health checks** for monitoring
-**TimescaleDB support** for time-series optimization
## Architecture
```
Redis Streams → Consumer Group → Transform → Database → Acknowledge
Failed messages
Dead Letter Queue
```
### Components
- **`main.py`**: Service orchestration and processing loop
- **`redis_reader.py`**: Redis stream consumer with fault tolerance
- **`db_writer.py`**: Database operations with connection pooling
- **`schema.py`**: Data transformation and validation
- **`config.py`**: Configuration management
## Quick Start
### Prerequisites
- Python 3.13+
- [uv](https://github.com/astral-sh/uv) package manager
- Redis server with streams
- PostgreSQL or TimescaleDB
### Installation
1. **Navigate to the service directory**:
```bash
cd services/db_write
```
2. **Copy and configure environment variables**:
```bash
cp .env.example .env
# Edit .env with your DATABASE_URL and other settings
```
3. **Install dependencies**:
```bash
uv sync
```
4. **Setup database schema** (IMPORTANT - do this before running):
```bash
# Review the schema in models.py first
cat models.py
# Create initial migration
chmod +x migrate.sh
./migrate.sh create "initial schema"
# Review the generated migration
ls -lt alembic/versions/
# Apply migrations
./migrate.sh upgrade
```
5. **Run the service**:
```bash
uv run main.py
```
Or use the standalone script:
```bash
chmod +x run-standalone.sh
./run-standalone.sh
```
### ⚠️ Important: Schema Management
This service uses **Alembic** for database migrations. The service will NOT create tables automatically.
- Schema is defined in `models.py`
- Migrations are managed with `./migrate.sh` or `alembic` commands
- See `SCHEMA_MANAGEMENT.md` for detailed guide
## Schema Management
This service uses **SQLAlchemy** for models and **Alembic** for migrations.
### Key Files
- **`models.py`**: Define your database schema here (SQLAlchemy models)
- **`alembic/`**: Migration scripts directory
- **`migrate.sh`**: Helper script for common migration tasks
- **`SCHEMA_MANAGEMENT.md`**: Comprehensive migration guide
### Quick Migration Commands
```bash
# Create a new migration after editing models.py
./migrate.sh create "add new column"
# Apply pending migrations
./migrate.sh upgrade
# Check migration status
./migrate.sh check
# View migration history
./migrate.sh history
# Rollback last migration
./migrate.sh downgrade 1
```
**See `SCHEMA_MANAGEMENT.md` for detailed documentation.**
## Configuration
All configuration is done via environment variables. See `.env.example` for all available options.
### Required Settings
```bash
# Redis connection
REDIS_HOST=localhost
REDIS_PORT=6379
# Database connection
DATABASE_URL=postgresql://user:password@localhost:5432/iot_dashboard
```
### Optional Settings
```bash
# Consumer configuration
CONSUMER_GROUP_NAME=db_writer # Consumer group name
CONSUMER_NAME=worker-01 # Unique consumer name
BATCH_SIZE=100 # Messages per batch
BATCH_TIMEOUT_SEC=5 # Read timeout
PROCESSING_INTERVAL_SEC=1 # Delay between batches
# Stream configuration
STREAM_PATTERN=mqtt_stream:* # Stream name pattern
DEAD_LETTER_STREAM=mqtt_stream:failed
# Database
TABLE_NAME=sensor_readings # Target table name
ENABLE_TIMESCALE=false # Use TimescaleDB features
# Logging
LOG_LEVEL=INFO # DEBUG, INFO, WARNING, ERROR
LOG_FORMAT=json # json or console
```
## Data Flow
### Input (Redis Streams)
The service reads from Redis streams with the format:
```
mqtt_stream:{device_id}:{sensor_type}
```
Each message contains:
```
{
"value": "23.5",
"timestamp": "2023-10-18T14:30:00Z",
"metadata": "{...}" (optional)
}
```
### Output (Database)
Data is written to the `sensor_readings` table:
```sql
CREATE TABLE sensor_readings (
id BIGSERIAL PRIMARY KEY,
timestamp TIMESTAMPTZ NOT NULL,
device_id VARCHAR(100) NOT NULL,
sensor_type VARCHAR(100) NOT NULL,
value DOUBLE PRECISION NOT NULL,
metadata JSONB,
created_at TIMESTAMPTZ DEFAULT NOW()
);
```
**Note**: The table is automatically created if it doesn't exist.
## Running with Docker
### Build the image
```bash
docker build -t db-writer:latest .
```
### Run the container
```bash
docker run -d \
--name db-writer \
-e REDIS_HOST=redis \
-e DATABASE_URL=postgresql://user:pass@postgres:5432/iot \
db-writer:latest
```
## Consumer Groups
The service uses Redis consumer groups for reliable, distributed processing:
- **Multiple instances**: Run multiple workers for load balancing
- **Fault tolerance**: Messages are not lost if a consumer crashes
- **Acknowledgments**: Messages are only removed after successful processing
- **Pending messages**: Unacknowledged messages can be reclaimed
### Running Multiple Workers
```bash
# Terminal 1
CONSUMER_NAME=worker-01 uv run main.py
# Terminal 2
CONSUMER_NAME=worker-02 uv run main.py
```
All workers in the same consumer group will share the load.
## Error Handling
### Dead Letter Queue
Failed messages are sent to the dead letter stream (`mqtt_stream:failed`) with error information:
```
{
"original_stream": "mqtt_stream:esp32:temperature",
"original_id": "1634567890123-0",
"device_id": "esp32",
"sensor_type": "temperature",
"value": "23.5",
"error": "Database connection failed",
"failed_at": "1634567890.123"
}
```
### Retry Strategy
- **Transient errors**: Automatic retry with backoff
- **Data errors**: Immediate send to DLQ
- **Connection errors**: Reconnection attempts
## Monitoring
### Health Checks
Check service health programmatically:
```python
from main import DatabaseWriterService
service = DatabaseWriterService()
health = service.health_check()
print(health)
# {
# 'running': True,
# 'redis': True,
# 'database': True,
# 'stats': {...}
# }
```
### Logs
The service outputs structured logs:
```json
{
"event": "Processed batch",
"rows_written": 100,
"messages_acknowledged": 100,
"timestamp": "2023-10-18T14:30:00Z",
"level": "info"
}
```
### Statistics
Runtime statistics are tracked:
- `messages_read`: Total messages consumed
- `messages_written`: Total rows inserted
- `messages_failed`: Failed messages sent to DLQ
- `batches_processed`: Number of successful batches
- `errors`: Total errors encountered
## Development
### Project Structure
```
db_write/
├── config.py # Configuration management
├── db_writer.py # Database operations
├── redis_reader.py # Redis stream consumer
├── schema.py # Data models and transformation
├── main.py # Service entry point
├── pyproject.toml # Dependencies
├── .env.example # Configuration template
└── README.md # This file
```
### Adding Dependencies
```bash
uv add package-name
```
### Running Tests
```bash
uv run pytest
```
## Troubleshooting
### Service won't start
1. **Check configuration**: Verify all required environment variables are set
2. **Test connections**: Ensure Redis and PostgreSQL are accessible
3. **Check logs**: Look for specific error messages
### No messages being processed
1. **Check streams exist**: `redis-cli KEYS "mqtt_stream:*"`
2. **Verify consumer group**: The service creates it automatically, but check Redis logs
3. **Check stream pattern**: Ensure `STREAM_PATTERN` matches your stream names
### Messages going to dead letter queue
1. **Check DLQ**: `redis-cli XRANGE mqtt_stream:failed - + COUNT 10`
2. **Review error messages**: Each DLQ entry contains the error reason
3. **Validate data format**: Ensure messages match expected schema
### High memory usage
1. **Reduce batch size**: Lower `BATCH_SIZE` in configuration
2. **Check connection pool**: May need to adjust pool size
3. **Monitor pending messages**: Use `XPENDING` to check backlog
## Performance Tuning
### Throughput Optimization
- **Increase batch size**: Process more messages per batch
- **Multiple workers**: Run multiple consumer instances
- **Connection pooling**: Adjust pool size based on load
- **Processing interval**: Reduce delay between batches
### Latency Optimization
- **Decrease batch size**: Process smaller batches more frequently
- **Reduce timeout**: Lower `BATCH_TIMEOUT_SEC`
- **Single worker**: Avoid consumer group coordination overhead
## Production Deployment
### Recommended Settings
```bash
BATCH_SIZE=500
PROCESSING_INTERVAL_SEC=0.1
LOG_LEVEL=INFO
LOG_FORMAT=json
ENABLE_TIMESCALE=true
```
### Monitoring
- Monitor consumer lag using Redis `XPENDING`
- Track database insert latency
- Alert on error rate > 5%
- Monitor DLQ depth
### Scaling
1. **Horizontal**: Add more consumer instances with unique `CONSUMER_NAME`
2. **Vertical**: Increase resources for database writes
3. **Database**: Use TimescaleDB for better time-series performance
## License
Part of the IoT Dashboard project.

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"""
Configuration management for the database writer service.
Loads settings from environment variables with sensible defaults.
"""
import os
from dataclasses import dataclass
from typing import Optional
import dotenv
dotenv.load_dotenv()
@dataclass
class RedisConfig:
"""Redis connection configuration"""
host: str
port: int = 6379
db: int = 0
password: Optional[str] = None
@dataclass
class DatabaseConfig:
"""Database connection configuration"""
url: Optional[str] = None
host: Optional[str] = None
port: int = 5432
name: Optional[str] = None
user: Optional[str] = None
password: Optional[str] = None
table_name: str = "sensor_readings"
enable_timescale: bool = False
def get_connection_string(self) -> str:
"""Build connection string from components or return URL"""
if self.url:
return self.url
if not all([self.host, self.name, self.user, self.password]):
raise ValueError("Either DATABASE_URL or all DB_* variables must be set")
return f"postgresql://{self.user}:{self.password}@{self.host}:{self.port}/{self.name}"
@dataclass
class ConsumerConfig:
"""Redis consumer group configuration"""
group_name: str = "db_writer"
consumer_name: str = "worker-01"
batch_size: int = 100
batch_timeout_sec: int = 5
processing_interval_sec: float = 1.0
block_time_ms: int = 5000
@dataclass
class StreamConfig:
"""Redis stream configuration"""
pattern: str = "mqtt_stream:*"
dead_letter_stream: str = "mqtt_stream:failed"
max_retries: int = 3
trim_maxlen: int = 10000 # Keep last N messages in each stream
@dataclass
class LogConfig:
"""Logging configuration"""
level: str = "INFO"
format: str = "json" # json or console
class Config:
"""Main configuration class"""
def __init__(self):
self.redis = RedisConfig(
host=os.getenv('REDIS_HOST', 'localhost'),
port=int(os.getenv('REDIS_PORT', 6379)),
db=int(os.getenv('REDIS_DB', 0)),
password=os.getenv('REDIS_PASSWORD', None) or None
)
self.database = DatabaseConfig(
url=os.getenv('DATABASE_URL', None),
host=os.getenv('DB_HOST', None),
port=int(os.getenv('DB_PORT', 5432)),
name=os.getenv('DB_NAME', None),
user=os.getenv('DB_USER', None),
password=os.getenv('DB_PASSWORD', None),
table_name=os.getenv('TABLE_NAME', 'sensor_readings'),
enable_timescale=os.getenv('ENABLE_TIMESCALE', 'false').lower() == 'true'
)
self.consumer = ConsumerConfig(
group_name=os.getenv('CONSUMER_GROUP_NAME', 'db_writer'),
consumer_name=os.getenv('CONSUMER_NAME', 'worker-01'),
batch_size=int(os.getenv('BATCH_SIZE', 100)),
batch_timeout_sec=int(os.getenv('BATCH_TIMEOUT_SEC', 5)),
processing_interval_sec=float(os.getenv('PROCESSING_INTERVAL_SEC', 1.0)),
block_time_ms=int(os.getenv('BLOCK_TIME_MS', 5000))
)
self.stream = StreamConfig(
pattern=os.getenv('STREAM_PATTERN', 'mqtt_stream:*'),
dead_letter_stream=os.getenv('DEAD_LETTER_STREAM', 'mqtt_stream:failed'),
max_retries=int(os.getenv('MAX_RETRIES', 3)),
trim_maxlen=int(os.getenv('TRIM_MAXLEN', 10000))
)
self.log = LogConfig(
level=os.getenv('LOG_LEVEL', 'INFO'),
format=os.getenv('LOG_FORMAT', 'json')
)
def validate(self):
"""Validate configuration"""
errors = []
# Validate Redis config
if not self.redis.host:
errors.append("REDIS_HOST is required")
# Validate database config
try:
self.database.get_connection_string()
except ValueError as e:
errors.append(str(e))
# Validate consumer config
if self.consumer.batch_size < 1:
errors.append("BATCH_SIZE must be >= 1")
if errors:
raise ValueError(f"Configuration errors: {', '.join(errors)}")
return True
# Global config instance
config = Config()

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import logging
from typing import List
from sqlalchemy import create_engine, select
from sqlalchemy.orm import sessionmaker
from sqlalchemy.pool import QueuePool
from config import config
from schema import SensorReading
from models import SensorReading as SensorReadingModel
class DatabaseWriter:
"""
Database writer using SQLAlchemy.
Schema is defined in models.py and should be managed using Alembic migrations.
This class only handles data insertion, NOT schema creation.
To manage schema:
1. Edit models.py to define your schema
2. Generate migration: alembic revision --autogenerate -m "description"
3. Apply migration: alembic upgrade head
"""
def __init__(self):
self.logger = logging.getLogger(__name__)
# Initialize SQLAlchemy engine with connection pooling
connection_string = config.database.get_connection_string()
self.engine = create_engine(
connection_string,
poolclass=QueuePool,
pool_size=5,
max_overflow=10,
pool_pre_ping=True
)
# Create session factory
self.SessionLocal = sessionmaker(bind=self.engine)
self.logger.info("Database writer initialized")
def write_batch(self, readings: List[SensorReading]) -> bool:
"""Write a batch of sensor readings to the database"""
if not readings:
return True
session = self.SessionLocal()
try:
# Convert to database objects
db_objects = [
SensorReadingModel(
timestamp=reading.timestamp,
device_id=reading.device_id,
sensor_type=reading.sensor_type,
value=reading.value,
metadata=reading.metadata
)
for reading in readings
]
# Bulk insert
session.bulk_save_objects(db_objects)
session.commit()
self.logger.debug(f"Wrote {len(readings)} readings to database")
return True
except Exception as e:
self.logger.error(f"Failed to write batch: {e}")
session.rollback()
return False
finally:
session.close()
def health_check(self) -> bool:
"""Check if database connection is healthy"""
try:
with self.engine.connect() as conn:
result = conn.execute(select(1))
return result.fetchone()[0] == 1
except Exception as e:
self.logger.error(f"Database health check failed: {e}")
return False
def close(self):
"""Close database engine and all connections"""
if hasattr(self, 'engine') and self.engine:
self.engine.dispose()
self.logger.info("Database engine closed")

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"""
Main entry point for the database writer service.
Orchestrates the read → transform → write cycle with error handling.
"""
import logging
import signal
import sys
import time
import structlog
from typing import List
from config import config
from redis_reader import RedisReader
from db_writer import DatabaseWriter
from schema import SchemaHandler, StreamMessage, SensorReading
def configure_logging():
"""Configure structured logging"""
if config.log.format == 'json':
structlog.configure(
processors=[
structlog.stdlib.filter_by_level,
structlog.stdlib.add_logger_name,
structlog.stdlib.add_log_level,
structlog.stdlib.PositionalArgumentsFormatter(),
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.StackInfoRenderer(),
structlog.processors.format_exc_info,
structlog.processors.JSONRenderer()
],
wrapper_class=structlog.stdlib.BoundLogger,
context_class=dict,
logger_factory=structlog.stdlib.LoggerFactory(),
cache_logger_on_first_use=True,
)
# Configure standard logging
logging.basicConfig(
level=getattr(logging, config.log.level.upper(), logging.INFO),
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
class DatabaseWriterService:
"""Main service class that orchestrates the data pipeline"""
def __init__(self):
self.running = False
self.redis_reader: RedisReader = None
self.db_writer: DatabaseWriter = None
self.schema_handler: SchemaHandler = None
self.logger = logging.getLogger(__name__)
# Setup signal handlers for graceful shutdown
signal.signal(signal.SIGTERM, self._signal_handler)
signal.signal(signal.SIGINT, self._signal_handler)
# Statistics
self.stats = {
'messages_read': 0,
'messages_written': 0,
'messages_failed': 0,
'batches_processed': 0,
'errors': 0
}
def _signal_handler(self, signum, frame):
"""Handle shutdown signals"""
self.logger.info(f"Received signal {signum}, initiating graceful shutdown...")
self.stop()
def start(self) -> bool:
"""Start the service"""
self.logger.info("Starting Database Writer Service...")
try:
# Validate configuration
config.validate()
self.logger.info("Configuration validated successfully")
# Initialize components
self.schema_handler = SchemaHandler()
self.logger.info("Schema handler initialized")
self.redis_reader = RedisReader()
self.logger.info("Redis reader initialized")
self.db_writer = DatabaseWriter()
self.logger.info("Database writer initialized")
# Start the processing loop
self.running = True
self.logger.info("Service started successfully, entering processing loop")
self._processing_loop()
return True
except Exception as e:
self.logger.error(f"Service startup failed: {e}", exc_info=True)
return False
def _processing_loop(self):
"""Main processing loop"""
consecutive_errors = 0
max_consecutive_errors = 5
while self.running:
try:
# Read batch from Redis
messages = self.redis_reader.read_batch()
if not messages:
# No messages, sleep briefly
time.sleep(config.consumer.processing_interval_sec)
continue
self.stats['messages_read'] += len(messages)
self.logger.debug(f"Read {len(messages)} messages from Redis")
# Transform messages to sensor readings
readings = self._transform_messages(messages)
if not readings:
self.logger.warning("No valid readings after transformation")
# Acknowledge the messages anyway (they were invalid)
self.redis_reader.acknowledge_batch(messages)
continue
# Write to database
result = self.db_writer.write_batch(readings)
if result.success:
# Successfully written, acknowledge the messages
ack_count = self.redis_reader.acknowledge_batch(messages)
self.stats['messages_written'] += result.rows_written
self.stats['batches_processed'] += 1
consecutive_errors = 0
self.logger.info(
f"Processed batch: {result.rows_written} readings written, "
f"{ack_count} messages acknowledged"
)
else:
# Write failed, send to dead letter queue
self.logger.error(f"Failed to write batch: {result.error}")
self._handle_failed_batch(messages, result.error)
self.stats['messages_failed'] += len(messages)
self.stats['errors'] += 1
consecutive_errors += 1
# Check for too many consecutive errors
if consecutive_errors >= max_consecutive_errors:
self.logger.error(
f"Too many consecutive errors ({consecutive_errors}), "
"pausing for 30 seconds"
)
time.sleep(30)
consecutive_errors = 0
# Brief pause between batches
if config.consumer.processing_interval_sec > 0:
time.sleep(config.consumer.processing_interval_sec)
except KeyboardInterrupt:
self.logger.info("Keyboard interrupt received")
break
except Exception as e:
self.logger.error(f"Error in processing loop: {e}", exc_info=True)
self.stats['errors'] += 1
consecutive_errors += 1
time.sleep(5) # Back off on errors
self.logger.info("Processing loop terminated")
def _transform_messages(self, messages: List[StreamMessage]) -> List[SensorReading]:
"""Transform stream messages to sensor readings"""
readings = []
for msg in messages:
reading = self.schema_handler.transform_message(msg)
if reading:
readings.append(reading)
else:
self.logger.warning(
f"Failed to transform message {msg.message_id} from {msg.stream_key}"
)
return readings
def _handle_failed_batch(self, messages: List[StreamMessage], error: str):
"""Handle a batch that failed to write to database"""
# Send all messages to dead letter queue
for msg in messages:
self.redis_reader.send_to_dead_letter(msg, error)
# Acknowledge them so they don't block the consumer group
self.redis_reader.acknowledge_batch(messages)
self.logger.warning(f"Sent {len(messages)} messages to dead letter queue")
def stop(self):
"""Stop the service gracefully"""
if not self.running:
return
self.logger.info("Stopping service...")
self.running = False
# Print final statistics
self.logger.info(
f"Final statistics: "
f"messages_read={self.stats['messages_read']}, "
f"messages_written={self.stats['messages_written']}, "
f"messages_failed={self.stats['messages_failed']}, "
f"batches_processed={self.stats['batches_processed']}, "
f"errors={self.stats['errors']}"
)
# Close connections
if self.redis_reader:
self.redis_reader.close()
if self.db_writer:
self.db_writer.close()
self.logger.info("Service stopped")
def health_check(self) -> dict:
"""Check service health"""
health = {
'running': self.running,
'redis': False,
'database': False,
'stats': self.stats
}
if self.redis_reader:
health['redis'] = self.redis_reader.health_check()
if self.db_writer:
health['database'] = self.db_writer.health_check()
return health
def main():
"""Entry point"""
# Configure logging
configure_logging()
logger = logging.getLogger(__name__)
logger.info("=" * 60)
logger.info("Database Writer Service")
logger.info(f"Consumer Group: {config.consumer.group_name}")
logger.info(f"Consumer Name: {config.consumer.consumer_name}")
logger.info(f"Batch Size: {config.consumer.batch_size}")
logger.info(f"Stream Pattern: {config.stream.pattern}")
logger.info("=" * 60)
service = DatabaseWriterService()
try:
success = service.start()
if not success:
logger.error("Service failed to start")
sys.exit(1)
except KeyboardInterrupt:
logger.info("Received keyboard interrupt")
except Exception as e:
logger.error(f"Unexpected error: {e}", exc_info=True)
sys.exit(1)
finally:
service.stop()
logger.info("Service exited")
if __name__ == '__main__':
main()

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[project]
name = "db-write"
version = "0.1.0"
description = "Database writer service that reads from Redis streams and writes to PostgreSQL/TimescaleDB"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"redis>=6.4.0",
"psycopg2-binary>=2.9.0",
"python-dotenv>=1.0.0",
"pydantic>=2.0.0",
"structlog>=24.0.0",
"alembic>=1.13.0",
"sqlalchemy>=2.0.0",
]
[project.optional-dependencies]
dev = [
"pytest>=7.0.0",
"fakeredis>=2.0.0",
"pytest-asyncio>=0.21.0",
]

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import redis
import logging
from typing import List, Optional, Dict
from config import config
from schema import SchemaHandler, StreamMessage
class RedisReader:
"""Redis stream consumer with consumer groups for reliability"""
def __init__(self, streams: Optional[List[str]] = None):
self.logger = logging.getLogger(__name__)
self.schema_handler = SchemaHandler()
self.redis_client = redis.StrictRedis(
host=config.redis.host,
port=config.redis.port,
db=config.redis.db,
password=config.redis.password,
decode_responses=False
)
self.redis_client.ping()
self.logger.info(f"Connected to Redis at {config.redis.host}:{config.redis.port}")
if streams:
self.streams = streams
else:
pattern = config.stream.pattern
keys = self.redis_client.keys(pattern)
self.streams = [k.decode('utf-8') if isinstance(k, bytes) else k for k in keys]
self.logger.info(f"Monitoring {len(self.streams)} streams")
# Initialize consumer groups
self._setup_consumer_groups()
def _setup_consumer_groups(self):
"""Create consumer groups for streams"""
group_name = config.consumer.group_name
for stream in self.streams:
try:
self.redis_client.xgroup_create(stream, group_name, id='0', mkstream=True)
self.logger.info(f"Created consumer group '{group_name}' for '{stream}'")
except redis.exceptions.ResponseError as e:
if 'BUSYGROUP' in str(e):
self.logger.debug(f"Consumer group '{group_name}' already exists for '{stream}'")
else:
self.logger.error(f"Error creating consumer group for {stream}: {e}")
def read_batch(self, batch_size: Optional[int] = None,
timeout_ms: Optional[int] = None) -> List[StreamMessage]:
"""Read a batch of messages from streams using consumer group"""
if batch_size is None:
batch_size = config.consumer.batch_size
if timeout_ms is None:
timeout_ms = config.consumer.block_time_ms
if not self.streams:
return []
# Prepare stream dict for XREADGROUP
stream_dict = {stream: '>' for stream in self.streams}
try:
results = self.redis_client.xreadgroup(
groupname=config.consumer.group_name,
consumername=config.consumer.consumer_name,
streams=stream_dict,
count=batch_size,
block=timeout_ms
)
if not results:
return []
# Parse results into StreamMessage objects
messages = []
for stream_key, entries in results:
stream_name = stream_key.decode('utf-8') if isinstance(stream_key, bytes) else stream_key
for message_id, fields in entries:
msg_id = message_id.decode('utf-8') if isinstance(message_id, bytes) else message_id
stream_msg = self.schema_handler.parse_stream_entry(stream_name, msg_id, fields)
if stream_msg:
messages.append(stream_msg)
if messages:
self.logger.debug(f"Read {len(messages)} messages")
return messages
except Exception as e:
self.logger.error(f"Error reading from Redis: {e}")
return []
def acknowledge_batch(self, messages: List[StreamMessage]) -> int:
"""Acknowledge multiple messages at once"""
ack_count = 0
# Group messages by stream
by_stream: Dict[str, List[str]] = {}
for msg in messages:
if msg.stream_key not in by_stream:
by_stream[msg.stream_key] = []
by_stream[msg.stream_key].append(msg.message_id)
# Acknowledge each stream's messages
for stream_key, message_ids in by_stream.items():
try:
result = self.redis_client.xack(
stream_key,
config.consumer.group_name,
*message_ids
)
ack_count += result
except Exception as e:
self.logger.error(f"Failed to acknowledge messages from {stream_key}: {e}")
if ack_count > 0:
self.logger.debug(f"Acknowledged {ack_count} messages")
return ack_count
def health_check(self) -> bool:
"""Check if Redis connection is healthy"""
try:
self.redis_client.ping()
return True
except redis.RedisError:
return False
def close(self):
"""Close Redis connection"""
try:
self.redis_client.close()
self.logger.info("Redis connection closed")
except Exception as e:
self.logger.error(f"Error closing Redis connection: {e}")

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"""
Schema definitions and data transformation logic.
Handles conversion between Redis stream messages and database records.
"""
from dataclasses import dataclass
from datetime import datetime
from typing import Optional, Dict, Any
import json
@dataclass
class StreamMessage:
"""Represents a message from Redis stream"""
stream_key: str
message_id: str
device_id: str
sensor_type: str
value: float
timestamp: str
metadata: Optional[Dict[str, Any]] = None
@property
def stream_name(self) -> str:
"""Return the stream name without prefix"""
return self.stream_key
@dataclass
class SensorReading:
"""Represents a sensor reading ready for database insertion"""
timestamp: datetime
device_id: str
sensor_type: str
value: float
metadata: Optional[Dict[str, Any]] = None
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for database insertion"""
return {
'timestamp': self.timestamp,
'device_id': self.device_id,
'sensor_type': self.sensor_type,
'value': self.value,
'metadata': json.dumps(self.metadata) if self.metadata else None
}
@dataclass
class ValidationResult:
"""Result of data validation"""
valid: bool
error: Optional[str] = None
class SchemaHandler:
"""Handles schema transformation and validation"""
def __init__(self):
self.logger = self._get_logger()
def _get_logger(self):
"""Get logger instance"""
import logging
return logging.getLogger(__name__)
def transform_message(self, stream_message: StreamMessage) -> Optional[SensorReading]:
"""
Transform a Redis stream message into a SensorReading.
Returns None if transformation fails.
"""
try:
# Parse timestamp
timestamp = self._parse_timestamp(stream_message.timestamp)
# Create sensor reading
reading = SensorReading(
timestamp=timestamp,
device_id=stream_message.device_id,
sensor_type=stream_message.sensor_type,
value=float(stream_message.value),
metadata=stream_message.metadata
)
# Validate the reading
validation = self.validate_reading(reading)
if not validation.valid:
self.logger.error(f"Invalid reading: {validation.error}")
return None
return reading
except Exception as e:
self.logger.error(f"Failed to transform message: {e}", exc_info=True)
return None
def validate_reading(self, reading: SensorReading) -> ValidationResult:
"""Validate a sensor reading"""
try:
# Check required fields
if not reading.device_id:
return ValidationResult(False, "device_id is required")
if not reading.sensor_type:
return ValidationResult(False, "sensor_type is required")
if reading.value is None:
return ValidationResult(False, "value is required")
# Validate timestamp
if not isinstance(reading.timestamp, datetime):
return ValidationResult(False, "timestamp must be a datetime object")
# Validate value is numeric
if not isinstance(reading.value, (int, float)):
return ValidationResult(False, "value must be numeric")
# Check for reasonable value ranges (can be customized)
if reading.value < -1000000 or reading.value > 1000000:
self.logger.warning(f"Value {reading.value} is outside typical range")
return ValidationResult(True)
except Exception as e:
return ValidationResult(False, f"Validation error: {str(e)}")
def _parse_timestamp(self, timestamp_str: str) -> datetime:
"""Parse timestamp string into datetime object"""
# Try ISO format first
try:
return datetime.fromisoformat(timestamp_str.replace('Z', '+00:00'))
except ValueError:
pass
# Try common formats
formats = [
'%Y-%m-%dT%H:%M:%S.%fZ',
'%Y-%m-%dT%H:%M:%SZ',
'%Y-%m-%d %H:%M:%S.%f',
'%Y-%m-%d %H:%M:%S',
]
for fmt in formats:
try:
return datetime.strptime(timestamp_str, fmt)
except ValueError:
continue
# If all else fails, use current time and log warning
self.logger.warning(f"Could not parse timestamp '{timestamp_str}', using current time")
return datetime.utcnow()
def parse_stream_entry(self, stream_key: str, message_id: str, fields: Dict[bytes, bytes]) -> Optional[StreamMessage]:
"""
Parse a raw Redis stream entry into a StreamMessage.
Expected stream key format: mqtt_stream:{device_id}:{sensor_type}
Expected fields: value, timestamp (and optionally metadata)
"""
try:
# Extract device_id and sensor_type from stream key
# Format: mqtt_stream:{device_id}:{sensor_type}
parts = stream_key.split(':')
if len(parts) < 3:
self.logger.error(f"Invalid stream key format: {stream_key}")
return None
device_id = parts[1]
sensor_type = ':'.join(parts[2:]) # Handle sensor types with colons
# Extract fields from message
value_bytes = fields.get(b'value')
timestamp_bytes = fields.get(b'timestamp') or fields.get(b'time')
if not value_bytes or not timestamp_bytes:
self.logger.error(f"Missing required fields in message: {fields}")
return None
# Parse metadata if present
metadata = None
metadata_bytes = fields.get(b'metadata')
if metadata_bytes:
try:
metadata = json.loads(metadata_bytes.decode('utf-8'))
except json.JSONDecodeError:
self.logger.warning(f"Could not parse metadata: {metadata_bytes}")
return StreamMessage(
stream_key=stream_key,
message_id=message_id,
device_id=device_id,
sensor_type=sensor_type,
value=float(value_bytes.decode('utf-8')),
timestamp=timestamp_bytes.decode('utf-8'),
metadata=metadata
)
except Exception as e:
self.logger.error(f"Failed to parse stream entry: {e}", exc_info=True)
return None

View File

@@ -0,0 +1 @@
3.13

View File

@@ -0,0 +1,3 @@
# Device Manager
This is a service that has a key role in devices and their management. It is used to provision, create, manage the lifecycle of devices and everything connected to it.

View File

@@ -0,0 +1,6 @@
def main():
print("Hello from device-manager!")
if __name__ == "__main__":
main()

View File

@@ -0,0 +1,7 @@
[project]
name = "device-manager"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = []

View File

@@ -0,0 +1,6 @@
.env
.venv/
__pycache__/
*.pyc
*.pyo
*.pyd

View File

@@ -0,0 +1,13 @@
# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0
REDIS_PASSWORD=
# MQTT Configuration
MQTT_BROKER=localhost
MQTT_PORT=1883
MQTT_USERNAME=
MQTT_PASSWORD=
MQTT_TOPIC_PATTERN=devices/#
MQTT_KEEPALIVE=60

View File

@@ -0,0 +1 @@
3.13

View File

@@ -0,0 +1,32 @@
# Stage 1: Builder - Install dependencies with uv
FROM ghcr.io/astral-sh/uv:python3.13-alpine AS builder
WORKDIR /app
ENV UV_COMPILE_BYTECODE=1
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev --no-install-project
COPY . .
RUN uv sync --frozen --no-dev
FROM python:3.13-alpine
WORKDIR /app
COPY --from=builder /app/.venv /app/.venv
COPY --from=builder /app/*.py /app/
RUN adduser -D -u 1000 appuser && \
chown -R appuser:appuser /app
USER appuser
ENV PATH="/app/.venv/bin:$PATH"
CMD ["python", "main.py"]

View File

@@ -0,0 +1,3 @@
# MQTT Ingestion service
This service ingests all the data from the MQTT topics and sends the data to Redis

View File

@@ -18,7 +18,8 @@ class MQTTConfig:
port: int = 1883
username: Optional[str] = None
password: Optional[str] = None
topic: str = "#"
topic_pattern: str = "devices/#"
keepalive: int = 60
@dataclass
class Payload:
@@ -40,8 +41,9 @@ class Config:
broker=os.getenv('MQTT_BROKER', 'localhost'),
port=int(os.getenv('MQTT_PORT', 1883)),
username=os.getenv('MQTT_USERNAME', None),
password=None,
topic="#"
password=os.getenv('MQTT_PASSWORD', None),
topic_pattern=os.getenv('MQTT_TOPIC_PATTERN', 'devices/#'),
keepalive=int(os.getenv('MQTT_KEEPALIVE', 60))
)
config = Config()

View File

@@ -1,11 +1,9 @@
import logging
import signal
import sys
from config import config
from mqtt_client import MQTTClient
from redis_writer import RedisWriter
# Setup logging
logging.basicConfig(
level=getattr(logging,'INFO'),
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
@@ -18,7 +16,6 @@ class MQTTIngestionService:
self.redis_writer = None
self.mqtt_client = None
# Setup signal handlers for graceful shutdown
signal.signal(signal.SIGTERM, self._signal_handler)
signal.signal(signal.SIGINT, self._signal_handler)
@@ -43,13 +40,10 @@ class MQTTIngestionService:
logger.info("Starting MQTT Ingestion Service...")
try:
# Initialize Redis writer
self.redis_writer = RedisWriter()
# Initialize MQTT client with our message handler
self.mqtt_client = MQTTClient(self._handle_sensor_data)
# Connect to MQTT
if not self.mqtt_client.connect():
logger.error("Failed to connect to MQTT, exiting")
return False
@@ -57,7 +51,6 @@ class MQTTIngestionService:
self.running = True
logger.info("Service started successfully")
# Start MQTT loop (this blocks)
self.mqtt_client.start_loop()
except Exception as e:
@@ -74,11 +67,9 @@ class MQTTIngestionService:
logger.info("Stopping service...")
self.running = False
# Stop MQTT client
if self.mqtt_client:
self.mqtt_client.stop()
# Close Redis connection
if self.redis_writer:
self.redis_writer.close()
@@ -89,7 +80,6 @@ class MQTTIngestionService:
if not self.running:
return False
# Check Redis connection
if not self.redis_writer or not self.redis_writer.health_check():
return False
@@ -97,13 +87,9 @@ class MQTTIngestionService:
def main():
"""Entry point"""
service = MQTTIngestionService(
redis_config=config.redis,
mqtt_config=config.mqtt
)
service = MQTTIngestionService()
try:
# Start the service (blocks until shutdown)
success = service.start()
if not success:
sys.exit(1)

View File

@@ -20,7 +20,6 @@ class MQTTClient:
self.client.on_message = self._on_message
self.client.on_disconnect = self._on_disconnect
# Set credentials if provided
if config.mqtt.username:
self.client.username_pw_set(
config.mqtt.username,
@@ -30,7 +29,6 @@ class MQTTClient:
def _on_connect(self, client, userdata, flags, rc):
if rc == 0:
logger.info(f"Connected to MQTT broker {config.mqtt.broker}")
# Subscribe to all device topics: devices/+/+
client.subscribe(config.mqtt.topic_pattern)
logger.info(f"Subscribed to {config.mqtt.topic_pattern}")
else:
@@ -38,7 +36,6 @@ class MQTTClient:
def _on_message(self, client, userdata, msg):
try:
# Parse topic: devices/{device_id}/{sensor_type}
topic_parts = msg.topic.split('/')
if len(topic_parts) != 3 or topic_parts[0] != 'devices':
logger.warning(f"Invalid topic format: {msg.topic}")
@@ -47,14 +44,12 @@ class MQTTClient:
device_id = topic_parts[1]
sensor_type = topic_parts[2]
# Parse payload as float
try:
value = float(msg.payload.decode())
except ValueError:
logger.error(f"Invalid payload for {msg.topic}: {msg.payload}")
return
# Call the handler (this will be our Redis writer)
self.message_handler(device_id, sensor_type, value)
except Exception as e:

View File

@@ -0,0 +1,11 @@
[project]
name = "mqtt-ingestion"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"dotenv>=0.9.9",
"paho-mqtt>=2.1.0",
"redis>=6.4.0",
]

View File

@@ -1,37 +1,67 @@
import redis
import logging
from datetime import datetime
from typing import Optional
from config import Payload
from config import config
logger = logging.getLogger(__name__)
class RedisWriter:
def __init__(self, host: str, port: int, db: int, password: Optional[str] = None):
def __init__(self):
"""Initialize Redis writer with config from environment"""
self.logger = logging.getLogger(__name__)
self.redis_client = redis.StrictRedis(host=host, port=port, db=db, password=password)
self.redis_client = redis.StrictRedis(
host=config.redis.host,
port=config.redis.port,
db=config.redis.db,
password=config.redis.password
)
try:
self.redis_client.ping()
self.logger.info("Connected to Redis server successfully.")
self.logger.info(f"Connected to Redis at {config.redis.host}:{config.redis.port}")
except redis.ConnectionError as e:
self.logger.error(f"Failed to connect to Redis server: {e}")
raise
def write_message(self, topic: str, payload: Payload):
def write_sensor_data(self, device_id: str, sensor_type: str, value: float) -> bool:
"""
Write a message to a Redis stream with the topic and payload.
- Stream: mqtt_stream: {device_id}:{sensor_type}
Write sensor data to Redis streams and latest values hash.
- Stream: mqtt_stream:{device_id}:{sensor_type}
- Hash: mqtt_latest:{device_id}
"""
device_id = payload.device_id
sensor_type = payload.sensor_type
timestamp = datetime.utcnow().isoformat()
stream_key= f"mqtt_stream:{device_id}:{sensor_type}"
stream_key = f"mqtt_stream:{device_id}:{sensor_type}"
hash_key = f"mqtt_latest:{device_id}"
stream_data = {
"value": str(payload),
"source": "mqtt",
"value": str(value),
"timestamp": timestamp
}
try:
message_id = self.redis_client.xadd(stream_key, stream_data,maxlen=1000)
self.logger.info(f"Message written to Redis: {stream_data}")
return message_id
# Write to stream
self.redis_client.xadd(stream_key, stream_data, maxlen=1000)
# Update latest value hash
self.redis_client.hset(hash_key, sensor_type, str(value))
self.redis_client.hset(hash_key, f"{sensor_type}_time", timestamp)
return True
except redis.RedisError as e:
self.logger.error(f"Failed to write message to Redis: {e}")
self.logger.error(f"Failed to write to Redis: {e}")
return False
def health_check(self) -> bool:
"""Check if Redis connection is healthy"""
try:
self.redis_client.ping()
return True
except redis.RedisError:
return False
def close(self):
"""Close Redis connection"""
try:
self.redis_client.close()
self.logger.info("Redis connection closed")
except Exception as e:
self.logger.error(f"Error closing Redis connection: {e}")

View File

@@ -1,2 +0,0 @@
redis
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