Compression of Resources
| Authors | Michael Amadi | 
|---|
It can be argued that applying compression to data resources can make data package publishing more cost-effective and sustainable. Compressing data resources gives publishers the benefit of reduced storage and bandwidth costs and gives consumers the benefit of shorter download times.
Implementations
- tabulator-py (Gzip and Zip support)
 - datapackage-connector (Gzip support)
 - datapackage-m (Gzip support)
 
Specification
All compressed resources MUST have a path that allows the compression property to be inferred. If the compression can’t be inferred from the path property (e.g. a custom file extension is used) then the compression property MUST be used to specify the compression.
Supported compression types:
- gz
 - zip
 
Example of a compressed resource with implied compression:
{  "name": "data-resource-compression-example",  "path": "http://example.com/large-data-file.csv.gz",  "title": "Large Data File",  "description": "This large data file benefits from compression.",  "format": "csv",  "mediatype": "text/csv",  "encoding": "utf-8",  "bytes": 1073741824}Example of a compressed resource with the compression property:
{  "name": "data-resource-compression-example",  "path": "http://example.com/large-data-file.csv.gz",  "title": "Large Data File",  "description": "This large data file benefits from compression.",  "format": "csv",  "compression" : "gz",  "mediatype": "text/csv",  "encoding": "utf-8",  "bytes": 1073741824}