Skip to main content

VPC Peering Limitations


To create a VPC peering connection with another VPC, you need to be aware of the following limitations and rules:
  • You cannot create a VPC peering connection between VPCs that have matching or overlapping CIDR blocks.
  • You cannot create a VPC peering connection between VPCs in different regions.
  • You have a limit on the number active and pending VPC peering connections that you can have per VPC. For more information, see Amazon VPC Limits in the Amazon VPC User Guide.
  • VPC peering does not support transitive peering relationships; in a VPC peering connection, your VPC does not have access to any other VPCs that the peer VPC may be peered with. This includes VPC peering connections that are established entirely within your own AWS account. For more information about unsupported peering relationships, see Invalid VPC Peering Connection Configurations. For examples of supported peering relationships, see VPC Peering Scenarios.
  • You cannot have more than one VPC peering connection between the same two VPCs at the same time.
  • The Maximum Transmission Unit (MTU) across a VPC peering connection is 1500 bytes.
  • A placement group can span peered VPCs; however, you do not get full-bisection bandwidth between instances in peered VPCs. For more information about placement groups, see Placement Groups in the Amazon EC2 User Guide for Linux Instances.
  • Unicast reverse path forwarding in VPC peering connections is not supported. For more information, see Routing for Response Traffic.

Comments

Popular posts from this blog

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...

Data Wrangling vs EDA

  Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...

What's replicated, what's not?

Logged operations are replicated. These include, but are not limited to: DDL DML Create/alter table space Create/alter storage group Create/alter buffer pool XML data. Logged LOBs Not logged operations are not replicated. These include, but are not limited to: Database configuration parameters (this allows primary and standby databases to be configured differently). "Not logged initially" tables Not logged LOBs UDF (User Defined Function) libraries. UDF DDL is replicated. But the libraries used by UDF (such as C or Java libraries)  are not replicated, because they are not stored in the database. Users must manually copy the libraries to the standby. Note: You can use database configuration parameter  BLOCKNONLOGGED  to block not logged operations on the primary.