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ShopifyQL Commerce Data Querying Language

2 posts · Jun 2022 – Sep 2023

What this trail covers

ShopifyQL is an accessible, commerce-focused querying language designed to empower merchants to explore their data without requiring deep technical expertise. It simplifies traditional data querying concepts like visualization, period comparisons, and commerce-specific date filtering. The language is supported by exploration data models tailored for commerce use cases, focusing on products, orders, and customers. Future development includes user-defined date ranges and enhanced period comparisons.

All Shopify trails
  1. Building a ShopifyQL Code Editor - ShopifyThis post details the technical implementation of integrating the ShopifyQL Language Server with the CodeMirror editor. Key contributions include: developing a custom adapter to bridge LSP and Lezer, handling ANTLR's relative token offsets by creating a `TokenIterator` class, and connecting the language server's features (validation, completion, hover) to CodeMirror's plugins. The post also explains the challenges of ANTLR's token offset system and how it was adapted for CodeMirror's document-relative offsets.
  2. ShopifyQL Notebooks: Simplifying Querying with Commerce Data Models - ShopifyThis post details a new data modeling process for ShopifyQL, focusing on purpose-driven, explorable, and extensible data models. It outlines a six-step approach: 1. Start with a list of business questions, 2. Create mock data, 3. Find the data, 4. Assess data quality and consistency, 5. Assess model freshness, and 6. Assess model performance. It also elaborates on the properties of good data models (purpose-driven, explorable, extensible) and provides examples of commerce-focused data models and the challenges faced in their creation, such as reconciling granularity differences and ensuring quick results for large merchants.
  3. Introducing ShopifyQL: Our New Commerce Data Querying Language  - ShopifyThis post introduces ShopifyQL, a new commerce-focused querying language designed for accessibility. It details the language's syntax, including keywords like FROM, SHOW, VISUALIZE, BY, WHERE, SINCE, UNTIL, ORDER BY, COMPARE TO, and DURING. It highlights features such as direct visualization from queries, native period comparisons (e.g., year-over-year), and commerce-specific date range filtering (e.g., Black Friday). The post also discusses the underlying exploration data models that simplify commerce-specific analysis.