Many modern applications need to be fast, scalable, and always available. To achieve this, they often use distributed systems. These systems run on many computers and work together to store and process data.

One important type of distributed system is the key-value store. It saves data as key-value pairs. This model is simple but very powerful. Many popular tools—like databases, caches, and service discovery systems—use this structure.

However, building a key-value store that works across multiple machines is not easy. We need to think about problems like:

In modern high-traffic or data-intensive applications, distributed data systems are crucial for handling large volumes of information. But when we deal with huge sets of integer IDs—such as user IDs, IP addresses, or event indices—we need a data structure that is not only memory-efficient but also supports fast set operations. This is where RoaringBitmap comes in.

RoaringBitmap stores large sets of integer values in a compressed format and offers rapid operations like intersection, union, and difference. This blog will explore how RoaringBitmap can make distributed data systems more efficient and easier to manage.

In the era of modern software systems, observability has become a critical aspect of system management. It enables engineers to monitor, debug, and optimize their applications effectively. However, traditional observability tools often come with high resource costs, limited visibility, and performance overhead. This is where eBPF (extended Berkeley Packet Filter) steps in to revolutionize observability solutions.

eBPF is a cutting-edge technology that empowers developers to collect detailed insights about system behavior directly from the Linux kernel, all while keeping resource usage to a minimum. It offers a flexible, efficient, and secure way to understand what’s happening inside your systems, without significantly affecting performance.

Handling massive volumes of data writes in distributed environments requires data structures built to minimize I/O, balance loads, and retain data integrity. Log-Structured Merge Trees (LSM Trees) accomplish this by intelligently managing writes, compactions, and reads across hierarchical storage levels. This guide will dissect the internal mechanics of LSM Trees, from write batching and SSTable lifecycle management to advanced techniques in compaction and read amplification reduction, all essential for high-performance distributed storage solutions.

When working with concurrent applications, synchronization primitives are essential tools. While Go provides sync.WaitGroup, sometimes we need more sophisticated control like timeouts. Java’s CountDownLatch offers this functionality, so let’s implement it in Go.

The Implementation

Our CountDownLatch combines Go’s sync.WaitGroup with atomic operations for thread-safe counting:

type CountDownLatch struct {
   sync.WaitGroup
   counter uint64
}

The struct embeds sync.WaitGroup and adds a counter field using uint64 to store two 32-bit counters in one atomic value.