We're excited to announce the release of BitDive 1.2.6, bringing new capabilities that make runtime intelligence for JVM applications even more powerful and developer-friendly. This update expands protocol coverage, adds a new analytics view, and introduces flexible agent configuration - all designed to help teams move faster from problem detection to resolution.
Testing for JVM applications-Java, Kotlin, Spring Boot-often struggles to keep up with distributed systems, asynchronous flows, and frequent code changes. Traditional methods rely on mocks and black‑box checks that don't reflect how the system really behaves. The result: flaky tests, missed bugs, and uncertainty before release.
BitDive provides a single platform for the entire testing lifecycle. It captures real execution data, curates meaningful scenarios, replays them across builds, and validates results where it matters most-at the level of API responses, database queries, and messaging flows.
JVM-first • Testcontainers-ready • Kafka/gRPC/JDBC support • CI/CD friendly • Flake-resistant
We're excited to announce the launch of BitDive SaaS - our fully hosted, cloud-based observability platform built for modern Java microservices. This launch marks a major milestone in making deep performance tracing and real-time insight available to everyone, instantly and effortlessly.
Now, instead of setting up infrastructure or maintaining backend components, developers can get started in just minutes. The BitDive agent connects your application to our platform, and you're immediately able to trace distributed requests, profile methods, visualize service dependencies, and capture runtime data - all in a single place.
Yesterday, we announced the MCP Server (Model Context Protocol) integration for BitDive. This new capability allows you to access full runtime context for any method executed in production — including input parameters, return values, exceptions, SQL queries, and downstream calls. MCP turns observability into a daily development tool, not just a monitoring system.
In today's cloud-native environments, maintaining optimal application performance requires multiple complementary approaches. While these tools share some features, they serve distinct purposes in modern performance management.
APM's evolution reflects the changing landscape of application architecture. Originally designed for monolithic applications, modern APM tools now handle the complexities of distributed systems and microservices.
Performance testing is crucial for software success, ensuring systems meet user expectations and business requirements. This guide explores core performance testing concepts and provides actionable strategies for effective implementation.
At BitDive.io, we leverage flame graphs not for traditional CPU profiling, but for visualizing the time (in milliseconds) that each method in a span consumes. This approach is especially suited for distributed systems with microservices running across different servers and processors. In this post, we will explain why flame graphs are effective for this use case, how they can help uncover bottlenecks in distributed architectures, and why this method could be more suitable for your system's needs.
Imagine you're an engineer at a bustling tech company, racing against time to make your app lightning-fast. Your code is a maze, and somewhere in there lurks a sneaky performance bottleneck. Enter flame graphs: the secret weapon of modern software optimization. Born from the brilliant mind of Brendan Gregg, these visual tools have become the go-to for tech giants and startups alike. They're not just graphs; they're x-ray vision for your code, revealing hidden CPU hogs that could be costing your business millions. In a world where milliseconds can make or break user experience (and profits), flame graphs have emerged as the unsung heroes of the enterprise tech world. Curious about how they're transforming the way we build and optimize software? Let's dive in and uncover the fiery truth behind these game-changing tools.