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Version: tdbench-8.01.04 - Created: 14 Oct 2024

TdBench is a tool designed to simulate realistic database workloads for applications and platforms.This tool can be used with any DBMS supporting JDBC to:Measure performance before vs after a change to add indexes, partitioning, compression, etcMeasure the impact to your DBMS of changes to settings, a patch, or a new software releaseSimulate a workload for a new application or a proof of conceptCompare the performance of one platform to anotherCompare performance of different data base vendor’s productsGetting Software and Help for TdBench:You can download the latest package of the TdBench jar file and setup information with scripts for Teradata and non-Teradata DBMSs from this page and unzip it in a directory on a server of PC with connectivity to your DBMSTerdata's JDBC driver is included.  Search the web for other vendor's JDBC drivers and save them on your server or PCAdditional DBMS setup scripts and information may be found at https://github.com/Teradata/tdbench.  You can submit issues, questions or contribute DBMS setup information at https://github.com/Teradata/tdbench/discussions.  Manuals, white papers and videos are reference at the bottom of this page. What does TdBench do?TdBench simulates realistic production systems by allowing definition of the different types of work and adjusting the number of concurrent executions for each type of work;It captures the results each query execution in its internal database.It facilitates analysis host DBMS resource consumption by maintaining test metadata on the host DBMS to join with its query logs.Tests are defined with:queues of SQL queries and scripts or OS commandsvariable number of execution threads (workers) per queuecommands to pace queries by time or percentageparameterized queries to simulate different usersoptional query prepare reducing DBMS parsingscheduled start of processes or individual queriesFixed work or fixed period execution modelsScripting language to automate multiple testsTests can be defined as simply as 4 statements. Analysis capabilities have been used to track individual query performance over hundreds of runs during projects with constraints like:WHERE RunID in (79, 81, 105)Example:  Basic test of all queries in 1 worker session:define serial Test of queries executed seriallyqueue thequeries scripts/queries/*.sqlworker thequeries mydbmsrunExample:  Fixed period test of 10 minutes with 2 queues and a total of 5 worker sessions:define workload5 Test of 1 heavy and 4 reporting worker sessionsqueue hvy scripts/queries/hvy*.sqlworker hvy mydbms 1queue rpt scripts/queries/rpt*.sql;worker rpt mydbms 4run 10mThere are nearly 60 commands for defining and scripting multiple tests.  You could use:the PACE command with an interval reference command to control arrival of queries on a queue, orPACE with a percentage  to limit the percentage of total queries executed from one queue, orAT command to schedule events, or QUERY LIST to replay query starting as the executed in production There are built-in variables, user variables, IF and GOTO statements.There are 69 built-in help files and a TdBench 8.01 User Guide to help you get started.TdBench Documentation:TdBench 8.01 User GuideTdbench 8.01 Tri-Fold Command ReferenceWhite Papers:Essential Guide to Benchmarks for DBAs1-Page Essential Guide to Benchmarks for ExecutivesBenchmark DeceptionBenchmark Deception And How to Avoid Benchmark TricksVideos:TdBench Overview  - Why it was created and what it does (0:10:09)TdBench Command Language - Demonstration of use (0:14:19)Design of a Good Benchmark - Training session on constructing a benchmark that models realistic database workloads (0:41:33)

Version: 20.00.00.07 - Created: 04 Sep 2024

Teradata SQL Driver Dialect for SQLAlchemyThis package enables SQLAlchemy to connect to the Teradata Database.This package requires 64-bit Python 3.4 or later, and runs on Windows, macOS, and Linux. 32-bit Python is not supported.For community support, please visit the Teradata Community forums.For Teradata customer support, please visit Teradata Access.Copyright 2024 Teradata. All Rights Reserved.

Version: 20.00.00.03 - Created: 29 Mar 2024

Overviewteradatamlspk is a Python package, built as an extension of teradataml, Teradata Python package. Syntax and user accessibility of teradatamlspk APIs are kept similar to PySpark APIs, allowing, the existing PySpark workloads, that run on Spark engine, can be easily run on Teradata Vantage with minimal changes to migrate PySpark workloads to Vantage.teradatamlspk offers another function pyspark2teradataml that enables conversion of a PySpark script to a teradatamlspk Python script. It also generates the HTML report for the conversion, that is useful for the user to understand the changes done and also carry out any manual changes in the generated script, so that the script can be run on Vantage.Dependent Python Packages: teradataml >= 20.00.00.06PrettyTableNbformatpytzpygments==2.19.0jinja2mistunePrerequisite: Python >= 3.9.0 on the client machine 

Version: 20.0.47 - Created: 16 Nov 2023

The Teradata SQL Driver for Node.js enables Node.js applications to connect to the Teradata database.For documentation, license information, and sample programs, please visit the driver GitHub page.For community support, please visit Teradata Community.For Teradata customer support, please visit Teradata Customer Service.We recommend that you follow the Installation instructions listed on the driver GitHub page.If the recommended installation procedure is not possible for you, then follow these manual installation steps:Download the .tgz file from the link above.In your command prompt or shell, run npm install tgzFileName

Version: 01.04.00.00 - Created: 15 Sep 2023

Teradata Partner API The Partner API is composed of two separate components.  The first component is an in-database function (a Table Operator named API_Request) that enables Vantage users to predict (score) machine learning models that are built on 3rd party partner platforms against Vantage data through a Vantage query.  For example, the release 1.4.0 release supports AWS SageMaker, Azure Machine Learning (and OpenAI), Google Vertex AI, and OpenAI model endpoints, with data passed from Vantage to these external platforms using a web services call, and the results are returned to the Vantage end user as results from the query. The second component is a Python library (named tdapiclient), which is a companion package of the Teradata Package for Python (teradataml), Teradata's Python package for client-side processing.  The tdapiclient Python packages allows AWS SageMaker, AzureML and/of Google VertexAI users of Vantage to call each CSP’s Python library interfaces to train and predict using data in Teradata Vantage tables.  tdapiclient also transparently converts and copies the Teradata DataFrame to S3, AzureBlob Storage or Google Cloud Storage for training as part of the fit() method which can invoke any of the API’s offered by the CSP.  For inference, it will use the data directly from Vantage input tables or queries via API_Request.  Test inferences on small amounts of data can also be done directly through the client library.  The tdapiclient also wraps the required BYOM calls to productionize the scoring process.  ** Note that for OpenAI and Azure OpenAI, only the API_Request static API is available in tdapiclient.  Download tdapiclient from:  https://pypi.org/project/tdapiclient/ For more information, see the Partner API Integration Guide on docs.teradata.com.