Skip to main content

Hot Standby Nodes

Hot standby nodes allow spare nodes to be incorporated into the production environment. Teradata Database can use spare nodes to improve availability and maintain performance levels in the event of a node failure. A hot standby node is a node that:

• Is a member of a clique.
• Does not normally participate in Teradata Database operations.
• Can be brought in to participate in Teradata Database operations to compensate for the loss of a node in the clique.

Configuring a hot standby node can eliminate the system-wide performance degradation associated with the loss of a node. A hot standby node is added to each clique in the system.

When a node fails, all AMPs and all LAN-attached PEs on the failed node migrate to the node designated as the hot standby. The hot standby node becomes a production node. When the failed node returns to service, it becomes the new hot standby node
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