ClickHouse architecture: 4 key components and optimization tips

What is ClickHouse?

ClickHouse is a columnar database management system known for its high performance and efficiency. Its architecture is built for OLAP queries that handle large volumes of data with low latency. ClickHouse’s architecture has a columnar storage model that optimizes data retrieval by storing similar types of data together, increasing query processing speed.

Additionally, ClickHouse uses a distributed architecture, allowing data to be partitioned and processed in parallel across multiple nodes, which balances load and accelerates data queries. ClickHouse also focuses on data compression and storage efficiency. The system uses compression algorithms to minimize storage space requirements, reducing I/O operations, and speeding up data access.

Core components of ClickHouse architecture

The ClickHouse architecture includes the following components.

1. Columns and data types

In ClickHouse, data is organized into columns, emphasizing a columnar database approach rather than a traditional row-based system. This columnar storage structure offers significant improvements in memory usage and processing speed, as it allows the database to only read the necessary columns required by a query.

As a result, ClickHouse can perform faster aggregations, which are common in OLAP workloads. The use of specific data types for each column further optimizes storage and query execution. ClickHouse supports various data types, including integers, floating-point numbers, strings, and complex data types, ensuring flexibility in handling different data forms.

2. Blocks and data compression

In ClickHouse, data is internally managed in blocks, a logical unit for organizing data processing and storage. Each block contains columnar data and is processed together to enhance performance during query execution. This block-oriented structure helps ClickHouse utilize CPU cores efficiently by minimizing cache misses and facilitating vectorized execution.

Data compression is aimed at reducing storage requirements while preserving data access speed. ClickHouse uses various compression algorithms, such as LZ4, ZSTD, and Delta, to compress data in blocks. These algorithms reduce data size and enhance disk I/O efficiency, translating to quicker data retrieval times.

3. Processors and query execution

ClickHouse uses a query execution engine featuring a variety of processors to optimize query performance. Each processor in the engine is responsible for a different aspect of query execution, such as filtering, aggregation, or joins. By using multiple processors concurrently, ClickHouse can efficiently manage the workload distribution.

A key feature of ClickHouse’s query execution is the pipeline model, which organizes the flow of data through various processors in a structured manner. This allows ClickHouse to handle multiple stages of query execution simultaneously, resulting in reduced query execution time and increased throughput.

4. Merge tree and storage engines

The Merge Tree engine is designed to handle massive amounts of data with high write and read throughput. Merge Tree structures the data in a format that aids in fast insertion and querying, while maintaining the order of data with a primary key. This structure is optimal for analytical use cases where large datasets need to be sorted and aggregated quickly.

Merge operations are periodically performed to organize and optimize the data segments, improving overall query performance and system efficiency. ClickHouse also supports other storage engines, including the Log family for simple data logging scenarios, the AggregatingMergeTree for pre-aggregated data operations, and the CollapsingMergeTree for handling distributed state changes.

Data distribution and sharding in ClickHouse

Here’s a look at the mechanisms that enable the distributing and sharding of data in ClickHouse.

Replication mechanisms

ClickHouse implements replication mechanisms to ensure data redundancy and consistency across distributed systems. This allows for failover support and load balancing across different nodes, which improves system reliability and availability. The replication process involves copying parts of data, known as shards, to multiple servers.

This redundancy ensures that if one server fails, others can continue to provide uninterrupted data access. The replication is managed through predefined replica configurations, ensuring that data copies are synchronized and consistent at all times. The replication mechanism in ClickHouse also supports automatic failover and recovery processes.

Distributed query execution

Distributed query execution in ClickHouse allows queries to be processed across multiple servers, improving performance and scalability. With this feature, data is spread across different nodes, and queries are split and run in parallel over these nodes. This parallel execution allows ClickHouse to handle large-scale data analytics efficiently.

Distributed query execution relies on data locality and the minimization of data transfer between nodes. ClickHouse optimizes query execution plans by ensuring queries are processed on nodes where the data resides when possible, reducing network overhead. Partitioned data enables load balancing across servers, keeping performance consistent.

Tips from the expert

Andrew Mills

Senior Solution Architect

Andrew Mills is an industry leader with extensive experience in open source data solutions and a proven track record in integrating and managing Apache Kafka and other event-driven architectures

In my experience, here are tips that can help you better optimize and manage ClickHouse:

Use the MergeTree family wisely: Choose the appropriate MergeTree engine variant for your use case: ReplacingMergeTree for managing data deduplication, CollapsingMergeTree for handling events with a lifecycle (like state changes), or SummingMergeTree for data that benefits from pre-aggregation. This can drastically reduce query processing time and storage.
Avoid frequent updates and deletes: ClickHouse isn’t designed for high-frequency updates or deletes, so design your tables for write-once, read-many scenarios. If your application needs frequent changes, consider restructuring or use techniques like partitioning by date to periodically delete or overwrite old data.
Partition data effectively for fast querying: Use time-based or ID-based partitions to restrict the data scope queried. For example, partitioning by month or week helps limit the amount of data scanned, optimizing query performance and making merges and data deletion more manageable.
Pre-calculate common aggregates with materialized views: Materialized views allow you to store pre-computed aggregations for frequently accessed metrics. Use them for metrics that are computationally expensive, enabling faster analytics without recalculating the same metrics.
Optimize I/O with asynchronous reads: ClickHouse supports asynchronous reads, which can improve throughput in environments with high I/O demands. Set max_distributed_connections and other concurrency settings to enable non-blocking reads, especially useful for clusters with high data volume.

Concurrency and performance optimization in ClickHouse

The ClickHouse architecture supports a number of functions to help optimize performance.

Threads and job scheduling

ClickHouse uses a concurrency management system to optimize performance through threads and job scheduling. The ClickHouse architecture leverages multi-threading to execute queries faster by harnessing CPU cores. Each query is broken down into smaller tasks that can run concurrently, improving throughput and reducing latency.

Job scheduling in ClickHouse involves a queue-based system that prioritizes tasks based on their complexity and resource requirements. The scheduler intelligently assigns tasks to available threads, optimizing CPU usage and ensuring balanced workload distribution. This approach minimizes contention and maximizes system responsiveness.

Vectorized query execution

Vectorized query execution in ClickHouse refers to the practice of processing batches of data at a time instead of single-row processing. This approach uses modern CPU instruction sets like SIMD (Single Instruction, Multiple Data) to perform operations across multiple data points simultaneously. ClickHouse thus boosts performance by reducing the number of CPU cycles required per query operation.

By processing data in vectors, ClickHouse reduces overhead and improves cache utilization, which is critical for query performance. This method ensures that CPU resources are used efficiently, which is particularly beneficial for large dataset operations where compute-bound tasks can otherwise become bottlenecks.

Just-In-Time (JIT) compilation

Just-In-Time (JIT) compilation in ClickHouse is an optimization technique to improve query execution efficiency. JIT converts high-level query instructions into optimized machine code during runtime, allowing the database to execute queries more rapidly. This dynamic compilation process tailors execution paths for the current workload, adapting to the CPU architecture.

JIT compilation minimizes the overhead typically associated with interpreting and executing complex queries, significantly increasing throughput. It takes advantage of CPU optimizations and can eliminate redundant computations, optimizing execution paths for each query.

Best practices for implementing ClickHouse

Here are some recommended practices for working with ClickHouse.

Optimize data types and compression

Selecting the appropriate data type for each column can lead to significant storage savings and performance improvements. For example, using fixed-point numbers instead of floating-point when possible, or choosing ENUM types to reduce string storage overhead, can optimize how data is stored and retrieved.

Applying appropriate compression methods enhances storage utilization and speeds up data retrieval. ClickHouse supports various compression codecs, and selecting the right one is crucial depending on the data characteristics and query patterns. The chosen codecs should provide a balance between compression rate and decompression speed.

Leverage in-memory processing

By keeping frequently accessed data in memory, ClickHouse reduces the latency associated with disk I/O operations. This approach enables rapid access to data and faster execution of queries, which is particularly beneficial for time-sensitive analytical tasks.

Properly configuring memory settings ensures that available resources are optimally utilized without overextending system capabilities. This includes setting limits on in-memory data storage and intelligently caching hot data sets. By carefully managing memory resources, administrators can prevent bottlenecks commonly associated with disk-bound operations.

Regularly backup and test data

Implementing a backup strategy protects against data loss from hardware failures, human error, or software bugs. Frequent snapshots of database states can capture critical data points, enabling quick restoration. These backups should be stored securely in multiple locations to protect against localized failures or environmental threats.

Testing these backups regularly is as important as creating them, ensuring that restoration processes are reliable. By simulating disaster recovery scenarios, administrators can verify that data recovery will proceed smoothly during an actual failure.

Learn more in our detailed guide to Clickhouse backup (coming soon)

Customize settings for hardware optimization

Memory disk configurations, CPU architecture, and network capabilities should all inform system settings adjustments. For example, tuning ClickHouse’s caching mechanisms can reduce read latencies when high-speed SSDs are employed, while multi-core CPUs might benefit from adjusted concurrency and threading settings.

Network settings should be calibrated to minimize latency and maximize throughput, particularly in distributed environments where data exchange between nodes is frequent. By refining these settings to complement the capacity and capabilities of the hardware, ClickHouse administrators can improve data processing performance and system efficiency.

Monitor and tune performance metrics

Monitoring involves tracking key performance indicators like query execution times, resource utilization rates, and system latency. These metrics provide insights into how well the database responds to query loads and help identify any potential bottlenecks or inefficiencies that could hinder performance.

Regularly tuning the system based on these observations is vital. Adjusting configurations, such as memory allocation, data distribution strategies, or query optimization settings, can resolve detected inefficiencies. Automated monitoring tools and analytics can help assess these metrics, allowing administrators to make ongoing adjustments.

Efficiency and scalability amplified: The benefits of Instaclustr for ClickHouse

Instaclustr provides a range of benefits for ClickHouse, making it an excellent choice for organizations seeking efficient and scalable management of these deployments. With its managed services approach, Instaclustr simplifies the deployment, configuration, and maintenance of ClickHouse, enabling businesses to focus on their core applications and data-driven insights.

Some of these benefits are:

Infrastructure provisioning, configuration, and security, ensuring that organizations can leverage the power of this columnar database management system without the complexities of managing it internally. By offloading these operational tasks to Instaclustr, organizations can save valuable time and resources, allowing them to focus on utilizing ClickHouse to its full potential.
Seamless scalability to meet growing demands. With automated scaling capabilities, ClickHouse databases can expand or contract based on workload requirements, ensuring optimal resource utilization and cost efficiency. Instaclustr’s platform actively monitors the health of the ClickHouse cluster and automatically handles scaling processes, allowing organizations to accommodate spikes in traffic and scale their applications effectively.
High availability and fault tolerance for ClickHouse databases. By employing replication and data distribution techniques, Instaclustr ensures that data is stored redundantly across multiple nodes in the cluster, providing resilience against hardware failures and enabling continuous availability of data. Instaclustr’s platform actively monitors the health of the ClickHouse cluster and automatically handles failover and recovery processes, minimizing downtime and maximizing data availability for ClickHouse deployments.

Furthermore, Instaclustr’s expertise and support are invaluable for ClickHouse databases. Our team of experts has deep knowledge and experience in managing and optimizing ClickHouse deployments. We stay up-to-date with the latest advancements in ClickHouse technologies, ensuring that the platform is compatible with the latest versions and providing customers with access to the latest features and improvements. Instaclustr’s 24/7 support ensures that organizations have the assistance they need to address any ClickHouse-related challenges promptly.

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