Choosing between ClickHouse® and CrateDB means weighing raw analytical speed against operational simplicity and search capabilities. Both databases handle large-scale analytics, but they take different approaches to storage, query execution, and developer experience.
This comparison covers performance benchmarks, architectural differences, SQL capabilities, operational complexity, and when each database makes sense for your application.
Performance benchmarks at a glance
ClickHouse is an open-source columnar database built for analytical query speed, while CrateDB is a distributed SQL database designed for real-time analytics and search. The main difference shows up in how they handle large-scale analytical queries: ClickHouse uses columnar storage and vectorized execution to process aggregations faster, while CrateDB offers PostgreSQL-compatible SQL and built-in full-text search.
When you run complex aggregations across billions of rows, ClickHouse typically returns results in under a second. CrateDB handles the same queries but usually takes longer because its hybrid storage model reads more data from disk than ClickHouse's pure columnar approach.
Ingest throughput results
ClickHouse can ingest hundreds of thousands to millions of rows per second through its Kafka table engine and batch loading capabilities. The exact rate depends on your hardware and how complex your data schema is.
CrateDB reaches tens of thousands to hundreds of thousands of rows per second for similar workloads. The gap widens as data volume grows, where ClickHouse's columnar compression keeps write speeds higher.
Analytical query latency results
For queries with GROUP BY operations across large datasets, ClickHouse processes data in batches using SIMD instructions. This vectorized execution means the CPU can apply the same operation to multiple values at once, which speeds up calculations significantly.
CrateDB executes analytical queries effectively but takes longer for scan-heavy operations. The difference becomes more noticeable when you're reading specific columns from wide tables, where ClickHouse only touches the columns you need.
Concurrency scaling results
ClickHouse maintains consistent query latency even when multiple users run queries simultaneously. The shared-nothing architecture means queries run independently without fighting over resources.
CrateDB scales horizontally for concurrent queries too, though analytical performance under high concurrency typically trails ClickHouse. For workloads mixing point lookups with aggregations, CrateDB's architecture provides more balanced results.
Architectural differences that drive speed
The performance gap between ClickHouse and CrateDB comes from different storage and execution choices. ClickHouse stores each column separately and compresses them independently, while CrateDB uses a hybrid model built on Apache Lucene.
Columnar vs hybrid storage layout
When ClickHouse stores a table with 50 columns, each column lives in its own file on disk. A query that reads 3 columns only touches those 3 files, leaving the other 47 untouched. This reduces I/O dramatically for analytical queries.
CrateDB's hybrid approach combines row and columnar elements, which gives you flexibility for different query patterns including full-text search. However, this design doesn't achieve the same compression ratios or scan speeds as pure columnar storage for aggregations.
Vectorized execution engines
ClickHouse processes 1,000 rows at a time instead of one row at a time. Modern CPUs have SIMD instructions that can add 8 numbers in a single CPU cycle, and vectorized execution takes advantage of this. When you sum a column with a billion rows, this approach makes a measurable difference.
CrateDB's query engine processes data more traditionally, which works fine but doesn't leverage SIMD instructions as aggressively. You'll notice this most in queries with heavy computation like complex math operations across millions of rows.
Sharding and replication approaches
Both databases split data across multiple nodes through sharding. ClickHouse gives you explicit control over shard placement using ReplicatedMergeTree table engines with ZooKeeper or ClickHouse Keeper for coordination.
CrateDB handles sharding and replication automatically, which simplifies cluster setup but offers less control over data placement. Adding nodes triggers automatic rebalancing, whereas ClickHouse requires you to plan data redistribution.
Ingestion throughput and latency under load
Real-world applications ingest data continuously while serving queries. ClickHouse and CrateDB handle this differently, with ClickHouse providing more native streaming options.
Kafka and streaming pipelines
ClickHouse has a Kafka table engine that pulls data directly from Kafka topics and writes it to the database. You can transform data during ingestion and the engine handles backpressure automatically when the database can't keep up with the stream.
CrateDB connects to Kafka through external tools or custom code rather than a native engine. This adds moving parts to your pipeline and typically increases the latency between when data hits Kafka and when you can query it, though data visibility within 1 second is achievable.
Batch CSV or Parquet loads
Both databases import files like CSV and Parquet, but ClickHouse excels at parallel loading. When you have 100 Parquet files to load, ClickHouse can ingest them simultaneously across cluster nodes.
CrateDB handles batch imports well but at lower throughput for large analytical datasets. The difference matters when you're loading terabytes of historical data where ClickHouse's columnar format and compression reduce both load time and storage size.
Back-pressure and reliability features
ClickHouse offers Buffer tables for batching small writes, materialized views for transforming data during ingestion, and distributed tables for writing across clusters. These features let you control exactly how ingestion behaves when things go wrong.
CrateDB provides simpler ingestion with automatic retry logic built in. This reduces configuration work but gives you fewer options for custom error handling.
SQL feature parity and limitations
Both databases speak SQL, but their dialects and capabilities differ in ways that matter for complex analytics.
Joins and subqueries support
ClickHouse supports standard SQL joins but optimizes for patterns where one table is much smaller than the other. The database lacks a cost-based optimizer, so complex multi-way joins often require manual tuning to get good performance.
CrateDB offers more familiar join behavior if you're coming from PostgreSQL, with better support for complex join patterns. However, joins on very large tables typically run slower than ClickHouse for analytical workloads where you can denormalize data.
Window functions and rollups
Both databases support window functions like ROW\_NUMBER(), RANK(), LAG(), and LEAD() for calculations across related rows. ClickHouse's vectorized execution typically processes these faster on large datasets.
ClickHouse also supports ROLLUP, CUBE, and GROUPING SETS for generating subtotals and totals in a single query. CrateDB offers similar functionality through its SQL interface.
Materialized views and TTL policies
ClickHouse materialized views update automatically as new data arrives, pre-computing aggregations that would otherwise require scanning large amounts of raw data. For example, you can maintain a materialized view that calculates hourly user counts, and it updates incrementally as new events stream in.
CrateDB doesn't offer the same materialized view capabilities. Applications either implement their own aggregation layers or accept longer query times for complex analytics.
ClickHouse TTL policies automatically delete or move old data based on timestamp columns. If you only need the last 90 days of raw events but want to keep aggregated data longer, TTL policies handle this without custom code.
Data model fit for time-series, JSON and vectors
Modern analytics often involves more than just numbers and strings. Both databases handle specialized data types, but with different strengths.
Time-series functions and retention
ClickHouse includes functions like exponentialMovingAverage() and timeSeriesGroupSum() built specifically for time-series analysis. These functions are optimized for columnar storage and execute quickly on billions of timestamped events.
CrateDB handles time-series data using standard SQL date and time functions rather than specialized operations. It provides time-based partitioning for managing data retention, which works well for organizing data by time periods.
JSON column handling and search
ClickHouse stores JSON as strings or using the experimental JSON type that allows querying nested fields efficiently. Functions like JSONExtract() and JSONHas() let you work with JSON without flattening the structure first.
CrateDB provides stronger native JSON support where you can store and query JSON objects using familiar SQL syntax. You don't write explicit extraction functions, which makes working with dynamic schemas easier.
For full-text search within JSON documents, CrateDB's Lucene foundation provides more powerful capabilities than ClickHouse. If you're searching across text fields in JSON documents, CrateDB's search features are more comprehensive.
Vector similarity search support
ClickHouse recently added vector similarity search for AI applications that store embeddings. Functions like cosineDistance() and L2Distance() work with specialized indexes for approximate nearest neighbor search.
CrateDB doesn't currently offer vector search features, making ClickHouse the better choice for applications combining analytical queries with vector similarity operations like semantic search.
Operational complexity and scaling paths
Running these databases in production involves different operational tradeoffs. ClickHouse gives you more control but requires more expertise, while CrateDB automates more operations.
Cluster elasticity and autoscaling
Adding nodes to a ClickHouse cluster requires planning how data will be redistributed. The database doesn't automatically rebalance existing data when you add capacity, so you implement resharding strategies or use coordination tools.
CrateDB rebalances data automatically when you add or remove nodes. This makes scaling operations more straightforward but provides less control over where data lives and how queries route.
Rolling upgrades and versioning
ClickHouse supports rolling upgrades where you update nodes one at a time without downtime. Some versions introduce breaking changes that require coordinated upgrades across the cluster, so you plan version migrations carefully.
CrateDB also supports rolling upgrades with automatic version compatibility checks. The upgrade process is generally more automated, reducing the risk of version mismatch issues.
Observability and alerting hooks
ClickHouse exposes metrics through system tables that you can query like any other data. This makes building custom monitoring dashboards straightforward, but you set up comprehensive observability from scratch.
CrateDB provides built-in monitoring endpoints that integrate with standard observability tools more easily. This reduces initial setup time but may offer less flexibility for custom metrics.
Developer experience from local dev to prod
The workflow for building applications differs between these databases in ways that affect how quickly you can ship features.
Docker compose and local mocks
Both databases run locally in Docker for development. ClickHouse requires more configuration to set up a realistic multi-node cluster locally, though single-node testing works fine for many use cases.
CrateDB offers simpler local setup with fewer configuration requirements. Its single-node mode works well for development, and scaling to multiple nodes locally is more straightforward.
Tinybird provides a local development runtime that gives you a ClickHouse environment with simplified configuration, letting you work with realistic ClickHouse features locally before deploying.
CI/CD workflow and schema migration
ClickHouse schema changes require coordination in production, especially for distributed tables. Adding columns or changing types can be done online, but operations like changing primary keys require rebuilding tables.
CrateDB handles schema changes more dynamically, allowing fields to be added without explicit ALTER statements in some cases. This flexibility speeds up development but can lead to schema drift without careful management.
Tinybird manages schemas and queries as code in .datasource and .pipe files, enabling version control and automated deployments through standard CI/CD pipelines.
Secure API endpoint generation
ClickHouse doesn't generate APIs automatically. Applications query the database directly using HTTP or native protocol clients, which means you build your own API layer with parameter validation, authentication, and rate limiting.
CrateDB also requires a custom API layer for exposing query results. The database provides a PostgreSQL-compatible wire protocol and HTTP endpoint, but applications handle authentication and access control separately.
Tinybird generates secure, parameterized REST APIs directly from SQL queries, handling authentication, rate limiting, and parameter validation automatically. This eliminates custom API code and reduces the time from query to production endpoint. Sign up for a free Tinybird plan to see how quickly you can turn ClickHouse queries into production APIs.
Ecosystem and integrations you'll actually use
The tools and libraries available for each database affect how easily they fit into existing data stacks.
BI and dashboard tools
ClickHouse integrates with popular BI tools through JDBC/ODBC drivers and native connectors:
- Grafana for time-series visualization and monitoring dashboards
- Tableau for business intelligence and data exploration
- Looker for embedded analytics and data modeling
- Metabase for self-service analytics
CrateDB's PostgreSQL compatibility means it works with any tool supporting PostgreSQL. This provides broader compatibility with BI and visualization platforms, reducing integration friction.
Python and Node drivers
ClickHouse offers official and community-maintained client libraries for multiple languages. The clickhouse-driver for Python and @clickhouse/client for Node.js provide idiomatic interfaces for querying and ingesting data.
Spark, Flink and dbt adapters
ClickHouse has connectors for Apache Spark and Flink, enabling integration with large-scale data processing pipelines. The dbt adapter for ClickHouse allows analytics engineers to build transformation pipelines using dbt's workflow.
CrateDB offers similar integration capabilities with data processing frameworks, though the ecosystem of pre-built connectors is smaller.
Deployment options and managed services compared
Infrastructure choices affect operational costs and team responsibilities. Both databases offer self-hosted and managed options with different tradeoffs.
Self-hosted Kubernetes
Running ClickHouse on Kubernetes requires configuring StatefulSets, persistent volumes, and networking. The ClickHouse Operator for Kubernetes simplifies some deployment aspects but still requires expertise in both ClickHouse and Kubernetes.
CrateDB is designed with Kubernetes in mind and provides operators that automate much of the deployment and scaling process.
ClickHouse Cloud vs Tinybird
ClickHouse Cloud is the official managed service from ClickHouse, Inc., providing a hosted environment with automatic scaling and management. It offers full ClickHouse compatibility and works well for teams that want managed infrastructure while maintaining control over cluster configuration.
Tinybird provides a managed ClickHouse platform optimized for developers building real-time analytics into applications. Unlike ClickHouse Cloud, Tinybird includes managed ingestion, API generation, and a CLI-first workflow that treats data pipelines as code. This approach reduces the infrastructure work required to get from data to production API.
You can read a detailed Tinybird vs Clickhouse comparison guide here.
CrateDB Cloud and Aiven
CrateDB offers a managed cloud service that handles cluster provisioning, scaling, and maintenance. The service includes automatic backups, monitoring, and support.
Aiven provides managed CrateDB as part of its multi-database platform, offering integration with other Aiven services and unified billing.
Pricing and total cost of ownership
The full cost of running these databases includes more than just infrastructure expenses. Operational overhead, scaling costs, and support requirements all factor in.
Hardware footprint and storage costs
ClickHouse's columnar storage and compression typically result in 10x to 100x smaller storage footprint compared to row-oriented databases for analytical data. This compression directly reduces storage costs, especially for large historical datasets.
CrateDB's storage efficiency is good but generally doesn't achieve the same compression ratios as ClickHouse for analytical workloads.
Managed service pricing models
Managed ClickHouse services typically charge based on compute and storage resources, with pricing that scales as data volume and query load increase.
Tinybird uses a consumption-based pricing model that charges for data processed and API requests, making costs more predictable for application developers.
Support and enterprise add-ons
Both databases offer commercial support options for production deployments. ClickHouse, Inc. provides enterprise support packages with SLAs, while CrateDB offers support tiers through its commercial offering.
For managed services, support is typically included in the service pricing. Tinybird includes support in all plans, with response time SLAs based on plan tier.
When to choose ClickHouse or CrateDB
The decision between ClickHouse and CrateDB depends on your specific application requirements and performance priorities.
Choose ClickHouse when your primary goal is achieving the fastest possible query performance for analytical workloads on large datasets. Its columnar storage, vectorized execution, and specialized analytical functions make it the best choice for applications where query speed directly impacts user experience.
ClickHouse also makes sense when you want advanced features like materialized views, TTL policies, and sophisticated time-series functions. For applications combining analytical queries with vector similarity search, ClickHouse's vector capabilities provide both in a single database.
Choose CrateDB when you want a database that combines SQL analytics with full-text search capabilities, or when your team values PostgreSQL compatibility for easier integration with existing tools. CrateDB's hybrid storage model and automatic scaling work well for applications with mixed workloads that include both analytical queries and search operations.
CrateDB is also appropriate when operational simplicity matters more than maximum query performance. Its more automated cluster management and easier horizontal scaling reduce the expertise required to run production clusters.
FAQs about ClickHouse vs CrateDB
How difficult is migrating from CrateDB to ClickHouse?
Migration complexity depends on your use of CrateDB-specific features like full-text search or dynamic schema capabilities. The process involves exporting data from CrateDB, converting schemas to ClickHouse table definitions, and rewriting queries to use ClickHouse SQL dialect. For straightforward analytical workloads, migration is relatively simple, but applications relying heavily on CrateDB's search features may require architectural changes.
Do either database offer full ACID transaction guarantees?
Neither database provides full ACID transaction guarantees in the traditional sense. Both prioritize analytical performance and eventual consistency over transactional integrity. ClickHouse offers atomic inserts and some consistency guarantees through ReplicatedMergeTree engines, while CrateDB provides similar eventual consistency models.
Can you run OLTP and OLAP workloads on the same cluster?
Both databases are optimized for OLAP workloads rather than OLTP operations. While they can handle some point lookups and updates, neither is designed for high-frequency transactional workloads with many small writes and updates. Applications needing both OLTP and OLAP capabilities typically use separate databases for each workload type.
/