ClickHouse vs. Apache Druid: A Detailed Comparison

Overview of ClickHouse and Apache Druid

What is ClickHouse?

ClickHouse is a high-performance, columnar database designed for Online Analytical Processing (OLAP) and near real-time analytics on large datasets. While it supports real-time workloads, ClickHouse processes data in micro-batches rather than row-by-row streaming ingestion, making it better optimized for high-speed historical analytics rather than instant event-driven data.

Key Features:

• Columnar Storage – Optimized for analytical queries by reducing I/O overhead and improving compression.
• MergeTree Engine – Provides indexing, partitioning, and data replication for efficient query execution.
• Vectorized Execution – Uses SIMD (Single Instruction, Multiple Data) operations to accelerate computation.
• Distributed Query Processing – Supports sharding and replication to enable parallel query execution.
• Materialized Views – Precomputes and stores query results for improved performance.

ClickHouse delivers exceptional performance for analytical workloads but requires careful schema design and denormalization to optimize query execution.

What is Apache Druid?

Apache Druid is a distributed, real-time analytics database built for fast queries on streaming and historical data. Originally developed by MetaMarkets and later adopted by the Apache Software Foundation, Druid specializes in high-concurrency, low-latency analytics, making it a popular choice for operational dashboards, clickstream analysis, and monitoring.

Key Features:

• Segmented Columnar Storage – Data is stored in immutable segments, enabling fast retrieval and highly efficient aggregations.
• Automatic Indexing – Uses bitmap, inverted, and dictionary encoding indexes for fast filtering and lookups.
• Hybrid Ingestion Model – Supports real-time streaming ingestion (Kafka, Kinesis) and batch ingestion (HDFS, S3, local files).
• Independent Scaling – Ingestion, storage, and query processing scale separately, allowing dynamic resource allocation.
• Native Approximate Queries – Utilizes sketch-based algorithms (HyperLogLog, Theta Sketch) for fast, approximate aggregations.

Druid is well-suited for real-time event analytics but has limited support for complex multi-table joins. While denormalization is often recommended for performance, Druid's automatic indexing and columnar storage allow it to optimize query execution efficiently for many workloads. Its immutable segments reduce update flexibility but enable high-speed analytics on pre-aggregated datasets.

Key Similarities Between ClickHouse and Apache Druid

Despite their differences, ClickHouse and Apache Druid share several architectural and functional similarities that make them both powerful options for high-performance analytics:

Columnar Storage

Both store data in a columnar format, which enables:

• Efficient compression – Reducing disk I/O.
• Faster analytical queries – By reading only the necessary columns.

Real-Time Data Processing

• ClickHouse: Supports near real-time ingestion but typically processes data in micro-batches.
• Druid: Built for true real-time ingestion, making it highly suitable for event-driven workloads that require immediate data availability.

Optimized for Analytical Workloads

Both databases specialize in OLAP use cases, enabling fast aggregations, filtering, and analytics on large-scale datasets.

Scalability

Both systems scale out horizontally:

• ClickHouse: Uses sharding and replication to distribute queries across nodes.
• Druid: Druid allows independent scaling of ingestion, storage, and query layers, providing flexibility for resource allocation. However, this modular architecture also introduces operational complexity, requiring careful tuning to balance ingestion speed, storage optimization, and query performance.

Distributed Query Execution

Both databases distribute queries across multiple nodes to improve performance under high workloads.

Primary Differences: ClickHouse vs. Apache Druid

Architecture and Design: Key Differences

ClickHouse Architecture

ClickHouse is optimized for complex analytical workloads, particularly when dealing with structured, relational datasets. It relies on:

• Columnar Storage – Reducing I/O overhead and improving query speed.
• MergeTree Engine – Supports partitioning and indexing but requires careful tuning.
• Sparse Indexing – Unlike traditional databases, ClickHouse does not use B-trees, relying on primary key ordering for optimized lookups.
• Distributed Execution – Queries can be executed across multiple nodes for parallel processing.
• Materialized Views & Projections – Enables query pre-aggregation to speed up frequent queries.

Strengths:

• Excellent for structured, batch-oriented analytics.
• High-performance aggregations and deep analytical queries.
• Scales well with sharding and replication.

Limitations:

• Expensive joins – ClickHouse supports multi-table joins, but its performance is optimized when queries operate on denormalized data. Large joins can become resource-intensive, requiring pre-aggregations, materialized views, or careful indexing strategies to maintain efficiency.

• Streaming ingestion is not native – ClickHouse supports streaming via Kafka, but it processes data in micro-batches, making it less suited for event-driven architectures.

Apache Druid Architecture

Druid is designed for low-latency, real-time analytics and works well for event-driven workloads. It is structured around:

• Segmented Columnar Storage – Stores immutable columnar segments, optimizing query performance.
• Automatic Indexing – Uses bitmap and dictionary encoding for high-cardinality datasets.
• Hybrid Ingestion Model – Supports real-time streaming ingestion (Kafka, Kinesis) and batch ingestion (HDFS, S3).
• Independent Scaling – Allows ingestion, storage, and query nodes to scale separately.

Strengths:

• Optimized for real-time analytics, event-driven data, and high-concurrency workloads.
• Sub-second query performance, making it ideal for dashboards and operational monitoring.
• Automated indexing simplifies query optimization.

Limitations:

• Limited support for complex joins, making it less suitable for multi-dimensional analytics.
• Higher storage costs due to immutable segment storage.

Primary Differences: ClickHouse vs. Apache Druid

Key Considerations for Each Database

Indexing & Query Optimization

• ClickHouse: Uses primary key ordering and sparse indexing, requiring manual query tuning for efficiency.
• Druid: Automatically indexes data, reducing manual tuning but limiting join performance.

Join Handling: Denormalization vs. Native Support

• ClickHouse: Supports joins, but performance can degrade for large datasets unless denormalization or pre-aggregation is applied.
• Druid: Limited native JOIN support – requires pre-aggregated event-driven data before ingestion.

Data Ingestion: Batch vs. Real-Time

• ClickHouse: Optimized for batch processing, supports streaming but processes in micro-batches.
• Druid: Built for real-time streaming ingestion, partitions and indexes data as it arrives.

Query Performance & Aggregation Strategies

• ClickHouse: Optimized for deep analytical queries, requiring denormalization for best performance.
• Druid: Optimized for real-time aggregations and time-series workloads.

Scalability & Resource Allocation

• ClickHouse: Sharding and replication require manual management to prevent uneven workload distribution.
• Druid: Independently scales ingestion, storage, and querying, making it better for fluctuating workloads.

Concurrency: Large Workloads vs. High QPS

• ClickHouse: Handles thousands of concurrent queries but requires denormalization to optimize complex joins.
• Druid: Handles high-concurrency, low-latency queries, but complex joins are not its strength.

Schema Flexibility

• ClickHouse: Explicit schema definitions required, making schema evolution harder.
• Druid: Schema-on-read, dynamically adapts to changes.

Introducing StarRocks: Overcoming ClickHouse & Druid Limitations

ClickHouse and Druid are powerful, but they each have limitations:

• ClickHouse struggles with real-time ingestion and complex joins, requiring heavy denormalization.
• Druid lacks robust join support and has higher storage costs due to immutable segments.

What is StarRocks?

StarRocks is a new-generation, high-performance OLAP database that combines the strengths of ClickHouse and Druid while removing their limitations.

How StarRocks Bridges These Gaps

Why Choose StarRocks Over ClickHouse & Druid?

While ClickHouse and Apache Druid are widely used for analytical workloads, they come with limitations in handling complex queries, real-time ingestion, and high-concurrency workloads. StarRocks was designed to bridge these gaps, providing a more flexible, performant, and cost-efficient solution for modern analytics.

Key Limitations of ClickHouse and Druid

• ClickHouse struggles with complex joins: It performs best with denormalized datasets, which can lead to massive data duplication and increased storage costs. Managing pre-aggregated tables also requires heavy ETL pipelines, adding operational complexity.
• Druid has limited support for complex queries: It is optimized for real-time aggregations and time-series data but does not handle multi-table joins efficiently. Users often preprocess data heavily before ingestion, adding another layer of maintenance overhead.
• Concurrency & Scalability Issues: ClickHouse supports high query concurrency but requires careful tuning to avoid resource contention. Druid, on the other hand, caps out at around 200 concurrent queries before experiencing performance degradation.

How StarRocks Solves These Issues

• Seamless Real-Time and Batch Ingestion

  • ClickHouse is optimized for batch processing, while Druid excels in real-time ingestion. StarRocks natively supports both, allowing businesses to process fresh and historical data efficiently without maintaining separate pipelines.
  • This eliminates the need for additional ETL transformations, reducing maintenance overhead.

• Optimized Indexing and Query Performance

  • ClickHouse requires manual index tuning and sparse indexing, while Druid automatically indexes data but lacks advanced join optimizations.
  • StarRocks automatically manages indexing and leverages advanced query optimizations, delivering better performance without heavy tuning.

• Superior Join Performance Without Denormalization

  • ClickHouse and Druid encourage data denormalization to avoid expensive join operations. This leads to data duplication and higher storage costs.
  • StarRocks optimizes real-time multi-table joins using runtime filtering, query optimizations, and distributed execution, allowing efficient joins without requiring full denormalization. This makes it a strong choice for interactive analytics where normalized datasets need to be queried in real-time without performance penalties.

• Better Handling of High-Concurrency Workloads

  • ClickHouse can handle high query loads but requires significant manual tuning of resources to avoid bottlenecks.
  • Druid is optimized for high QPS but experiences performance degradation past 200 concurrent queries.
  • StarRocks is designed for high-concurrency analytics, distributing queries efficiently and scaling seamlessly without extensive tuning.

• Lower Operational Costs

  • ClickHouse and Druid require additional data engineering efforts to manage indexing, optimize queries, and preprocess data before ingestion.
  • StarRocks reduces infrastructure complexity by eliminating the need for pre-aggregation and extensive ETL processes, leading to lower storage, compute, and operational costs.

When to Choose StarRocks

StarRocks is the right choice when:

• You need real-time analytics but also require batch processing for historical data.
• You want to run complex, multi-table queries without denormalization.
• You require high concurrency for interactive dashboards and user-facing analytics.
• You’re looking to reduce infrastructure costs and simplify your ETL pipelines.

By addressing the shortcomings of ClickHouse and Druid, StarRocks offers a more balanced, scalable, and efficient OLAP solution for real-time and batch analytics.

Real-World Case Studies

Pinterest: Migrating from Apache Druid to StarRocks

Pinterest, a visual discovery platform, required real-time analytics to provide advertisers with insights into ad performance. Initially, they utilized Apache Druid for this purpose.

Challenges with Apache Druid:

• Complex Joins: Limited support necessitated pre-aggregation, increasing complexity.
• Operational Costs: High infrastructure costs due to deep storage requirements and segment compaction.
• Scalability: Difficulties in handling multi-dimensional queries for real-time dashboards.

Transition to StarRocks:

• Enhanced SQL Support: Full support for SQL semantics, enabling on-the-fly complex joins.
• Cost Efficiency: Achieved a 50% reduction in p90 query latency and required only 32% of the instances previously used with Druid, leading to a threefold increase in cost-performance efficiency.
• Data Freshness: Streamlined ingestion process achieving data freshness within 10 seconds.

Demandbase: Migrating from ClickHouse to StarRocks

Demandbase, a leader in account-based marketing, relied on ClickHouse for their analytics needs.

Challenges with ClickHouse

Demandbase, a leader in account-based marketing, relied on ClickHouse for analytics but faced key challenges:

• Denormalization Overhead: Extensive denormalization was required to handle complex queries, increasing storage costs by 90%.
• Operational Complexity: Maintaining 49 ClickHouse clusters (147 nodes total) resulted in high engineering effort and infrastructure costs.
• Query Performance Bottlenecks: Performance was inconsistent for complex joins and real-time analytics.

Why StarRocks?

StarRocks' on-the-fly JOIN capabilities allowed Demandbase to migrate to a single 45-node cluster, eliminating the need for denormalization and reducing operational overhead.

Results

• 60% Cluster Reduction: Replaced 49 ClickHouse clusters with a single 45-node StarRocks cluster.
• 90% Storage Savings: Eliminated redundant, pre-aggregated data.
• Simplified Data Pipelines: Removed costly ETL jobs, reducing engineering complexity.
• Improved Query Performance: Faster and more efficient real-time analytics.

By switching to StarRocks, Demandbase significantly cut costs, simplified operations, and improved analytics performance.

FAQ

What is the main difference between ClickHouse, Apache Druid, and StarRocks?

• ClickHouse is a high-performance OLAP (Online Analytical Processing) columnar database optimized for fast analytical queries over large datasets. It is best for historical analytics, batch processing, and deep queries but requires denormalization for optimal performance.
• Apache Druid is a real-time analytics database designed for low-latency, high-concurrency queries, making it ideal for event-driven and time-series workloads. It has native real-time ingestion but limited multi-table join support.
• StarRocks is a newer OLAP database that combines the strengths of both ClickHouse and Druid while addressing their limitations. It supports real-time and batch ingestion, efficient indexing, and native multi-table joins without requiring denormalization.

Key takeaway:

• Use ClickHouse for high-speed analytical queries on structured data.
• Use Druid for real-time streaming ingestion and sub-second query responses.
• Use StarRocks if you need real-time analytics, batch processing, and complex joins without denormalization.

Can ClickHouse, Apache Druid, and StarRocks handle time-series data?

Yes, all three databases support time-series data, but they have different strengths:

• ClickHouse is optimized for historical time-series analysis, using MergeTree-based storage, partitioning by time, and advanced aggregation functions. It is best for log analytics, observability, and financial data processing.
• Apache Druid is designed for real-time ingestion and interactive time-series analytics, automatically indexing high-cardinality data. It is best for real-time dashboards, IoT data, and network telemetry.
• StarRocks supports both real-time ingestion and historical analysis, offering low-latency queries without requiring denormalized storage. It is a strong choice for hybrid analytics workloads where real-time and historical data need to be analyzed together.

Key takeaway:

• Use ClickHouse for historical time-series analysis.
• Use Druid for real-time monitoring dashboards.
• Use StarRocks if you need both real-time and batch analytics in a single system.

Which database is better for real-time analytics?

• Apache Druid is built for real-time analytics, with native streaming ingestion (Kafka, Kinesis) and automatic indexing for fast, high-concurrency queries. However, it struggles with complex joins and has higher storage costs due to immutable segments.
• ClickHouse supports real-time ingestion, but it processes data in micro-batches, meaning there is a slight delay before data is queryable. It is better for near-real-time analytics and historical batch processing.
• StarRocks provides real-time ingestion with on-the-fly joins, enabling low-latency analytics without requiring pre-aggregated data. Unlike Druid, it supports complex queries and multi-table joins, making it more flexible for real-time use cases.

Recommendation:

• Use Druid for high-concurrency, sub-second queries on streaming data.
• Use ClickHouse for fast but primarily batch-oriented analytics.
• Use StarRocks if you need real-time analytics with complex joins and mixed workloads.

How does ClickHouse compare to MySQL, Cassandra, and StarRocks?

ClickHouse is significantly faster than MySQL and Cassandra for analytical queries, thanks to its columnar storage format and distributed execution. However, it lacks ACID transactions and requires denormalization for optimal performance.

• Compared to MySQL

  • ClickHouse is designed for analytical workloads, while MySQL is optimized for transactional workloads (OLTP).
  • MySQL stores data in row format, which is inefficient for large-scale analytical queries, whereas ClickHouse’s columnar storage reduces I/O and improves compression.
  • ClickHouse provides full SQL compatibility, but it requires denormalization for optimal join performance. StarRocks, on the other hand, enhances multi-table joins with cost-based optimization and runtime filtering, making it better suited for structured, high-concurrency analytics.

• Compared to Cassandra

  • Cassandra is a NoSQL, write-optimized database designed for low-latency, distributed workloads. It excels in write-heavy applications like real-time messaging and recommendation engines.
  • ClickHouse is a read-optimized analytical database best for high-performance aggregations and complex queries.
  • StarRocks, compared to both, offers the best of both worlds—handling both real-time streaming and batch analytics efficiently.

Key takeaway:

• Use ClickHouse for high-speed analytical queries.
• Use MySQL for transactional OLTP workloads.
• Use Cassandra for distributed, write-intensive workloads.
• Use StarRocks if you need high-speed analytics with flexible joins and real-time ingestion.

Is schema management easier in ClickHouse, Apache Druid, or StarRocks?

• Apache Druid provides schema flexibility by allowing late-binding schema adjustments during ingestion. While it does not fully support schema-on-read like some NoSQL databases, it enables schema evolution without requiring predefined table structures, unlike ClickHouse.
• ClickHouse requires explicit schema definitions, meaning you must define tables and columns upfront. This provides better performance and control but makes schema evolution more complex.
• StarRocks supports flexible schema changes while maintaining full SQL support, making it easier to manage structured data without the constraints of ClickHouse or the semi-structured approach of Druid.

Key takeaway:

• Use Druid if you need schema flexibility and frequently changing data structures.
• Use ClickHouse if you prefer structured data with strict schema definitions.
• Use StarRocks if you need SQL support with more schema flexibility and real-time adaptability.