A Pandas-like SQL-wrapper for in-database analytics with IBM dashDB/DB2.
Project description
Accelerating Python Analytics by In-Database Processing
The ibmdbpy project provides a Python interface for data manipulation and access to in-database algorithms in IBM dashDB and IBM DB2. It accelerates Python analytics by seamlessly pushing operations written in Python into the underlying database for execution, thereby benefitting from in-database performance-enhancing features, such as columnar storage and parallel processing.
IBM dashDB is a database management system available on IBM Bluemix, the cloud application development and analytics platform powered by IBM. The ibmdbpy project can be used by Python developers with very little additional knowledge, because it copies the well-known interface of the Pandas library for data manipulation and the Scikit-learn library for the use of machine learning algorithms.
The ibmdbpy project is compatible with Python releases 2.7 up to 3.4 and can be connected to dashDB or DB2 instances via ODBC or JDBC.
The project is still at an early stage and many of its features are still in development. However, several experiments have already demonstrated that it provides significant performance advantages when operating on medium or large amounts of data, that is, on tables of 1 million rows or more.
The latest version of ibmdbpy is available on the Python Package Index.
How ibmdbpy works
The ibmdbpy project translates Pandas-like syntax into SQL and uses a middleware API (pypyodbc/JayDeBeApi) to send it to an ODBC or JDBC-connected database for execution. The results are fetched and formatted into the corresponding data structure, for example, a Pandas.Dataframe or a Pandas.Series.
The following scenario illustrates how ibmdbpy works.
Assuming that all ODBC connection parameters are correctly set, issue the following statements to connect to a database (in this case, a dashDB instance named DASHDB) via ODBC:
>>> from ibmdbpy import IdaDataBase, IdaDataFrame
>>> idadb = IdaDataBase('DASHDB')
A few sample data sets are included in ibmdbpy for you to experiment. We can firstly load the well-known IRIS table into this dashDB instance.
>>> from ibmdbpy.sampledata import iris >>> idadb.as_idadataframe(iris, "IRIS") <ibmdbpy.frame.IdaDataFrame at 0x7ad77f0>
Next, we can create an IDA data frame that points to the table we just uploaded. Let’s use that one:
>>> idadf = IdaDataFrame(idadb, 'IRIS')
Note that to create an IDA data frame using the IdaDataFrame object, we need to specify our previously opened IdaDataBase object, because it holds the connection.
Now let us compute the correlation matrix:
>>> idadf.corr()
In the background, ibmdbpy looks for numerical columns in the table and builds an SQL request that returns the correlation between each pair of columns. Here is the SQL request that was executed for this example:
SELECT CORRELATION("sepal_length","sepal_width"),
CORRELATION("sepal_length","petal_length"),
CORRELATION("sepal_length","petal_width"),
CORRELATION("sepal_width","petal_length"),
CORRELATION("sepal_width","petal_width"),
CORRELATION("petal_length","petal_width")
FROM IRIS
The result fetched by ibmdbpy is a tuple containing all values of the matrix. This tuple is formatted back into a Pandas.DataFrame and then returned:
sepal_length sepal_width petal_length petal_width sepal_length 1.000000 -0.117570 0.871754 0.817941 sepal_width -0.117570 1.000000 -0.428440 -0.366126 petal_length 0.871754 -0.428440 1.000000 0.962865 petal_width 0.817941 -0.366126 0.962865 1.000000
Et voilà !
How the spatial functions work
The geospatial extension translates geopandas-like syntax into SQL and uses a middleware API (pypyodbc/JayDeBeApi) to send it to an ODBC or JDBC-connected database for execution. It identifies the geometry column for spatial tables and enables the user to perform spatial queries based upon this column. The results are fetched and formatted into the corresponding data structure, for example, an IdaGeoDataframe.
The following scenario illustrates how spatial functions work.
Assuming that all ODBC connection parameters are correctly set, issue the following statements to connect to a database (in this case, a dashDB instance named DASHDB) via ODBC:
>>> from ibmdbpy import IdaDataBase, IdaGeoDataFrame
>>> idadb = IdaDataBase('DASHDB')
We can create an IDA geo data frame that points to a sample table in dashDB:
>>> idadf = IdaGeoDataFrame(idadb, 'SAMPLES.GEO_COUNTY')
Note that to create an IDA geo data frame using the IdaDataFrame object, we need to specify our previously opened IdaDataBase object, because it holds the connection.
Now let us compute the area of the counties in the GEO_COUNTY table. The result of the area will be stored as a new column ‘area’ in the IdaGeoDataFrame:
>>> idadf['area'] = idadf.area(colx = 'SHAPE')
OBJECTID NAME SHAPE area
1 Wilbarger MULTIPOLYGON (((-99.4756582604 33.8340108094, ... 0.247254
2 Austin MULTIPOLYGON (((-96.6219873342 30.0442882117, ... 0.162639
3 Logan MULTIPOLYGON (((-99.4497297204 46.6316377481, ... 0.306589
4 La Plata MULTIPOLYGON (((-107.4817473750 37.0000108736,... 0.447591
5 Randolph MULTIPOLYGON (((-91.2589262966 36.2578866492, ... 0.170844
In the background, ibmdbpy looks for geometry columns in the table and builds an SQL request that returns the area of each geometry. Here is the SQL request that was executed for this example:
SELECT t.*,db2gse.ST_Area(t.SHAPE) as area FROM SAMPLES.GEO_COUNTY t;
That’s as simple as that!
Project Roadmap
Full test coverage (a basic coverage is already provided)
Add more functions and improve what already exists
Add wrappers for several ML-Algorithms
Feature selection extension
Add Spark as computational engine
Contributors
The ibmdbpy project was initiated in April 2015, and developed by Edouard Fouché and the geospatial extension was contributed by Avipsa Roy and Rafael Rodriguez Morales in March,2016 under the supervision of Dr. Gregor Moehler, at IBM Deutschland Reasearch & Development, Böblingen.
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