You’ve heard that Redis has an embedded scripting language, but haven’t given it a try yet? Here’s a tour of what you need to understand to use the power of Lua with your Redis server.
A while back, I wrote about writing a shell in C, a task which lets you peek under the covers of a tool you use daily. Underneath even a simple shell are many operating system calls, like read, fork, exec, wait, write, and chdir (to name a few). Now, it’s time to continue this journey down another level, and learn just how these system calls are implemented in Linux.
What is a system call?
Before we start implementing system calls, we’d better make sure we understand exactly what they are. A naive programmer—like me not that long ago—might define a system call as any function provided by the C library. But this isn’t quite true. Although many functions in the C library align nicely with system calls (like chdir), other ones do quite a bit more than simply ask the operating system to do something (such as fork or fprintf). Still others simply provide programming functionality without using the operating system, such as qsort and strtok.
In fact, a system call has a very specific definition. It is a way of requesting that the operating system kernel do something on your behalf. Operations like tokenizing a string don’t require interacting with the kernel, but anything involving devices, files, or processes definitely does.
System calls also behave differently under the hood than a normal function. Rather than simply jumping to some code from your program or a library, your program has to ask the CPU to switch into kernel mode, and then go to a predefined location within the kernel to handle your system call. This can be done in a few different ways, such as a processor interrupt, or special instructions such as
sysenter. In fact, the modern way of making a system call in Linux is to let the kernel provide some code (called the VDSO) which does the right thing to make a system call. Here’s an interesting SO question on the topic.
Thankfully, all that complexity is handled for us. No matter how a system call is made, it all comes down to looking up the particular system call number in a table to find the correct kernel function to call. Since all you need is a table entry and a function, it’s actually very easy to implement your own system call. So let’s give it a shot!
List of Computer Science courses with video lectures.
- Please note:
- Focus would be to keep the list to the point so that it is readable and usable. To access syllabus/notes/assignments, please visit link to the course or use Google search with course number/name.
- Only MOOCs with comprehensive lecture material which may be equivalent to a standard University course will be added.
- NPTEL contains large number of good Computer Science courses. To check courses by Indian IIT’s, please refer nptel site.
I am thrilled by all of the excitement that I see around AWS Lambda and serverless application development. I have shared many serverless success stories, tools, and open source projects in the AWS Week in Review over the last year or two.
Today I would like to tell you about two important additions to Lambda: environment variables and the new Serverless Application Model.
You can now provision and manage resources for AWS Lambda-based applications using AWS CloudFormation and the AWS Serverless Application Model (AWS SAM). SAM helps you more effectively model, package, and deploy “serverless” applications which use services like AWS Lambda, Amazon DynamoDB, and Amazon API Gateway. SAM is a specification for describing Lambda-based applications and offers a syntax designed specifically for expressing serverless resources. Learn more about SAM here.
You can now also use new AWS CLI commands for package and deploy, which simplify the process of uploading source code from your local drive and creating your resources. For example, you can use the package command to upload source code for Lambda functions, API Gateway Swagger files, or AWS Elastic Beanstalk applications.
CloudFormation has also introduced the following updates:
There’s a lot of buzzword around the term “Sentiment Analysis” and the various ways of doing it. Great! So you report with reasonable accuracies what the sentiment about a particular brand or product is.
After publishing this report, your client comes back to you and says “Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?” – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy. Over time, some powerful methods have been developed using Natural Language Processing and computational linguistics to extract these subjective opinions.
In this blog we will study the stepping stone to Opinion Mining – grammatically tagging a sentence. It will help us break a sentence down into its underlying grammatical structure – nouns, verbs, adjectives etc. that will help us associate what was said about what. Once we are capable enough to do that, we can extract useful opinions that will help us answer the question posed by our client above.
Bayesian inference is a way to get sharper predictions from your data. It’s particularly useful when you don’t have as much data as you would like and want to juice every last bit of predictive strength from it.
Although it is sometimes described with reverence, Bayesian inference isn’t magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.
Bayesian inference is based on the ideas of Thomas Bayes, a nonconformist Presbyterian minister in London about 300 years ago. He wrote two books, one on theology, and one on probability. His work included his now famous Bayes Theorem in raw form, which has since been applied to the problem of inference, the technical term for educated guessing. The popularity of Bayes’ ideas was aided immeasurably by another minister, Richard Price. He saw their significance, refined them and published them. It would be more accurate and historically just to call Bayes’ Theorem the Bayes-Price Rule.