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![Carbonfact logo](https://cdn.collective.work/clvz6pv5y0xyo55ee4mv29584.png)
Carbonfact
•Apparel & Fashion•Small
Improving Data Import Efficiency
Implementing a background job platform and introducing parallelism at the application level to enhance performance and significantly reduce computing time.
Carbonfact is developing carbon management software for the fashion industry. The company imports and processes extensive data to provide comprehensive reporting and support brands in reducing their carbon footprint. However, some data imports took over 10 hours and required more than 250 GB of disk space, significantly slowing down the process. This delay left limited time for retries and prevented timely client feedback.
Process
Setup BullMQ
Introduced BullMQ to run data import jobs asynchronously with retry capabilities.Identify Slowness
Conducted a comprehensive analysis to identify bottlenecks and rewrote slow parts of the data import process.Implement Streaming Approach
Redesigned the import script to utilize a streaming approach, significantly reducing disk space and memory usage.Leverage Parallelism
Rewrote the script to take advantage of parallel processing and fully utilize all available machine cores.
Impact
Job Monitoring
The engineering team can now monitor their jobs through existing tools like Sentry.Easy Job Creation
Engineers can easily create new jobs using the provided documentation and SDK.Processing Time Reduction
Reduced data import time from 10 hours to just 30 minutes.Disk Space Reduction
Reduced disk space usage from 250 GB to just 1 GB.Memory Usage Optimization
Capped memory usage at 4 GB.
By implementing these changes, Carbonfact successfully streamlined their data import process, improved efficiency, and enhanced customer satisfaction by delivering quicker and more accurate insights.
Members
Expertises