Most products don’t have the luxury of retaining users beyond the first session solely on their solid reputations, and in order to do so, we need to optimize for the best user experience possible.
Have you recently launched a cool new feature you spent so much time on? What data do you use to determine the impact of the feature? Take a look at this blog, this may be just for you!
Ted Lasso has some real-life lessons we can all take in. How is this possible? Take a look!
Why is it important to remove underperforming features to improve the product’s key metrics? Find out here.
We wanted to see if there was a way we could sync our Kubernetes NetworkPolicies dynamically with tools we already use, like Consul and Calico.
Read this article to learn more about what conversions are, how Taboola handle billions of daily events at scale, and how it all presents meaningful data to customers.
Something strange happened while I worked with Kafka. While adding a new consumer from Kafka to one of our services, the service stopped consuming from ALL other existing consumers. As part of my job at Taboola as a team leader on a production team in the Infrastructure group, we’re supposed to remove bottlenecks, not create them. This post will describe how I investigated the issue, explain what I discovered, and share my insights into the whole situation. Some background Before I get into the rest of the story, here’s some background on how we use Kafka at Taboola’s events handling pipeline and why it’s critical to our infrastructure. Taboola’s recommendations appear on tens of thousands of web pages and mobile apps every second. As users engage with the content, multiple events are fired to signal that recommendations are rendered, opened, clicked, and so on. Each event triggers one or more Kafka messages, […]
Find out the secrets to how Taboola deploys and manages the thousands of servers that bring you recommendations every day.
Taboola is one of the largest content recommendation companies in the world. We maintain hundreds of servers in multiple data centers around the world, while obligated to strict SLAs. Thus, you might understand why our engineers would appreciate a little heads up when the system gets overloaded. Like most companies today, we use metrics to visualize our services’ health, and our challenge is to create an automatic system that will detect issues in multiple metrics as soon as possible, without any performance impact. A real life example Wouldn’t it be nice if we could predict the impact on our response time metric when major events are about to happen? For example “Black Friday”, “Cyber Monday” or even the “Kobe Bryant’s tragedy”, on the 26/1/2020? – as can be seen below: Figure 1: Kobe Bryant’s downtime 26/1/20 And yes – the gap with no metrics around the 26/1 is the downtime […]
Taboola is responsible for billions of daily recommendations, and we are doing everything we can to make those recommendations fit each viewer’s personal taste and interests. We do so by updating our Deep-Learning based models, increasing our computational resources, improving our exploration techniques and many more. All those things though, have one thing in common – we need to understand if a change is for the better or not, and we need to do so while allowing many tests to run in parallel. We can think of many KPI’s for new algorithmic modifications – system latency, diversity of recommendations or user-interaction to name a few – but at the end of the day, the one metric that matters most for us in Taboola is RPM (revenue per mill, or revenue per 1,000 recommendations), which indicates how much money and value we create for our customers on both sides – the […]