ndnc

A DSL interpreter for transparent distributed execution over NDN (Named Data Networking).

Write code without thinking about where it runs — local or remote. The system handles it.

Installation

pip install ndnc

Usage

Run a .ndn script

ndnc run path/to/script.ndn

Run with arguments

ndnc run path/to/script.ndn arg0 arg1

Start NDN server (producer)

ndnc serve

Examples

Fetch data by NDN name and print it:

let height = interest "/height/Mt.Fuji/"
print height

Fetch data and convert units with a local function:

let height_m = interest "/height/Mt.Fuji/"
let height_ft = m_to_feet(height_m)
print height_ft

These examples use locally registered data and run without NFD. When NFD and NLSR are running, interest first checks local data, and if not found, fetches it over the NDN network transparently.

Requirements

• Python 3.10+
• python-ndn

NDN Network Requirements

To fetch data or execute functions over an actual NDN network, the following must be running on your machine:

• NFD (NDN Forwarding Daemon) — handles NDN packet forwarding
• NLSR (Named-data Link State Routing) — manages routing between NDN nodes

Without these, interest expressions will fall back to local data only.

Function return values

Use return to produce a function result. It stops execution of the current script/function. print writes to stdout only and is never used as the result. Without a return, the result is None (an empty response over the text NDN/gRPC interface).

Function (average.ndn):

let avg = (arg0 + arg1) / 2
return avg

Caller:

let result = average(10, 20)
print result

For CLI integrations, keep logs on stdout and write the return value separately:

ndnc run --result-file result.json average.ndn 10 20

result.json contains JSON 15. ndnc run does not automatically print return values. This also applies to cached function execution and exec_in_context. Existing function scripts that used print as their return value must change to return. Caller scripts that print results remain unchanged.

Release: the existing release-branch workflow increments the package version from 0.0.6 to 0.0.7. Publish that version before building the function runtime and invoker images pinned to 0.0.7; deploy both images before registering scripts using return. Source-cache entries from before the migration may remain until their TTL expires (300 seconds).