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Last pushed: 4 months ago
Short Description
SQLAlchemy models and DDL and ERD generation from chop-dbhi/data-models style JSON endpoints.
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Data Models SQLAlchemy

SQLAlchemy models and DDL and ERD generation for chop-dbhi/data-models style JSON endpoints.

Web service available at http://dmsa.a0b.io/

SQLAlchemy Models

In your shell, hopefully within a virtualenv:

pip install dmsa

In python:

from dmsa.omop.v5.models import Base

for tbl in Base.metadata.sorted_tables():
    print tbl.name

Or:

from dmsa.pedsnet.v2.models import Person, VisitPayer

print VisitPayer.columns

These models are dynamically generated at runtime from JSON endpoints provided by chop-dbhi/data-models-service, which reads data stored in chop-dbhi/data-models. It should be simple to add modules for any additional data models that become available, but the currently provided ones are:

  • OMOP V4 at omop.v4.models
  • OMOP V5 at omop.v5.models
  • PEDSnet V1 at pedsnet.v1.models
  • PEDSnet V2 at pedsnet.v2.models
  • i2b2 V1.7 at i2b2.v1_7.models
  • i2b2 PEDSnet V2 at i2b2.pedsnet.v2.models
  • PCORnet V1 at pcornet.v1.models
  • PCORnet V2 at pcornet.v2.models
  • PCORnet V3 at pcornet.v3.models

DDL and ERD Generation

Use of the included Dockerfile is highly recommended to avoid installing DBMS and graphing specific system requirements.

The following DBMS dialects are supported when generating DDL:

  • PostgreSQL called as postgresql
  • MySQL called as mysql
  • MS SQL Server called as mssql
  • Oracle called as oracle

With Docker:

Retrieve the image:

docker pull dbhi/data-models-sqlalchemy

Usage for DDL generation:

docker run --rm dbhi/data-models-sqlalchemy ddl -h

Generate OMOP V5 DDL for Oracle:

docker run --rm dbhi/data-models-sqlalchemy ddl omop v5 oracle

Usage for ERD generation:

docker run --rm dbhi/data-models-sqlalchemy erd -h

Generate i2b2 PEDSnet V2 ERD (the image will land at ./erd/i2b2_pedsnet_v2_erd.png):

docker run --rm -v $(pwd)/erd:/erd dbhi/data-models-sqlalchemy erd i2b2_pedsnet v2 /erd/i2b2_pedsnet_v2_erd.png

The graphviz graphing package supports a number of other output formats, listed here (link pending), which are interpreted from the passed extension.

Without Docker:

Install the system requirements (see Dockerfile for details):

  • Python 2.7
  • graphviz for ERD generation
  • Oracle instantclient-basic and -sdk and libaio1 for Oracle DDL generation
  • libpq-dev for PostgreSQL DDL generation
  • unixodbc-dev for MS SQL Server DDL generation

Install the python requirements, hopefully within a virtualenv (see Dockerfile for details):

pip install cx-Oracle            # for Oracle DDL generation
pip install psycopg2             # for PostgreSQL DDL generation
pip install PyMySQL              # for MySQL DDL generation
pip install pyodbc               # for MS SQL Server DDL generation

Install the data-models-sqlalchemy python package:

pip install dmsa

Usage for DDL generation:

dmsa ddl -h

Generate OMOP V5 DDL for Oracle:

dmsa ddl omop v5 oracle

Usage for ERD generation:

dmsa erd -h

Generate i2b2 PEDSnet V2 ERD (the image will land at ./erd/i2b2_pedsnet_v2_erd.png):

mkdir erd
dmsa erd i2b2_pedsnet v2 ./erd/i2b2_pedsnet_v2_erd.png

Web Service

The web service uses a simple Flask debug server for now. It exposes the following endpoints:

  • DDL at /<model>/<version>/ddl/<dialect>/
  • DDL for only table, constraint, or index elements at /<model>/<version>/ddl/<dialect>/<elements>
  • ERDs at /<model>/<version>/erd/

With Docker:

Usage:

docker run  dbhi/data-models-sqlalchemy start -h

Run:

docker run dbhi/data-models-sqlalchemy  # Uses Dockerfile defaults of 0.0.0.0:80

Without Docker:

Install Flask:

pip install Flask

Usage:

dmsa start -h

Run:

dmsa start                              # Uses Flask defaults of 127.0.0.1:5000
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