reshard-c4-data

To reorganize datasets into constraint-compliant shards, you calculate required shard and group nesting, split oversized files into chunks, and distribute them across a hierarchical directory tree. This ensures every root, intermediate, and leaf directory respects maximum item counts.

Hierarchical data sharding is the process of splitting large file collections into nested directories to satisfy recursive per-directory item limits. You need it for scalable storage or distribution workflows where every directory level must comply with strict item and file-size constraints.

To split oversized files during data partitioning, you divide them into chunks and evenly distribute those chunks across computed shards. You track the chunk mappings and original path relationships by generating a metadata file for later reconstruction.

Checksums and reconstruction metadata are generated by emitting a .metadata.json file alongside the resharded output. This file records original path mappings, chunk order, and checksums to enable round-trip reconstruction and verify data integrity across all directories.

Yes, you can redistribute a 10k-file dataset with a 30-item-per-directory cap by computing the required shard and group counts, splitting any oversized files, and distributing the items to meet per-directory limits while producing verification metadata.

The limitation of recursive directory constraints is that they require calculating exact shard, group, and nesting depth for root, intermediate, and leaf directories. Failing to split oversized files or validate item counts will break compliance and prevent accurate reconstruction.