📄️ Attributes
A cornerstone principle in KDB/Q, integral to query optimization, is the concept of Attributes. Attributes serve as metadata that can be attached to lists of special forms, dictionaries, or table columns (which are basically lists), amplifying data retrieval speed and consequently enhancing query efficiency and performance. By attaching metadata about the implied structure of the list associated with a specific Attribute, the Q interpreter can enact optimizations, resulting in a notable performance boost. This post not only provides a comprehensive explanation of various Attributes and their properties but also emphasizes their strategic utilization and optimal placement in different scenarios.
📄️ Memory Management
This blog post dives into a crucial aspect of real-time data handling: Memory Management. Despite its significance in architectural system design, this topic is not extensively covered in the official KX documentation. A profound comprehension of Memory Management in KDB/Q is crucial for understanding the workings of Garbage Collection in the same environment. In this blog post, I aim to provide a comprehensive overview of Memory Management in KDB/Q, shedding light on essential considerations for designing an efficient system.
📄️ Garbage Collection
In this blog post, we cover another crucial concept, which if fully understood, can set you apart. Today's topic is Garbage Collection, and here's the news upfront: There's no such thing as Garbage in KDB/Q!! Yes, you read it correctly. Don't be caught off guard like I was, discovering this information in a job interview with "the QGOD" among all KDB/Q developers. Keep reading and learn everything you need to know about this essential topic.
📄️ Amend, Amend At: The Swiss Army knife among KDB/Q operators
Whenever I encounter the @ or . amend or amend at operators, I'm inevitably reminded of a Swiss Army pocketknife. Just like this versatile multi-tolled knife, the @ or . operators in KDB/Q are multi-functional, capable of solving various tasks. When combined with the expertise of a skilled KDB/Q developer, akin to the resourcefulness of a highly trained soldier or agent like MacGyver, the possibilities are limitless – you can conquer any challenge with ease.
📄️ Dictionaries and Tables
In this blog post, we explore two key data types native to KDB/Q: Dictionaries and Tables. Unlike mainstream programming languages like Java, which lack native support for these data types and require alternative structures (e.g., Java HashMap for dictionaries or Java ArrayList for lists of rows to represent tables), KDB/Q inherently supports both Dictionaries and Tables. Understanding these data structures is crucial for leveraging the speed and performance of KDB/Q to build efficient big data applications. In the following sections, we will examine the most important concepts of both data types, a in detail explanation of dictionaries and tables is beyond the scope of this blog post and can be found in Chapter 5 - Dictionaries and Chapter 8 - Tables of Q for Mortals.