---
title: "92x faster queries: How Open Electricity uses ClickHouse to track Australia’s energy transition in real time"
description: "Open Electricity uses ClickHouse to serve live Australian energy data through a public platform and API spanning roughly one billion records. Migrating from Postgres and TimescaleDB addressed queries that could take more than five minutes, reducing a benchmark to just over three seconds and to 60 milliseconds with a materialized view. The new setup keeps heavy relational workloads in Postgres while ClickHouse handles analytics, using ReplacingMergeTree base tables and more than 20 materialized views for different aggregation patterns. It also uses substantially less hardware than the previous configuration: four cores, four GB RAM, and 200 GB disk versus 16 cores, 32 GB RAM, and 1.6 TB, while serving data added daily and API users."
---

# 92x faster queries: How Open Electricity uses ClickHouse to track Australia’s energy transition in real time

[Clickhouse](https://yomu.fyi/company/clickhouse) · Jun 17, 2026

**Type:** Problem & solution

## Summary

Open Electricity uses ClickHouse to serve live Australian energy data through a public platform and API spanning roughly one billion records. Migrating from Postgres and TimescaleDB addressed queries that could take more than five minutes, reducing a benchmark to just over three seconds and to 60 milliseconds with a materialized view. The new setup keeps heavy relational workloads in Postgres while ClickHouse handles analytics, using ReplacingMergeTree base tables and more than 20 materialized views for different aggregation patterns. It also uses substantially less hardware than the previous configuration: four cores, four GB RAM, and 200 GB disk versus 16 cores, 32 GB RAM, and 1.6 TB, while serving data added daily and API users.

## Context

Open Electricity processes five-minute generation and market data from Australia’s NEM and WEM, totaling around one billion records and growing by approximately 500,000 records per day. Its Postgres and TimescaleDB setup was strained by analytical queries, especially joins between market and generation data, with some queries taking upwards of five minutes.

## Approach / What changed

Open Electricity moved analytical workloads to ClickHouse while retaining Postgres for heavy relational data. The ClickHouse design uses ReplacingMergeTree base tables and a hierarchy of more than 20 materialized views, allowing the team to add views for query hot spots and precompute different aggregation patterns.

## Takeaways

- A benchmark query fell from over five minutes on the previous setup to just over three seconds after migration, and to 60 milliseconds when served through a materialized view.
- The ClickHouse deployment uses four cores, 4 GB RAM, and 200 GB disk, compared with 16 cores, 32 GB RAM, and 1.6 TB for Postgres.
- Open Electricity retains Postgres for relational workloads and uses ClickHouse for analytics, with materialized views supporting multiple representations of its energy data.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Migrations](https://yomu.fyi/topic/migration), [Performance](https://yomu.fyi/topic/performance), [Postgres](https://yomu.fyi/topic/postgres)

- Source: [Clickhouse](https://clickhouse.com/blog/openelectricity-australia-energy-transition)
- Source URL: https://clickhouse.com/blog/openelectricity-australia-energy-transition
- Ingested by Yomu: 2026-08-29T01:35:23.887Z

[Read original post](https://clickhouse.com/blog/openelectricity-australia-energy-transition)