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ClickHouse vs BigQuery: Performance and Cost Comparison

JayJay

ClickHouse and BigQuery both handle analytical workloads on large datasets, but they're fundamentally different products. ClickHouse is open-source software you operate. BigQuery is a fully managed service you rent. The choice affects not just performance, but how you work, what you pay, and what expertise you need.

What You're Comparing

ClickHouse is an open-source column-oriented database created at Yandex for web analytics. It's designed for real-time analytical queries on large datasets, billions of rows returning in milliseconds. You can self-host it or use managed services like ClickHouse Cloud.

BigQuery is Google's serverless data warehouse. You don't manage infrastructure. You send queries, and Google handles the rest. It's built on Google's internal systems and integrates tightly with the Google Cloud ecosystem.

Performance Characteristics

Query Speed

ClickHouse is fast. Remarkably fast. It's optimized for scanning large amounts of data quickly:

SQL
-- ClickHouse: Aggregate 1 billion rows
SELECT
  toDate(timestamp) AS date,
  count() AS events,
  uniq(user_id) AS unique_users
FROM events
WHERE timestamp >= today() - 30
GROUP BY date
ORDER BY date
-- Returns in seconds, sometimes sub-second

BigQuery is also fast, but it trades raw speed for convenience:

SQL
-- BigQuery: Same query
SELECT
  DATE(timestamp) AS date,
  COUNT(*) AS events,
  COUNT(DISTINCT user_id) AS unique_users
FROM `project.dataset.events`
WHERE timestamp >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY date
ORDER BY date
-- Returns in seconds to tens of seconds

In benchmarks:

  • ClickHouse often wins on raw query speed, especially for well-optimized schemas
  • BigQuery is more consistent, less variance based on configuration
  • ClickHouse excels at high-frequency queries (dashboards, real-time analytics)
  • BigQuery handles complex ad-hoc queries without tuning

Concurrency

ClickHouse handles many concurrent queries well, making it suitable for user-facing analytics (embedded dashboards, customer-facing reporting).

BigQuery has slot-based concurrency. Free tier gets limited slots; paid tier can have thousands. But it's designed for fewer, larger queries rather than many small ones.

Ingestion

ClickHouse supports real-time inserts:

SQL
-- ClickHouse: Insert data immediately
INSERT INTO events (timestamp, user_id, event_type)
VALUES (now(), 12345, 'page_view')

BigQuery prefers batch loading:

SQL
-- BigQuery: Streaming inserts cost extra
-- Batch loads from GCS are free
LOAD DATA INTO events
FROM 'gs://bucket/events/*.parquet'

BigQuery streaming inserts exist but cost $0.01 per 200 MB, meaningful at scale.

Cost Models

This is where the products differ most significantly.

ClickHouse (Self-Hosted)

You pay for infrastructure:

  • Compute: VMs or containers running ClickHouse
  • Storage: Disk space for data
  • Network: Egress if applicable

Costs are predictable but require capacity planning. Under-provision and queries slow down. Over-provision and you waste money.

Rough estimates for moderate workloads: $500-2000/month for infrastructure capable of handling billions of rows with good query performance.

ClickHouse Cloud

Managed ClickHouse with usage-based pricing:

  • Compute: ~$0.20/hour for small instances
  • Storage: ~$0.03/GB/month
  • Scales automatically

More expensive than self-hosted but simpler to operate.

BigQuery

Pay for queries and storage:

  • Queries: $5 per TB scanned (first 1 TB/month free)
  • Storage: $0.02/GB/month (active), $0.01/GB/month (long-term)
  • Streaming inserts: $0.01 per 200 MB

The per-query cost is the trap. A carelessly written query that scans 10 TB costs $50. Do that regularly and bills add up.

Cost control strategies:

  • Partition tables by date
  • Use clustered tables
  • Implement query quotas
  • Use slot reservations for predictable pricing ($2000/month for 100 slots)

When to Choose ClickHouse

Real-Time Analytics

If you need sub-second query response for dashboards or user-facing analytics, ClickHouse excels:

SQL
-- Power a real-time dashboard
SELECT
  toStartOfMinute(timestamp) AS minute,
  count() AS requests,
  avg(response_time_ms) AS avg_latency
FROM requests
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY minute
ORDER BY minute

This query running hundreds of times per second is ClickHouse's sweet spot.

High Query Volume

If you have many users running queries (embedded analytics, multi-tenant SaaS), ClickHouse's concurrency and fixed infrastructure cost make sense.

Cost Sensitivity at Scale

If you're scanning petabytes regularly, BigQuery's per-query pricing becomes prohibitive. ClickHouse's infrastructure costs plateau while BigQuery's scale linearly with usage.

Data Sovereignty / Control

Self-hosted ClickHouse means complete control over data location, access, and retention. No data leaves your infrastructure.

When to Choose BigQuery

Serverless Simplicity

No capacity planning, no server management, no upgrades. Load data, write queries, get results.

SQL
-- Just query. No cluster tuning.
SELECT * FROM `project.dataset.table`
WHERE condition

Google Cloud Integration

BigQuery integrates natively with:

  • Cloud Storage (load data directly)
  • Dataflow (streaming pipelines)
  • Looker (visualization)
  • Vertex AI (ML)
  • Cloud Functions (automation)

If you're already in GCP, BigQuery slots in naturally.

Variable Workloads

If your analytics usage is sporadic (heavy one week, nothing the next), BigQuery's pay-per-query model might be cheaper than maintaining ClickHouse infrastructure.

Complex ETL

BigQuery has strong SQL support for transformations:

SQL
-- Scheduled queries, views, materialized views
CREATE MATERIALIZED VIEW monthly_stats AS
SELECT
  DATE_TRUNC(date, MONTH) AS month,
  SUM(revenue) AS total_revenue
FROM sales
GROUP BY month

Team Without Database Expertise

BigQuery requires no DBA skills. ClickHouse performance depends on schema design, indexing, and tuning.

Feature Comparison

| Feature | ClickHouse | BigQuery | |---------|------------|----------| | Query language | SQL (ClickHouse dialect) | SQL (GoogleSQL) | | Real-time inserts | ✅ Native | ⚠️ Streaming (costs extra) | | Partitioning | ✅ Flexible | ✅ Date-based primarily | | Materialized views | ✅ | ✅ | | UDFs | ✅ (C++, SQL) | ✅ (SQL, JavaScript) | | ML integration | ⚠️ Limited | ✅ BigQuery ML | | Geospatial | ✅ | ✅ | | JSON support | ✅ Excellent | ✅ Good | | Nested data | ✅ | ✅ (STRUCT, ARRAY) | | Time-travel | ⚠️ Limited | ✅ 7 days |

Migration Considerations

From BigQuery to ClickHouse

Export data to Cloud Storage, then load into ClickHouse:

BASH
# Export from BigQuery to GCS
bq extract --destination_format=PARQUET \
  project:dataset.table gs://bucket/export/*.parquet

# Load into ClickHouse
INSERT INTO table
SELECT * FROM s3('https://storage.googleapis.com/bucket/export/*.parquet')

Adjust data types (BigQuery's STRUCT → ClickHouse Nested/Tuple).

From ClickHouse to BigQuery

Export to Parquet, upload to GCS, load into BigQuery:

SQL
-- ClickHouse export
INSERT INTO FUNCTION s3('s3://bucket/export.parquet', 'Parquet')
SELECT * FROM table
SQL
-- BigQuery load
LOAD DATA INTO table
FROM 'gs://bucket/export.parquet'

The Bottom Line

Choose ClickHouse if:

  • Real-time analytics with sub-second latency
  • High query volume (user-facing dashboards)
  • You have database expertise or want to build it
  • Cost control at large scale matters
  • Data sovereignty is required

Choose BigQuery if:

  • Serverless simplicity is priority
  • Variable/unpredictable workloads
  • Deep Google Cloud integration
  • Team lacks database operations expertise
  • Occasional heavy analytical workloads

For most data teams without strong database operations expertise, BigQuery is the safer choice. The per-query costs are manageable with good practices, and you avoid the operational burden entirely.

For teams building analytics products or running high-frequency dashboards, ClickHouse's performance and cost model at scale are hard to beat, if you're willing to invest in operating it.