What is ClickHouse?
ClickHouse is an open source columnar database developed by Yandex. It is used for online analytical processing (OLAP) and known for managing large volumes of data with high performance. ClickHouse achieves this by storing data in a compressed format and executing queries directly on the compressed data.
This allows for fast query execution and efficient storage usage, making it suitable for environments where rapid data analysis and minimal latency are critical. ClickHouse is known for its high throughput and low response times, especially in real-time analytics scenarios.
ClickHouse supports SQL queries and offers compatibility with various data formats, contributing to its flexibility and ease of integration. The database provides horizontal scalability, allowing it to handle increasing data volumes by distributing load across multiple nodes. Its ability to process billions of rows per second helps optimize analytical workloads.
Limitations of ClickHouse
While ClickHouse offers some advantages, it also comes with limitations that users should be aware of. These limitations were reported by users on the G2 platform:
- Lack of support for custom functions: Users cannot create custom functions, which limits flexibility in tailoring ClickHouse to specific analytical needs.
- Materialized view complexity: While materialized views are valuable for improving query performance, they can be difficult to work with. Current support is limited to a single join.
- Absence of triggers: Unlike some traditional databases, ClickHouse does not support triggers.
- Immutability of records: ClickHouse’s immutable data storage design makes updating or deleting existing records challenging.
- Challenges in handling updates and deletes: Users coming from traditional databases like SQL or Oracle may find the approach to updates and deletes in ClickHouse unconventional and cumbersome.
- Stability and predictability issues: In some scenarios, ClickHouse may not provide the level of stability or predictable behavior necessary for production environments.
- Time-consuming troubleshooting: Resolving issues can be slow, partly due to the lack of comprehensive documentation and challenges in obtaining answers from forums or community support.
- Documentation limitations: While functional, ClickHouse’s documentation can be sparse or insufficiently detailed.
- Cluster setup complexity: Setting up a ClickHouse cluster can be tricky and time-consuming, requiring advanced knowledge and careful configuration.
- Slow startup with large data volumes: When handling extensive data sets, ClickHouse may take significantly longer to start.
ClickHouse vs open source alternatives
ClickHouse operates on an open source license, which means users and organizations like NetApp Instaclustr are free to use, contribute updates and patches, and offer managed services for ClickHouse under a different logo.
Although ClickHouse is currently open source, there is always a risk that the founding company will revoke its open source license and change it to a commercial license. Most recent examples of this include Redis revoking its open source license in 2024 and Elasticsearch switching to a commercial license in 2021, leading to the forking and creation of open source Valkey and OpenSearch, respectively.
Tips from the expert
Suresh Vasanthakumar
Site Reliability Engineer
Suresh is a seasoned database engineer with over a decade of experience in designing, deploying and optimizing high-performance distributed systems. Specializing in in-memory data stores, Suresh has deep expertise in managing Redis and Valkey clusters for enterprise-scale applications.
In my experience, here are tips that can help you better evaluate and utilize alternatives Instaclustr for ClickHouse for OLAP and real-time analytics:
- Define workload-specific requirements: Clearly identify whether the workload requires high-speed ingestion, complex analytics, or scalability across distributed environments. This helps in choosing the right alternative that aligns with specific needs.
- Evaluate schema flexibility: For use cases with frequently evolving data models, prioritize systems like Snowflake or Databricks, which support both structured and semi-structured data seamlessly.
- Assess real-time capabilities: If real-time analytics or streaming ingestion is critical, tools like Google BigQuery or ScyllaDB excel in handling live data without performance degradation.
- Test query performance on large datasets: Use sample datasets representative of production workloads to benchmark query latency and throughput across candidates, focusing on the most complex queries.
- Check compatibility with existing tools: Ensure the database integrates well with the current analytics stack, such as BI tools, ETL pipelines, or machine learning workflows. This minimizes the overhead of adapting infrastructure.
Notable ClickHouse alternatives
1. Instaclustr Managed ClickHouse
Google Cloud BigQuery is a serverless, fully-managed data warehouse that enables fast SQL queries using the processing capabilities of Google’s infrastructure. It is used for large-scale data analytics.
License: Commercial
Key features include:
- Serverless architecture: Eliminates the need for managing infrastructure, allowing users to focus on querying and analyzing data.
- Real-time analytics: Supports streaming data ingestion, enabling real-time analysis of live data.
- High scalability: Automatically scales to accommodate workloads of any size, ensuring consistent performance even as data volumes grow.
- Standard SQL support: Uses a familiar SQL dialect, making it easy for database users to adopt.
- Built-in machine learning: Integrates with BigQuery ML, allowing users to create and train machine learning models directly within the platform.
2. Google Cloud BigQuery
Google Cloud BigQuery is a serverless, fully-managed data warehouse that enables fast SQL queries using the processing capabilities of Google’s infrastructure. It is used for large-scale data analytics.
License: Commercial
Key features include:
- Serverless architecture: Eliminates the need for managing infrastructure, allowing users to focus on querying and analyzing data.
- Real-time analytics: Supports streaming data ingestion, enabling real-time analysis of live data.
- High scalability: Automatically scales to accommodate workloads of any size, ensuring consistent performance even as data volumes grow.
- Standard SQL support: Uses a familiar SQL dialect, making it easy for database users to adopt.
- Built-in machine learning: Integrates with BigQuery ML, allowing users to create and train machine learning models directly within the platform.
Source: Google Cloud
3. Snowflake
Snowflake is a managed data platform that enables organizations to connect, store, and analyze data of all types and scales. Snowflake’s architecture eliminates data silos, integrates structured and unstructured data, and supports diverse workloads, including analytics, AI, and application development.
License: Commercial
Key features include:
- Optimized storage: Combines unstructured, semi-structured, and structured data into a unified platform, enabling near-infinite scalability with secure, compressed storage.
- Elastic compute: Supports diverse workloads through a single, flexible compute engine, including streaming pipelines, AI, analytics, and interactive applications, with isolated compute for consistent performance.
- Interoperability: Provides flexibility to work with data on-premises or in open table formats, preventing vendor lock-in and supporting various architectural patterns.
- Cortex AI: Offers serverless large language models (LLMs) for natural language processing and summarization at scale, along with tools for building conversational interfaces for structured and unstructured data.
- Cloud services: Delivers a managed service that automates complex operations, reduces overhead, and applies performance enhancements through automated updates.
Source: Snowflake
4. Amazon Redshift
Amazon Redshift is a managed cloud data warehouse for data analytics. It integrates with the Amazon SageMaker Lakehouse and supports unified analytics across data warehouses and data lakes.
License: Commercial
Key features include:
- Price performance: Achieves better price performance and better throughput compared to most other cloud data warehouses.
- Lakehouse integration: Leverages powerful SQL capabilities to query data in Amazon Redshift and Amazon S3 without duplication.
- Near real-time analytics: Supports zero-ETL integrations to ingest real-time data from streaming services like Amazon Kinesis and operational databases.
- Serverless scalability: Automatically adjusts compute resources based on workload demands with Amazon Redshift Serverless.
- Generative AI integration: Enhances applications with natural language SQL authoring through Amazon Q and allows tasks like text summarization and sentiment analysis via integration with Amazon Bedrock and SageMaker.
Source: Amazon
5. Databricks
Databricks is a data and AI platform. Built on a unified lakehouse architecture, Databricks combines data engineering, analytics, governance, and AI capabilities into a single platform.
License: Commercial
Key features include:
- Unified lakehouse architecture: Combines the flexibility of data lakes with the performance of data warehouses, providing an open and unified foundation for all data and governance needs.
- Data intelligence engine: Optimizes performance by understanding the unique semantics of company data, automating infrastructure management, and tailoring operations to the business.
- Simplified user experience: Uses natural language processing to enable easy search, discovery, and code development.
- End-to-end AI development: Supports a full MLOps lifecycle for building, deploying, and managing AI models, including integration with APIs like OpenAI and custom-built AI solutions.
- Governance and security: Provides enterprise-grade data privacy, governance, and intellectual property control.
Source: Databricks
6. DuckDB
License: MIT
Key features include:
- Simplicity: DuckDB requires no external dependencies and runs in-process within its host application or as a standalone binary.
- Portability: Compatible with Linux, macOS, Windows, and major hardware architectures, DuckDB offers client APIs for programming languages like Python, R, Java, and Node.js.
- Rich SQL dialect: Supports advanced SQL capabilities and works with file formats including CSV, Parquet, and JSON, from local file systems or remote endpoints like Amazon S3.
- Performance: Optimized for speed with a columnar engine that supports parallel execution and processes large datasets.
- Extensibility: Allows for integration of third-party features such as custom data types, functions, file formats, and new SQL syntax.
Source: DuckDB
7. ScyllaDB
ScyllaDB is a distributed database offering low latencies, high availability, and scalability. Built in C++, its architecture leverages multi-core servers and cloud infrastructure.
License: AGPL-3.0
Key features include:
- Shard-per-core architecture: Assigns data fragments to specific CPU cores, along with their memory and storage, for maximum efficiency and a “shared-nothing” design that reduces contention and lock overhead.
- Distributed cluster architecture: Organizes nodes in a peer-to-peer, fault-tolerant virtual ring. Supports geographically dispersed clusters with multi-datacenter replication.
- Wide-column data model: Offers a “key-key-value” structure compatible with Cassandra and DynamoDB, supporting efficient querying and sparse data storage.
- Dynamic data distribution: Transitioning from static vNodes to tablets (ScyllaDB 6.0) for more granular, adaptive distribution of data, reducing hot spots and improving performance.
- Scalability: Supports vertical scaling (scaling up nodes) and horizontal scaling (adding nodes) to optimize resource utilization and reduce complexity.
Source: ScyllaDB
8. CockroachDB
CockroachDB is a cloud-native, distributed SQL database designed to support mission-critical applications with availability, scalability, and control over data placement. Its architecture eliminates the complexities of traditional databases, such as manual sharding, while delivering consistency and reliability for transaction-heavy workloads.
License: Proprietary
Key features include:
- Horizontal scalability: Scales by adding nodes, eliminating the need for manual sharding. Distributes workloads across nodes, enabling consistent performance for reads, writes, and storage growth.
- Distributed SQL with ACID transactions: Offers standard SQL and supports fully distributed, ACID-compliant transactions, ensuring data consistency and correctness across workloads, even at scale.
- High availability: Built to handle node, availability zone, or even entire region failures without downtime. Features such as online schema changes and rolling upgrades ensure uninterrupted service during maintenance.
- Multi-active availability: Every node can handle both reads and writes, leveraging hardware efficiently. Guarantees data consistency via consensus replication while achieving zero Recovery Point Objective (RPO) and Recovery Time Objective (RTO).
- Multi-region, multi-cloud deployments: Can run across multiple clouds or on-premises environments, ensuring no vendor lock-in. Supports migrating apps and data between environments to meet business and regulatory needs.
Source: CockroachDB
Conclusion
When selecting a database for analytics and data processing, it is important to consider the specific requirements of your use case, including performance, scalability, ease of integration, and operational complexity. It is critical to evaluate each tool in the context of workload demands, team expertise, and long-term business goals. A well-chosen database can optimize workflows, improve decision-making, and adapt to evolving data needs over time.