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AWS Lambda layer is a ZIP archive containing libraries, frameworks or custom code that can be added to AWS Lambda functions. [9] As of December 2024, AWS Lambda layers have significant limitations: [10] [11] No semantic versioning support. Incompatibility with major security scanning tools. Contribution to Lambda's 250MB size limit. Impeded ...
An Amazon Machine Image (AMI) is a special type of virtual appliance that is used to create a virtual machine within the Amazon Elastic Compute Cloud ("EC2"). It serves as the basic unit of deployment for services delivered using EC2.
Now, the multiple dispatch occurs in the call issued from the body of the anonymous function, and so traverse is just a mapping function that distributes a function application over the elements of an object. Thus all traces of the Visitor Pattern disappear, except for the mapping function, in which there is no evidence of two objects being ...
AWS launches AWS Lambda, its Functions as a Service (FaaS) tool. With Lambda, AWS customers can define and upload functions with specific triggers and execution code. AWS takes care of executing the function on the trigger occurring, and the AWS customer does not have to provision or manage the compute resources.
Amazon EC2 price varies from $2.5 per month for "nano" instance with 1 vCPU and 0.5 GB RAM on board to "xlarge" type of instances with 32 vCPU and 488 GB RAM billed up to $3997.19 per month. The charts above show how Amazon EC2 pricing is compared to similar Cloud Computing services: Microsoft Azure, Google Cloud Platform, Kamatera, and Vultr. [69]
AWS CodeDeploy facilitates blue–green deployments by automating the entire process across services such as Amazon EC2 and AWS Lambda. The service shifts traffic between the old (blue) environment and the new (green) environment, minimizing downtime and ensuring a smooth transition.
In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.