lyzs90 / MstrERD

Pretty schema diagrams for MicroStrategy

Pretty schema diagrams for MicroStrategy

This notebook demonstrates how to convert the generally unhelpful MicroStrategy schema into human-readable JSON. We can then use a diagramming library such as JointJS or GoJS to draw aesthetically pleasing entity relationship diagrams.

Export MicroStrategy project schema

We will use the schema from the MicroStrategy Tutorial. The schema can be obtained in Developer from 'Schema' > 'Export Project Schema'. Export the Table Catalog - Logical View which contains a list of all warehouse tables and their attributes and facts.

from pyexcel_xls import get_data
import json

data = get_data("schema.xls")
logical_view = data["Logical View"]
temp = [[item[0], item[1], item[2]] for item in logical_view]

WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero

import pandas as pd
df = pd.DataFrame(temp[1:], columns = logical_view[0])
df.head(10)

	Table Name	Object	Type
0	CITY_CTR_SLS	Call Center	Attribute (K)
1	CITY_CTR_SLS	Customer City	Attribute (K)
2	CITY_CTR_SLS	Cost	Fact
3	CITY_CTR_SLS	Profit	Fact
4	CITY_CTR_SLS	Revenue	Fact
5	CITY_CTR_SLS	Units Sold	Fact
6	CITY_CTR_SLS	Gross Revenue	Fact
7	CITY_MNTH_SLS	Month	Attribute (K)
8	CITY_MNTH_SLS	Customer City	Attribute (K)
9	CITY_MNTH_SLS	Cost	Fact

Node list

# form node list

graph_spec = {"nodes": [], "links": []}

df2 = df["Table Name"].drop_duplicates()

for table_id in df2:
    props_list = []
    temp = pd.DataFrame()
    temp = df[df["Table Name"] == table_id]
    for index, row in temp.iterrows():
        props_list.append(row["Object"])
    graph_spec["nodes"].append({"id": table_id, "props": props_list})

Edge List

# cartesian product to find possible edges
df3 = pd.merge(df, df, how='left', on='Object')

# drop rows if Type eq Fact because can't join on Facts
df3 = df3[df3.Type_x != "Fact"]

# drop rows if same Table Name because it is a duplicate record
df3 = df3[df3["Table Name_x"] != df3["Table Name_y"]]

df3.head(10)

	Table Name_x	Object	Type_x	Table Name_y	Type_y
1	CITY_CTR_SLS	Call Center	Attribute (K)	DAY_CTR_SLS	Attribute (K)
2	CITY_CTR_SLS	Call Center	Attribute (K)	F_TUTORIAL_TARGETS	Attribute (K)
3	CITY_CTR_SLS	Call Center	Attribute (K)	ITEM_CCTR_MNTH_SLS	Attribute (K)
4	CITY_CTR_SLS	Call Center	Attribute (K)	LU_CALL_CTR	Attribute (K)
5	CITY_CTR_SLS	Call Center	Attribute (K)	LU_EMPLOYEE	Attribute
6	CITY_CTR_SLS	Call Center	Attribute (K)	SUBCATEG_MNTH_CTR_SLS	Attribute (K)
8	CITY_CTR_SLS	Customer City	Attribute (K)	CITY_MNTH_SLS	Attribute (K)
9	CITY_CTR_SLS	Customer City	Attribute (K)	CITY_SUBCATEG_SLS	Attribute (K)
10	CITY_CTR_SLS	Customer City	Attribute (K)	LU_CUST_CITY	Attribute (K)
11	CITY_CTR_SLS	Customer City	Attribute (K)	LU_CUSTOMER	Attribute

# only keep unique pairs of nodes

unique_pairs = set()
temp = []
for index, row in df3.iterrows():
    if ((row[0], row[3]) in unique_pairs):
        pass
    else:
        unique_pairs.add((row[0], row[3]))
        temp.append(row)
        
df4 = pd.DataFrame(temp)

for index, row in df4.iterrows():
    graph_spec["links"].append({"source": row[0], "target": row[3]})

Export JSON

import json

with open('data.json', 'w') as f:
    json.dump(graph_spec, f)