When a project has dozens of configuration files that need similar updates — changing an API endpoint, updating a version number, or modifying a feature flag — manual editing is tedious and error-prone. Batch processing automates these repetitive tasks, applying consistent changes across all files at once.
Common Batch Processing Scenarios
Updating a shared API base URL across multiple environment configs. Incrementing version numbers in package.json, Info.plist, and build.gradle simultaneously. Validating that all JSON configuration files are syntactically correct before deployment. Converting a set of YAML files to JSON for a system that only accepts JSON.
Tools for Batch Processing
Command-line tools like `jq` for JSON, `yq` for YAML, and `xmlstarlet` for XML enable scriptable transformations. For more complex logic, Python scripts with `json`, `pyyaml`, and `xml.etree` libraries provide full programming capabilities. Shell scripts combining these tools create reusable batch processing pipelines.
Building Reliable Pipelines
Always validate before and after transformation. Back up original files before batch modifications. Use dry-run modes when available to preview changes. Log every modification for audit purposes. Treat batch config changes like code changes — review and test them.
Handling Multiple Formats
Real projects mix formats: JSON for web configs, YAML for CI/CD, XML for Android, plist for iOS. A robust batch processing pipeline handles format differences transparently, applying the same logical change (update this URL) across different syntactic representations.
Previewing Before Processing
ParseLab for iOS lets you preview data files before and after changes, supporting JSON, YAML, XML, and other formats. Use it to verify that batch transformations produced the expected results on your phone before committing changes.