Skip to main content

CAP Theoram


C - Consistency means a client should get same view of data at a given point in time irrespective of node it is looked up from. All the servers in the system will have the same data so anyone using the system will get the same copy regardless of which server answers their request.
A - Availability here means that any given request should receive a response [success/failure]. The system will always respond to a request (even if it's not the latest data or consistent across the system or just a message saying the system isn't working).
P - Partition Tolerance means the system remains operational despite node or other hardware failures, the system is tolerant enough to these kind of failures. The system continues to operate as a whole even if individual servers fail or can't be reached.
In distributed systems, consistency, availability and partition tolerance exists in a mutually dependent relationship. You cannot have all 3, for example, if you choose strict consistency you have to give away availability, therefore pick any two.
Theoretically it is impossible to fulfill all three requirements. Therefore current NoSQL databases follow the different combinations of the C,A,P from the CAP theorem.
CA – Single site cluster, therefore all nodes are always in contact. When a partition occurs, the systems blocks.
CP – Some data may be not accessible, but the rest is still consistent/accurate.
AP – System is still available under partitioning, but some of the data returned may be inaccurate.
 

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