Skip to main content

Limitations of using Transportable Tablespace

  1. Movement between different character sets
     
  2. Movement between different OS (depending on RDBMS version)
     
  3. Some objects are not transferred via TTS. Objects with underlying objects (such as materialized views) or contained objects (such as partitioned tables) are not transportable unless all of the underlying or contained objects are in the tablespace set.
     
  4. Oracle Server -- Standard Edition vs. Enterprise Edition; Standard Edition can only import TTS (no export)
  5.  System, undo, sysaux and temporary tablespaces cannot be transported.

Transporting tablespaces with XMLTypes has the following limitations:
  • The destination database must have XML DB installed.
  • Schemas referenced by XMLType tables cannot be the XML DB standard schemas.
  • Schemas referenced by XMLType tables cannot have cyclic dependencies.
  • XMLType tables with row level security are not supported, because they cannot be exported or imported.
  • If the schema for a transported XMLType table is not present in the destination database, it is imported and registered. If the schema already exists in the destination database, an error is returned unless the ignore=y option is set.
  • If an XMLType table uses a schema that is dependent on another schema, the schema that is depended on is not exported. The import succeeds only if that schema is already in the destination database.

Encrypted tablespaces have the following the limitations

  • Before transporting an encrypted tablespace, you must copy the Oracle wallet manually to the destination database, unless the master encryption key is stored in a Hardware Security Module (HSM) device instead of an Oracle wallet. When copying the wallet, the wallet password remains the same in the destination database. However, it is recommended that you change the password on the destination database so that each database has its own wallet password. See Oracle Database Advanced Security Administrator's Guide for information about HSM devices, about determining the location of the Oracle wallet, and about changing the wallet password with Oracle Wallet Manager.
  • You cannot transport an encrypted tablespace to a database that already has an Oracle wallet for transparent data encryption. In this case, you must use Oracle Data Pump to export the tablespace's schema objects and then import them to the destination database. You can optionally take advantage of Oracle Data Pump features that enable you to maintain encryption for the data while it is being exported and imported. See Oracle Database Utilities for more information.

Labels:

Comments

Post a Comment

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...
Post a Comment
Read more

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

Got this during the execution of following command in R > dat 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' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...
Post a Comment
Read more

Training LLM model requires more GPU RAM than storing same LLM

Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th...
Post a Comment
Read more