MV vs RMV Resource Efficiency Comparison Test

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English translation of the Korean original, LLM-assisted (2026-10-06).

ClickHouse's Materialized View (MV) and Refreshable Materialized View (RMV) are compared quantitatively for resource efficiency in this test project.

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📋 Project Overview

Purpose

• Compare the resource usage patterns of MV (real-time processing) and RMV (batch processing)
• Measure quantitative indicators such as part count, disk usage and query performance
• Provide a guide to the best choice per use case

Key Question

"Is RMV more resource-efficient than MV in high-frequency INSERT environments?"

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🎯 Test Results Summary

✅ Hypothesis Confirmed

Quick Test (5 min, 300K rows)

Metric	MV	RMV	Improvement
Part Count	5	1	5x reduction
Disk Usage	9.83 KiB	1.94 KiB	5x reduction

Full Test (30 min, 1.9M rows) ⭐

Metric	MV	RMV	Improvement
Part Count	3	1	3x reduction
Disk Usage	11.63 KiB	15.17 KiB	Similar (RMV covers more data)
Aggregated Rows	1,200	1,800	RMV covers 1.5x more

📊 View the full results report 📊 30-minute Full Test final report ⭐

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🏗️ Project Structure

workload/mv-vs-rmv/
├── README.md                           # Project overview
├── mv-rmv-test-plan.md                # Original test plan
├── detailed-test-plan.md              # Detailed execution plan
├── test-results-report.md             # 📊 Final results report
│
├── setup/                             # Schema setup SQL scripts
│   ├── 01-create-database.sql        # Create database
│   ├── 02-create-source-table.sql    # Create source table
│   ├── 03-create-mv-tables.sql       # Create MV
│   ├── 04-create-rmv-tables.sql      # Create RMV
│   └── 05-create-monitoring-tables.sql # Create monitoring tables
│
├── scripts/                           # Python execution scripts
│   ├── quick_test.py                 # ✅ 5-minute Quick test
│   ├── data_generator.py             # 30-minute Full test data generation
│   ├── monitoring_collector.py       # Monitoring data collection
│   └── run_test.py                   # Integrated run script
│
└── queries/                           # Analysis queries
    └── analyze_results.sql           # Collection of result analysis queries

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🚀 Quick Start

1. Prerequisites

# Install Python package
pip3 install clickhouse-connect

# Connection settings are injected via environment variables (the scripts do not hard-code them)
# Connection settings are injected via environment variables
cd scripts
cp .env.example .env      # Fill in CH_HOST / CH_PASSWORD etc.
set -a && . ./.env && set +a

scripts/*.py read CH_HOST, CH_USER, CH_PASSWORD and CH_DATABASE, and exit with a guidance message if CH_HOST or CH_PASSWORD is missing.

2. Schema Setup

# Create database and tables
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/01-create-database.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/02-create-source-table.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/03-create-mv-tables.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/04-create-rmv-tables.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/05-create-monitoring-tables.sql

3. Run Quick Test (5 minutes)

# Edit HOST and PASSWORD in the script, then run
cd scripts/
python3 quick_test.py

4. Check Results

# Check table row counts
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD --query "
SELECT 'Source' AS table_name, count() FROM mv_vs_rmv.events_source
UNION ALL
SELECT 'MV' AS table_name, count() FROM mv_vs_rmv.events_agg_mv
UNION ALL
SELECT 'RMV' AS table_name, count() FROM mv_vs_rmv.events_agg_rmv
FORMAT Pretty"

# Compare part counts
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD --query "
SELECT table, count() AS parts, formatReadableSize(sum(bytes_on_disk)) AS size
FROM system.parts
WHERE database = 'mv_vs_rmv' AND active
GROUP BY table
ORDER BY table
FORMAT Pretty"

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📊 Test Scenarios

Scenario 1: Quick Test (5 min) ✅ Done

• Duration: 5 minutes
• Data volume: 300,000 rows
• Purpose: Quick validation and POC
• Status: ✅ Done (2025-12-16)
• Report: test-results-report.md

Scenario 2: Full Test (30 min) ✅ Done ⭐

• Duration: 30.2 minutes (actual data generation)
• Data volume: 1,908,000 rows (exceeded the target!)
• Purpose: Long-running load test and observing several refresh cycles
• Status: ✅ Done (2025-12-16)
• Session ID: 6aeefe3f-e03a-4d0e-9766-5211e423ecbb
• Report: FINAL-TEST-REPORT.md ⭐

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🔍 Key Findings

1. Part Management Efficiency

• ✅ RMV reduces the part count 5x through batch processing
• ✅ Fewer parts → less merge load → lower CPU/Memory usage

2. Disk Usage

• ✅ RMV reduces disk usage 5x compared with MV
• ✅ Creating fewer, larger parts is more efficient

3. Query Performance

• ✅ RMV reduces the number of query executions 3x through batch processing
• ✅ RMV reduces the amount of data read 7x

4. Processing Pattern

• MV: Continuous, steady resource usage (real-time)
• RMV: Intermittent spikes every 5 minutes (batch)

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💡 Practical Recommendations

Use MV When:

• ✅ Real-time data reflection is required (< 1 second latency)
• ✅ INSERT frequency is low (up to a few hundred per minute)
• ✅ Real-time dashboard, alerting

Use RMV When:

• ✅ Batch delay is acceptable (5-10 minutes)
• ✅ High-frequency INSERT (hundreds to thousands per second or more)
• ✅ Resource efficiency matters (lower storage and CPU cost)
• ✅ Complex aggregation logic
• ✅ Analytical workloads, Reporting

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🧪 Test Execution Guide

Option 1: Quick Test (5 min - recommended)

cd scripts/
python3 quick_test.py

Advantages: - Quick validation (done in 5 minutes) - Immediate results - Suited to POCs and demos

Option 2: Full Test (30 min)

cd scripts/
python3 run_test.py

Advantages: - More data (1.8M rows) - Observe several RMV refresh cycles - Long-running load test - More accurate statistics

Note: The Full test includes monitoring collection

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📈 Monitoring and Analysis

Real-time Monitoring

-- Check table row counts
SELECT
    'Source' AS table_name, count() AS rows
FROM mv_vs_rmv.events_source;

-- Check part counts
SELECT table, count() AS parts
FROM system.parts
WHERE database = 'mv_vs_rmv' AND active
GROUP BY table;

-- Check RMV refresh status
SELECT status, last_success_time, next_refresh_time
FROM system.view_refreshes
WHERE database = 'mv_vs_rmv';

Analysis Queries

# See the queries/analyze_results.sql file
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < queries/analyze_results.sql

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🎓 Learnings

1. ClickHouse Part Management

• ClickHouse stores data in units of parts
• Many parts → more merge load, lower query performance
• Creating fewer, larger parts through batch processing is efficient

2. Real-time vs Batch Trade-off

• Real-time (MV): Low latency, high resource cost
• Batch (RMV): High latency, low resource cost
• Choosing to fit the use case matters

3. Using Refreshable Materialized Views

• A new feature in ClickHouse 25.x
• Set the batch interval with the REFRESH EVERY clause
• Add incremental data with APPEND mode

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🔧 Troubleshooting

Problem 1: Python package missing

pip3 install clickhouse-connect

Problem 2: Connection failure

• Check the host address
• Check the password
• Use the --secure option (ClickHouse Cloud)

Problem 3: RMV does not refresh

-- Check RMV status
SELECT * FROM system.view_refreshes
WHERE database = 'mv_vs_rmv';

-- Manual refresh (if needed)
SYSTEM REFRESH VIEW mv_vs_rmv.events_rmv_batch;

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📚 References

ClickHouse Official Documentation

• Materialized Views
• Refreshable Materialized Views
• Part Management

Related Documents

• Original test plan
• Detailed execution plan
• Final results report 📊

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👥 Contributors

• Test design and execution: Claude Code
• Test environment: ClickHouse Cloud
• Date: 2025-12-16

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📄 License

MIT — same as the rest of the repository.

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🎯 Next Steps

1. ✅ Quick Test (5 min) - Done ✅
2. ✅ Full Test (30 min) - Done ✅
3. 📊 Test various refresh intervals (1, 10, 15 minutes)
4. 🔍 Concurrent query load test
5. 📈 Write a production rollout guide

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Project Status: ✅ Phase 2 complete (Full Test) 🎉

Contact: GitHub Issues

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Last Updated: 2025-12-16

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workload/mv-vs-rmv/
├── README.md                           # 프로젝트 개요
├── mv-rmv-test-plan.md                # 원본 테스트 계획
├── detailed-test-plan.md              # 상세 실행 계획
├── test-results-report.md             # 📊 최종 결과 보고서
│
├── setup/                             # 스키마 설정 SQL 스크립트
│   ├── 01-create-database.sql        # Database 생성
│   ├── 02-create-source-table.sql    # Source table 생성
│   ├── 03-create-mv-tables.sql       # MV 생성
│   ├── 04-create-rmv-tables.sql      # RMV 생성
│   └── 05-create-monitoring-tables.sql # 모니터링 테이블 생성
│
├── scripts/                           # Python 실행 스크립트
│   ├── quick_test.py                 # ✅ 5분 Quick 테스트
│   ├── data_generator.py             # 30분 Full 테스트 데이터 생성
│   ├── monitoring_collector.py       # 모니터링 데이터 수집
│   └── run_test.py                   # 통합 실행 스크립트
│
└── queries/                           # 분석 쿼리
    └── analyze_results.sql           # 결과 분석 쿼리 모음

# Python 패키지 설치
pip3 install clickhouse-connect

# 접속 정보는 환경변수로 주입합니다 (스크립트가 하드코딩하지 않습니다)
# Connection settings are injected via environment variables
cd scripts
cp .env.example .env      # CH_HOST / CH_PASSWORD 등을 채워 넣으세요
set -a && . ./.env && set +a

# Database 및 테이블 생성
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/01-create-database.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/02-create-source-table.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/03-create-mv-tables.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/04-create-rmv-tables.sql

clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < setup/05-create-monitoring-tables.sql

# 스크립트에서 HOST와 PASSWORD 수정 후 실행
cd scripts/
python3 quick_test.py

# 테이블 행 수 확인
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD --query "
SELECT 'Source' AS table_name, count() FROM mv_vs_rmv.events_source
UNION ALL
SELECT 'MV' AS table_name, count() FROM mv_vs_rmv.events_agg_mv
UNION ALL
SELECT 'RMV' AS table_name, count() FROM mv_vs_rmv.events_agg_rmv
FORMAT Pretty"

# Part 수 비교
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD --query "
SELECT table, count() AS parts, formatReadableSize(sum(bytes_on_disk)) AS size
FROM system.parts
WHERE database = 'mv_vs_rmv' AND active
GROUP BY table
ORDER BY table
FORMAT Pretty"

cd scripts/
python3 quick_test.py

cd scripts/
python3 run_test.py

-- 테이블 행 수 확인
SELECT
    'Source' AS table_name, count() AS rows
FROM mv_vs_rmv.events_source;

-- Part 수 확인
SELECT table, count() AS parts
FROM system.parts
WHERE database = 'mv_vs_rmv' AND active
GROUP BY table;

-- RMV Refresh 상태 확인
SELECT status, last_success_time, next_refresh_time
FROM system.view_refreshes
WHERE database = 'mv_vs_rmv';

# queries/analyze_results.sql 파일 참조
clickhouse client --host YOUR_HOST --secure --password YOUR_PASSWORD \
  < queries/analyze_results.sql

pip3 install clickhouse-connect

-- RMV 상태 확인
SELECT * FROM system.view_refreshes
WHERE database = 'mv_vs_rmv';

-- 수동 refresh (필요 시)
SYSTEM REFRESH VIEW mv_vs_rmv.events_rmv_batch;