Elian  Harber

Elian Harber


Pyhdb: SAP HANA Connector in pure Python

SAP HANA Database Client for Python

Important Notice

This public repository is read-only and no longer maintained.

The active maintained alternative is SAP HANA Python Client: https://pypi.org/project/hdbcli/

A pure Python client for the SAP HANA Database based on the SAP HANA Database SQL Command Network Protocol.

pyhdb supports Python 2.7, 3.3, 3.4, 3.5, 3.6 and also PyPy on Linux, OSX and Windows. It implements a large part of the DBAPI Specification v2.0 (PEP 249).

Table of contents


Install from Python Package Index:

$ pip install pyhdb

Install from GitHub via pip:

$ pip install git+https://github.com/SAP/pyhdb.git

You can also install the latest version direct from a cloned git repository.

$ git clone https://github.com/SAP/pyhdb.git
$ cd pyhdb
$ python setup.py install

Getting started

If you do not have access to a SAP HANA server, go to the SAP HANA Developer Center and choose one of the options to get your own trial SAP HANA Server.

For using PyHDB with hanatrial instance, follow this guide.

The basic pyhdb usage is common to database adapters implementing the DBAPI 2.0 interface (PEP 249). The following example shows how easy it's to use the pyhdb module.

>>> import pyhdb
>>> connection = pyhdb.connect(

>>> cursor = connection.cursor()
>>> cursor.execute("SELECT 'Hello Python World' FROM DUMMY")
>>> cursor.fetchone()
(u"Hello Python World",)

>>> connection.close()

Establish a database connection

The function pyhdb.connect creates a new database session and returns a new Connection instance. Please note that port isn't the instance number of you SAP HANA database. The SQL port of your SAP HANA is made up of 3<instance-number>15 for example the port of the default instance number 00 is 30015.

Currently pyhdb only supports the user and password authentication method. If you need another authentication method like SAML or Kerberos than please open a GitHub issue. Also there is currently no support of encrypted network communication between client and database.

Cursor object

With the method cursor of your Connection object you create a new Cursor object. This object is able to execute SQL statements and fetch one or multiple rows of the resultset from the database.

Example select

>>> cursor = connection.cursor()

After you executed a statement you can fetch one or multiple rows from the resultset.

>>> cursor.fetchone()
(u'SYS', u'DUMMY')

>>> cursor.fetchmany(3)

You can also fetch all rows from your resultset.

>>> cursor.fetchall()

Example Create table

With the execute method you can also execute DDL statements like CREATE TABLE.

>>> cursor.execute('CREATE TABLE PYHDB_TEST("NAMES" VARCHAR (255) null)')

Example insert

You can also execute DML Statements with the execute method like INSERT or DELETE. The Cursor attribute rowcount contains the number of affected rows by the last statement.

>>> cursor.execute("INSERT INTO PYHDB_TEST VALUES('Hello Python World')")
>>> cursor.rowcount


Three different types of LOBs are supported and corresponding LOB classes have been implemented: * Blob - binary LOB data * Clob - string LOB data containing only ascii characters * NClob - string (unicode for Python 2.x) LOB data containing any valid unicode character

LOB instance provide a file-like interface (similar to StringIO instances) for accessing LOB data. For HANA LOBs lazy loading of the actual data is implemented behind the scenes. An initial select statement for a LOB only loads the first 1024 bytes on the client:

>>> mylob = cursor.execute('select myclob from mytable where id=:1', [some_id]).fetchone()[0]
>>> mylob
<Clob length: 2000 (currently loaded from hana: 1024)>

By calling the read(<num-chars>)-method more data will be loaded from the database once <num-chars> exceeds the number of currently loaded data:

>>> myload.read(1500)   # -> returns the first 1500 chars, by loading extra 476 chars from the db
>>> mylob
<Clob length: 2000 (currently loaded from hana: 1500)>
>>> myload.read()   # -> returns the last 500 chars by loading them from the db
>>> mylob
<Clob length: 2000 (currently loaded from hana: 2000)>

Using the seek() methods it is possible to point the file pointer position within the LOB to arbitrary positions. tell() will return the current position.

LOBs can be written to the database via insert or update-statemens with LOB data provided either as strings or LOB instances:

>>> from pyhdb import NClob
>>> nclob_data = u'朱の子ましける日におえつかうまつ'
>>> nclob = NClob(nclob_data)
>>> cursor.execute('update mynclob set nclob_1=:1, nclob_2=:2 where id=:3', [nclob, nclob_data, myid])


Currently LOBs can only be written in the database for sizes up to 128k (entire amount of data provided in one update or insert statement). This constraint will be removed in one of the next releases of PyHDB. This limitation does however not apply when reading LOB data from the database.

Stored Procedures

Rudimentary support for Stored Procedures call, scalar parameters only:

The script shall call the stored procedure PROC_ADD2 (source below):

>>> sql_to_prepare = 'call PROC_ADD2 (?, ?, ?, ?)'
>>> params = {'A':2, 'B':5, 'C':None, 'D': None}
>>> psid = cursor.prepare(sql_to_prepare)
>>> ps = cursor.get_prepared_statement(psid)
>>> cursor.execute_prepared(ps, [params])
>>> result = cursor.fetchall()
>>> for l in result:
>>>     print l

from the stored procedure:

create procedure PROC_ADD2 (in a int, in b int, out c int, out d char)
language sqlscript
reads sql data as
    c := :a + :b;
    d := 'A';

Transaction handling

Please note that all cursors created from the same connection are not isolated. Any change done by one cursor is immediately visible to all other cursors from same connection. Cursors created from different connections are isolated as the connection based on the normal transaction handling.

The connection objects provides to method commit which commit any pending transaction of the connection. The method rollback undo all changes since the last commit.


If you found bugs or have other issues than you are welcome to create a GitHub Issue. If you have questions about usage or something similar please create a Stack Overflow Question with tag pyhdb.

Run tests

pyhdb provides a test suite which covers the most use-cases and protocol parts. To run the test suite you need the pytest and mock package. Afterwards just run py.test inside of the root directory of the repository.

$ pip install pytest mock
$ py.test

You can also test different python version with tox.

$ pip install tox
$ tox


For debugging purposes it is sometimes useful to get detailed tracing information about packages sent to hana and those received from the database. There are two ways to turn on the print out of tracing information:

  1. Set the environment variable HDB_TRACING=1 before starting Python, e.g. (bash-syntax!):
$ HDB_TRACE=1 python
  1. Import the pyhdb module and set pyhdb.tracing = True

Then perform some statements on the database and enjoy the output.

To get tracing information when running pytest provide the -s option:

$ HDB_TRACE=1 py.test -s


  • Allow execution of stored database procedure
  • Support of SELECT FOR UPDATE
  • Authentication methods
    • SAML
    • Kerberos

Author: SAP-archive
Source Code: https://github.com/SAP-archive/PyHDB 
License: Apache-2.0 License


What is GEEK

Buddha Community

Pyhdb: SAP HANA Connector in pure Python
Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type