LDMDump: A Complete Guide for Developers
What LDMDump is
LDMDump is a command-line utility for extracting and exporting data from LDM (Lightweight Data Model) repositories and related binary formats. It converts internal storage formats into readable, interoperable outputs (JSON, CSV, or plain text) so developers can inspect, migrate, or analyze dataset contents.
Key features
- Format extraction: Read proprietary or compact LDM binary blobs and emit JSON/CSV.
- Schema-aware parsing: Uses stored schema/metadata to map binary fields to human-readable names and types.
- Selective export: Filter by record type, timestamp range, or field selection.
- Batch processing: Stream large datasets with low memory usage.
- Pluggable output: Support for custom serializers and post-processing hooks.
- Error reporting: Detailed logs for malformed records and schema mismatches.
Typical use cases
- Inspecting repository contents during development or debugging.
- Migrating data from LDM-based storage to other systems.
- Auditing or verifying data integrity and schemas.
- Building ETL pipelines that consume LDM artifacts.
Installation and setup
- Install via package manager or download prebuilt binary for your OS (assume Linux/macOS/Windows releases available).
- Place binary on PATH or symlink to /usr/local/bin.
- Create a minimal config file (yaml/json) pointing to repository root and schema directory.
- Ensure read access to repository files and any encryption keys if blobs are encrypted.
Basic commands (examples)
- Export all records to JSON:
Code
ldmdump export –repo /path/to/repo –format json > all.json - Export specific record type:
Code
ldmdump export –repo ./repo –type userprofile –format csv - Stream records between timestamps:
Code
ldmdump export –repo ./repo –from 2025-01-01 –to 2025-12-31 –stream
Filtering and transforms
- Field selection:
Code
ldmdump export –fields id,name,email - Inline transform with JS/Python hook:
Code
ldmdump export –transform ./scripts/normalize_emails.js
Performance tips
- Use streaming mode for very large datasets to avoid high memory usage.
- Increase worker threads for parallel parsing when CPU-bound.
- Prefetch schema metadata into memory to reduce I/O overhead.
- Compress output on-the-fly (gzip) when writing large JSON exports.
Error handling
- Use
–skip-errorsto continue on malformed records while logging details. - Run
ldmdump validateto check schema compatibility before export. - Enable verbose logging (
-v/–debug) for stack traces on parse failures.
Security considerations
- Keep encryption keys secure; LDMDump will need keys to decrypt encrypted blobs.
- Run with least privilege to avoid accidental overwrite of repository files.
- Sanitize any transform scripts to avoid code injection when processing untrusted data.
Integration examples
- As part of a CI job to verify repository integrity before deploy.
- Hooked into an ETL pipeline: ldmdump -> Apache Kafka -> downstream consumers.
- Use with analytics: ldmdump export –format parquet -> query with Spark.
Troubleshooting checklist
- Repo path correct and accessible.
- Correct schema version loaded.
- Matching decryption keys present (if used).
- Sufficient disk space for temporary output files.
- Compatible ldmdump binary for platform/architecture.
Further resources
- Command reference: run
ldmdump –help. - Schema guide: include mapping conventions and versioning best practices.
- Example transform scripts repository (JS/Python).
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