Sometimes developers face optimization challenges in their code — regardless of the programming language they use. One of the most common optimization challenges is the complexity (or performance) of an algorithm that increases and grows infinitely when the number of arguments it has to process increases. This is called big-O complexity or big-O notation.
In this blog post, you’ll learn how to perform DataWeave code optimization to improve performance by following the big-O notation principles.
Testing is an essential part of the software development process used to ensure code quality, but the reality is that it can be a daunting and arduous task — especially in larger applications. This can make it difficult for some users to know where to begin when they’re ready to test their Mule applications in MUnit.
It’s a new year and people are re-inventing themselves with their new year resolutions. We can apply this same concept to other aspects of our days, such as the way we integrate systems! Here are five things that you should start (or stop) doing when it comes to data integration in 2020.
Recently, the RAML team decided it was time for an updated server infrastructure. The original site used a web-based Content Management System (CMS) that required a lot of costly server resources. Each client request to the CMS invoked scripts that rendered the pages from outside sources, such as the database and theme;
This led to significant processing time before providing what was, in most cases, a static page. Of course, we ran a caching layer in front of the web server to speed up some requests,
The holiday shopping season is upon us, with Black Friday and Cyber Monday just around the corner. Last year, shoppers spent over $655.8 billion during Black Friday and that amount is expected to increase by 47% this year. And during Cyber Monday last year, shoppers spent a record-breaking $3.45 billion, a 12.1% increase from the previous year.
Welcome back! Following the series about new batch features in Mule 3.8, the second most common request was being able to configure the batch block size.
What’s the block size?
In a traditional online processing model, each request is usually mapped to a worker thread. Regardless of the processing being synchronous, asynchronous, one-way, request-response, or even if the requests are temporarily buffered before being processed (like in the Disruptor or SEDA models),
We often get asked to help tune applications running on Mule for optimal performance. Over time, we have developed a methodology that helps us deliver on that request — and we wanted to share that methodology with you.
To-Do Before Tuning
Here are a few questions to ask before tuning. Performance tuning requires affirmative answers for (1) and (2), plus a concise response to (3).
DataWeave, our new data query and transformation language, offers significant performance advantages over Anypoint DataMapper, our earlier data mapping and transformation solution. The below benchmark shows transformations performance using DataWeave and Anypoint DataMapper with payload size of 100KB (1000 records) with simple complexity.
When deployed as an API Gateway and managed with API Manager, the highly performant Anypoint Platform enables you to control which traffic is authorized to pass through your APIs to various backend services, meter the traffic flowing through your API, log transactions, and apply runtime policies.
A policy is a mechanism the gateway uses to enforce filters on traffic as it flows through the gateway.
MuleSoft provides the most widely used integration platform for connecting any application, data source or API, whether in the cloud or on-premises. With Anypoint Platform®, MuleSoft delivers a complete integration experience built on proven open source technology, eliminating the pain and cost of point-to-point integration. Anypoint Platform includes CloudHub™ iPaaS, Mule ESB™, and a unified solution for API management™, design and publishing.