---
title: The Problem and Solution | Tiger Data Docs
description: Learn why Continuous Aggregates matter for analytics
---

**Module:** Tiger Data 101 → An Introduction to TimescaleDB\
**Section:** Continuous Aggregates\
**Estimated time:** 10–12 minutes total (this page: \~3 minutes)

---

## Learning objectives

By the end of this module, you will be able to:

- **Explain** what Continuous Aggregates are and how they improve query performance (Understand)
- **Describe** the refresh mechanisms and **apply** them appropriately (Understand/Apply)
- **Design** hierarchical Continuous Aggregates for complex analytical queries (Create)
- **Compare** materialized views with traditional approaches (Analyze)

---

## The Problem with Repeated Rollups

Imagine querying hourly average temperatures across thousands of sensors, daily. Traditional approach:

```
-- Run this query every time—scans millions of raw rows
SELECT
  time_bucket('1 hour', time) as bucket,
  avg(temperature) as avg_temp
FROM sensor_readings
WHERE time > NOW() - INTERVAL '30 days'
GROUP BY bucket;
```

**The problem:**

- Scans 30 days × 24 hours × 1000s of sensors = millions of rows
- Recomputes the same aggregations every query
- Dashboard becomes slow as data grows

---

## What are continuous aggregates?

**Continuous Aggregates (CAGGs)** are materialized views that TimescaleDB automatically keeps fresh as new data arrives:

```
CREATE MATERIALIZED VIEW hourly_temp_summary
WITH (timescaledb.continuous) AS
SELECT
  time_bucket('1 hour', time) AS bucket,
  device_id,
  avg(temperature) AS avg_temperature,
  max(temperature) AS max_temperature
FROM sensor_readings
GROUP BY bucket, device_id
WITH NO DATA;


-- Add automatic refresh policy
SELECT add_continuous_aggregate_policy('hourly_temp_summary',
  start_offset => INTERVAL '3 hours',
  end_offset => INTERVAL '1 hour',
  schedule_interval => INTERVAL '1 hour'
);
```

Now queries hit the pre-computed results—milliseconds instead of seconds:

```
-- Instead of scanning raw data, query the CAGG
SELECT bucket, device_id, avg_temperature
FROM hourly_temp_summary
WHERE bucket > NOW() - INTERVAL '30 days';
```

---

→NEXT MODULE

[**Refresh Policies** — Understand how TimescaleDB keeps aggregates fresh automatically.](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/refresh-policies-and-hierarchies/index.md)