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ClickHouse > Case Studies > Coinpaprika Enhances Cryptocurrency Data Aggregation with ClickHouse
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Coinpaprika Enhances Cryptocurrency Data Aggregation with ClickHouse

Technology Category
  • Application Infrastructure & Middleware - Blockchain
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Maintenance
  • Product Research & Development
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • System Integration
  • Training
The Challenge
Coinpaprika, a leading cryptocurrency market data platform, was facing challenges with their existing data management system. They were using InfluxDB for their time-series data and MySQL for transactional data. However, as their data volume grew, they encountered several issues with InfluxDB. The team found it difficult to extract useful metrics from the system, and extending the timeframe for queries often led to server overload. They also experienced problems with response times due to merging data blocks. The open-source version of InfluxDB lacked built-in replication and scalability, which were critical for Coinpaprika's infrastructure. Coinpaprika needed a solution that could handle their increasing data volume, provide useful metrics, and offer improved performance and scalability.
About The Customer
Coinpaprika is a leading cryptocurrency market data platform that provides comprehensive insights into price data, historical trends, market capitalization, trading volume, and other relevant metrics for numerous cryptocurrencies across hundreds of exchanges. The platform connects directly to almost 370 exchanges using open APIs and is actively working on integrating decentralized exchanges. Coinpaprika aims to provide developers, investors, and businesses with a user-friendly, transparent, and reliable source of information in the cryptocurrency space. Their system design consists of many services distributed across public and private infrastructure, with most CPU and disk intensive parts hosted on-premises and consumer-facing interfaces distributed globally in public cloud infrastructure.
The Solution
Coinpaprika decided to switch to ClickHouse as their time-series database after evaluating several other options. The decision was based on several factors including ClickHouse's single binary architecture, positive press coverage, availability of training materials, supportive community, impressive performance metrics, sales team engagement, and mature technology stack. The transition to ClickHouse resulted in significant improvements in performance, query concurrency, and scalability. Coinpaprika was able to develop the initial version of their product using ClickHouse within just three months. They experimented with different codecs and optimized data structures, and after the successful initial setup, they migrated all their data to ClickHouse. Coinpaprika also integrated ClickHouse with their existing Go and Gorm infrastructure, which allowed them to build and maintain their solution more efficiently.
Operational Impact
  • The transition to ClickHouse has significantly improved Coinpaprika's platform capabilities. The enhanced performance, query concurrency, and scalability have allowed Coinpaprika to offer a reliable and user-friendly experience for developers, investors, and businesses in the cryptocurrency space. The seamless integration with their existing Go and Gorm infrastructure has further optimized their development process. Materialized views and precomputed aggregations have helped to optimize data storage and retrieval, enhancing performance for both free and paid users seeking historical information. As the cryptocurrency market continues to evolve, Coinpaprika can confidently depend on ClickHouse's robust and flexible data storage solution to support their growth and maintain their position as a leading market data platform.
Quantitative Benefit
  • Developed the initial version of their product using ClickHouse within just three months
  • Efficiently handles around 150 requests per second
  • Significant improvements in performance, query concurrency, and scalability

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