Skip to main content

A simple, yet elegant MDX library for TM1

Project description

Logo

MDXpy

A simple, yet elegant MDX library for TM1

Install

pip install mdxpy

Usage

Create MDX queries programmatically with the Member, MdxTuple, MdxHierarchySet, MdxBuilder classes.

Benefits of using MDXpy over hacking raw MDX queries in your code

  • Faster to write
  • Requires less MDX knowledge
  • Eliminates syntax errors (e.g. forget }, ], ) in a query) forever
  • Makes code more robust and easier to refactor
  • Escaping of ] in object names is taken care of

Member

Member is used in MdxTuple and MdxHierarchySet. create a Member with the static Member.of(*args: str) method.

>>> member = Member.of("Product", "Product1")
>>> print(member.unique_name)
[PRODUCT].[PRODUCT].[PRODUCT1]

>>> member = Member.of("Region", "ByGeography", "UK")
>>> print(member.unique_name)
[REGION].[BYGEOGRAPHY].[UK]

MdxTuple

Create a MdxTuple with the static of(*args: Member) method. The MDX expression of the tuple is generated with the to_mdx method.

>>> mdx_tuple = MdxTuple.of(Member.of("Product", "Product1"), Member.of("Region", "US"))

>>> print(mdx_tuple.to_mdx())
([PRODUCT].[PRODUCT].[PRODUCT1],[REGION].[REGION].[US])

>>> mdx_tuple = MdxTuple.of(Member.of("Product", "ByType", "Product1"), Member.of("Region", "ByGeography", "North America"))

>>> print(mdx_tuple.to_mdx())
([PRODUCT].[BYTYPE].[PRODUCT1],[REGION].[BYGEOGRAPHY].[North America])

you can add a Member to a MdxTuple

>>> mdx_tuple = MdxTuple.of(Member.of("Product", "ByType", "Product1"))

>>> mdx_tuple.add_member(Member.of("Region", "ByGeography", "North America"))

>>> print(mdx_tuple.to_mdx())
([PRODUCT].[BYTYPE].[PRODUCT1],[REGION].[BYGEOGRAPHY].[NORTHAMERICA])

MdxHierarchySet

MdxHierarchySet is created with any of the static methods on the MdxHierarchySet class. The MDX expression of the set is generated with the to_mdx method.

>>> mdx_set = MdxHierarchySet.tm1_subset_all("Product")
>>> print(mdx_set.to_mdx())
{TM1SUBSETALL([Product].[Product])}

>>> mdx_set = MdxHierarchySet.tm1_subset_to_set("Region", "By Geography", "Default")
>>> print(mdx_set.to_mdx())
{TM1SUBSETTOSET([REGION].[BYGEOGRAPHY],'Default')}

>>> mdx_set = MdxHierarchySet.all_leaves("Region")
>>> print(mdx_set.to_mdx())
{TM1FILTERBYLEVEL({TM1SUBSETALL([REGION].[REGION])},0)}

>>> mdx_set = MdxHierarchySet.members([Member.of("Region", "US"), Member.of("Product", "Product1")])
>>> print(mdx_set.to_mdx())
{[REGION].[REGION].[US],[PRODUCT].[PRODUCT].[PRODUCT1]}

Functions on MdxHierarchySet can be concatenated to arbitrary length in a functional style:

>>> mdx_set = MdxHierarchySet.tm1_subset_all("Region").filter_by_level(0).filter_by_pattern("I*").tm1_sort()
>>> print(mdx_set.to_mdx())
{TM1SORT({TM1FILTERBYPATTERN({TM1FILTERBYLEVEL({TM1SUBSETALL([REGION].[REGION])},0)},'I*')},ASC)}

MdxBuilder

The MdxBuilder is used to build MDX queries. MdxHierarchySet or MdxTuple are placed on the axes. Zero suppression can be switched on or off per axis. The actual MDX expression is generated with the to_mdx method.

>>> query = MdxBuilder.from_cube("Cube").add_hierarchy_set_to_column_axis(MdxHierarchySet.all_leaves("Product"))
>>> print(query.to_mdx())
SELECT {TM1FILTERBYLEVEL({TM1SUBSETALL([PRODUCT].[PRODUCT])},0)} ON 0
FROM [CUBE] 

>>> query = MdxBuilder.from_cube("Cube").add_hierarchy_set_to_column_axis(MdxHierarchySet.member(Member.of("Product", "Product1")))
>>> print(query.to_mdx())
SELECT {[PRODUCT].[PRODUCT].[PRODUCT1]} ON 0
FROM [CUBE] 

>>> query =  MdxBuilder.from_cube("Cube").add_member_tuple_to_axis(0, Member.of("Product", "Product1"), Member.of("Region", "EMEA"))
>>> print(query.to_mdx())
SELECT
{([PRODUCT].[PRODUCT].[PRODUCT1],[REGION].[REGION].[EMEA])} ON 0
FROM [CUBE] 

>>> query = MdxBuilder.from_cube("Cube").columns_non_empty().add_hierarchy_set_to_column_axis(MdxHierarchySet.member(Member.of("Product", "Product1")))
>>> print(query.to_mdx())
SELECT
NON EMPTY {[PRODUCT].[PRODUCT].[PRODUCT1]} ON 0 
FROM [CUBE]

MDX queries can have any number of axes. Axis 0 (=columns) must be defined.

>>> mdx = MdxBuilder.from_cube("Cube") \
    .add_hierarchy_set_to_axis(0, MdxHierarchySet.member(Member.of("Region", "US"))) \
    .add_hierarchy_set_to_axis(1, MdxHierarchySet.all_leaves("Product")) \
    .add_hierarchy_set_to_axis(2, MdxHierarchySet.member(Member.of("Version", "Actual"))) \
    .add_hierarchy_set_to_axis(3, MdxHierarchySet.tm1_subset_to_set("Time", "Time", "2020-Q1")) \
    .to_mdx()

>>> print(mdx)
SELECT
{[REGION].[REGION].[US]} ON 0,
{TM1FILTERBYLEVEL({TM1SUBSETALL([PRODUCT].[PRODUCT])},0)} ON 1,
{[VERSION].[VERSION].[ACTUAL]} ON 2,
{TM1SUBSETTOSET([TIME].[TIME],'2020-Q1')} ON 3
FROM [CUBE]

The CalculatedMember class is used to define query-scoped calculated members. They are used with the MdxBuilder through the with_member function.

>>> mdx = MdxBuilder.from_cube(cube="Record Rating").with_member(
        CalculatedMember.avg(
            dimension="Period",
            hierarchy="Period",
            element="AVG 2016",
            cube="Record Rating",
            mdx_set=MdxHierarchySet.children(member=Member.of("Period", "2016")),
            mdx_tuple=MdxTuple.of(Member.of("Chart", "Total Charts"), Member.of("Record Rating Measure", "Rating")))) \
        .add_hierarchy_set_to_row_axis(
        MdxHierarchySet
            .children(Member.of("Record", "Total Records"))
            .top_count(cube="Record Rating", mdx_tuple=MdxTuple.of(Member.of("Period", "AVG 2016")), top=5)) \
        .add_member_tuple_to_columns(Member.of("Period", "AVG 2016")) \
        .where(Member.of("Chart", "Total Charts"), Member.of("Record Rating Measure", "Rating")) \
        .to_mdx()

>>> print(mdx)
WITH 
MEMBER [PERIOD].[PERIOD].[AVG2016] AS AVG({[PERIOD].[PERIOD].[2016].CHILDREN},[Record Rating].([CHART].[CHART].[TOTALCHARTS],[RECORDRATINGMEASURE].[RECORDRATINGMEASURE].[RATING]))
SELECT
{([PERIOD].[PERIOD].[AVG2016])} ON 0,
{TOPCOUNT({[RECORD].[RECORD].[TOTALRECORDS].CHILDREN},5,[RECORDRATING].([PERIOD].[PERIOD].[AVG2016]))} ON 1
FROM [RECORDRATING]
WHERE ([CHART].[CHART].[TOTALCHARTS],[RECORDRATINGMEASURE].[RECORDRATINGMEASURE].[RATING])

The DimensionProperty class is used to query attributes in conjunction with data. It is used with the MdxBuilder through the add_properties_to_row_axis, add_hierarchy_set_to_column_axis functions.

from mdxpy import DimensionProperty, MdxHierarchySet, MdxBuilder, Member

query = MdxBuilder.from_cube("Sales")

query = query.rows_non_empty()
query = query.add_hierarchy_set_to_row_axis(MdxHierarchySet.all_leaves("Product"))
query = query.add_properties_to_row_axis(DimensionProperty.of("Product", "Description"))

query = query.columns_non_empty()
query = query.add_hierarchy_set_to_column_axis(MdxHierarchySet.member(Member.of("Sales Measure", "Revenue")))

query = query.where(Member.of("Year", "2022"), Member.of("Region", "Switzerland"))

print(query.to_mdx())

>>> print(mdx)
SELECT
NON EMPTY {[salesmeasure].[salesmeasure].[revenue]} DIMENSION PROPERTIES MEMBER_NAME ON 0,
NON EMPTY {TM1FILTERBYLEVEL({TM1SUBSETALL([product].[product])},0)} DIMENSION PROPERTIES [product].[product].[description] ON 1
FROM [sales]
WHERE ([year].[year].[2022],[region].[region].[switzerland])

To see all samples checkout the test.py file

Supported MDX Functions

  • TM1SUBSETALL
  • MEMBERS
  • TM1SUBSETTOSET
  • DEFAULTMEMBER
  • PARENT
  • FIRSTCHILD
  • LASTCHILD
  • CHILDREN
  • ANCESTORS
  • ANCESTOR
  • DRILLDOWNLEVEL
  • FILTER
  • TM1FILTERBYPATTERN
  • TM1FILTERBYLEVEL
  • TM1SORT
  • HEAD
  • TAIL
  • SUBSET
  • TOPCOUNT
  • BOTTOMCOUNT
  • UNION
  • INTERSECT
  • EXCEPT
  • ORDER

Tests

All tests in test.py

Contribution

Contribution is welcome. If you find a bug or feel like you can contribute please fork the repository, update the code and then create a pull request so we can merge in the changes.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mdxpy-1.3.1.tar.gz (83.4 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page