Frequently Asked Questions¶
Why the name giraffez?¶
The original intention was to create a C++ command-line tool that quickly dumped a Teradata table to a file. The first name was “Teradumps” (which is an awful name), for Teradata and “dump-s” (aka “dump to string”). When trying to think up a better name, we found ourselves thinking, “Hadoop has an elephant, what’s another cool animal? How about a giraffe?”. And, thus, GiraffeDumps was the name at the time. Later as more functionality was added to the package it was decided to come up with a final name that better represents the project (it moved beyond just dumping from Teradata), and the name giraffez was where we landed.
What are the Teradata Call-Level Interface Version 2 and Teradata Parallel Transporter API and how do I get them?¶
- Teradata Call-Level Interface Version 2 (CLIv2)
The official Teradata C API, which provides an interface for communication with a Teradata server. This library is the same used by Teradata to create all their client-side tools. It is supported on most major platforms of Windows, Linux, Unix, and Unix-like operation systems (Mac OS) and supports processor architectures for x86 (32-bit and 64-bit), Power PC, Itanium 2, PA-RISC, and Opteron.
- Teradata Parallel Transporter API (TPT API)
Official Teradata library that exposes many common and simplified interfaces to Teradata via C++. This library is built upon the CLIv2 and gives support for bulk operations such as BulkExport and BulkLoad.
While these libraries have wide support, the platform-specific installation of these can vary wildly and be problematic. Since these are dependencies for almost all giraffez features, it means that these will need to be installed and configured correctly for giraffez to be able to work properly. If having trouble with these contact your system administrator and/or checkout the Teradata support forums. Teradata is a commercial product only, and these drivers are both under proprietary license, so getting these drivers may not be available outside of a relationship with Teradata.
The CLIv2 is offered for free for Windows, Linux, HP-UX, AIX, and Solaris via http://downloads.teradata.com (login required) and is bundled in the Teradata Tools and Utilities for Windows download. The TPT API is only available with the Teradata Tools and Utilities for Windows, and is distributed for other platforms via their paid support portal and via the media provided with the purchased server software. The TTU packages are not publicly offered for Linux or MacOS, however, both libraries should be available in this package (unsure if TPT was included in packages prior to 15.00).
Why is ujson an optional requirement?¶
When performing a BulkExport to json, the ujson package is much faster than Python’s standard library json. Python’s json package is much slower because it provides a more complete json marshaling package, whereas ujson is sufficiently complete for the purpose of creating well-formed json from this type of data.
Why are the column aliases incorrect when using BulkExport?¶
Update This should no longer apply as the new method for fetching columns does not rely on the broken TPT API
A regression was introduced after version 13.10 of the Teradata Parallel Transporter API that causes inconsistent behavior with column aliases in BulkExport operations. When dealing with columns that are aliased the Teradata library does not return the name specified in the alias. It seemed that when dealing with columns that are aliased that it would instead return the underlying column name or when dealing with computed fields returning a blank string.
This affects any part of giraffez that makes use of the
giraffez.BulkExport class (it does not affect
giraffez.Cmd). Unfortunately, this is rooted in the proprietary Teradata libraries and cannot be fixed within giraffez, so the recommendation is to simply hardcode the column names with the query in those cases.
Why implicitly coerce Teradata decimals?¶
In previous versions of giraffez, Teradata decimal types fixed precision was preserved via Python’s
decimal.Decimal type. The decision was made to allow implicit conversion of Teradata decimal types to the Python float because of the many times unnecessary performance burden with using
decimal.Decimal and this cost not being transparent when using
decimal.Decimal. For example, numpy will take a
decimal.Decimal in place of a float because
decimal.Decimal has the appropriate magic methods defined, but will be many times slower. This cost ends up being invisible to users because the fixed-point arithmetic is hidden by the Python magic methods. While giraffez is very convered with correctness, this decision was made to balance that with performance and what we feel is the most likely use case.
Why use python’s “with” context?¶
Interacting with Teradata involves correctly managing a connection to the server. In order to reduce complexity and opportunity for mistakes to be made, the giraffez python API classes
giraffez.BulkLoad provide the python “magic” methods
__exit__, which allow easier automatic management of stateful resources. These methods are called when an object is created as part of a
with block, as in:
with giraffez.Cmd() as cmd: for row in cmd.execute(my_query): print(row)
Python guarantees that the object of a
with statement will have its
__enter__ method called immediately, and its
__exit__ method called whenever leaving the following block. This means that even if an exception is raised within the block, the managed object will be given a chance to clean up and exit gracefully. Because the procedure for closing an underlying Teradata connection is complicated, it is important that these methods are always invoked at the proper time. If they are not, it is easy to create a situation where tables are left in a locked state indefinitely, or server resources are tied up by open idle connections.
As of giraffez version 2.0.6, it is probitied to create instances of the above Teradata connection classes outside of a
with context. Usage of the giraffez python API without context managment is dangerous and discouraged. If you feel that there is a legitimate need for allowing this behavior, please describe your use-case in an issue.