Operations | Monitoring | ITSM | DevOps | Cloud

Announcing HAProxy Data Plane API 2.3

The HAProxy Data Plane API 2.3 expands its service discovery mechanisms and introduces native support for discovering AWS EC2 instances and auto-scaling groups. It also adds a new configuration file that supports HCL and YAML, an Inotify configuration watcher, and Syslog support. HAProxy Data Plane API version 2.3 is now available and you will find it in the 2.3 version of the Alpine Docker image.

We raised $100M in our Series F: here's what we're building next

Today we announced our Series F round of $100M led by Greenspring Associates, with Eleven Prime, IVP, Sapphire Ventures, Top Tier Capital Partners, Baseline Ventures, Threshold, Scale, Owl Rock, and Next Equity Partners. Thank you to our customers, community, partners, investors, and team. This latest investment allows us to invest as well; in our product, our community, and in our people. We build for the builders of the digital age: developers.

Concrete Steps to Reducing MTTR

In today’s data-centric world, metrics or numbers define all performance benchmarks. The time between when an event starts and ends shows how well a system can handle and process such events. One of such metrics is MTTR. MTTR usually stands for Mean Time To Resolution, but it has held several meanings over the years. MTTR is a metric used to measure how well a system can bounce back from errors and provide long-lasting solutions.

Digging into AWS Fargate runtime security approaches: Beyond ptrace and LD_PRELOAD

Fargate offers a great value proposition to AWS users: forget about virtual machines and just provision containers. Amazon will take care of the underlying hosts, so you will be able to focus on writing software instead of maintaining and upgrading a fleet of Linux instances. Fargate brings many benefits to the table, including small maintenance overhead, lower attack surface, and granular pricing. However, as any cloud asset, leaving your AWS Fargate tasks unattended can lead to nasty surprises.

No-code Lambda Monitoring

Auto-instrumenting Lambda Monitoring didn’t originate through a focus group or business plan. It started as a hackathon project in which our growth team used Cloudwatch to build a prototype that could instrument Lambda functions with Sentry. We did this by using Cloudformation’s stack to automatically create resources in a customer environment while streaming CloudWatch Logs to Sentry through the Kinesis Firehose.

A guide for CTO: 8 questions to ask before using Kubernetes

Congratulations, you finally consider moving your apps to Kubernetes. It is a big day! Here is a checklist to ensure you did not forget anything essential to increase your chances of success using Kubernetes. We divided those points into three sections, from the most important to the least. Let’s go.

How shuffle sharding in Cortex leads to better scalability and more isolation for Prometheus

For many years, it has been possible to scale Cortex clusters to hundreds of replicas. The relatively simple Dynamo-style replication relies on quorum consistency for reads and writes. But as such, more than a single replica failure can lead to an outage for all tenants. Shuffle sharding solves that issue by automatically picking a random “replica set” for each tenant, allowing you to isolate tenants and reduce the chance of an outage.