---
title: Refresh Policies & Hierarchies | Tiger Data Docs
description: Master automatic refresh and layered aggregates
---

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

---

## Refresh mechanisms

### Invalidation tracking

TimescaleDB tracks which time buckets have been modified:

```
When new data arrives for bucket 2025-01-15 10:00:
  → Invalidation log records: bucket "2025-01-15 10:00" needs refresh
On next policy refresh:
  → Only recompute that bucket, not all historical buckets
```

### Lookback window

The refresh policy includes a lookback to handle late-arriving data:

```
add_continuous_aggregate_policy('hourly_temp_summary',
  start_offset   => INTERVAL '3 hours',   -- How far back to look
  end_offset     => INTERVAL '1 hour',    -- How far into future
  schedule_interval => INTERVAL '1 hour'  -- How often to refresh
);
```

---

## Hierarchical Continuous Aggregates

Build layers: hourly → daily → monthly. Each level reads from the next finer level, avoiding redundant raw data scans:

```
-- Layer 1: Hourly (reads raw sensor_readings)
CREATE MATERIALIZED VIEW hourly_summary WITH (timescaledb.continuous) AS
SELECT time_bucket('1 hour', time) AS bucket, device_id, avg(temp) AS avg_temp
FROM sensor_readings GROUP BY bucket, device_id;


-- Layer 2: Daily (reads hourly_summary, not raw data)
CREATE MATERIALIZED VIEW daily_summary WITH (timescaledb.continuous) AS
SELECT time_bucket('1 day', bucket) AS day, device_id, avg(avg_temp) AS avg_temp
FROM hourly_summary GROUP BY day, device_id;


-- Layer 3: Monthly (reads daily_summary)
CREATE MATERIALIZED VIEW monthly_summary WITH (timescaledb.continuous) AS
SELECT time_bucket('1 month', day) AS month, device_id, avg(avg_temp) AS avg_temp
FROM daily_summary GROUP BY month, device_id;
```

**Query performance:**

- Hourly dashboard: instant (materialized hourly data)
- Daily dashboard: instant (materialized daily data)
- Monthly dashboard: instant (materialized monthly data)
- No raw data scans—every query hits pre-computed results!

---

## Real-Time Aggregates

For combine real-time uncomputed data with pre-computed aggregates:

```
-- Real-time aggregate (data not yet in CAGG)
SELECT
  bucket,
  avg(avg_temp) AS combined_avg
FROM hourly_summary
WHERE bucket > NOW() - INTERVAL '7 days'
UNION ALL
SELECT
  time_bucket('1 hour', time) AS bucket,
  avg(temp)
FROM sensor_readings
WHERE time > (SELECT MAX(bucket) FROM hourly_summary)
GROUP BY bucket;
```

---

[NEXT PAGE→](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/knowledge-check/index.md)

[Knowledge Check](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/knowledge-check/index.md)

[Test your understanding of Continuous Aggregates concepts and refresh policies.](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/knowledge-check/index.md)