The JMESPath language is described in an ABNF grammar with a complete specification. This ensures that the language syntax is precisely defined.
JMESPath has a full suite of data driven testcases. This ensures parity for multiple libraries, and makes it easy for developers to implement JMESPath in their language of choice.
The way this article starts might make you imagine that it was written by someone in a grumpy state of mind who, after several hours swearing at the computer, decided to rant about it on the internet. That’s not at all the case. I was just making my morning coffee in no hurry, when someone asked me on Twitter what is my opinion on Promises. I thought about it while sipping my coffee, then wrote a couple of tweets. Some people thought it would better presented as a blog post, so here we go!
- Eager, not lazy
- No cancellation
- Never synchronous
then() is a mix of
Every day on our feeds we find news about new cryptocurrency or someone who says that they are a big bubble that will soon explode and of which only the blockchain will remain. But, what is the blockchain?
Is a continuously growing list of records, called blocks, which are linked and secured using cryptography.
So, the blockchain is an immutable, sequential chain of records called Blocks. Each block can contain transactions, files or any data you like. The important thing is that they’re chained together using hashes.
Blockchains are secure by design and are an example of a distributed computing system with high Byzantine fault tolerance. This makes blockchains potentially suitable for the recording of events, medical records, and other records management activities, such as identity management, transaction processing or voting.
One of the keys to writing a successful web application is being able to make dozens of AJAX calls per page.
This is a typical asynchronous programming challenge, and how you choose to deal with asynchronous calls will, in large part, make or break your app, and by extension potentially your entire startup.
A little over a year ago, I joined Cloud Foundry to work on Loggregator, Cloud Foundry’s application logging component. Its core concern is best-effort log delivery without pushing back on upstream writers. Loggregator is written entirely in Go.
After spending more than a thousand hours working with Go in a non-trivial code base, I still admire the language and enjoy using it. Nonetheless, our team struggled with a number of problems, many of which seem unique to Go. What follows is a list of the most salient problems.
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible.