---
## 🔍 What is TimescaleDB?
**TimescaleDB** is a PostgreSQL extension built for **time-series data**, offering:
| Feature | Benefit |
|-------------------------------|----------------------------------------------------------|
| ⏱️ Hypertables | Automatic time-based partitioning for massive datasets |
| 📊 Continuous Aggregates | Precomputed rollups for fast queries |
| 🧊 Compression | Reduces storage, improves read performance |
| 🔁 Time Bucketing | Easy `GROUP BY time_interval` for summaries |
| 💥 PostgreSQL Compatibility | Works with Django ORM via PostgreSQL backend |
---
## 🔧 1. Install and Enable TimescaleDB
### 🐳 Docker Example:
```yaml
services:
db:
image: timescale/timescaledb:latest-pg15
ports:
- "5432:5432"
environment:
POSTGRES_PASSWORD: password
```
### Manual Install (Linux):
```bash
sudo apt install timescaledb-postgresql-15
```
Update `postgresql.conf`:
```ini
shared_preload_libraries = 'timescaledb'
```
Then restart PostgreSQL and enable the extension:
```sql
CREATE EXTENSION IF NOT EXISTS timescaledb;
```
---
## ⚙️ 2. Django Project Setup
### Install dependencies:
```bash
pip install psycopg2 django
```
### `settings.py`:
```python
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'yourdb',
'USER': 'youruser',
'PASSWORD': 'yourpass',
'HOST': 'localhost',
'PORT': '5432',
}
}
```
---
## 📦 3. Django Model for Time-Series Data
```python
# models.py
from django.db import models
class SensorReading(models.Model):
timestamp = models.DateTimeField()
sensor_id = models.CharField(max_length=50)
temperature = models.FloatField()
class Meta:
indexes = [models.Index(fields=["timestamp"])]
```
### Migrate:
```bash
python manage.py makemigrations
python manage.py migrate
```
---
## 🔁 4. Convert to Hypertable
Run this via `psql` or Django `connection.cursor()`:
```sql
SELECT create_hypertable('yourapp_sensorreading', 'timestamp', if_not_exists => TRUE);
```
Or dynamically in Django:
```python
from django.db import connection
with connection.cursor() as cursor:
cursor.execute("SELECT create_hypertable('yourapp_sensorreading', 'timestamp', if_not_exists => TRUE);")
```
---
## ⏱️ 5. Time Bucket + Aggregation (Rollups)
### Basic hourly average query:
```sql
SELECT time_bucket('1 hour', timestamp) AS hour,
AVG(temperature)
FROM yourapp_sensorreading
GROUP BY hour
ORDER BY hour;
```
Use this in Django with raw SQL and convert to Pandas for dashboards.
---
## 📊 6. Continuous Aggregates
```sql
CREATE MATERIALIZED VIEW hourly_temp
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 hour', timestamp) AS bucket,
sensor_id,
AVG(temperature) AS avg_temp
FROM yourapp_sensorreading
GROUP BY bucket, sensor_id;
```
### Automate refresh:
```sql
SELECT add_continuous_aggregate_policy('hourly_temp',
start_offset => INTERVAL '7 days',
end_offset => INTERVAL '1 hour',
schedule_interval => INTERVAL '30 minutes');
```
---
## 🧊 7. Data Compression
```sql
ALTER TABLE yourapp_sensorreading
SET (timescaledb.compress, timescaledb.compress_segmentby = 'sensor_id');
SELECT compress_chunk(i) FROM show_chunks('yourapp_sensorreading') i;
```
### Policy:
```sql
SELECT add_compression_policy('yourapp_sensorreading', INTERVAL '30 days');
```
---
## 🧠 8. Query via Django ORM or Raw SQL
### Django ORM (limited for rollups):
```python
from yourapp.models import SensorReading
SensorReading.objects.filter(
timestamp__gte=timezone.now() - timedelta(days=7)
).aggregate(Avg('temperature'))
```
### Raw SQL with Pandas:
```python
from django.db import connection
import pandas as pd
query = """
SELECT time_bucket('1 hour', timestamp) AS hour,
AVG(temperature) as avg_temp
FROM yourapp_sensorreading
WHERE timestamp > now() - interval '7 days'
GROUP BY hour ORDER BY hour;
"""
df = pd.read_sql(query, connection)
```
---
## 🧮 9. Visualize with Jupyter
Perfect for exploring time-series:
```python
import matplotlib.pyplot as plt
df.plot(x='hour', y='avg_temp', title='Hourly Avg Temp')
plt.grid(True)
plt.show()
```
---
## 📘 Summary Table
| Feature | TimescaleDB Usage |
|---------------------------|------------------------------------------------------|
| Store time-series | Regular Django model + `create_hypertable()` |
| Partitioning | Automatic via hypertables |
| Aggregations | `time_bucket()`, `GROUP BY` |
| Continuous aggregates | Fast rollups with materialized views |
| Compression | Save space, improve historical query performance |
| Compatibility | Full Django & PostgreSQL support |
---
## 🛠️ Tools & Add-ons
- 📊 [Grafana](https://grafana.com/) for live dashboards
- 📦 [pgAdmin](https://www.pgadmin.org/) for TimescaleDB queries
- 🔁 [pg_cron](https://github.com/citusdata/pg_cron) for DB-side scheduling
- 🧪 [tsbs](https://github.com/timescale/tsbs) for benchmarking
---
## 🧪 Starter Ideas
- IoT metrics dashboard (sensor data)
- Health tracker (heart rate over time)
- Financial tick data rollups
- Website traffic analysis (time-bucketed)
---
Would you like:
- A **starter Django + TimescaleDB project** on GitHub?
- A **Jupyter notebook dashboard** with Django integration?
- A **live analytics dashboard** using Django REST + Chart.js?
Let me know your use case and I can help set it up!TimescaleDB
Here’s a comprehensive, forward-thinking guide to using TimescaleDB in Django, including what it is, why it matters, and how to use it for time-series data analysis, dashboards, and efficient data storage.