Analytical queries that run in seconds on small datasets can take minutes or time out entirely as data grows to hundreds of millions of rows. Teams running MySQL or Aurora for both transactional and analytical workloads eventually hit a wall where dashboard queries compete with user-facing features for CPU and memory.

This guide compares Aurora MySQL and ClickHouse^® across architecture, query performance, scaling models, and cost to help you decide which database fits your workload. You'll see benchmark results for analytical queries, learn when a hybrid approach makes sense, and understand the tradeoffs between transactional and analytical database architectures.

Understanding OLTP and OLAP workloads

ClickHouse^® is an analytical database optimized for OLAP (Online Analytical Processing) using a columnar storage model, while Amazon Aurora for MySQL is a transactional database designed for OLTP (Online Transaction Processing) with row-based storage. Pick ClickHouse^® when you're running analytical queries on large datasets, and pick Aurora when you're handling transactional workloads like user logins and order updates.

OLTP systems handle frequent, small transactions. Think of a user logging in, placing an order, or updating their profile. OLAP systems process large volumes of data for reporting and aggregations. Think of calculating daily active users across millions of events or summing revenue across all orders in the last quarter.

Read vs write patterns

Transactional systems see many small writes scattered across different rows, with reads targeting specific records using primary keys. A typical query fetches a single user's profile or updates one order status.

Analytical systems perform bulk inserts of thousands or millions of rows at once. Reads scan large portions of tables for aggregations. A typical query might scan 10 million events to calculate user retention by week.

Query latency and throughput metrics

Query latency measures how long a single query takes to return results, typically in milliseconds. OLTP databases aim for response times under 10ms for point lookups, keeping user-facing features fast.

Query throughput measures how many queries a system processes per second or how much data it scans per second. OLAP databases optimize for scanning billions of rows in seconds rather than minimizing individual query latency.

Why engineers compare ClickHouse^® and Aurora MySQL

Teams compare these databases when their application outgrows a single-purpose architecture. Applications often start with Aurora handling both transactional and analytical workloads, but performance degrades as data grows and analytical queries compete with transactions for CPU and memory.

Pain points with MySQL analytics

Aurora's row-based storage reads entire rows even when queries only need a few columns. A query summing revenue across millions of orders reads customer names, addresses, shipping details, and every other column, wasting I/O on unused data.

Query performance degrades as tables grow beyond tens of millions of rows. Aggregations that once took seconds start taking minutes. Dashboard queries that refresh every few minutes begin timing out.

Long-running aggregations consume CPU and memory that transactional queries need for user-facing features. Connection pools get exhausted when analytical queries hold connections open for 30+ seconds.

Goals of real-time dashboards

Real-time dashboards require sub-second query response times even as data volumes grow. Users expect dashboards to refresh every few seconds with fresh data, not minutes-old snapshots.

The database handles both high write throughput and concurrent analytical queries. When 50 users view dashboards simultaneously, each making dozens of API calls, the database processes hundreds of concurrent queries without degrading performance.

Aurora MySQL architecture and performance profile

Aurora separates compute and storage into independent layers, with up to 15 read replicas sharing a single distributed storage volume. Storage scales automatically up to 128 TB without downtime.

The storage layer replicates data across three availability zones automatically. When a write occurs, the storage layer replicates it across six copies before acknowledging the write.

Storage layer separation

Aurora's compute nodes connect to a shared storage layer that handles all writes and replication. Read replicas connect to the same storage volume, receiving updates within milliseconds of the write. This eliminates the replication lag seen in standard MySQL read replicas.

Query planner strengths

Aurora's MySQL-compatible query optimizer excels at point lookups using primary keys or secondary indexes. For queries like SELECT * FROM users WHERE user_id = 123, Aurora locates and returns the row in milliseconds using B-tree indexes.

Join performance for small to medium result sets benefits from Aurora's buffer pool caching. Queries joining a few tables on indexed columns with selective filters typically complete in tens of milliseconds.

Limits for large aggregations

Aurora's row-based storage becomes inefficient for analytical queries that scan large portions of tables. A query calculating average order value across millions of orders reads every column in every row, even though it only needs the order value column.

Single-threaded query execution limits Aurora's ability to parallelize complex aggregations. While Aurora can use multiple CPU cores for different queries, a single analytical query runs on one core.

ClickHouse^® columnar engine and performance profile

ClickHouse^® stores data in columns rather than rows, reading only the columns a query needs. A query summing revenue across millions of orders reads just the revenue column, ignoring customer names, addresses, and other unused fields. This reduces I/O by 10x to 100x compared to row-based storage.

The columnar format enables aggressive compression because adjacent values in a column are often similar. Timestamp columns compress to a few bytes per value using delta encoding.

Compression and vectorized execution

ClickHouse^® achieves compression ratios between 10:1 and 100:1 depending on data types. A 1 TB Aurora table might compress to 10-50 GB in ClickHouse^®, reducing both storage costs and the amount of data queries scan from disk.

Vectorized execution processes thousands of values at once using CPU SIMD instructions. Instead of calculating SUM(revenue) one row at a time, ClickHouse^® loads thousands of revenue values into CPU registers and sums them in parallel.

Parallelism and MPP scaling

ClickHouse^® automatically parallelizes queries across all available CPU cores. A query scanning 100 million rows on an 8-core machine splits the work into 8 parallel tasks, reducing query time by nearly 8x.

Distributed queries split work across multiple nodes in a cluster. A query on a 3-node cluster divides the data into thirds, with each node processing its portion in parallel, then combining results.

Common OLAP optimizations

Primary keys in ClickHouse^® determine physical data ordering on disk, unlike traditional databases where primary keys enforce uniqueness. Ordering data by commonly filtered columns like timestamp allows ClickHouse^® to skip entire data blocks that don't match query filters.

• Materialized views: Pre-aggregate data at write time, trading slightly higher write costs for significantly faster query performance
• Projection columns: Create alternative sort orders for the same data, optimized for different query patterns
• Sorting keys: Order data by frequently queried columns to enable data skipping and faster scans

Head-to-head benchmark results for analytical queries

Performance differences between Aurora and ClickHouse^® become significant as data volumes grow. While Aurora performs well for simple queries on small datasets, ClickHouse^® maintains consistent performance as data scales to billions of rows.

One billion row aggregation test

A test aggregating one billion events by day and event type takes 45-90 seconds on Aurora with proper indexes. The same query on ClickHouse^® completes in 0.5-2 seconds, a 20-50x performance improvement.

SELECT 
    toDate(timestamp) AS day,
    event_type,
    COUNT(*) AS event_count
FROM events
WHERE timestamp >= '2024-01-01'
GROUP BY day, event_type
ORDER BY day DESC;

Aurora builds large in-memory hash tables for the GROUP BY operation, often exceeding available memory and spilling to disk. ClickHouse^® streams aggregations with minimal memory overhead, processing billions of rows with just a few gigabytes of RAM.

Concurrent dashboard workload

Testing 50 concurrent users each running different dashboard queries shows Aurora's performance degrading as query queue depth increases. Response times increase from 2-3 seconds with 10 concurrent users to 30-60 seconds with 50 users.

ClickHouse^® maintains consistent query times even under high concurrency. The same 50 concurrent queries complete in 0.5-2 seconds each, with minimal variation as concurrency increases.

Write ingestion stress test

Aurora handles transactional inserts efficiently, supporting thousands of small INSERT statements per second. Bulk loading large datasets is slower, typically ingesting 10,000-50,000 rows per second using LOAD DATA INFILE.

ClickHouse^® optimizes for bulk ingestion, handling 100,000-1,000,000 rows per second per node depending on data complexity. The columnar format and lack of row-level locking allow ClickHouse^® to write large batches efficiently, though it performs poorly for small single-row inserts.

Cost comparison at 100 GB and 1 TB scales

Total cost includes compute, storage, and operational expenses. At 100 GB, Aurora costs roughly $200-400 per month for a db.r6g.large instance with storage. ClickHouse^® Cloud or Tinybird costs $150-300 per month for equivalent compute resources, though compression reduces actual storage needs by 5-10x.

At 1 TB, cost differences become more pronounced. Aurora storage costs $0.10 per GB-month, totaling $100 for storage alone, plus $400-800 for compute. ClickHouse^® compresses that 1 TB to 100-200 GB, reducing storage costs to $10-20 per month.

Compute and storage pricing

Aurora charges separately for compute instances and storage. Compute costs range from $0.29/hour for a db.r6g.large to $4.66/hour for a db.r6g.8xlarge. Storage costs $0.10 per GB-month for the first 128 TB.

ClickHouse^® Cloud pricing varies by provider, with compute costs typically ranging from $0.20-0.50 per hour for small instances to $3-6 per hour for large instances. Tinybird offers usage-based pricing starting at $0 for the free tier, scaling based on data processed and API requests.

• Compression advantages: ClickHouse^®'s 10-50x compression means a 1 TB Aurora dataset might only consume 20-100 GB in ClickHouse^®
• Compute efficiency: ClickHouse^® processes the same queries with fewer CPU resources due to columnar storage
• Scaling costs: Aurora requires larger instances for analytical workloads, while ClickHouse^® maintains performance with smaller instances

Scaling and high availability models

Aurora scales read capacity by adding up to 15 read replicas, each serving read-only queries while sharing the same storage volume. Write capacity scales vertically by moving to larger instance types, though this requires brief downtime.

ClickHouse^® scales horizontally by adding more nodes to a cluster, distributing both data and query load. Each node handles a portion of the data, and queries automatically parallelize across all nodes.

Vertical vs horizontal growth

Aurora's vertical scaling approach works well for moderate growth but hits limits at the largest instance sizes. A db.r6g.16xlarge with 512 GB RAM and 64 vCPUs costs $9,000+ per month.

ClickHouse^®'s horizontal scaling adds commodity hardware nodes as needed. A 10-node cluster with 16 cores per node provides 160 cores for parallel query execution, far exceeding what's possible with vertical scaling alone.

Read replicas and sharding

Aurora read replicas share storage with the primary instance, providing read scaling without data duplication. Replicas receive updates within milliseconds through the shared storage layer.

ClickHouse^® sharding distributes data across nodes, with each node storing a subset of the data. Queries automatically route to the relevant shards, and ClickHouse^® combines results from multiple shards.

SQL compatibility and migration paths

Aurora supports standard MySQL syntax and most MySQL features, making it compatible with existing MySQL applications. ClickHouse^® uses SQL with extensions for analytical operations but lacks some transactional features like UPDATE and DELETE on individual rows.

Migration from Aurora to ClickHouse^® typically involves extracting data, transforming it for ClickHouse^®'s data model, and loading it into ClickHouse^® tables. The specific approach depends on whether you're migrating all data or running a hybrid architecture.

Dual-write with CDC

Change Data Capture (CDC) tracks changes in Aurora and replicates them to ClickHouse^® in near real-time. Tools like Debezium or AWS DMS capture INSERT, UPDATE, and DELETE operations from Aurora's binary logs and stream them to ClickHouse^®.

CDC works well for hybrid architectures where transactional data stays in Aurora and analytical queries run against ClickHouse^®. Applications write to Aurora as usual, and CDC automatically propagates changes to ClickHouse^® within seconds.

Federated queries with JDBC

ClickHouse^® can query Aurora tables directly using the JDBC table engine, though performance is limited by network latency and Aurora's row-based storage.

SELECT 
    ch.event_date,
    au.customer_name,
    SUM(ch.revenue) AS total_revenue
FROM clickhouse_events ch
JOIN jdbc('jdbc:mysql://aurora-endpoint/db', 'customers') au
    ON ch.customer_id = au.id
GROUP BY ch.event_date, au.customer_name;

Federated queries help during migration phases when some data remains in Aurora. You can query both databases from ClickHouse^®, though copying frequently accessed Aurora data into ClickHouse^® provides better performance.

Offline backfill using S3

Bulk migration exports Aurora data to S3 in formats like Parquet or CSV, then loads it into ClickHouse^®. This approach handles large datasets efficiently, with export and import both parallelizable across multiple workers.

When to run a hybrid ClickHouse^® plus Aurora stack

Many teams run both databases, using each for its strengths. Aurora handles transactional workloads requiring strong consistency and complex updates, while ClickHouse^® handles analytical queries and reporting.

Real-time analytics sidecar

The sidecar pattern keeps transactional data in Aurora and replicates it to ClickHouse^® for analytics. Applications write to Aurora for user-facing features, and CDC streams changes to ClickHouse^® within seconds.

This architecture separates concerns, allowing each database to scale independently. Aurora scales for transactional load, and ClickHouse^® scales for analytical query volume.

Cost-optimized cold storage

Tiered storage keeps recent data in Aurora for transactional access and moves older data to ClickHouse^® for analytical queries. After 90 days, orders might move from Aurora to ClickHouse^®, reducing Aurora storage costs while maintaining queryability.

ClickHouse^®'s compression makes it cost-effective for storing years of historical data. Data that would cost $100/month in Aurora might cost $5-10/month in ClickHouse^® after compression.

Decision checklist for your use case

Picking between Aurora and ClickHouse^® depends on workload characteristics, team capabilities, and business requirements. Most teams benefit from using both databases, though some workloads clearly favor one over the other.

Query pattern fit

If most queries are point lookups or small updates, Aurora excels. If most queries scan large datasets for aggregations or time-series analysis, ClickHouse^® provides 10-50x better performance.

Team skill set

Aurora requires standard MySQL administration skills, which most backend engineers already have. Setting up read replicas, configuring backups, and optimizing queries use familiar MySQL tools.

ClickHouse^® has a steeper learning curve, requiring understanding of columnar storage, sorting keys, and distributed query execution. Managed services like Tinybird eliminate most operational complexity.

Time to market considerations

Aurora integrates easily with existing MySQL applications, often requiring no code changes beyond connection string updates. Migration from standard MySQL to Aurora typically completes in hours or days.

ClickHouse^® migration takes longer, requiring schema redesign, data transformation, and query rewrites. However, managed services like Tinybird reduce time to market by handling infrastructure setup, providing streaming ingestion, and offering instant API endpoints. Teams can go from zero to production-ready ClickHouse^® APIs in hours rather than weeks.

FAQs about ClickHouse^® vs Aurora MySQL

Can I query ClickHouse^® with standard MySQL clients?

ClickHouse^® supports the MySQL wire protocol for basic queries, allowing MySQL clients to connect and run simple SELECT statements. Advanced ClickHouse^® features like array functions, nested data types, and distributed queries require the native ClickHouse^® client or HTTP interface.

Does Aurora Serverless v2 close the analytics performance gap?

Aurora Serverless v2 improves scaling flexibility by automatically adjusting compute capacity based on load, but it doesn't change the fundamental row-based storage limitations. Analytical queries still read entire rows and execute single-threaded, so performance for large aggregations remains 10-50x slower than ClickHouse^®.

Is Tinybird open source or managed only?

Tinybird is a managed ClickHouse^® service built on top of open-source ClickHouse^®. While ClickHouse^® itself is open source and can be self-hosted, Tinybird provides managed infrastructure, developer-friendly APIs, streaming ingestion, and operational tooling.

Start building real-time analytics today with Tinybird

Tinybird eliminates ClickHouse^® operational complexity by providing managed infrastructure, streaming ingestion, and instant API endpoints. Developers define data pipelines as code, test locally, and deploy to production in minutes without managing clusters or configuring replication.

The platform handles scaling automatically as data volumes and query loads grow. When your dashboard traffic increases or data ingestion spikes, Tinybird adjusts resources without manual intervention.

Sign up for a free Tinybird plan

Sign up for a free Tinybird plan to start building ClickHouse^®-powered APIs without infrastructure work. The free tier includes enough resources to build proof-of-concept analytics features and test performance with realistic data volumes.

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