PostgreSQL is a traditional RDBMS that is widely used because of its SQL support and versatile features. On the other hand, ClickHouse, though comparatively new, has established itself as an effective data analytics solution. To choose one of these databases, you will have to clearly understand your data-related work objectives, data volume, and budget.
Here, we are comparing ClickHouse vs PostgreSQL features to help you select one of these databases for performing data-driven tasks in your organization.
What is ClickHouse?
ClickHouse is an SQL database management system used to manage online analytical processing (OLAP) workloads. It has a column-oriented database structure and allows you to store data in compressed format. For more compact data storage, ClickHouse supports codecs, which are software programs that enable you to compress and decompress large amounts of data using algorithms.
To speed up the querying of the stored data, ClickHouse offers a vectorized query execution feature. This helps you process data quickly in chunks, making ClickHouse a fast-performing database. It also supports parallel query processing to provide better query execution performance.
The highly functional capabilities of this platform make it a suitable database for performing real-time analytics, helping you make well-informed business decisions.
Key Features of ClickHouse
- SQL Support: ClickHouse supports SQL, a user-friendly and fully-featured programming language that allows you to perform basic and advanced queries on large datasets.
- Approximated Calculation Capabilities: It offers a set of aggregate functions to calculate mean, median, or distinct values. You can also run a query based on partial data to get approximated results if you prefer fast performance over accuracy.
- Data Replication: ClickHouse facilitates asynchronous multi-master data replication where data is replicated across a group of servers. While there can be some latency, it is useful when replicating data at remote locations.
What is PostgreSQL?
PostgreSQL is an object-relational database that allows you to store and query data using SQL. It is an ACID-compliant database and supports different data types, including integer, boolean, date/time, geometrical data, and non-relational JSON documents.
To query data stored in Postgres, you can use various SQL functions and clauses such as ORDER BY, GROUP BY, or WHERE clause. The database allows you to perform JOIN operations on multiple tables, including LEFT JOIN, INNER JOIN, or CROSS JOIN. You can also write nested subqueries and correlated subqueries using Postgres.
Due to such robust features, the StackOverflow survey shows that PostgreSQL is the most popular database tool among developers in 2024. It is also used as a data warehousing solution to query complex and high-volume datasets. You can use it to manage OLTP workloads such as financial transaction datasets.
Key Features of PostgreSQL
- Extensibility: You can expand the functionality of Postgres using different extensions. For instance, the pg_stat_statement extension is used to monitor the statistics of executed queries. It also supports the pgvector extension, helping you to store vector data.
- Indexes: Indexes help you retrieve data faster in databases. Postgres offers different index types, such as B-tree, Hash, GiST, SP-GiST, GIN, and BRIN.
- Data Security: This database offers a strong security mechanism through user authentication, column and row-level security, and role-based access control to ensure data protection. Some authentication methods supported by Postgres are Trust-based, password-based, GSSAPI, and Kerberos authentication.
Key Differences Between ClickHouse vs PostgreSQL
Some of the key differences between ClickHouse vs PostgreSQL databases are as follows:
Architecture
The core components of ClickHouse architecture are clusters, servers, storage layer, query processor, and ClickHouse Keeper. The server allows you to store data, execute queries, and handle data replication across multiple nodes in a cluster. Next, is the storage layer that is divided further into shards, which are horizontal data partitions containing subsets of the total dataset. It is the responsibility of the storage layer to facilitate data compression, partitioning, and indexing in ClickHouse.
The query processor is responsible for parsing and optimizing SQL queries before execution. This component also provides an execution plan to reduce data processing time. Another important element of ClickHouse’s architecture is ClickHouse Keeper, a distributed coordination service compatible with Apache ZooKeeper. It facilitates the management of cluster configuration, data synchronization, and system failover.
PostgreSQL, on the other hand, operates on a client/server model. In this architecture, the server is responsible for managing the database and interacting with client applications. It enables you to handle data storage, query processing, and return results to the client.
The other is the client, which can be any application that interacts with the Postgres database. The client could be a text-oriented tool (psql), a web server, or any graphical application. You can send requests to the database through a server. This server helps you perform the requested operations and then send the results as responses back to the client. You can process these responses for further use.
Analytical Workloads
You can use ClickHouse to manage OLAP data workloads. This database helps in real-time data analysis due to its columnar storage format, which facilitates faster data retrieval and quick processing of analytical queries. The vectorized query execution further reduces the querying time and optimizes the usage of compute resources.
Contrarily, Postgres is used to manage OLTP workloads. For effective data analysis, you can use SQL clauses such as JOIN or WHERE and aggregate functions such as AVG() or COUNT(). You can also utilize it to execute nested and correlated subqueries for advanced analytics.
Data Compression
The column-oriented data storage makes it easier for you to compress data in ClickHouse. With its support for codecs and granular data types, you can further refine the compression techniques.
Alternatively, Postgres supports a technique called TOAST (The Oversized-Attribute Storage Technique) for compressing specific data types that can exceed the size of a standard Postgres page. A page is a basic data storage unit in Postgres. The data types that you can compress using TOAST are JSON, JSONB, large text strings, or Varchar.
Deployment Options
ClickHouse facilitates both on-premise and cloud deployment options. For on-premise usage, you can choose the quick install option to setup ClickHouse on Linux, macOS, and FreeBSD. The production deployment option enables you to run the database on Linux, FreeBSD, or macOS with x86-64, modern arm, or PowerPC64LE CPU architecture. You can also run this database inside Docker.
Similar to ClickHouse, you can deploy PostgreSQL locally or on the cloud. However, it can be installed on several operating systems, such as Windows, macOS, Linux, BSD, and Solaris. PostgreSQL also allows you to leverage its services through cloud platforms such as AWS, Azure, or GCP.
Here is a tabular comparison of PostgreSQL vs ClickHouse:
Factors to Consider when Choosing ClickHouse or PostgreSQL
You should consider the following factors while choosing ClickHouse or PostgreSQL:
Scalability
ClickHouse allows horizontal scaling using the sharding technique, which involves adding more nodes to the database clusters. This helps you to distribute data across multiple nodes to accommodate the high volumes of datasets.
Alternatively, PostgreSQL supports vertical scaling. However, horizontal scaling can be achieved through replication and partitioning.
Use Cases
You can use ClickHouse for real-time data analytics and business intelligence applications.
Conversely, PostgreSQL is used in enterprise applications such as CRM, ERP, and HR management. It supports geospatial data type through PostGIS extension, so you can use it in location-based web and mobile applications.
Costs
ClickHouse offers its services through three editions: development, production, and dedicated. Each version allows you to use various features at a different usage cost.
On the contrary, PostgreSQL is released under the PostgreSQL license, which is an open-source license. You can use it freely for individual purposes and even for commercial software applications.
Streamline Data Integration with ClickHouse and Postgres Using Airbyte
To leverage the services of ClickHouse and PostgreSQL, you can load your data from the source system into these databases. You can also transfer data from these databases into a destination data system of your choice. Airbyte, a data movement platform, can help you with this data integration process.
To achieve this, it offers a vast library of 400+ connectors and an intuitive interface to streamline your data pipelines. With these features, you can quickly load data from PostgreSQL to ClickHouse without writing a single line of code.
Here are some key features of Airbyte:
- Change Data Capture (CDC): The CDC feature of Airbyte helps you to capture the incremental changes made at the source system and reflect them into the target data system. This ensures that source and destination databases are in sync with each other.
- RAG Transformations: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations such as chunking. This helps in optimizing the accuracy of LLM outcomes.
- Orchestrate Your Pipelines: You can orchestrate your data workflows by integrating Airbyte with data orchestration tools like Dagster, Apache Airflow, or Kestra.
Conclusion
ClickHouse and Postgres are powerful database solutions with unique functionalities. The former is helpful for real-time data analysis, whereas the latter is a traditional RDBMS that helps you manage OLTP workloads. This blog provides a detailed comparison of ClickHouse vs PostgreSQL to help you understand their capabilities. You can use any of these two databases depending on your organizational goals, performance requirements, and data volume.