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);
Labels:

Comments

Post a Comment

Popular posts from this blog

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

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more