This guide walks through provisioning two Alibaba Cloud ECS instances, installing Docker, launching Citus 12.1 coordinator and worker containers, configuring the cluster, and verifying data distribution using a sample microservice backend.
This tutorial walks through setting up a PostgreSQL 16.1 + Citus 12.1 cluster with Docker, creating separate schemas and roles for user, time, and ping microservices, distributing them via schema‑based sharding, and demonstrating CRUD operations and verification of data placement across three worker nodes.
This guide explains what a sharded PostgreSQL cluster is, how StackGres implements it with Citus and Patroni, and provides step‑by‑step YAML manifests and scripts to create both a basic and a custom production‑grade high‑availability sharded cluster on Kubernetes.
The article explains how to boost a distributed system's throughput by optimizing middleware clusters—using Longhorn for storage, Citus for sharding Sentry's nodestore_node table into 64 shards across six workers, and adding PgPool, repmgr, and PgAdmin for read‑write splitting, high availability, and management.
This article explains how to automate and run HammerDB benchmarks for OLTP, OLAP, and HTAP workloads on Azure-hosted Citus‑extended PostgreSQL, discusses workload characteristics, benchmark specifications, challenges of HTAP testing, and shows how a 2 million new‑orders‑per‑minute result was achieved using open‑source tooling and configuration tips.
Citus 11.0 makes the entire extension open source, adds a non‑blocking shard rebalancer, enables automatic schema and metadata synchronization so distributed queries can run on any cluster node, and introduces trigger support on distributed tables, with clear upgrade steps and deprecation notes.
This article provides a comprehensive reference of Citus‑provided user‑defined functions—including table DDL, metadata management, cluster maintenance, and shard rebalancing utilities—detailing their purpose, required permissions, parameters, and behavior within a distributed PostgreSQL environment.
This reference outlines Citus 11.x distributed PostgreSQL SQL features, covering DDL operations, DML commands, rollup cache aggregation, distributed table queries, query processing, manual query propagation, and supported SQL workarounds, with links to detailed documentation.
This guide walks through migrating a single‑node Django multi‑tenant application to a distributed PostgreSQL backend using Citus, covering schema changes, primary‑key and unique‑constraint adjustments, integration of the django‑multitenant library, data distribution, range‑query updates, and middleware automation.
This guide explains how Hyperscale (Citus) extends PostgreSQL with distributed tables, enabling developers to start on a single node and seamlessly scale to multiple nodes for high‑throughput SaaS, real‑time analytics, and time‑series workloads across many industry verticals.
This guide explains how Citus shards data across machines to horizontally scale SQL queries for workloads over 100 GB, then walks through cloning the Citus 11 docs repository, setting up a pyenv‑based Python environment, installing Sphinx requirements, and launching the documentation in watch mode for local browsing.
The article explains how Citus extends PostgreSQL to provide distributed tables with full single‑node SQL coverage, enumerates the cross‑node SQL features it cannot handle, and demonstrates concrete workarounds using CTEs and temporary tables with detailed code examples.
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.
This guide shows how to combine PostgreSQL declarative time‑range partitioning with Citus distributed sharding to create a scalable time‑series database, covering table creation, distribution, automatic monthly partition generation, columnar archiving, maintenance with pg_cron, and performance benefits such as faster inserts, queries, and data expiration.
This tutorial shows how to use Citus, a distributed PostgreSQL extension, to ingest immutable HTTP event logs, shard them by site_id, create real‑time and pre‑aggregated tables, schedule roll‑up jobs, handle data expiration, estimate distinct counts with HyperLogLog, and store unstructured JSONB counters for a low‑latency analytics dashboard.
This guide walks through creating a multi‑tenant ad‑analytics SaaS using Citus, covering schema design, sharding by company_id, data ingestion, reference tables, online schema changes, tenant‑specific JSONB fields, hardware scaling, large‑tenant isolation, and migration steps with concrete SQL examples and commands.
Citus extends PostgreSQL into a shared‑nothing distributed system where a coordinator node routes queries to worker nodes, supporting distributed, reference, and local tables, shard placement, replication modes, co‑location, parallel execution, and configurable connection‑pool settings to scale storage and CPU across a cluster.
Citus is an open‑source PostgreSQL extension that adds sharding, distributed tables and parallel query execution, enabling horizontal and vertical scaling for multi‑tenant SaaS, real‑time analytics, and massive workloads, while outlining acquisition options, practical use cases, and scenarios where it is unsuitable.
The 10th PostgreSQL China Technical Conference in January 2021 featured expert talks on scaling Citus clusters with logical replication and on applying PostgreSQL to Suning’s retail digitalization, highlighting architecture, challenges, technology choices, and future directions for database-driven business transformation.
This article details Suning's journey of replacing commercial databases with PostgreSQL and Citus, covering background, feature and performance comparisons, reliability tests, deployment architecture, real‑world use cases, operational tips, and a comprehensive Q&A on migration and maintenance.
Suning’s 2020 PostgreSQL case study, presented at PostgresConf.CN & PGConf.Asia, details how the company deployed over 4,000 instances, leveraged Citus, JSON, PostGIS and roaringbitmap, and achieved cost savings and smart‑retail capabilities across billing, analytics, location search and user profiling.
Facing the post‑pandemic slowdown of e‑commerce growth, SuNing pioneered an O2O “smart retail” strategy by integrating online and offline channels through its “Anytime Business System,” which combines PostGIS‑enabled geospatial analysis, PostgreSQL high‑availability clustering with Citus, and seamless data flow across front‑end, transaction, and support layers.
The article explains how Citus enables sharding and MapReduce‑style parallel query execution in PostgreSQL, showing performance gains, example bucket algorithms, and how standard SQL can replace custom MapReduce code for large‑scale data analytics.
The December 2017 DB‑Engines ranking shows Oracle still on top, while PostgreSQL maintains steady growth, gaining momentum with JSON support and the Citus extension, as developers turn to it over NoSQL alternatives, signaling a potential shift in the database landscape.
