While building Hyperswitch, we found ourselves in need of real time analytics pretty quick. While there are data points that we don't necessarily need to know in real time, there are ones like payment status that we absolutely need. In addition we wanted to optimize the infrastructure cost our analytics pipeline would incur. We wanted something that is cost efficient, easy to maintain and reliably delivers on realtime analytics. So here's why we love ClickHouse and how we wen about the setup, let's jump right in!
Data warehouses serve as foundational tools for storing and processing data, but the shift from conventional warehouses to ClickHouse marks a significant leap in analytics and search capabilities. Initially, traditional warehouses offer structured storage and querying but may falter as data volumes escalate. ClickHouse's emergence as a high-performance analytical database brings forth unparalleled advantages. Its columnar storage engine, optimized for analytical workloads, delivers lightning-fast query execution on vast datasets, outshining traditional warehouses in speed and efficiency.
The migration to ClickHouse allows for real-time analytics, efficient compression, and superior performance in point searches, transforming the approach to data processing. This strategic transition optimizes resource utilization, reduces query times, and helps to swiftly extract critical insights. ClickHouse, an open-source column-oriented database management system, has emerged as a powerful solution for handling analytics workloads and real-time querying. Combining ClickHouse with Kafka as an ingestion pipeline for real-time analytics, along with S3 integration for event and log data storage, opens up a world of possibilities for businesses seeking rapid insights from their data.
ClickHouse for Analytics and ID-Based Search
ClickHouse's columnar storage design and powerful query engine make it an ideal choice for analytics workloads. Its ability to handle large volumes of data with blazing fast query speeds is well-suited for performing various analytics tasks, including complex aggregations, time-series analysis, and ad-hoc queries.
One of the standout features of ClickHouse is its efficient handling of ID-based searches. Whether searching for specific events, transactions, or user interactions, ClickHouse's indexing capabilities and optimized storage format enable lightning-fast retrieval of data based on unique identifiers.
Kafka as an Ingestion Pipeline for Real-Time Analytics
Integrating Kafka with ClickHouse creates a seamless pipeline for real-time data ingestion and analytics. Kafka's distributed streaming platform facilitates the collection, processing, and delivery of large volumes of data in real time. By leveraging Kafka's robustness and fault-tolerance, businesses can ensure the continuous flow of data into ClickHouse for instant analysis.
Using Kafka's table engine with ClickHouse allows for the direct consumption of Kafka topics as tables, enabling real-time querying and analysis on the incoming data streams. This approach empowers organizations to make immediate decisions based on the most up-to-date information available.
ClickHouse Cluster with Replication and Advantages
Deploying a ClickHouse cluster with replication ensures data redundancy, fault tolerance, and high availability. With a replication factor of one, each piece of data is duplicated across nodes, providing resilience against node failures. In the event of a node going offline, data remains accessible from replicas, maintaining uninterrupted analytics operations.
Advantages of ClickHouse replication include:
- Fault Tolerance: Redundancy safeguards data against node failures.
- High Availability: Ensures continuous access to data, even during node outages.
- Load Distribution: Distributes query load across nodes for enhanced performance.
- Scalability: Facilitates horizontal scaling by adding more nodes to the cluster.
S3 Integration for Events/Log Data Storage
Leveraging S3 buckets as storage for events and log data complements ClickHouse's capabilities by providing cost-effective, durable, and scalable storage. S3's reliability and scalability make it an excellent choice for long-term storage of historical data, ensuring that valuable information is retained without compromising performance.
By using ClickHouse's capabilities to query data directly from S3, organizations can seamlessly access and analyze archived data alongside real-time streams, enabling comprehensive analytics across historical and current datasets.
Zookeeper for ClickHouse Replication Management
Zookeeper plays a critical role in maintaining ClickHouse replication across nodes. It acts as a centralized service for configuration management, synchronization, and coordination among distributed systems. ClickHouse leverages Zookeeper for leader election, fault detection, and ensuring consistency among replicas.
By utilizing Zookeeper, ClickHouse clusters maintain synchronization and consistency, enabling seamless failover, automatic recovery, and streamlined management of replication across nodes.
The synergy between ClickHouse, Kafka, S3, and Zookeeper presents a robust framework for organizations to perform real-time analytics, efficiently manage large volumes of data, and derive actionable insights.