ClickHouse vs AWS DynamoDB
A detailed comparison
Compare ClickHouse and AWS DynamoDB for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 AWS DynamoDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how ClickHouse and AWS DynamoDB 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 AWS DynamoDB Breakdown
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| Database Model | Columnar database |
Key-value and document store |
| Architecture | ClickHouse can be deployed on-premises, in the cloud, or as a managed service. |
DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations |
| License | Apache 2.0 |
Closed source |
| Use Cases | Real-time analytics, big data processing, event logging, monitoring, IoT, data warehousing |
Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing |
| Scalability | Horizontally scalable, supports distributed query processing and parallel execution |
Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication |
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ClickHouse Overview
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.
AWS DynamoDB Overview
Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.
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.
AWS DynamoDB for Time Series Data
DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.
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.
AWS DynamoDB Key Concepts
Some of the key terms and concepts specific to DynamoDB include:
- Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
- Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
- Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
- Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.
ClickHouse Architecture
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.
AWS DynamoDB Architecture
DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:
- Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
- Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
- Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.
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ClickHouse Features
Real-time analytics
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.
Data compression
ClickHouse’s columnar storage format enables efficient data compression, reducing storage requirements and improving query performance.
Materialized views
ClickHouse supports materialized views, which can significantly improve query performance by precomputing and storing query results as tables.
AWS DynamoDB Features
Auto scaling
DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.
Backup and restore
DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.
Global tables
DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.
Streams
DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.
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.
Real-time reporting
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.
AWS DynamoDB Use Cases
Session management
DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.
Gaming
DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.
Internet of Things
DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.
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.
AWS DynamoDB Pricing Model
DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.
On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.
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