No more stumbling around in the dark—find performance bottlenecks quickly with Sciagraph

Sciagraph features

• For Python data pipelines and batch jobs: You’re not running a web application: you’re running a long-running data processing job that has a beginning, middle, and end. You need profiling and performance visualizations designed for your particular use case.
• Find slow code—in production: Identify which parts of your program are slow, as it runs in production—and whether the bottleneck is coming from a bad algorithm, a slow database query, or lack of parallelism.
• Discover why your program is using so much memory: With memory profiling built-in, you can identify the source of peak memory usage, the critical bottleneck for hardware costs and the hazard of the Linux out-of-memory killer.
• Fast setup: For simple Python processes, using Sciagraph may be as easy as setting an environment variable. And with MLFlow and Celery integration built-in, and other framework support planned, Sciagraph is designed to work out of the box with the frameworks you use. Need support for another framework? Send me an email to get it prioritized.
• Fast and robust enough to run in production: Sciagraph is designed to minimize impact on your job’s performance.
• Cloud storage for reports: No need to spend time figuring out how to store profiling reports: they can be automatically and securely stored in the cloud. That means you can easily access performance reports even if your runtime environment is ephemeral, like a container.
• Data privacy: Profiling reports never include user data, and when they’re uploaded to Sciagraph’s cloud storage they are encrypted end-to-end, so no one but you can access them.