Skip to main content

Can we configure RMAN backup for very large database to use all the nodes instead of the node where it is scheduled through crontab?

 YES

RMAN and Oracle Net in Real Application Clusters

In any RMAN connection made through a net service name, each net service name must specify one and only one instance. This rule applies to all RMAN connections whether they are made from the command line or through the CONNECT clause in ALLOCATE CHANNEL or CONFIGURE CHANNEL commands. Therefore, you cannot specify a net service name that uses Oracle Net features to distribute RMAN connections to more than one instance.

Channel Connections to Cluster Instances

When making backups in a Real Application Clusters configuration, each allocated channel can connect to a different instance in the cluster, and each channel connection must resolve to one and only one instance. For example, configure automatic channels as follows:

CONFIGURE DEFAULT DEVICE TYPE TO sbt;
CONFIGURE DEVICE TYPE sbt PARALLELISM 3;
CONFIGURE CHANNEL 1 DEVICE TYPE sbt CONNECT = 'SYS/oracle@node1';
CONFIGURE CHANNEL 2 DEVICE TYPE sbt CONNECT = 'SYS/oracle@node2';
CONFIGURE CHANNEL 3 DEVICE TYPE sbt CONNECT = 'SYS/oracle@node3';

If the instance to which one of the channels is connected does not have the database open, then the database must not be open by any instance. In other words, either all channels must be connected to open instances, or all channels must be connected to instances that are not open. For example, if the node1 instance has the database mounted while the node2 and node3 instances have the database open, then the backup fails.

Node Affinity Awareness

In some cluster database configurations, some nodes of the cluster have faster access to some datafiles than to other datafiles. RMAN automatically detects this affinity, which is known as node affinity awareness.
When deciding which channel to use to back up a particular datafile, RMAN gives preference to channels allocated at the nodes that have affinity to the datafiles you want to back up. For example, if you have a three-node cluster, and if node 1 has faster read/write access to datafiles 7, 8, and 9 than the other nodes, then node 1 has greater node affinity to those files than nodes 2 and 3.

To use node affinity, configure RMAN channels on the nodes of the cluster that have affinity to the datafiles you want to back up. For example, use the syntax:

CONFIGURE CHANNEL 1 DEVICE TYPE sbt CONNECT 'user1/password1@node1';
CONFIGURE CHANNEL 2 DEVICE TYPE sbt CONNECT 'user2/password2@node2';
CONFIGURE CHANNEL 3 DEVICE TYPE sbt CONNECT 'user3/password3@node3';

You can manually override the automatic node affinity by specifying which channels should back up which datafiles. For example:


BACKUP
  # channel 1 gets datafile 1
  (DATAFILE 1 CHANNEL ORA_SBT_TAPE_1)
  # channel 2 gets datafiles 2-4
  (DATAFILE 2,3,4 CHANNEL ORA_SBT_TAPE_2)
  # channel 3 gets datafiles 5-10
  (DATAFILE 5,6,7,8,9,10 CHANNEL ORA_SBT_TAPE_3);  

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