Skip to main content

Hugepages and concern on Linux environment

HugePages is crucial for faster Oracle database performance on Linux if you have a large RAM and SGA. If the combined database SGAs is large (like more than 8GB, can even be important for smaller), you will need HugePages configured. Note that the size of the SGA matters. 

Applies to:

Linux OS - Version Enterprise Linux 4.0 to Oracle Linux 6.0 with Unbreakable Enterprise Kernel [2.6.32] [Release RHEL4 to OL6]
Oracle Server - Enterprise Edition - Version 9.2.0.1 and later
Linux x86-64
Oracle Linux
Red Hat Enterprise Linux (RHEL)
SUSE Linux Enterprise Server (SLES)

Hugepages is a delicate features of Oracle and one should understand certain concern before setting this.

Concern before setting Huge Pages for Linux environment

The performed configuration is basically based on the RAM installed and combined size of SGA of database instances you are running. Based on that when:
  • Amount of RAM installed for the Linux OS changed
  • New database instance(s) introduced
  • SGA size / configuration changed for one or more database instances
One should revise their HugePages configuration to make it suitable to the new memory framework. If not you may experience one or more problems below on the system:
  • Poor database performance
  • System running out of memory or excessive swapping
  • Database instances cannot be started
  • Crucial system services failing
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