Skip to main content

FAQ on using Transportable Tablespace

Q1: Can I move/migrate to both a different RDBMS version and OS platform at the same time?

Yes; must be 10g or higher to move across OS platforms; charactersets must be the same regardless of version.

See "Limitations on Transportable Use" in Document 371556.1 How to move tablespaces across platforms using Transportable Tablespaces with RMAN


Q2: Do I have to convert the datafiles?

Yes, if the endianness is different.  If the endianness is not different and no undo is in any of the tablespaces being transported, then the convert step is not needed. 

Document 243304.1 10g : Transportable Tablespaces Across Different Platforms confirms the answer.

Q3: Can I use TTS with ASM?

Yes, with RMAN, ASM files can be moved.

See "Transportable tablespace EXP/IMP of ASM files" in Document 371556.1 How to move tablespaces across platforms using Transportable Tablespaces with RMAN


Q4: Can I move raw files?

Yes, with RMAN.

See "Transportable tablespace EXP/IMP of ASM files" in Document 371556.1 How to move tablespaces across platforms using Transportable Tablespaces with RMAN

Q5: Can I transport just a single partition?

Yes.
See Document 731559.1 How to move or transport table partition using Transportable Table Space (TTS)?

Q6: Is there a size limitation?

No, except for a couple of size-related bugs, one which is a potential-corruption bug in < 11g.  Please see note for description and patch information.

Document 566875.1 Size Limitations On Cross Platform Transportable Tablespaces

Comments

Popular posts from this blog

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat <- colindex="colIndex," endrow="23," file="NGAP.xlsx" header="TRUE)</p" read.xlsx="" sheetindex="1," startrow="18,"> Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip&

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data