Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of ClickHouse and Redis so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how ClickHouse and Redis perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
ClickHouse vs Redis Breakdown
ClickHouse can be deployed on-premises, in the cloud, or as a managed service.
Redis can be deployed on-premises, in the cloud, or as a managed service
Real-time analytics, big data processing, event logging, monitoring, IoT, data warehousing
Caching, message brokering, real-time analytics, session storage, geospatial data processing
Horizontally scalable, supports distributed query processing and parallel execution
Horizontally scalable via partitioning and clustering, supports data replication
ClickHouse is an open source columnar database management system designed for high-performance online analytical processing (OLAP) tasks. It was developed by Yandex, a leading Russian technology company. ClickHouse is known for its ability to process large volumes of data in real-time, providing fast query performance and real-time analytics. Its columnar storage architecture enables efficient data compression and faster query execution, making it suitable for large-scale data analytics and business intelligence applications.
Redis, which stands for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It was created by Salvatore Sanfilippo in 2009 and has since gained significant popularity due to its high performance and flexibility. Redis supports various data structures, such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, and geospatial indexes with radius queries.
ClickHouse for Time Series Data
ClickHouse can be used for storing and analyzing time series data effectively, although it is not explicitly optimized for working with time series data. While ClickHouse can query time series data very quickly once ingested, it tends to struggle with very high write scenarios where data needs to be ingested in smaller batches so it can be analyzed in real time.
Redis for Time Series Data
Redis has a dedicated module for working with time series data called RedisTimeSeries. RedisTimeSeries offers functionality like downsampling, data retention policies, and specialized queries for time series data in Redis. Being an in-memory database, Redis will be very fast for reading and writing time series data, but due to the cost of RAM compared to disk using Redis could become expensive depending on the size of your dataset. If your use case doesn’t require extremely fast response times, you could save money by going with a more traditional time series database.
ClickHouse Key Concepts
- Columnar storage: ClickHouse stores data in a columnar format, which means that data for each column is stored separately. This enables efficient compression and faster query execution, as only the required columns are read during query execution.
- Distributed processing: ClickHouse supports distributed processing, allowing queries to be executed across multiple nodes in a cluster, improving query performance and scalability.
- Data replication: ClickHouse provides data replication, ensuring data availability and fault tolerance in case of hardware failures or node outages.
- Materialized Views: ClickHouse supports materialized views, which are precomputed query results stored as tables. Materialized views can significantly improve query performance, as they allow for faster data retrieval by avoiding the need to recompute the results for each query.
Redis Key Concepts
- In-memory store: Redis stores data in memory, which allows for faster data access and manipulation compared to disk-based databases .
- Data structures: Redis supports a wide range of data structures, including strings, hashes, lists, sets, and more, which provide flexibility in how data is modeled and stored.
- Persistence: Redis offers optional data persistence, allowing data to be periodically saved to disk or written to a log for durability.
- Pub/Sub: Redis provides a publish/subscribe messaging system, enabling real-time communication between clients without the need for a centralized message broker.
ClickHouse’s architecture is designed to support high-performance analytics on large datasets. ClickHouse stores data in a columnar format. This enables efficient data compression and faster query execution, as only the required columns are read during query execution. ClickHouse also supports distributed processing, which allows for queries to be executed across multiple nodes in a cluster. ClickHouse uses the MergeTree storage engine as its primary table engine. MergeTree is designed for high-performance OLAP tasks and supports data replication, data partitioning, and indexing.
Redis is a NoSQL database that uses a key-value data model, where each key is associated with a value stored as one of Redis’ supported data structures. The database is single-threaded, which simplifies its internal architecture and reduces contention. Redis can be deployed as a standalone server, a cluster, or a master-replica setup for scalability and high availability. The Redis Cluster mode automatically shards data across multiple nodes, providing data partitioning and fault tolerance.
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ClickHouse is designed for real-time analytics and can process large volumes of data with low latency, providing fast query performance and real-time insights.
ClickHouse’s columnar storage format enables efficient data compression, reducing storage requirements and improving query performance.
ClickHouse supports materialized views, which can significantly improve query performance by precomputing and storing query results as tables.
Redis supports atomic operations on complex data types, allowing developers to perform powerful operations without worrying about race conditions or other concurrent processing issues.
Broad data structure support
Redis supports a range of data structures such as lists, sets, sorted sets, hashes, bitmaps, hyperloglog, and geospatial indexes. This flexibility allows developers to use Redis for a wide variety of tasks by using data structures that are optimized for their data in terms of performance characteristics.
Redis provides a publish/subscribe messaging system for real-time communication between clients.
Developers can run Lua scripts in the Redis server, enabling complex operations to be executed atomically in the server itself, reducing network round trips.
ClickHouse Use Cases
Large-scale data analytics
ClickHouse’s high-performance query engine and columnar storage format make it suitable for large-scale data analytics and business intelligence applications.
ClickHouse’s real-time analytics capabilities enable organizations to generate real-time reports and dashboards, providing up-to-date insights for decision-making.
Log and event data analysis
ClickHouse’s ability to process large volumes of data in real-time makes it a suitable choice for log and event data analysis, such as analyzing web server logs or application events.
Redis Use Cases
Redis is often used as a cache to store frequently accessed data and reduce the load on other databases or services, improving application performance and reducing latency.
Redis can be used to implement task queues, which are useful for managing tasks that take longer to process and should be executed asynchronously. This is particularly common in web applications, where background tasks can be processed independently of the request/response cycle
Real-time analysis and machine learning
Redis’ high performance and low-latency data access make it suitable for real-time analysis and machine learning applications, such as processing streaming data, media streaming, and handling time-series data. This can be achieved using Redis’ data structures and capabilities like sorted sets, timestamps, and pub/sub messaging.
ClickHouse Pricing Model
ClickHouse is an open source database and can be deployed on your own hardware. The developers of ClickHouse have also recently created ClickHouse Cloud which is a managed service for deploying ClickHouse.
Redis Pricing Model
Redis is open-source software, which means it can be deployed and used freely on your own infrastructure. However, there are also managed Redis services available, such as Redis Enterprise which offer additional features, support, and ease of deployment. Pricing for these services typically depends on factors like the size of the instance, data storage, and data transfer.
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