ClickHouse^® vs StarRocks: Which one is actually faster?

Choosing between ClickHouse^® and StarRocks often comes down to a single benchmark number that doesn't tell the whole story. One database might complete a query in 50 milliseconds while the other takes 200 milliseconds, but that difference becomes meaningless when the faster one crashes under 100 concurrent users or takes hours to process data updates.

This article compares the architectures, performance characteristics, and operational trade-offs of both databases across single-table scans, multi-table joins, high-concurrency workloads, and real-time data updates. You'll learn which database fits specific use cases and why "faster" depends entirely on what you're asking it to do.

Benchmark methodology and why "faster" is tricky

ClickHouse^® is a columnar database known for speed on flat tables and single-table queries, while StarRocks is an MPP database optimized for multi-table joins and high-concurrency workloads. The answer to which is "faster" depends entirely on what you're asking it to do.

Performance benchmarks measure specific scenarios. A database that dominates single-table aggregations might struggle with complex joins, and one that handles 500 concurrent users gracefully might be slower for batch processing.

ClickBench vs SSB vs TPCH workloads

ClickBench tests single-table analytical queries on a denormalized web analytics dataset. ClickHouse^® typically wins this benchmark because its MergeTree storage engine excels at scanning wide, flat tables without joins.

ClickHouse^® generally outperforms StarRocks on ClickBench queries, though not by much:

The Star Schema Benchmark (SSB) introduces dimensional modeling with fact and dimension tables. StarRocks outperforms ClickHouse^® by 1.87x on SSB because its cost-based optimizer can plan join execution more efficiently across multiple tables.

TPC-H includes complex multi-table joins with varied cardinalities. StarRocks outperforms ClickHouse^® by 3-5x on these queries because ClickHouse^®'s query planner struggles with join ordering and data movement between nodes.

Streaming ingestion benchmark at one million rows per second

Both databases handle high-throughput ingestion well. ClickHouse^® can sustain over one million rows per second through its native protocol or Kafka table engine, achieving sustained 2 million rows/s with optimized batching, with data immediately available after each batch commit.

StarRocks offers similar ingestion rates through Stream Load and Routine Load for Kafka. The difference shows up when queries run during heavy ingestion: StarRocks maintains more consistent query latency because of better resource isolation.

P50 and P95 latency under five hundred concurrent users

ClickHouse^® was not designed for hundreds of simultaneous users. Under 500 concurrent queries, P95 latency degrades significantly as queries compete for memory and CPU.

StarRocks handles high concurrency better through query queuing and resource pools. In practice, StarRocks maintains sub-second P95 latency with 100x more concurrent sessions than ClickHouse^® before similar degradation occurs.

Quick primer on ClickHouse^® and StarRocks internals

Both are columnar OLAP databases designed for analytical workloads rather than transactions. They store data in columns instead of rows, which allows scanning only needed columns and compressing data more effectively.

Columnar storage and vectorized execution

Columnar storage organizes data by column rather than row, so reading a single column from millions of rows requires fewer disk operations. Both databases compress columnar data using LZ4, ZSTD, and dictionary encoding.

Vectorized execution processes data in batches of thousands of rows at once. This leverages SIMD (Single Instruction, Multiple Data) CPU instructions to perform the same operation on multiple values simultaneously, which dramatically improves throughput for aggregations and filters.

Cost-based optimizer versus rule-based planner

StarRocks uses a cost-based optimizer that estimates execution plan costs by analyzing table statistics and data distribution. The optimizer chooses the plan with the lowest estimated cost, which typically means better join ordering.

ClickHouse^® relies on rule-based query planning where predefined rules determine execution. This works well for simple queries but struggles with complex multi-table joins because it can't adapt to actual data characteristics.

Storage engines and primary key models

ClickHouse^®'s MergeTree family uses a sparse primary key index storing one entry per 8,192 rows by default. This optimizes range scans but doesn't enforce uniqueness—multiple rows can share the same primary key value.

StarRocks uses a similar approach but adds native support for primary key tables where updates and deletes work more efficiently. The primary key in StarRocks can enforce uniqueness and handle upserts without complex workarounds.

Where the engines are similar

Both databases share fundamental choices that make them fast for analytics. Understanding these similarities clarifies where differences actually matter.

MPP architecture with shared-nothing nodes

Both distribute data across multiple nodes in a shared-nothing architecture where each node has its own CPU, memory, and storage. Queries break into tasks that execute in parallel across nodes, with each processing its local data independently.

The coordinator node aggregates results from worker nodes and returns the final result. This parallelism allows horizontal scaling by adding more nodes as data volume grows.

Pipeline execution with SIMD acceleration

Query execution uses a pipeline model where operators stream data through stages without materializing intermediate results. This reduces memory pressure and allows more efficient CPU processing.

Both leverage SIMD instructions on modern x86 and ARM processors. Operations like filtering, hashing, and arithmetic on numeric columns can process 4, 8, or 16 values in a single CPU instruction.

ANSI SQL support and standard connectors

Both support a large subset of ANSI SQL, including common table expressions, window functions, and subqueries. This makes migrating existing SQL queries from other databases easier.

Standard connectors exist for both to integrate with tools like Kafka, dbt, and Apache Flink. JDBC and ODBC drivers allow connections from BI tools like Tableau and Looker, though ClickHouse^® has a more mature ecosystem.

Performance differences that affect raw speed

Architectural differences create measurable performance gaps depending on workload. These matter most when queries involve joins, high concurrency, or frequent updates.

Join execution and network shuffle cost

ClickHouse^® executes joins by pulling data from remote nodes to the coordinating node, creating significant network overhead for large joins. The lack of a cost-based optimizer means ClickHouse^® often chooses suboptimal join orders.

StarRocks shuffles data between nodes more efficiently because its MPP engine was designed for distributed joins. The cost-based optimizer analyzes table sizes and join selectivity to choose between broadcast joins and shuffle joins.

• Hash joins: StarRocks allocates memory more efficiently and can spill to disk when joins exceed available memory
• Broadcast joins: StarRocks automatically detects when a dimension table is small enough to replicate to all nodes
• Shuffle joins: When both tables are large, StarRocks redistributes data across nodes based on the join key

Aggregations on high cardinality dimensions

Both handle low-cardinality aggregations efficiently. Performance diverges when grouping by high-cardinality dimensions like user IDs, where distinct groups approach or exceed available memory.

ClickHouse^® uses two-level aggregation that splits high-cardinality aggregations into smaller hash tables. This works well but can still cause memory pressure on large datasets.

StarRocks implements more sophisticated memory management including adaptive hash table sizing and aggressive spilling to disk. This means StarRocks handles higher cardinality aggregations without running out of memory.

Materialized views and rollups

ClickHouse^® supports materialized views that precompute aggregations and store them in separate tables. You define the view with a SELECT query, and ClickHouse^® populates it as new data arrives, but you explicitly query the materialized view table in application code.

StarRocks also supports materialized views but adds automatic query rewriting where the optimizer transparently uses the materialized view even when querying the base table. This makes materialized views easier to use because you don't modify application queries.

Concurrency and throughput at scale

StarRocks handles high concurrency better than ClickHouse^®, which is well-supported by architectural differences. This matters significantly if your analytics involves many simultaneous users or API requests.

Thread scheduling and query queues

ClickHouse^® allocates threads from a global pool to execute queries. Under high concurrency, queries compete for CPU and memory with limited built-in queuing or prioritization.

StarRocks implements resource pools that isolate workloads and prevent expensive queries from monopolizing cluster resources. Query queuing ensures that when the cluster is saturated, new queries wait rather than failing.

Result set caching strategies

ClickHouse^® offers query result caching but it's relatively basic and requires manual configuration. Cached results store in memory and invalidate when underlying data changes.

StarRocks includes more sophisticated result set caching with automatic invalidation based on partition-level data changes. The cache remains valid even when new data arrives in different partitions, reducing unnecessary cache misses.

Resource isolation for multi-tenant workloads

Running multiple teams on a shared ClickHouse^® cluster often leads to resource contention. One team's expensive query can slow down or crash queries from other teams because ClickHouse^® has limited resource isolation mechanisms.

StarRocks provides resource groups that allocate CPU and memory quotas to different workloads. This allows guaranteeing that production dashboards get priority over ad-hoc queries, even on the same cluster.

Handling updates and upserts in practice

StarRocks is designed for real-time data updates and supports mutable data, while ClickHouse^® has more limited update support. This difference significantly affects data modeling and ingestion pipeline design.

MergeTree mutate workflow in ClickHouse^®

ClickHouse^® handles updates and deletes through mutations, which are background operations that rewrite affected data parts.

When you execute an UPDATE or DELETE statement, ClickHouse^® marks affected parts for mutation and asynchronously rewrites them.

This process is expensive because it involves reading entire data parts, applying the mutation, and writing new parts to disk. ReplacingMergeTree offers an alternative approach for handling updates without expensive mutations. Mutations can take minutes or hours on large tables, and query performance degrades while mutations run.

Primary key update model in StarRocks

StarRocks supports primary key tables where updates and deletes work more like traditional databases. When you update a row, StarRocks marks the old version deleted and writes a new version, with both coexisting until compaction merges them.

This design allows updates to complete immediately without waiting for background merges. Queries automatically filter out deleted versions, so you see updated data in real-time.

Impact on storage footprint and latency

Frequent updates in ClickHouse^® lead to storage amplification because mutations create new data parts until background merges consolidate them. This increases disk usage and can slow queries because ClickHouse^® reads more data parts.

StarRocks also experiences some storage amplification from versioned rows, but its compaction strategy is more aggressive. In practice, StarRocks maintains lower storage overhead and more consistent query latency even with frequent updates.

Scaling, elasticity, and operational overhead

Both databases scale horizontally by adding nodes, but operational complexity differs. This matters when you're maintaining production clusters or need to scale quickly for traffic spikes.

Vertical scaling limits and rebalancing time

ClickHouse^® scales vertically up to hardware limits, then you add more nodes. Adding nodes requires manually resharding data by creating new tables with different sharding keys and backfilling data.

StarRocks also scales vertically but makes horizontal scaling easier through online node expansion. You can add compute nodes without downtime, and StarRocks automatically rebalances data across new nodes.

Online node expansion and auto-sharding

Adding capacity to a ClickHouse^® cluster typically requires downtime or careful coordination to avoid query failures. You create new shards, backfill data, and update application code to query new shards.

StarRocks supports adding nodes to an existing cluster without downtime. The cluster automatically redistributes data to balance load across new nodes while queries continue running.

Observability, backup, and disaster recovery

ClickHouse^® provides basic system tables for monitoring query performance and cluster health. Building comprehensive observability requires integrating external tools like Grafana or Prometheus.

StarRocks includes more built-in observability features including query profiling, resource usage tracking, and audit logs. Backup and restore operations are also more straightforward with native support for incremental backups.

Ecosystem, tooling, and managed options

Ecosystem maturity affects how quickly you can integrate each database into your application. ClickHouse^® has been in production longer, so it has more third-party integrations.

Connectors for Kafka, Debezium, and Snowplow

ClickHouse^® has native Kafka table engine support and integrates well with Debezium for change data capture from transactional databases. The Kafka engine creates continuous ingestion pipelines that pull from Kafka topics and write to ClickHouse^® tables.

StarRocks also supports Kafka through Routine Load, which continuously consumes data from Kafka topics. Both integrate with Snowplow for event tracking, though ClickHouse^® has more mature Snowplow loaders.

CI/CD and schema migration workflows

Managing schema changes in ClickHouse^® typically involves writing DDL statements and applying them manually or through custom scripts. Version control for schema changes requires discipline and tooling you build yourself.

StarRocks follows a similar pattern but offers better support for online schema changes that don't require downtime. Both databases benefit from infrastructure-as-code approaches.

Managed services including Tinybird

ClickHouse^® Cloud is the official managed service offering fully hosted clusters with automatic scaling. However, it's primarily designed for data warehouse use cases rather than application backends.

Tinybird provides a managed ClickHouse^® platform specifically designed for developers building real-time analytics into applications. Tinybird handles infrastructure scaling, provides streaming ingestion, and generates hosted API endpoints from SQL queries. StarRocks is available through CelerData, a managed service from the StarRocks creators.

Cost to achieve sub-second analytics

Total cost of ownership includes infrastructure costs, engineering time, and operational overhead. Both databases can deliver sub-second query performance, but the cost to achieve that performance differs based on workload.

Hardware efficiency per query

ClickHouse^® typically requires less hardware for single-table analytical queries because its storage format and query execution are highly optimized for scanning flat tables. You can achieve excellent performance on modest hardware for append-only time-series workloads.

StarRocks requires more resources for similar single-table queries but delivers better hardware efficiency for complex multi-table joins and high-concurrency workloads. The trade-off depends on whether you're optimizing for raw single-table scan performance or diverse query patterns.

Engineer hours to production readiness

Getting ClickHouse^® into production for simple use cases takes days to weeks, especially with a managed service like Tinybird or ClickHouse^® Cloud. Complex use cases involving joins, updates, or high concurrency require more tuning and can take months to optimize.

StarRocks has a steeper initial learning curve because its feature set is broader. However, time to production can be shorter for workloads involving frequent updates or complex joins because you spend less time working around architectural limitations.

Licensing and support considerations

ClickHouse^® is open source under Apache 2.0, with commercial support available from ClickHouse^®, Inc. and other vendors. There are no licensing costs for self-hosted deployments, though managed services charge based on usage.

StarRocks is also open source under Apache 2.0, with commercial support through CelerData. The licensing model is similar to ClickHouse^®, with no costs for self-hosting and usage-based pricing for managed services.

When to choose one over the other

The choice between ClickHouse^® and StarRocks depends on specific workload characteristics, operational constraints, and team expertise. Neither database is universally better; each excels in different scenarios.

Choose ClickHouse^® when queries primarily scan and aggregate single tables, you can denormalize data to avoid joins, and you're comfortable with append-only or infrequent update patterns. ClickHouse^® excels at time-series analytics, log processing, and event tracking where data arrives continuously but rarely changes.

Choose StarRocks when queries involve complex multi-table joins, you need to support hundreds of concurrent users, or your data model requires frequent updates and deletes. StarRocks is better suited for operational analytics, customer-facing dashboards, and scenarios where normalized data models reduce storage and maintenance overhead.

If you're building real-time analytics into an application and want to avoid managing infrastructure, Tinybird offers a managed ClickHouse^® platform that handles scaling, ingestion, and API generation. This lets you focus on building features rather than operating databases.

Frequently asked questions about ClickHouse^® vs StarRocks

How difficult is migrating from PostgreSQL to ClickHouse^® or StarRocks?

Both databases require rethinking your schema for columnar storage rather than row-oriented tables. ClickHouse^® has more migration guides and community tools for PostgreSQL migrations, but both require denormalizing joins and changing how you handle updates.

Can either database handle mixed OLTP and OLAP workloads effectively?

Neither database is designed for transactional workloads that require ACID guarantees and point updates. StarRocks handles updates better than ClickHouse^® for analytical use cases, but you still want a traditional transactional database for OLTP and use ClickHouse^® or StarRocks for analytics.

Do ClickHouse^® or StarRocks support GPU acceleration for analytics queries?

Neither database currently offers native GPU acceleration. Both rely on CPU vectorization and SIMD instructions for performance, which is sufficient for most analytical workloads and more cost-effective than GPU-based solutions.

How do I implement row-level security without custom application middleware?

ClickHouse^® offers row policies that filter data based on user identity or session parameters. StarRocks requires view-based security or application-level filtering, which means you typically handle row-level security in application code rather than the database.

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