You might find something interesting here ^_^
The article explains how Citus lets you manually propagate SQL statements to workers, shards, or placements using functions like run_command_on_workers, run_command_on_shards, and run_command_on_placements, provides concrete examples, and outlines important limitations and safety considerations.
The article explains how a Citus cluster—comprising a coordinator and multiple workers—splits and routes SQL queries, detailing the distributed planner, executor, push‑pull handling of subqueries/CTEs, and a step‑by‑step EXPLAIN walkthrough of a top‑20 page‑views query.
This article explains how Citus extends PostgreSQL for distributed execution, detailing supported aggregation functions, count‑distinct handling, HyperLogLog columns, Top‑N estimation, percentile sketches with t‑digest, limit push‑down, view support, and various join strategies, while providing concrete SQL examples and configuration tips.
This guide explains how to use standard PostgreSQL INSERT, multi‑row INSERT, INSERT … SELECT, COPY for bulk loading, upserts with ON CONFLICT, and UPDATE/DELETE on Citus‑distributed tables, including routing by distribution column, performance tips, and two‑phase commit considerations.
This guide explains how to define schemas, create distributed and reference tables, manage co-location, run DDL commands, handle upgrades from Citus 5.x, and use utility functions such as create_distributed_table, create_reference_table, and mark_tables_colocated in a Citus cluster.
This tutorial walks through setting up a Citus‑enabled PostgreSQL cluster on Kubernetes, creating distributed tables, using co‑location and reference tables for high‑performance joins, and comparing row‑based versus columnar storage with concrete SQL commands and performance metrics.
This tutorial walks through setting up Citus, creating and distributing PostgreSQL tables for a multi‑tenant ad‑analytics application, loading sample data, executing CRUD and analytical queries, and extending the approach to real‑time GitHub event analysis, demonstrating end‑to‑end database operations in a distributed environment.
This article explains how to select an optimal distribution column for Citus tables by first identifying whether your workload is multitenant SaaS or real‑time analytics, then applying concrete guidelines such as using high‑cardinality tenant or entity IDs, avoiding timestamps, ensuring uniform distribution, and leveraging co‑location for better query performance.
This step‑by‑step guide shows how to set up both single‑node and multi‑node Citus clusters on Docker, Ubuntu/Debian, and Fedora/CentOS/Red Hat, covering repository addition, package installation, configuration of shared_preload_libraries, database initialization, worker node registration, and verification commands.
This guide demonstrates how to build a distributed multi‑tenant database using Django with Postgres and Citus, covering installation, supported Django versions, model modifications via mixins or inheritance, middleware for tenant context, automatic composite foreign keys, and the library’s API with concrete code examples.
