Skip to main content

System Requirements and Recommendations

Standby is maintained as a mirror image of the primary. Many aspects of the primary and standby databases need to be the same.

  • Primary and standby must have the same DB2 major version. For example, both on V10.1.
    • Standby database fix pack level must be same or higher than that of the primary (otherwise, primary could generate log records the standby cannot replay).
    • Same fix pack level is recommended on primary and standby, to minimize compatibility risk. Different primary and standby fix pack levels usually only occur duringrolling update
  • Primary and standby must have the same platform.
    • "Platform" here is defined as the combination of OS type (software) and machine architecture (hardware). For example, the followings are considered distinct platforms: Windows-x86, AIX-power, HP-IA, Solaris-Sparc, Solaris-x86, Linux-PPC, Linux-Z, Linux-390, Linux-x86,
    • Primary and standby must have the same endian (both big endian, or both small endian). This requirement is usually satisfied by the platform requirement already.
  • Same OS version (major and minor) is recommended on primary and standby. Different versions usually only occur during rolling update. DB2 does not enforce any check on OS version. But you should keep the different-version window as short as possible, to minimize compatibility risk.
  • The DB2 software on primary and standby must have the same bit size (both 64 bit, or both 32 bit).
  • Same bit size on the host platform is recommended, to minimize compatibility risk.
    • Host platform bit size could be different. For example, DB2 is 32 bit on both machines. Primary host is 64 bit, which can run both 64 bit and 32 bit applications. Standby host is 32 bit.
  • Primary and standby must have the same paths for tablespace containers, to support tablespace replication.
    • The container path requirement can often be satisfied with symbolic links. The standby devices should have same or larger capacity.
    • Redirected restore is not supported when creating the standby. However, database directory (for database metadata files) and transaction log directory changes are supported during the restore. Table space containers created by relative paths will be restored to paths relative to the new database directory.
  • Same hardware (CPU, memory, disk, etc.) is recommended on the primary and standby, so that standby has enough power for replay. Sufficient planning and testing should be done when deploying a less powerful standby.
  • Same amount of memory is recommended on the primary and standby, so that buffer pool replication is less likely to fail.
Labels:

Comments

Post a Comment

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.
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 <- 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&
Post a Comment
Read more

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
Post a Comment
Read more