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DSLs: The Good, the Bad, and the Ugly . Kathleen Fisher AT&T L a bs Research. Background. Data Warehouse. Hancock DSL for constructing customer „signatures” from transaction streams. Facilitated efficient, high-level programs. Used in production for > 8 years

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dsls the good the bad and the ugly

DSLs: The Good, the Bad, and the Ugly

Kathleen Fisher

AT&T Labs Research

background
Background

Data

Warehouse

  • Hancock
    • DSL for constructing customer „signatures” from transaction streams.
    • Facilitated efficient, high-level programs.
    • Used in production for > 8 years
  • PADS
    • Declarative DSL for describing ad hoc data.
    • Generate in-memory representation, parser, printer, XML conversion tools, statistical analyses...
    • Used at AT&T to vet and load data into databases.

Transaction Data

Hancock

Signature

Database

207.136.97.49 - - [15/Oct/2006:18:46:51 -0700] "GET /turkey/amnty1.gif HTTP/1.0" 200 3013

the good
The Good
  • Targeted abstractions mean DSL programs express important information & hide details.
    • “You can really see what we’re talking about”
    • Programs are shorter: easier to audit, maintain.
    • PADS descriptions serve as living documentation.
  • From declarative specifications, we can generate multiple artifacts:
    • parser, printer, XML translation, statistical analyses,...
  • Compiler can ensure properties of programs:
    • Parser will return meta-data that describes errors.
the bad and the ugly
The Bad and The Ugly
  • Challenge of routine features
    • Include in DSL: replicate a lot of effort!
    • Borrow from ‘host’ language: have to process host language code.
  • Lack of tools
    • DSLs often lack debuggers, profilers, IDE support, etc., because building them is labor intensive.
  • Reluctant customers
    • Learning new languages is hard!
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