Harnessing ClickHouse and Materialized Views for High-Performance Analytics: A Case Study of Inigo
Technology Category
- Application Infrastructure & Middleware - Database Management & Storage
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Equipment & Machinery
- National Security & Defense
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Real-Time Location System (RTLS)
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
Inigo is a leading company in the GraphQL API management industry, specializing in comprehensive security and governance at scale. Their platform empowers API and platform teams to fully control, manage, and secure GraphQL deployments by providing immediate visibility, security, access control, compliance, and continuous delivery. In addition to its security features, Inigo offers key developer experience enhancements and workflow efficiencies, ultimately enabling organizations to expedite GraphQL API development, lower costs, and improve end-user experiences. They process and analyze billions of GraphQL API requests, creating aggregated views on high cardinality data, generating alerts, and creating dashboards based on extensive data.
The Challenge
Inigo, a pioneering company in the GraphQL API management industry, was in search of a database solution that could handle a high volume of raw data for analytics. They explored various alternatives, including SQLite, Snowflake, and PostgreSQL, but none of these options met their needs. Snowflake was too slow and costly for their needs, especially when handling real-time customer data within a product. PostgreSQL, while an excellent transactional database, was unsuitable for large analytic workloads. The company was able to get it to work with around 100K rows, but past that, the indexes were growing out of control and the cost of running a PostgreSQL cluster didn’t make sense. There was significant performance degradation once they hit the 100K - 1M rows mark. Inigo needed a solution that could handle billions of GraphQL API requests, create aggregated views on high cardinality data, generate alerts, and create dashboards based on extensive data.
The Solution
Inigo chose ClickHouse, a columnar database specifically designed to handle high volumes of data while providing fast query execution. They paired ClickHouse with Materialized Views, which allow datasets to be pre-aggregated, significantly improving the performance of analytical queries. ClickHouse's ability to automatically manage and update materialized views as new data is ingested simplified the maintenance process. The open-source nature of ClickHouse and the ability to run it as a simple binary or docker on their laptops were major attractions. Inigo's current ClickHouse setup is a single database where nearly every Inigo service has its own table, with some tables containing billions of rows of data. They consolidated the data into several materialized views, each grouping the data into distinct time window buckets. By creating materialized views with different intervals, they could enhance query performance and enable more efficient data analysis across various timeframes.
Operational Impact
Quantitative Benefit
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