ClickHouse^® vs Amazon Athena for real-time analytics

Choosing between ClickHouse and Amazon Athena often comes down to a tradeoff between consistent sub-second query performance and the simplicity of serverless infrastructure. ClickHouse runs on dedicated compute and delivers predictable low-latency analytics, while Athena spins up resources on-demand to query data lakes without managing servers.

This guide compares their architectures, performance characteristics, cost models, and ideal use cases to help you decide which fits your real-time analytics requirements.

What is ClickHouse and what is Amazon Athena

ClickHouse is a columnar OLAP database built for analytical workloads that need sub-second query response times. Amazon Athena is AWS's serverless query service that lets you analyze data sitting in S3 using standard SQL. The main difference comes down to this: ClickHouse runs on dedicated infrastructure and delivers consistent performance for operational analytics, while Athena spins up compute on-demand and works better for occasional exploration of data lakes.

Storage layer

ClickHouse stores data using the MergeTree engine, which compresses columns separately and builds primary key indexes. When you run a query, ClickHouse only reads the specific columns you asked for, which cuts down on disk I/O and speeds up response times. A table with 50 columns that only queries 5 of them will read just 10% of the data.

Athena queries data directly in S3 without moving it anywhere. You can query Parquet, ORC, CSV, or JSON files that already live in your buckets. The data stays put, and Athena reads what it needs when you run a query.

Query execution model

ClickHouse runs queries on servers with persistent memory caches. After the first query loads data into memory, similar queries run faster because they hit the warm cache. If you're querying the same tables repeatedly, you get consistent sub-second latency.

Athena uses the Presto engine across temporary compute resources that start fresh for each query. Each query potentially begins cold, though Athena caches some metadata and recent query results. The compute resources disappear after your query finishes.

Serverless vs dedicated runtime

ClickHouse requires you to provision infrastructure, either by managing servers yourself or subscribing to a managed service like ClickHouse Cloud or Tinybird. You decide how much capacity to allocate, though managed services handle scaling and maintenance.

Athena operates as a fully serverless product. You don't provision any infrastructure, and AWS scales compute automatically based on your query complexity and data volume.

Query latency and throughput benchmarks

Query speed differs substantially between ClickHouse and Athena, especially when multiple users query at the same time. ClickHouse typically returns results in under a second for complex analytical queries, even with dozens of concurrent users. Athena's performance varies more, depending on how much data you scan, query complexity, and how busy AWS's shared infrastructure is at that moment.

Cold start versus warm cache

ClickHouse keeps a warm cache across queries. The first query after loading new data might take a bit longer, but subsequent queries benefit from cached data structures and indexes. For dashboards that refresh every 30 seconds, this means consistent response times, with P95 latencies under 145ms in production benchmarks.

Athena queries often start cold because compute resources spin up on-demand. A query scanning 100 GB of Parquet data might take 30-60 seconds on the first run and similar times on later runs, unless you hit Athena's result cache within 24 hours.

P95 latency under load

ClickHouse maintains predictable P95 latency even with hundreds of concurrent queries. If your median query takes 200ms, your P95 typically stays under 500ms because dedicated resources prevent queries from competing for the same compute.

Athena's P95 latency can spike during peak usage on AWS's shared infrastructure. You might see P95 times that are 5-10x higher than P50, with latencies spiking from 300ms to 10 seconds when scanning large datasets during busy periods.

Join and aggregation performance

ClickHouse optimizes analytical operations through vectorized execution, which processes data in batches using CPU SIMD instructions. Joining a 10 billion row events table with a 1 million row users table typically finishes in under a second with proper indexing.

Athena handles simple aggregations well but slows down on complex joins across large tables. The same 10 billion row join might take 2-5 minutes, depending on how you've partitioned your data and whether you're using columnar formats like Parquet.

Streaming ingestion and freshness

Data freshness matters differently for real-time analytics versus batch analysis. ClickHouse handles streaming ingestion natively, making data queryable within seconds of arrival. Athena relies on batch processing that introduces minutes to hours of latency between data arrival and query availability.

Kafka and Kinesis pipelines

ClickHouse offers native Kafka integration through the Kafka table engine. You point it at your Kafka topics, and it continuously pulls data and makes it immediately queryable. You can insert 2 million events per second with minimal configuration.

Athena requires services like Kinesis Data Firehose to batch stream data into S3 first. Firehose buffers data for 60-900 seconds or until reaching a size threshold, then writes files to S3. This batching introduces latency before queries can see new data.

Handling late-arriving events

ClickHouse engines like ReplacingMergeTree and CollapsingMergeTree handle out-of-order data efficiently. Late-arriving events update existing rows during background merges without requiring full table rewrites. If an event arrives 5 minutes late, it gets merged with existing data automatically.

Athena queries across multiple S3 partitions to catch late-arriving data, but you might run periodic compaction jobs to optimize performance. Some teams use AWS Glue to reorganize files nightly, trading data freshness for query speed.

Materialized view patterns

ClickHouse materialized views update automatically as new data arrives. A materialized view that counts events by user updates immediately when new events insert, with no orchestration needed. The pre-aggregated results stay current in real-time.

Cost model and total cost at scale

Cost structures work fundamentally differently between fixed infrastructure and pay-per-query pricing. ClickHouse costs stay constant each month regardless of query frequency. Athena charges based on data scanned, making costs heavily dependent on usage patterns.

Compute pricing

ClickHouse costs remain the same whether you run 10 queries or 10 million queries per month. A managed ClickHouse cluster might cost $500-5000/month depending on instance sizes, but query volume doesn't change that price.

Athena charges $5 per terabyte of data scanned. If you scan 100 GB per query and run 1,000 queries monthly, you'll pay around $500 just for compute, before S3 storage and data transfer costs.

Storage and data scanned fees

ClickHouse compresses data aggressively, often achieving 10-20x compression for analytical workloads. 1 TB of raw JSON data might occupy only 50-100 GB in ClickHouse, reducing both storage costs and query times.

Athena's costs scale directly with data volume scanned. Poorly optimized queries that scan entire tables become expensive quickly. Using columnar formats like Parquet and partitioning by common filter columns reduces costs, but you still pay for every byte scanned.

Concurrency surcharge scenarios

ClickHouse handles concurrent queries without additional charges. You might need larger instances as concurrency increases, but costs scale with infrastructure size, not query count.

Athena doesn't charge explicitly for concurrency, but performance degrades when too many queries run simultaneously. You might also hit service quotas, typically 25 concurrent queries per account, that require requesting limit increases from AWS.

Scalability for high-concurrency workloads

Scaling works differently between dedicated infrastructure and serverless architectures, especially when dealing with high-concurrency workloads. ClickHouse scales vertically by adding more powerful instances and horizontally by distributing data across nodes. Athena relies on AWS's automatic resource allocation.

ClickHouse distributes data across multiple nodes using sharding keys, letting queries run in parallel. A 10-node cluster processes 10x more data than a single node, with query latency staying constant if data distributes evenly.

Athena parallelizes queries across S3 partitions automatically, but parallelization depends on how you've organized your data. Well-partitioned data enables better parallelism, while poorly organized data forces sequential processing.

Developer experience and operational overhead

Infrastructure management requirements differ substantially. ClickHouse demands more upfront setup and ongoing maintenance when self-hosted. Athena requires minimal infrastructure work but offers less control over performance optimization.

Schema evolution

ClickHouse supports ALTER TABLE operations for adding columns, modifying types, and updating indexes. Some operations require table rewrites that can take time on large datasets. Adding a nullable column happens quickly, but changing a column type might rewrite the entire table.

Athena uses schema-on-read where you define schemas in AWS Glue Data Catalog without modifying underlying data files. Adding a new column means updating the Glue schema, which takes effect immediately with no data movement.

Observability and alerting

ClickHouse requires setting up monitoring for query performance, disk usage, memory consumption, and replication lag. You'd typically use tools like:

• Prometheus and Grafana: For metrics collection and visualization
• System tables: ClickHouse's built-in system.query\_log and system.metrics tables
• Custom alerting: Rules for slow queries, high memory usage, or disk space

Athena integrates with CloudWatch automatically, logging query execution times, data scanned, and errors. You can set CloudWatch alarms for failed queries or expensive operations without additional setup.

CI/CD workflow differences

ClickHouse deployments involve database schema migrations, index updates, and configuration changes that need version control and testing. Most teams use migration tools and staging environments to validate changes before production.

Athena workflows center around SQL queries and Glue schema definitions you can version control in Git. Deploying changes typically means updating Glue schemas or query definitions in your application code.

Best-fit use cases for each engine

Choosing between ClickHouse and Athena depends on your latency requirements, query frequency, and operational preferences. ClickHouse fits operational analytics where consistent sub-second performance matters. Athena works well for infrequent analysis of large datasets.

Application analytics

ClickHouse powers user-facing analytics dashboards displaying real-time metrics like active users, conversion rates, and feature usage. Product analytics platforms use ClickHouse to deliver instant query results to thousands of concurrent users.

Athena works better for internal business intelligence where analysts run ad-hoc queries a few times daily. Historical analysis of user behavior over months or years fits Athena's batch-oriented model well.

Ad hoc data exploration

ClickHouse enables interactive data exploration with sub-second response times. Data scientists can iterate quickly through multiple queries, testing hypotheses and discovering patterns without waiting.

Athena excels at one-time analysis of massive datasets already in S3, like analyzing years of application logs or processing raw event streams. You can query petabytes without moving data or setting up infrastructure.

Machine learning feature stores

ClickHouse serves real-time features for ML models needing low-latency access to aggregated user behavior. Recommendation systems or fraud detection models can retrieve features computed from billions of events in milliseconds.

Athena handles batch feature engineering for model training. Training jobs that process months of data can run overnight without time pressure, computing features from historical data once and storing results for later use.

Migration paths from Athena to ClickHouse

Moving from Athena to ClickHouse requires planning around data formats, ingestion pipelines, and query rewrites. Most teams migrate incrementally, starting with high-frequency queries that benefit most from ClickHouse's performance.

1. Assess data formats

Start by evaluating your current S3 data organization. Parquet and ORC files can be read directly by ClickHouse using the s3 table function, though native ClickHouse formats perform better for frequent queries.

2. Set up dual write

Implement parallel data pipelines that write to both S3 for Athena and ClickHouse during the transition. This approach lets you validate ClickHouse query results against Athena before cutting over production traffic.

3. Cut over queries

Gradually migrate query workloads from Athena to ClickHouse, starting with the most performance-sensitive queries. Monitor query latency and cost savings to validate that ClickHouse delivers expected improvements.

Build real-time APIs with Tinybird on ClickHouse

Tinybird eliminates the operational overhead of managing ClickHouse infrastructure while providing the same query performance. Developers can focus on building features rather than configuring clusters, setting up monitoring, or tuning performance. The platform handles infrastructure scaling automatically as your data and query volumes grow.

You write SQL queries and Tinybird deploys them as secure, parameterized REST APIs that your application calls directly. Here's how setup complexity compares:

Sign up for a free Tinybird account to start building real-time analytics APIs without managing infrastructure.

Frequently asked questions about ClickHouse vs Amazon Athena

Does ClickHouse support serverless deployment?

Traditional ClickHouse requires dedicated infrastructure, but managed services like Tinybird provide a serverless-like developer experience. You get ClickHouse's performance without operational complexity, and the platform scales automatically based on workload.

How do I secure API endpoints on top of ClickHouse?

Self-hosted ClickHouse requires custom authentication using database users and roles. Tinybird provides built-in token-based security and parameterized queries that prevent SQL injection, letting you create secure APIs without additional authentication code.

Can I keep data in S3 and still query it with ClickHouse?

ClickHouse supports querying S3 data directly using the s3 table function. Performance is better with data stored in ClickHouse's native format, but the s3 function works well for infrequently accessed historical data that doesn't need sub-second latency.

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