Skip to main content

Azure - Locally redundant storage

Locally redundant storage (LRS) replicates your data within the region in which you created your storage account. To maximize durability, every request made against data in your storage account is replicated three times. These three replicas each reside in separate fault domains and upgrade domains. A fault domain (FD) is a group of nodes that represent a physical unit of failure and can be considered as nodes belonging to the same physical rack. An upgrade domain (UD) is a group of nodes that are upgraded together during the process of a service upgrade (rollout). The three replicas are spread across UDs and FDs to ensure that data is available even if hardware failure impacts a single rack and when nodes are upgraded during a rollout. A request returns successfully only once it has been written to all three replicas.

While geo-redundant storage (GRS) is recommended for most applications, locally redundant storage may be desirable in certain scenarios:
  • LRS is less expensive than GRS, and also offers higher throughput. If your application stores data that can be easily reconstructed, you may opt for LRS.
  • Some applications are restricted to replicating data only within a single region due to data governance requirements.
  • If your application has its own geo-replication strategy, then it may not require GRS.
Labels:

Comments

Post a Comment

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...
Post a Comment
Read more

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

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