This comprehensive guide walks you through PromQL fundamentals, data types, query expressions, selectors, operators, aggregation, and essential functions, illustrating each concept with real‑world monitoring scenarios and code examples to help you effectively query and analyze time‑series data in Prometheus.
This article provides a comprehensive overview of TuGraph-DB's graph query language development, the capabilities of its 4.0 query engine, and the current and future architectural plans, including support for GQL, integration with GeaX, and optimization strategies for complex graph workloads.
This article introduces graph databases, explains Dgraph concepts and installation via Docker, details Dgraph4j Java client usage including schema setup, querying, mutations, and data monitoring, and explores applications such as equity relationship analysis and data visualization.
This article explores the structure and evaluation of PromQL queries, covering its nested functional language nature, expression types, time handling with instant and range queries, and practical examples using the PromLens visualizer, helping readers grasp how Prometheus processes and types queries.
This article introduces OAX (Open Analysis eXpressions), Kuaishou’s unified open‑analysis language, detailing its design background, guiding principles, five‑layer language model, syntax—including data types, compute capabilities and five analysis elements—its access protocol, runtime architecture, optimization steps, and the benefits it brings to the company’s big‑data analytics ecosystem.
This article chronicles the evolution of ArchGuard’s “Trend & Insight” feature—from a simple UI‑driven CRUD editor to a regex‑based SQL generator and finally a custom DSL powered by Monaco Editor—highlighting the trade‑offs between developer flexibility, user experience, and technical cost.
This article explains the functional, nested nature of PromQL, its expression types, how queries are parsed and evaluated over time, and the differences between instant and range queries, providing code examples and visual insights for better monitoring with Prometheus.
SPL is an open‑source structured data processing language that extends full SQL‑92 functionality to a wide range of data sources—including CSV, Excel, JSON, NoSQL and Hadoop—allowing developers to perform complex queries, multi‑step calculations, and mixed‑source analytics without a traditional relational database.
The SQL Quick Start Course introduces beginners to Structured Query Language, covering data insertion, querying, updating, deletion, schema creation, modification, and access control, and provides an online learning link and QR code for immediate access.
This article provides a comprehensive guide to Prometheus' PQL language, covering instant and range vectors, metric types, label selectors, offsets, arithmetic and logical operators, as well as a wide range of built‑in functions with practical code examples for effective monitoring.
This article introduces Prometheus' query language PromQL, explains instant and range vector selectors, label matching, offset handling, storage design, common functions and aggregation operators, and provides practical advice for efficient querying and avoiding performance issues.
This article provides a clear overview of the four main SQL query language categories—DDL, DML, DCL, and TCL—explaining their purposes, typical commands, practical examples, and why each is essential for effective database design and manipulation.
This article explains how GraphQL’s schema and type system model complex business data, and how its query language lets front‑end developers freely compose and customize data requests, covering core components, scalar and advanced types, queries, fragments, directives, and practical code examples.
