Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
- Cement
- Construction & Infrastructure
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Infrastructure Inspection
- Time Sensitive Networking
Services
- Data Science Services
- Testing & Certification
About The Customer
Instabug is an SDK that provides a suite of products for monitoring, prioritizing, and debugging performance and stability issues throughout the mobile app development lifecycle. The Instabug SDK offers crash reporting and application performance monitoring (APM), allowing users to monitor every aspect of their application’s performance like crashes, handled exceptions, network failures, UI hangs, launch and screen loading latency, and the ability to set up custom traces to monitor critical code sections. Instabug also provides automation of workflows via a rules and alerting engine, which integrates with other project and incident management tools like Jira, Opsgenie, Zendesk, Slack, Trello, and many more. Their backend is large scale, with APIs averaging approximately 2 million requests per minute and terabytes of data going in and out of their services daily.
The Challenge
Instabug, an SDK that provides a suite of products for monitoring and debugging performance issues throughout the mobile app development lifecycle, faced significant challenges with performance metrics. These metrics heavily relied on frequent and vast events, posing a challenge in receiving and efficiently storing these events. Additionally, the raw format of performance events was not useful for users, requiring heavy business logic for querying and data visualization. Instabug's backend is large scale, with APIs averaging approximately 2 million requests per minute and terabytes of data going in and out of their services daily. When building their Application Performance Monitoring (APM), they realized it would be their largest scale product in terms of data. They were storing approximately 3 billion events per day at a rate of approximately 2 million events per minute. They also had to serve complex data visualizations that depended heavily on filtering large amounts of data and calculating complex aggregations quickly for user experience. Initially, they designed APM like their other products, but faced performance issues with Elasticsearch, especially for reads, and writes were also not fast enough to handle their load.
The Solution
Instabug decided to experiment with different datastores to find an alternative to Elasticsearch for APM and discovered ClickHouse. After testing ClickHouse and finding it performed better for both reads and writes, they decided to migrate to ClickHouse. However, they couldn't freeze work on the product to migrate to a new datastore and didn't have experience with operating ClickHouse, so they decided to make their code and infrastructure versatile enough to allow for an incremental rollout and experimentation. They refactored the code to abstract dealing with the datastore, allowing it to read and write to different datastores based on some dynamically provided configuration. This allowed them to write all new data to both ClickHouse and Elasticsearch to minimize migration effort, have specific users write/read data from ClickHouse while all other users were writing/reading data from Elasticsearch, and add new features for both Elasticsearch and ClickHouse. The migration to ClickHouse took around 5 months, and the configurable and versatile infrastructure they built during the migration is still in use, allowing them to run multiple clusters and host different event metric data into different databases or clusters as per their needs.
Operational Impact
Quantitative Benefit
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