I’m currently developing an automation tool that can track and show the execution history of workflows, resembling what is found in services like Zapier or similar automation platforms.
The goal is to build a system where users can access their automated processes’ past executions, with details on when each step was carried out, whether it was successful or not, and the data handled at every stage.
Has anyone here worked on something like this? I’m especially looking for insights on:
How to design the database for efficient storage of execution logs
Good practices for presenting this information in an easy-to-understand manner
Techniques to manage large amounts of historical data without slowing down the system
Any advice on architectural strategies or relevant libraries that could assist me would be greatly appreciated. I’m also open to ideas regarding backend storage options and frontend display solutions.
elasticsearch saved my ass on this exact problem last year. handles massive log volumes way better than traditional databases, and the search/filtering is incredible when users need to dig through old executions. pair it with kibana for visualizations - users can slice data however they want without you building custom ui stuff. one heads up though - batch your writes. don’t hit it with every single step event or you’ll tank performance.
Built something like this for a mid-sized company two years back - learned some painful lessons. The execution data grows insanely fast. We went from small logs to terabytes in months once people started building complex workflows. For the database, I used PostgreSQL with time-series partitioning by month. Recent executions stayed fast while old data remained accessible. The big win was splitting execution metadata from payload data - keep lightweight execution info in main tables, dump heavy payloads in S3 with references. UI-wise, users loved having different views. Simple timeline for quick status checks, detailed drill-down for debugging failures. I screwed up initially by cramming everything into one view - total information overload. Performance tips: proper indexing on execution timestamps and workflow IDs is huge. Set up data retention policies early too - most people only care about recent stuff anyway.
Had a similar project about 18 months back. Biggest pain point? Users couldn’t wrap their heads around complex branching workflows - especially when there’s conditional logic or stuff running in parallel. I built a visual execution tree that shows the actual path taken, not just what’s configured. Game changer.
For storage, went hybrid - PostgreSQL for the structured stuff and execution states, then document storage for variable payloads. Workflow data’s all over the place structure-wise, so this combo worked great.
Here’s what’ll save you tons of headaches: execution context snapshots. When something breaks, you need the error AND the exact state of every variable at that moment. Makes debugging so much easier for users.
Also, build execution replay early - being able to rerun failed workflows from any step becomes huge once people start creating complex automations.