Real-World Success Stories with PostgreSQL on Azure
Organizations rarely leap into cloud migrations or AI-powered systems overnight. They progress in deliberate stages, establishing a reliable data foundation, optimizing for performance, and then accelerating innovation. Across healthcare, financial services, and AI startups, companies are navigating this journey on Azure Database for PostgreSQL: a fully managed, enterprise-ready PostgreSQL environment with 58% lower total cost of ownership (TCO) compared to on-premises deployments. This post walks through real customer stories that span the full arc, from lift-and-shift migration to production-grade AI agent development, illustrating how Azure Database for PostgreSQL supports scalability, performance, security, and AI-readiness at every stage. Migrating with Confidence: Apollo Hospitals & August AI Apollo Hospitals operates a network of more than 74 hospitals and needed to move beyond a legacy on-premises Oracle system that had become difficult to manage and couldn't keep pace with growing data volumes. IT teams were spending their time on maintenance rather than innovation. Apollo migrated its core hospital information system backend to Azure Database for PostgreSQL. Working with partner Quadrant Technologies, the team lifted and shifted critical applications while using Azure DevOps to orchestrate CI/CD pipelines and Azure Application Insights for telemetry and observability. The results: 99.95% availability across hospital systems Database transactions executing within 5 seconds 40% reduction in deployment times via modern CI/CD pipelines Decreased operational overhead, freeing IT staff for higher-value work With a stable, scalable PostgreSQL backend in place, Apollo is now exploring real-time analytics and AI-enabled tools like Microsoft 365 Copilot to advance patient care. "We saw Azure Database for PostgreSQL as the right foundation for the future. It's open, cost-effective, and capable of supporting the hospital information system we built in-house." — Shankar Krishna A., General Manager of IT, Apollo Hospitals Apollo's experience is not unique. August AI, a healthcare-tech startup offering an AI-driven medical companion, migrated its entire stack to Azure—with Azure Database for PostgreSQL storing mission-critical patient data while meeting strict compliance requirements such as HIPAA. The result: scaling from roughly 500,000 users to 3.5 million+ users worldwide, with zero downtime during the cutover, completed in just three months. As Founder and CEO Anuruddh Mishra noted: "We receive a log of queries that are not performing optimally, and within a couple of minutes we can optimize that query with PostgreSQL on Azure and move on". Modernizing at Scale: Nasdaq Migration is often the first step. Nasdaq demonstrates what becomes possible when organizations modernize their architecture on a scalable data foundation. To improve its Nasdaq Boardvantage platform—used by corporate boards to collaborate on governance documents—Nasdaq re-architected on Azure. The team containerized services with Azure Kubernetes Service (AKS) and adopted Azure Database for PostgreSQL alongside Azure Database for MySQL as persistent data stores for governance workloads. This architecture provided the flexibility, performance, and security required for a multitenant platform handling sensitive board materials. With the data layer in place, Nasdaq integrated Microsoft Foundry and Azure OpenAI to deliver AI-powered summarization and workflow automation. The measurable outcomes: 60% reduction in reading time through AI-powered document summarization 25% decrease in administrative preparation time across board workflows Up to 97% accuracy in AI-generated summaries and meeting minutes A reusable AI framework established for future extensibility "Both Azure Database for PostgreSQL and Azure Database for MySQL gave us the right balance of performance, security, and control. The governance workloads we handle are unique, so we needed something that could meet those isolation and encryption requirements." — Scott Ellison, Vice President of Technology, Nasdaq Building Intelligent Applications: SubgenAI and OpenAI Azure Database for PostgreSQL now supports native vector search via pgvector, high-performance DiskANN indexing, semantic operators and AI model management, and integrated graph capabilities for relationship reasoning—making it a production-ready foundation for intelligent applications. SubgenAI, a European generative AI company, built its flagship platform Serenity Star on Azure Database for PostgreSQL and Microsoft Foundry to transform AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. A core technical requirement: the platform's retrieval-augmented generation (RAG) system needs efficient vector search against embedded content while maintaining enterprise-grade reliability. After evaluating several database options, SubgenAI chose Azure Database for PostgreSQL with pgvector for its accurate and scalable vector similarity search. Serenity Star customers can now: Launch AI agents in as little as 15 minutes Cut coding and development time by 50% Resolve most AI agent queries in under 60 seconds [ "With Microsoft and Azure Database for PostgreSQL we have total control and an environment that is truly dynamic and can adapt to the evolution we're looking for." — Julia Schröder Langhaeuser, VP of Product Serenity Star, SubgenAI At the extreme end of scale, OpenAI runs PostgreSQL on Azure to support production systems behind ChatGPT. As write scalability limits emerged on an initially unsharded single primary instance, OpenAI offloaded write-heavy operations to other systems and optimized read workloads using PgBouncer for connection pooling. The Azure Database for PostgreSQL team responded by developing the elastic clusters feature, enabling horizontal scaling through row-based and schema-based sharding. The team reduced connection latency from approximately 50 ms to under 5 ms, scaled reads horizontally with multiple replicas, and improved reliability by prioritizing critical requests—all achieved by a small team making systematic optimizations on open-source PostgreSQL. "After all the optimization we did, we are super happy with Postgres right now for our read-heavy workloads. It's really scalable and reliable." — Bohan Zhang, Member of the Technical Staff, OpenAI Meeting You Where You Are Beyond these stories, organizations like BMW Group (cloud-native applications at global scale), Ahold Delhaize (highly available retail applications), Mott MacDonald (an AI agent accelerating onboarding and spreading best practices across 220,000 employees), and Multitude (scaling responsibly in regulated environments) all run on Azure Database for PostgreSQL. The service offers 99.99% availability with automatic failover and SLA, independent compute and storage scaling, and intelligent performance recommendations, available across 60+ Azure regions. Developer tooling including the PostgreSQL extension for Visual Studio Code with GitHub Copilot further accelerates productivity. Whether you are planning your first migration or building production AI agents, these stories share a clear signal: Azure Database for PostgreSQL delivers a scalable, secure, AI-ready data foundation at every stage of growth. Explore full customer stories in depth in the eBook: Customer Success Stories with Azure Database for PostgreSQL.Ultimate Guide to POSETTE: An Event for Postgres, 2026 edition
POSETTE: An Event for Postgres 2026 is back for its 5th year: free, virtual, and unapologetically all about Postgres. No travel budget required and no jet lag involved. Just your laptop, a decent internet connection, and curiosity. This year the POSETTE 2026 schedule has 4 livestreams (16-18 June) with 44 talks at ~25 minutes each—covering everything from query performance and partitioning to Postgres 19 features, extensions, and use cases. Which is awesome but also a bit of work to figure out which talks are for you. Hence this ultimate guide post. Every talk will land on YouTube afterward (un-gated, of course) so if you miss anything you care about, you can watch it later. But if you can catch a livestream in June, do it. That’s when the “virtual hallway track” happens on Discord—where you can ask the POSETTE speakers questions and compare notes with other attendees. Meeting other attendees who have the same weird Postgres problems you do can be reassuring somehow. And yes, there will be swag. This guide is your cheat sheet: I’ve categorized and tagged all 44 talks so you don’t have to read 44 abstracts back-to-back. In this post you'll get: “By the numbers” summary Map of the 44 talks 2 Keynote sessions 23 Postgres core talks 11 Postgres ecosystem talks 8 Azure Database talks Why participate on the virtual hallway track on Discord A big thank you to our amazing speakers Join us for POSETTE 2026 & mark your calendars Official POSETTE 2026 Trailer “By the numbers” summary for POSETTE 2026 Here’s a quick snapshot of what you need to know about POSETTE this year: 3 days 16-18 June 2026 4 livestreams In Americas & EMEA time zones but of course you can watch from anywhere 44 talks All free, all virtual 2 invited keynotes Driving Postgres forward at Microsoft (Livestream 1), and Postgres 19 Hackers Panel: What’s In, What’s Out, & What’s Next (Livestream 2) 25 minutes Average length per talk ~1100 minutes Total minutes in POSETTE 2026 talks 50 speakers POSETTE 2026 speakers include PostgreSQL hackers and contributors, users, application developers, PG community members, Azure engineers, & Azure customers 6 keynote speakers Affan Dar & Charles Feddersen (Livestream 1); and Álvaro Herrera, Heikki Linnakangas, Melanie Plageman, & Thomas Munro (Livestream 2) 19 countries Speakers reside in 19 different countries 23 companies Speakers hail from 23 different companies 17.6% CFP acceptance rate 42 talks selected from 238 submisssions 75% general Postgres talks 33 talks are not cloud-specific at all, they’re about the Postgres technology & ecosystem 25% Azure-related talks 11 of 44 talks feature Azure Database for PostgreSQL or Azure HorizonDB 1 organizing company Organized by the Postgres team at Microsoft, in partnership with AMD 17 languages Published talk videos will have captions available in 17 languages, including English, Czech, Dutch, French, German, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Turkish, Ukrainian, and Chinese Simplified & Chinese Traditional Map of the 44 talks To help you quickly navigate all 44 talks, here’s a map of the high-level categories and detailed topics. : A map of the POSETTE 2026 talks—high-level categories and detailed tags to help you find what you care about 2 Keynote sessions Affan Dar and Charles Feddersen lead the PostgreSQL engineering and product teams at Microsoft, In this keynote, they’ll walk through how Microsoft is contributing to Postgres, both upstream in the open source project and in the cloud database service they build on top of it. Driving Postgres forward at Microsoft, by Affan Dar & Charles Feddersen (Azure Database for PostgreSQL, Azure HorizonDB, VS Code, Dev tools, community, Postgres hacking, open source, PosetteConf, livestream-1) Want to understand how Postgres features get decided? This keynote panel with 4 PostgreSQL committers & hackers will peel back the curtain. You’ll hear what made it into Postgres 19, what didn’t (and why), and get a sneak peek into a few of the things in the oven for Postgres 20. Postgres 19 Hackers Panel: What’s In, What’s Out, & What’s Next, by Álvaro Herrera, Heikki Linnakangas, Melanie Plageman, & Thomas Munro (Postgres 19, Postgres hacking, panel, open source, collaboration, multithreading, livestream-2) 23 Postgres core talks Data Modeling JSON in PostgreSQL - evil data type or just needs to be tamed?, by Boriss Mejias (JSON, performance, data modeling, livestream-1) PostgreSQL Design Patterns, by Chris Ellis (data modeling, SQL, PG use cases, livestream-1) Graph Data Exploring property graphs with SQL/PGQ in PostgreSQL, by Ashutosh Bapat (SQL/PGQ, graph data, data modeling, Postgres 19, livestream-4) LISTEN/NOTIFY LISTEN Carefully: How NOTIFY Can Trip Up Your Database, by Jimmy Angelakos (LISTEN/NOTIFY, PG use cases, triggers, livestream-4) Performance Maintaining Large Tables in PostgreSQL, by Sarat Balijepalli (WAL, performance, scaling Postgres, vacuum, autovacuum, statistics, partitioning, monitoring, livestream-3) My Postgres partitioning cookbook, by Derk van Veen (partitioning, PG use cases, data modeling, performance, livestream-4) PostgreSQL 17 vs 18: Side‑by‑Side Performance Wins in Real‑World Queries, by Divya Bhargov (performance, PG use cases, livestream-3) Vacuuming Enhancements in PostgreSQL 18: Faster, Smarter, More Predictable, by Shashikant Shakya (vacuum, async IO, monitoring, performance, livestream-4) PG Internals Linux and PostgreSQL in the Multiverse of Connections, by Josef Machytka (Linux, PG internals, connection pooling, livestream-2) pg_stats: How Postgres Internal Stats Work, by Richard Yen (statistics, pg_stats, PG internals, query planner, livestream-2) Postgres isn’t slow, your storage is, by Sai Srirampur (storage, IO, performance, livestream-3) PostgreSQL queues done right with PgQ, by Alexander Kukushkin (queues, PG internals, extensions, livestream-2) random_page_cost in Postgres - why the default is 4.0 and should you lower it?, by Tomas Vondra (PG internals, IO, performance, livestream-1) The Wonderful World of WAL, by Bruce Momjian (WAL, PG internals, replication, livestream-3) What's new with constraints in Postgres 18, by Gülçin Yıldırım Jelínek (constraints, data modeling, livestream-2) Postgres Hacking Fuzzing PostgreSQL, by Adam Wolk (PG internals, testing, Dev tools, libpq, security, livestream-1) Journey of developing a performance optimization feature in PostgreSQL, by Rahila Syed (Postgres hacking, PG internals, performance, WAL, replication, livestream-4) The Hitchhiker’s Guide to PostgreSQL Hacking: Don’t Panic, Just Start Small, by Xuneng Zhou (Postgres hacking, PG internals, community, livestream-2) Replication Past, Present, and Future: Logical Decoding and Replication in PostgreSQL, by Hari Kiran (replication, logical decoding, PG internals, livestream-4) Where Does My INSERT Go? A Logical Replication Story, by Hamid Akhtar (replication, PG internals, WAL, livestream-4) Security From Dev to Prod: Securing Postgres the Right Way, by Sakshi Nasha (security, roles, PG use cases, extensions, monitoring, livestream-4) From trust to Tokens: A Short History of PostgreSQL Authentication, by Murat Tuncer (authentication, security, livestream-2) PostgreSQL vs. SQL Server: Security Model Differences, by Taiob Ali (security, authentication, SQL Server, roles, livestream-1) 11 Postgres ecosystem talks Analytics pg_lake: Postgres as a lakehouse, by Marco Slot (pg_lake, extensions, OLAP, data warehouse, Iceberg, DuckDB, analytics, livestream-2) Apache AGE Querying & Visualizing Graphs in Postgres with Apache AGE, by Christian Miles (Apache AGE, graph data, data visualization, SQL/PGQ, Azure HorizonDB, livestream-1) Autotuning Building safety tooling for risk-free AI tuning of Postgres: Fast cars need fast brakes, by Mohsin Ejaz (autotuning, AI, performance, monitoring, livestream-2) Change Data Capture Building Event-Driven Systems with PostgreSQL Logical Replication and Drasi, by Diaa Radwan (Drasi, replication, WAL, CDC, livestream-3) Citus Move Less, Move Faster: Speeding Up Citus Cluster Scaling, by Muhammad Usama (Citus, extensions, performance, scaling Postgres, livestream-4) Dev Tools An MCP for your Postgres DB, by Pamela Fox (MCP, AI, Python, Dev tools, livestream-1) pgcov: Bringing Real Test Coverage to PostgreSQL Code, by Pavlo Golub (testing, Postgres hacking, Dev tools, extensions, CI/CD, livestream-3) PostgreSQL Tooling Across AI Editors and Agents, by Matt McFarland (Dev tools, VS Code, Cursor, AI, data visualization, Apache AGE, graph data, Azure, MCP, Copilot, livestream-1) Django PostgreSQL Generated Columns by Example, by Paolo Melchiorre (app dev, Django, generated columns, livestream-2) Kubernetes Quorum-Based Consistency for Cluster Changes with CloudNativePG Operator, by Jeremy Schneider & Leonardo Cecchi (CloudNativePG, Kubernetes, PG use cases, livestream-3) Performance Modelling Postgres Performance Degradation on Burstable Cloud Instances, by Chun Lin Goh (performance, burstable, compute, QA, livestream-4) 8 Azure Database for PostgreSQL & Azure HorizonDB talks AI-related talks From Queries to Agents: The Next Era of Data Retrieval on PostgreSQL, by Abe Omorogbe (AI, MCP, Azure Database for PostgreSQL, graph data, Apache AGE, Azure HorizonDB, livestream-3) Production RAG at Scale with Azure Database for PostgreSQL, by Julia Schröder Langhaeuser & Paula Santamaría (Azure Database for PostgreSQL, AI, RAG, PG use cases, livestream-3) AMD Choose the Right Azure Infrastructure to Improve Postgres Performance by Over 60%, by Andrew Ruffin (AMD, performance, Azure, compute, Azure Database for PostgreSQL, livestream-1) Azure HorizonDB Why we built Azure HorizonDB for PostgreSQL, by Dingding Lu (Azure HorizonDB, scaling Postgres, livestream-3) Flexible Server pg_duckdb in Action: Accelerating Analytics on Azure Database for PostgreSQL, by Nitin Jadhav (DuckDB, Azure Database for PostgreSQL, extensions, OLAP, analytics, performance, livestream-4) The Rise of PostgreSQL as the Everything Database, by Varun Dhawan (Postgres history, extensions, graph data, Apache AGE, Azure Database for PostgreSQL, DuckDB, Citus, livestream-3) What I’ve Learned Teaching Postgres to 200+ field engineers at Microsoft, by Paula Berenguel (training, Azure, Postgres skilling, livestream-1) Oracle to Postgres Migrating VLDBs from Oracle to Azure Database for PostgreSQL, by Adithya Kumaranchath (migration, Azure Database for PostgreSQL, Oracle to Postgres, livestream-2) Why participate in the virtual hallway track on Discord If you’ve checked out the schedule and plan to watch some of the talks, you might still be wondering: why join live—and why bother with the virtual hallway track on Discord? Here’s how a few of last year’s attendees described the experience: “Very impressed by all the speakers and content I am absolutely shattered as there was so much great content in all the talks over the past 3 days but I have probably learnt more in these sessions than I could have in months of reading up.” “Want to let y’all know how much I got from this onine conference, the speakers were excellent, well-prepared and well-presented. The hosts were informative, engaging, & amusing. The discord hallway channel made me feel connected. I learned a lot and found some new inspiration. I’ll be back next year!” “I have no idea how I’m going to summarise all the interesting stuff for coworkers.” The common thread: the live, shared experience—being able to ask questions, compare notes, and learn alongside other people in real time. How to join the virtual hallway track Head to the #posetteconf channel on Discord (on the Microsoft Open Source Discord) That’s where speakers and attendees hang out during the livestreams—it’s where you can ask questions, share reactions, and just say hi Big thank you to our amazing speakers Every great event starts with great talks—and great talks start with great speakers. Want to learn more about the people behind these talks? Visit the POSETTE 2026 Speaker page Click a speaker’s bio to see their written interview (if available) If a speaker has been a guest on the Talking Postgres podcast in the past, then you’ll find a link to their episode there, too Join us for POSETTE 2026! Mark your calendars I hope you join us for POSETTE 2026. Consider yourself officially invited. As part of the talk selection team, I’m definitely biased—but I truly believe these speakers and talks are worth your time. I’ll be hosting Livestream 1 and you’ll find me in the #posetteconf Discord chat. I hope to see you there. And please: tell your Postgres friends, so they don’t miss out! 🗓️ Add the livestreams to your calendar Livestream 1: Tue 16 June, 8am–2pm PDT (UTC-7) [ register for updates ] and/or [ add to calendar ] Livestream 2: Wed 17 June, 8am–2pm CEST (UTC+2) [ register for updates ] and/or [ add to calendar ] Livestream 3: Wed 17 June, 8am–2pm PDT (UTC-7) [ register for updates ] and/or [ add to calendar ] Livestream 4: Thu 18 June, 8am–2pm CEST (UTC+2) [ register for updates ] and/or [ add to calendar ] Watch last year’s POSETTE 2025 talks in advance: And if you want to get ready, you can watch talks from the POSETTE 2025 playlist on YouTube anytime, anywhere. Lots of solid, useful, and evergreen Postgres talks in there. “Official Trailer” for POSETTE 2026 is on YouTube To help more developers, community members, and Postgres users discover POSETTE 2026, our team created this short video trailer. Take a peek and share it with friends as an invitation of sorts. We’re trying to make sure that people don’t miss their opportunity to be part of the livestreams and ask questions on the discord during the conference (as well as watch the talks on YouTube after the event is over.) Watch and share the trailer: Official Trailer for POSETTE: An Event for Postgres 2026 Acknowledgements & Gratitude I’ve already thanked the 50 amazing speakers above. In addition, thanks go to Silvano Coriani, Cornelia Biacsics, Aaron Wislang, and My Nguyen for reviewing parts of this post before publication. I also want to thank the team at AMD for their partnership and support of POSETTE this year! And of course, big thank you to the POSETTE 2026 organizing team and POSETTE talk selection team—without you, there would be no POSETTE! Figure 3: Visual invitation to join the virtual hallway track for POSETTE 2026 on the Microsoft Open Source Discord, so you can chat with the speakers & others in the Postgres community
End-to-end workload observability with Query Store for primary and replicas
Query performance doesn’t stop at the primary Most PostgreSQL architectures don’t run on a single node anymore. Reads get offloaded. Replica chains grow. And when performance issues hit, the hardest part is often simple: where did the queries actually run? With the latest query store capabilities in Azure Database for PostgreSQL flexible server, you can now capture workload executed not just on the primary, but also on read replicas—including cascading read replicas—and export the captured runtime stats, wait stats, and query text into Azure Monitor Logs (Log Analytics workspace / LAWS). See the real hotspot: isolate which node (primary vs replica) is slow. Know why: break down time by waits (CPU, I/O, locks) per query. Connect the dots: correlate query IDs to query text, and inspect sampled parameters locally in azure_sys on the primary when you need input context (parameters aren’t exported to LAWS). Centralize analysis: query everything with KQL in LAWS, across servers. What you’ll build This post walks through a reproducible demo that provisions a primary server, a read replica, and a cascading read replica, then runs a TPC-H–based workload across all three to generate query store data you can analyze locally and in Log Analytics. Enable query store capture (including query text) and parameter sampling for parameterized queries. Enable wait sampling so query store can record wait statistics. Export runtime stats, wait stats, and SQL text to LAWS using resource-specific tables. Validate capture on read replicas and cascading read replicas (not just the primary). Prerequisites Azure CLI logged in (az login) and permission to create a resource group, Log Analytics workspace, and PostgreSQL flexible servers. psql and curl available on your machine. PostgreSQL flexible server on General Purpose or Memory Optimized tier (query store and replicas aren’t supported on Burstable). PostgreSQL 14+ to test out cascading replicas. Networking: the script opens firewall access broadly for demos—tighten for production. Architecture (primary + replica chain + LAWS) You’ll deploy four resources: Primary server: read/write node. Read replica (level 1): read-only node created from the primary. Cascading read replica (level 2): read-only node created from replica level 1. Log Analytics workspace (LAWS): central place to query Query Store telemetry across all nodes. If Diagnostic Settings is properly configured, each server streams query store telemetry to LAWS—but how it’s kept locally differs by role. On the primary, query store data is recorded in-memory, then persisted locally in the azure_sys database, and then exported to LAWS. On read replicas (including cascading replicas), query store data is recorded in-memory only and then exported to LAWS. Bottom line: use LAWS for fleet-wide visibility, and use the primary’s azure_sys when you need deep local inspection (like parameter samples). Deploy the demo environment The fastest way to reproduce the scenario is to run the end-to-end bash script which you can download from https://raw.githubusercontent.com/Azure-Samples/azure-postgresql-query-store/refs/heads/main/may2026/script/query_store_demo.sh Save the file to a local directory in your Linux shell, and name the file query_store_demo.sh. To invoke the script, at minimum, you must assign a string password for the administrator login of the instances of the flexible servers it creates, and invoke the script like this: ADMIN_PASSWORD=<Your_Strong_Password> ./query_store_demo.sh Optionally, you can also override default values for other environment variables used by the script: Variable Purpose Default SUBSCRIPTION_ID Azure subscription ID to use (current default subscription) BASE_NAME Base name for all resources (used in naming servers, resource groups, etc.) pgqswait{YYYYMMDDHHMMSS} RESOURCE_GROUP Azure resource group name rg-{BASE_NAME} LOCATION Azure region for resources southeastasia PRIMARY_SERVER Name of primary PostgreSQL server {BASE_NAME}-primary REPLICA_1 Name of first-level read replica {BASE_NAME}-readreplica REPLICA_2 Name of second-level cascading read replica {BASE_NAME}-cascadereadreplica LOG_ANALYTICS_WORKSPACE Log Analytics workspace name law-{BASE_NAME} LOG_ANALYTICS_LOCATION Azure region for Log Analytics workspace southeastasia ADMIN_USER PostgreSQL admin username pgadmin ADMIN_PASSWORD PostgreSQL admin password (REQUIRED) SKU_NAME PostgreSQL server SKU (compute tier) Standard_D4ds_v5 TIER PostgreSQL pricing tier GeneralPurpose STORAGE_SIZE Storage size in GB 64 VERSION PostgreSQL version (minimum 14 for cascading replicas) 17 PRIMARY_DATABASE Initial database name postgres SQL_BASE_URL Base URL for downloading SQL scripts https://raw.githubusercontent.com/Azure-Samples/azure-postgresql-query-store/refs/heads/main/may2026/script/query_store_demo.sh TPCH_DDL_URL URL for TPC-H schema DDL file {SQL_BASE_URL}/schema/tpch_ddl.sql WORKLOAD_REPETITIONS Number of times to execute each workload query (minimum 5) 10 AUTO_APPROVE Skip confirmation prompt and proceed automatically false If, for example, you want to not only pass the ADMIN_PASSWORD but also override the LOCATION, you could do it like this: ADMIN_PASSWORD=<Your_Strong_Password> LOCATION=canadacentral ./query_store_demo.sh In a bit over 1 hour, the script will do the following steps: Step 1 — Provision first part of the infrastructure The infrastructure provisioned in this phase consists of: A resource group in which all resources are deployed. An instance of Log Analytics workspace, where all flexible server instances will send their query store related logs. A primary (read-write) flexible server. Step 2 — Configure primary server Now it's time to configure one new server parameters on your primary server so that query store emits query text to LAWS, so that we can correlate quey IDs to something recognizable. Query IDs are great for aggregation—but you still need the SQL. Turn on query text emission so you can correlate runtime and waits back to the actual statement text. Do this by setting pg_qs.emit_query_text to on. Refer to our documentation to learn how to set the value of a server parameter. Step 3 — Provision second part of the infrastructure The infrastructure provisioned in this phase consists of: A read replica (read-only) whose source is the primary server. A cascade read replica (read-only), whose source is the previously created read replica. Notice that when read replicas are created, they inherit the server parameter values from their source server. Because we have configured query store related settings on the primary server already, the intermediate read replica inherits its server parameters from that primary, and the cascade read replica inherits them from the intermediate replica. Step 4 — Export query store to Log Analytics (LAWS) Now for the payoff, we want to stream the data to Log Analytics so you can query across nodes, build dashboards, and alert. The script configures diagnostic settings on the primary and both replicas to send logs to a Log Analytics workspace using resource-specific tables. This is the key to cross-node visibility: each server exports its own captured telemetry, and you can slice by resource in a single KQL query. Query store runtime stats: execution counts, elapsed time, and other performance counters. Query store wait stats: wait breakdown attributed to queries. Query store SQL text: query text to decode query IDs. Note: Query store parameter samples are not included in the Log Analytics export. Parameters are stored locally per server in azure_sys, and on read replicas azure_sys is read-only—so don’t depend on replicas for parameter inspection. LAWS receives runtime stats, wait stats, and query text. Diagnostics settings for an instance of flexible server can be configured via portal. In the resource menu, under Monitoring, select Diagnostic settings. Add a new diagnostic setting, select a destination Log Analytics workspace, and the individual log catergories which you want to stream to that LAWS, and save the changes. For Destination table it's highly recommended to use Resource specific (one table per signal with proper schema) over Azure diagnostics (legacy one table for everything). With Azure diagnostics, all logs from all resource types land into a single table (AzureDiagnostics). It's a wide table with many columns. New columns get added as services emit new fields. If the 500 column limit is hit, extra fields go into the AdditionalFields column (a dynamic JSON). Querying on attributes stored in that column might have huge performance and query cost impact. The schema is inconsistent and difficult to discover. You must always filter events in that table by ResourceType and Category. On the other hand, with Resource specific, logs are written to separate tables per resource type and category. Therefore, each table has a well-defined schema and columns are strongly typed. Tables are smaller and faster to query. Queries on these tables are simpler don't need filtering by ResourceType and Category. Performance-wise, they also support faster ingestion and faster querying. They also support selecting different table plans and retention settings for each table. And, more importantly, role-based access control (RBAC) permissions can be applied at table level, allowing you to control access to telemetry in a more granular way. Note: If you want to see any of the images in this article in better quality, click on them to see them in their original size. This can also be configured using Azure CLI command az monitor diagnostic-settings create. Make sure that the --export-to-resource-specific parameter is set to true, which is the equivalent of selecting Resource specific for Destination table in portal UI. Setting this parameter to false, would mean that you want to use AzureDiagnostics as the destination table, which we don't recommend using. Step 5 — Run some workload In this phase the script loads a TPC-H schema and executes workload SQL across different nodes so that you can prove replica capture. Query it in Log Analytics Once the workload completed and data was streamed to Log Analytics, you can open your Log Analytics workspace, and start querying the relevant tables. If you don't know how to start issuing queries in a Log Analytics workspace, refer to Get started with log queries in Azure Monitor Logs. In your Log Analytics workspace, when you select Logs in the resource menu, you can access the Queries hub. By default, it should open automatically unless you have configured it to not show, in which case you can open by selecting Queries hub on the top right corner of the Logs home screen. If you add a filter in the queries hub for Resource type equals Azure Database for PostgreSQL Flexible Server, you'll be able to access multiple examples of queries which might help you get started querying the log categories we support for our service. You can run any of them by selecting Run on the summarization card that describes the query or, if you hover the mouse over the card, you can select Load to editor so that the query is copied over to the active query window, and you can run it or modified it further. Following, there are a few more query examples which can be useful to analyze the workload executed in this experiment. Top queries by total time (across all nodes) To get the list of 10 queries with higher duration from the ones that ran on any of the three nodes. KQL PGSQLQueryStoreRuntime | summarize total_time_ms = sum(TotalExecDurationMs) by QueryId, LogicalServerName | top 10 by total_time_ms desc Results Important: Results might be slightly different on each execution of the experiment. Where queries wait on each node List the most frequent wait events observed on user initiated queries across all nodes. KQL PGSQLQueryStoreWaits | join kind=inner (PGSQLQueryStoreRuntime) on QueryId | summarize total_waits_sampled = sum(Calls) by Event, EventType, LogicalServerName | order by total_waits_sampled desc Results Important: Results might be slightly different on each execution of the experiment. Decode query IDs (join runtime stats with SQL text) Top 20 queries the most frequent wait events observed on user initiated queries across all nodes. KQL PGSQLQueryStoreRuntime | join kind=inner (PGSQLQueryStoreQueryText) on QueryId | where QueryType == 'select' | project LogicalServerName, QueryId, TotalExecDurationMs, QueryText | top 20 by TotalExecDurationMs desc Results Important: Results might be slightly different on each execution of the experiment. Compare primary vs replicas (workload distribution) Find total number of query executions and accumulated duration of all those executions for each node. KQL PGSQLQueryStoreRuntime | summarize execs = sum(Calls), total_time_ms = sum(TotalExecDurationMs) by LogicalServerName | order by total_time_ms desc Results Important: Results might be slightly different on each execution of the experiment. Replica-only hotspots (find what’s slow off the primary) Find top 10 queries executed by their aggregated duration, focusing on what was executed on read replicas only. KQL let Replicas = dynamic(["pgqswait20260505220501-readreplica", "pgqswait20260505220501-cascadereadreplica"]); PGSQLQueryStoreRuntime | where LogicalServerName in (Replicas) | summarize total_time_ms = sum(TotalExecDurationMs) by QueryId, LogicalServerName | top 10 by total_time_ms desc Results Important: Results might be slightly different on each execution of the experiment. QPI now supports query store stats collected on replicas You can now use Query Performance Insight workbooks to analyze query store information not only on your primary server, as you were used to, but you can also get that valuable information on your read replicas. Why replica workload capture is a big deal This is the unlock: you can now answer performance questions in replica-heavy architectures without stitching together partial signals. Per-node truth: see the slow queries on the node where they actually ran (primary vs replica vs cascading replica). Faster root cause: runtime + waits gives you “slow” and “why” in one place. Replica tuning that sticks: identify replica-specific bottlenecks (I/O saturation, lock waits, CPU pressure) and tune with evidence. Centralized observability: export to LAWS so you can build dashboards, alerts, and cross-server comparisons with KQL. Unlock query visibility: Access query text without database permissions. Fine grain control on who can view query text: Using resource specific tables in LAWS, you can decide which users can access the table in which text of the queries is kept. Parameter-aware debugging: sampled parameters can help reproduce issues and explain plan changes, but they’re stored locally in azure_sys and not exported to LAWS. In practice, rely on the primary for parameter inspection (replicas have read-only azure_sys). Operational notes (quick but important) Expect a delay: Query store stats and LAWS ingestion aren’t instant. Give it a few minutes after running workload. Mind retention: Query store retention and Log Analytics retention are separate knobs. Tune them to balance troubleshooting value and cost. Production hygiene: don’t use wide-open firewall rules outside of a demo. Clean up When you’re done, delete the resource group: az group delete --name <RESOURCE_GROUP> --yes --no-wait Bottom line Query store in Azure Database for PostgreSQL flexible server now matches how modern architectures run—across primary, read replicas, and cascading replicas—and LAWS gives you a single place to query, compare, and act.When PostgreSQL v17 Chooses the Wrong Plan: A Deep Dive into CTEs with Data Skew
Common Table Expressions or CTEs provide temporary named result set that can be referenced within single complex SQL statements. CTE offer great flexibility in breaking down complex SQL queries into multiple readable parts and enables recursion. CTEs allows materialized result set which allows users to refer the result set multiple times. PostgreSQL 17 propagates column statistics from MATERIALIZED CTEs into the parent query, allowing the planner to estimate hash aggregation and join cardinalities more accurately. However, when queries combine highly skewed data distributions with runtime randomness (for example, ORDER BY random() LIMIT 1), this increased planner confidence can sometimes lead to execution plans that are correct in theory but catastrophic for specific runtime values. What is Data Skew and how it affects plans? Data skew is an uneven distribution of values in a column, where one or a few values occur far more often than the rest. To choose an efficient execution plan, PostgreSQL’s planner estimates row counts using statistics such as n_distinct, histograms, and most-common-values (MCV) frequencies stored in pg_statistic. If these estimates are off—especially if selectivity is overestimated—query performance can degrade significantly. Baseline Query and Observed Planner Behavior To gauge the performance impact of data skew with PGv17, we generated data into 3 tables relating to Accounts, Purchases and Audit. This baseline query selects a single random HIGH_RISK account and retrieves matching rows from a large audit_logs table via two materialized CTEs. Original Query with selected_account as materialized ( select account_id from accounts where account_type = 'HIGH_RISK' order by random() limit 1 ), audit_purchases as materialized ( select distinct p.account_id from purchases p join selected_account sa on p.account_id = sa.account_id ) select al.* from audit_logs al where exists ( select 1 from audit_purchases ap where al.account_id = ap.account_id); PostgreSQL 14 Execution Plan PostgreSQL 14 treats materialized CTEs as optimization fences and does not propagate detailed statistics into the parent query. As a result, the planner assumes relatively small CTE result sets and consistently favors nested loop joins with parameterized index scans. While these estimates are not strictly accurate, this conservative planning approach prevents full-table scans and avoids catastrophic performance under extreme data skew. In this workload, PostgreSQL 14 completes the query in approximately 7 ms. QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------ Nested Loop (cost=1412.94..212757485.16 rows=1500600064 width=27) (actual time=5.226..7.192 rows=30 loops=1) Buffers: shared hit=277 read=13 CTE selected_account -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.460..4.460 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.459..4.459 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.011..3.533 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 CTE audit_purchases -> HashAggregate (cost=317.44..355.57 rows=3813 width=4) (actual time=4.735..4.741 rows=1 loops=1) Group Key: p.account_id Batches: 1 Memory Usage: 217kB Buffers: shared hit=274 read=1 -> Nested Loop (cost=0.44..292.52 rows=9966 width=4) (actual time=4.722..4.726 rows=10 loops=1) Buffers: shared hit=274 read=1 -> CTE Scan on selected_account sa (cost=0.00..0.02 rows=1 width=4) (actual time=4.461..4.461 rows=1 loops=1) Buffers: shared hit=271 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..192.84 rows=9966 width=4) (actual time=0.260..0.262 rows=10 loops=1) Index Cond: (account_id = sa.account_id) Heap Fetches: 0 Buffers: shared hit=3 read=1 -> HashAggregate (cost=85.79..87.79 rows=200 width=4) (actual time=4.743..4.744 rows=1 loops=1) Group Key: ap.account_id Batches: 1 Memory Usage: 40kB Buffers: shared hit=274 read=1 -> CTE Scan on audit_purchases ap (cost=0.00..76.26 rows=3813 width=4) (actual time=4.737..4.742 rows=1 loops=1) Buffers: shared hit=274 read=1 -> Index Scan using idx_account_audit on audit_logs al (cost=0.58..817780.34 rows=24600001 width=27) (actual time=0.480..2.438 rows=30 loops=1) Index Cond: (account_id = ap.account_id) Buffers: shared hit=3 read=12 Planning Time: 1.121 ms Execution Time: 7.253 ms (37 rows) PostgreSQL 17 Execution Plan PostgreSQL 17 correctly estimates that the audit_purchases CTE may contain thousands of rows and that joining it with audit_logs could produce tens of millions of matches. Based on these estimates, the planner selects a Hash Semi Join with a Sequential Scan on audit_logs, which is optimal when many rows are expected to match. However, at runtime the selected account produces only ~30 matching audit rows. Despite this, PostgreSQL must execute the chosen plan fully, scanning millions of rows from disk. This results in an execution time of 521,625 ms (~8.7 minutes). Importantly, this behavior is not a cost estimation bug—the estimates are statistically correct. The failure occurs because the PostgreSQL 17 planner estimates a large matching result set based on the cardinality of values produced by the audit_purchases CTE, and consequently chooses a plan optimized for that estimated volume rather than for the single runtime-selected account. QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------- Hash Semi Join (cost=1691.60..93353890.16 rows=3001396992 width=26) (actual time=86577.858..521625.286 rows=30 loops=1) Hash Cond: (al.account_id = ap.account_id) Buffers: shared hit=28879 read=22040419 CTE selected_account -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.486..4.487 rows=1 loops=1) Buffers: shared hit=274 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.485..4.486 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=274 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.021..3.551 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 CTE audit_purchases -> HashAggregate (cost=658.72..673.28 rows=1456 width=4) (actual time=4.907..4.912 rows=1 loops=1) Group Key: p.account_id Batches: 1 Memory Usage: 73kB Buffers: shared hit=277 read=1 -> Nested Loop (cost=0.44..606.86 rows=20742 width=4) (actual time=4.898..4.902 rows=10 loops=1) Buffers: shared hit=277 read=1 -> CTE Scan on selected_account sa (cost=0.00..0.02 rows=1 width=4) (actual time=4.487..4.487 rows=1 loops=1) Buffers: shared hit=274 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..399.42 rows=20742 width=4) (actual time=0.410..0.411 rows=10 loops=1) Index Cond: (account_id = sa.account_id) Heap Fetches: 0 Buffers: shared hit=3 read=1 -> Seq Scan on audit_logs al (cost=0.00..52082989.92 rows=3001396992 width=26) (actual time=0.012..286755.571 rows=3001400050 loops=1) Buffers: shared hit=28602 read=22040418 -> Hash (cost=29.12..29.12 rows=1456 width=4) (actual time=4.919..4.920 rows=1 loops=1) Buckets: 2048 Batches: 1 Memory Usage: 17kB Buffers: shared hit=277 read=1 -> CTE Scan on audit_purchases ap (cost=0.00..29.12 rows=1456 width=4) (actual time=4.908..4.911 rows=1 loops=1) Buffers: shared hit=277 read=1 Planning: Buffers: shared hit=197 Planning Time: 1.831 ms Execution Time: 521625.433 ms (38 rows) Forced nested loop Disabling hash joins at the session level forces PostgreSQL 17 to revert to a nested loop plan using index scans. This diagnostic step completes in ~8 ms, confirming that index-based execution is sufficient for the actual runtime workload. However, disabling planner features is not suitable for production use and serves only to validate the root cause. QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------- Nested Loop (cost=1677.62..3361930001.36 rows=3001396992 width=26) (actual time=5.513..8.194 rows=30 loops=1) Buffers: shared hit=277 read=12 CTE selected_account -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.524..4.524 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.523..4.523 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.014..3.608 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 CTE audit_purchases -> HashAggregate (cost=658.72..673.28 rows=1456 width=4) (actual time=4.890..4.894 rows=1 loops=1) Group Key: p.account_id Batches: 1 Memory Usage: 73kB Buffers: shared hit=274 read=1 -> Nested Loop (cost=0.44..606.86 rows=20742 width=4) (actual time=4.882..4.885 rows=10 loops=1) Buffers: shared hit=274 read=1 -> CTE Scan on selected_account sa (cost=0.00..0.02 rows=1 width=4) (actual time=4.525..4.525 rows=1 loops=1) Buffers: shared hit=271 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..399.42 rows=20742 width=4) (actual time=0.354..0.356 rows=10 loops=1) Index Cond: (account_id = sa.account_id) Heap Fetches: 0 Buffers: shared hit=3 read=1 -> HashAggregate (cost=32.76..47.32 rows=1456 width=4) (actual time=4.896..4.899 rows=1 loops=1) Group Key: ap.account_id Batches: 1 Memory Usage: 73kB Buffers: shared hit=274 read=1 -> CTE Scan on audit_purchases ap (cost=0.00..29.12 rows=1456 width=4) (actual time=4.892..4.895 rows=1 loops=1) Buffers: shared hit=274 read=1 -> Index Scan using idx_account_audit on audit_logs al (cost=0.58..1782455.82 rows=52656088 width=26) (actual time=0.615..3.283 rows=30 loops=1) Index Cond: (account_id = ap.account_id) Buffers: shared hit=3 read=11 Planning Time: 0.184 ms Execution Time: 8.252 ms (37 rows) Fix Strategies While the fix isn’t straightforward, we attempt to modify the query plan through query rewrite using following methods: Lateral Join with Offset 0 Limit on Purchases Using subqueries Lateral Join with Offset 0 Adding OFFSET 0 inside a LATERAL subquery introduces an optimizer barrier that prevents join reordering. This forces PostgreSQL to execute a parameterized nested loop, passing the runtime account ID into an index scan on audit_logs. With this rewrite, PostgreSQL 17 executes the query in 6.519 ms, a 99.9988% improvement over the original plan. with selected_account as materialized ( select account_id from accounts where account_type = 'HIGH_RISK' order by random() limit 1 ), audit_purchases as materialized ( select p.account_id from purchases p join selected_account sa on p.account_id = sa.account_id ) select al.* from audit_purchases ap join lateral ( select * from audit_logs al where al.account_id = ap.account_id offset 0) al on true; Execution plan QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------- Nested Loop (cost=1578.44..42162640233.98 rows=1092192577296 width=26) (actual time=4.980..6.481 rows=300 loops=1) Buffers: shared hit=403 read=12 CTE selected_account -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.296..4.297 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.295..4.296 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.013..3.357 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 CTE audit_purchases -> Nested Loop (cost=0.44..606.86 rows=20742 width=4) (actual time=4.585..4.588 rows=10 loops=1) Buffers: shared hit=274 read=1 -> CTE Scan on selected_account sa (cost=0.00..0.02 rows=1 width=4) (actual time=4.298..4.298 rows=1 loops=1) Buffers: shared hit=271 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..399.42 rows=20742 width=4) (actual time=0.286..0.287 rows=10 loops=1) Index Cond: (account_id = sa.account_id) Heap Fetches: 0 Buffers: shared hit=3 read=1 -> CTE Scan on audit_purchases ap (cost=0.00..414.84 rows=20742 width=4) (actual time=4.587..4.591 rows=10 loops=1) Buffers: shared hit=274 read=1 -> Index Scan using idx_account_audit on audit_logs al (cost=0.58..1506157.19 rows=52656088 width=26) (actual time=0.040..0.185 rows=30 loops=10) Index Cond: (account_id = ap.account_id) Buffers: shared hit=129 read=11 Planning: Buffers: shared hit=8 Planning Time: 0.238 ms Execution Time: 6.519 ms (31 rows) Limit on Purchases Applying LIMIT 1 to the audit_purchases CTE bounds its cardinality to a single row. This makes nested loops cheaper than hash joins during planning. PostgreSQL 17 selects an index‑driven execution plan and completes in 7.742 ms, yielding a 99.9985% improvement. with selected_account as materialized ( select account_id from accounts where account_type = 'HIGH_RISK' order by random() limit 1 ), audit_purchases as materialized ( select p.account_id from purchases p join selected_account sa on p.account_id = sa.account_id limit 1 ) select al.* from audit_logs al where exists (select 1 from audit_purchases ap where al.account_id=ap.account_id); Execution plan QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------------------------------------- Nested Loop (cost=972.07..933697.99 rows=24600001 width=27) (actual time=5.693..7.686 rows=30 loops=1) Buffers: shared hit=276 read=13 CTE selected_account -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.816..4.817 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.816..4.817 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.014..3.825 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 CTE audit_purchases -> Limit (cost=0.44..0.47 rows=1 width=4) (actual time=5.133..5.134 rows=1 loops=1) Buffers: shared hit=274 read=1 -> Nested Loop (cost=0.44..292.52 rows=9966 width=4) (actual time=5.132..5.132 rows=1 loops=1) Buffers: shared hit=274 read=1 -> CTE Scan on selected_account sa (cost=0.00..0.02 rows=1 width=4) (actual time=4.817..4.817 rows=1 loops=1) Buffers: shared hit=271 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..192.84 rows=9966 width=4) (actual time=0.313..0.313 rows=1 loops=1) Index Cond: (account_id = sa.account_id) Heap Fetches: 0 Buffers: shared hit=3 read=1 -> HashAggregate (cost=0.02..0.03 rows=1 width=4) (actual time=5.138..5.139 rows=1 loops=1) Group Key: ap.account_id Batches: 1 Memory Usage: 24kB Buffers: shared hit=274 read=1 -> CTE Scan on audit_purchases ap (cost=0.00..0.02 rows=1 width=4) (actual time=5.135..5.135 rows=1 loops=1) Buffers: shared hit=274 read=1 -> Index Scan using idx_account_audit on audit_logs al (cost=0.58..686726.47 rows=24600001 width=27) (actual time=0.552..2.534 rows=30 loops=1) Index Cond: (account_id = ap.account_id) Buffers: shared hit=2 read=12 Planning Time: 0.216 ms Execution Time: 7.742 ms (35 rows) Using subqueries Rewriting the query using scalar subqueries converts the selected account ID into an InitPlan. The resulting value is then used as a runtime parameter in index scans against audit_logs. This eliminates join reordering opportunities and guarantees a parameterized access path. Both scalar subquery variants complete in approximately 7.3–7.6 ms, achieving over 99.9986% improvement. Subquery Example 1 select * from audit_logs al where al.account_id in (select p.account_id from purchases p where p.account_id=( select account_id from accounts where account_type = 'HIGH_RISK' order by random() limit 1)); Execution plan QUERY PLAN --------------------------------------------------------------------------------------------------------------------------------------------------------- Nested Loop Semi Join (cost=972.02..1302907.98 rows=24600001 width=27) (actual time=5.257..7.260 rows=30 loops=1) Buffers: shared hit=277 read=12 InitPlan 1 (returns $0) -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.612..4.613 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.611..4.611 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.012..3.674 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 -> Index Scan using idx_account_audit on audit_logs al (cost=0.58..686755.98 rows=24600001 width=27) (actual time=5.067..7.059 rows=30 loops=1) Index Cond: (account_id = $0) Buffers: shared hit=274 read=11 -> Materialize (cost=0.44..242.67 rows=9966 width=4) (actual time=0.006..0.006 rows=1 loops=30) Buffers: shared hit=3 read=1 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..192.84 rows=9966 width=4) (actual time=0.187..0.188 rows=1 loops=1) Index Cond: (account_id = $0) Heap Fetches: 0 Buffers: shared hit=3 read=1 Planning Time: 0.160 ms Execution Time: 7.286 ms (24 rows) Subquery Example 2 select * from audit_logs al where al.account_id=(select distinct p.account_id from purchases p where p.account_id=( select account_id from accounts where account_type = 'HIGH_RISK' order by random() limit 1)); Execution plan QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------ Index Scan using idx_account_audit on audit_logs al (cost=972.04..1507128.65 rows=52656088 width=26) (actual time=5.510..7.637 rows=30 loops=1) Index Cond: (account_id = (InitPlan 2).col1) Buffers: shared hit=277 read=12 InitPlan 2 -> Limit (cost=971.44..971.46 rows=1 width=4) (actual time=4.901..4.902 rows=1 loops=1) Buffers: shared hit=274 read=1 InitPlan 1 -> Limit (cost=971.00..971.00 rows=1 width=12) (actual time=4.455..4.456 rows=1 loops=1) Buffers: shared hit=271 -> Sort (cost=971.00..996.00 rows=10000 width=12) (actual time=4.455..4.455 rows=1 loops=1) Sort Key: (random()) Sort Method: top-N heapsort Memory: 25kB Buffers: shared hit=271 -> Seq Scan on accounts (cost=0.00..921.00 rows=10000 width=12) (actual time=0.018..3.522 rows=10000 loops=1) Filter: (account_type = 'HIGH_RISK'::text) Rows Removed by Filter: 40000 Buffers: shared hit=271 -> Index Only Scan using idx_purchases_account on purchases p (cost=0.44..399.42 rows=20742 width=4) (actual time=4.900..4.900 rows=1 loops=1) Index Cond: (account_id = (InitPlan 1).col1) Heap Fetches: 0 Buffers: shared hit=274 read=1 Planning: Buffers: shared hit=4 Planning Time: 0.154 ms Execution Time: 7.663 ms (25 rows) Query execution summary The table below summarizes execution time improvements across all tested rewrites relative to the original PostgreSQL 17 and 14 query execution plans. Setup / Rewrite Execution Time (ms) Speed‑up Factor (×) PG17 original (Hash Semi Join + Seq Scan) 521,625.433 1.0× PG14 original query shape 7.253 71,919× Forced nested loop (hashjoin off – diagnostic) 8.252 63,212× LATERAL JOIN + OFFSET 0 6.519 80,016× LIMIT on purchases 7.742 67,376× Subquery – IN with InitPlan (Example 1) 7.286 71,593× Subquery – scalar subquery (Example 2) 7.663 68,071× Key Takeaways While PostgreSQL 17’s planner improvements are correct, intentional, and beneficial for most workloads. However, queries that combine CTEs, runtime randomness, and heavily skewed data can cause the planner to select globally optimal plans that are locally inefficient. When runtime values drastically narrow result sets, query rewrites that force parameterized index scans—such as LATERAL joins, scalar subqueries, or bounded CTEs—provide predictable and stable performance.PgBouncer Best Practices in Azure Database for PostgreSQL – Part 1
Introduction Connection pooling is critical for scaling PostgreSQL workloads efficiently, especially in managed environments like Azure Database for PostgreSQL. PgBouncer, a lightweight connection pooler, helps manage thousands of client connections without overwhelming the database. Connection pooling is very important when managing multiple concurrent database requests, as PostgreSQL uses a process-per-connection model, which means too many active connections can: Increase context switching overhead Consume excessive CPU/memory Degrade performance under load PgBouncer addresses this by limiting active server connections and queuing the additional client requests. However, misconfiguring key settings such as default_pool_size can still lead to CPU/memory pressure, connection bottlenecks, and degraded performance. Careful planning and tuning are essential to avoid these pitfalls. Understanding connection pools Before diving into tuning, it’s important to understand how PgBouncer organizes connections: PgBouncer creates a separate pool for each unique (database, user) combination. For example: If you have 2 application roles/users connecting to 2 databases. In this scenario, PgBouncer will allocate 4 pools. Each pool maintains its own number of connections, determined by default_pool_size. So, the total number of potential server connections is: number_of_pools × default_pool_size This is why sizing default_pool_size correctly is critical. Azure PgBouncer defaults Azure Database for PostgreSQL comes with preconfigured PgBouncer settings optimized for most workloads. Understanding these defaults is essential before making any tuning changes: pool_mode: TRANSACTION (default in Azure; best for most workloads) default_pool_size: 50 (range: 1–4950) max_client_conn: 5000 (range: 1–50000) Transaction mode support for prepared statements PgBouncer now enables support for PostgreSQL PREPARED STATEMENTS when combined together with TRANSACTION mode pooling. Previously, in transaction mode cached plans were difficult to manage, as there was no way for PgBouncer to confirm whether a new connection allocated from the pool would benefit from any cached plans generated from prior PREPARED STATEMENT operations. To work around this scenario, PgBouncer now provides a parameter which controls how many globally cached plan statements remain in memory for any pooled connection to leverage. max_prepared_statements: 200 (range: 0-5000) PostgreSQL connection limits For large tiers (e.g., 96 vCores), the default max_connections is 5000, with 15 reserved for system use. That means 4985 user connections are available. For more details, see maximum connection. Sizing best practices Proper sizing ensures optimal performance and resource utilization. Here’s how to approach it: 1. Use transaction pooling Start by confirming that pool_mode = TRANSACTION is enabled. This is already the Azure default and provides the best pooling efficiency for most web applications. If your application is using prepared statements, ensure you configure max_prepared_statements accordingly. 2. Determine your maximum active concurrent database operations (max_concurrent_ops) Next, you need to estimate how many total concurrent active PostgreSQL backends your instance can maintain: For CPU-bound OLTP workloads: keep max_concurrent_ops near 1.5x -2x the number of CPU vCores. For I/O-heavy workloads: stay slightly higher than vCore count. Rule of thumb for 96 vCores: max_concurrent_ops ≈ 144–192. 3. Divide across pools Once you’ve estimated your max_concurrent_ops value, the next step is to distribute your capacity across all connection pools. default_pool_size ≈ max_concurrent_ops / number_of_pools Example: max_concurrent_ops = 144 number_of_pools = 4 default_pool_size = 144 / 4 = 36 Sample configuration To illustrate how these calculations translate into real-world settings, here’s a sample PgBouncer configuration tuned for a scenario with four pools and an Active_Backend_Target of 144. pool_mode = transaction default_pool_size = 36 ; tuned for 4 pools max_client_conn = 5000 Quick reference table For quick planning, the following table provides starting recommendations based on common Azure Database for PostgreSQL SKU sizes. Use these as a baseline and adjust according to your workload metrics. SKU Size Memory Default max_connections Pools Suggested max_concurrent_ops Starting default_pool_size 8 vCores 32 GiB 3437 2 12–16 6–12 16 vCores 64 GiB 5000 2 24–32 12–20 32 vCores 128 GiB 5000 2 48–64 30–40 48 vCores 192 GiB 5000 2 72–92 40–60 64 vCores 256 GiB 5000 2 96–128 50–70 96 vCores 384–672 GiB 5000 2 144–192 60–80 For all tiers ≥16 vCores, max_connections is capped at 5000 (with 15 reserved for system use). Notes: default_pool_size = max_concurrent_ops / number_of_pools These values are starting recommendations. You should validate them against actual workload metrics and adjust gradually. Always ensure: (number_of_pools × default_pool_size) < max_connections − 15 (reserved system slots) Monitoring and tuning After applying your configuration, continuous monitoring is key. Here’s how: Use PgBouncer metrics in Azure Monitor to track active, idle, and waiting connections. Run SHOW POOLS; for real-time stats; watch cl_waiting vs sv_idle. For detailed monitoring and management, visit the Admin Console. Recommended Alerts: Alert if waiting client connections > 0 while idle server connections = 0 (indicates pool exhaustion—consider increasing default_pool_size). Alert if active server connections approach the configured default_pool_size (may indicate need for tuning). Alert if max_client_conn utilization exceeds 80% (risk of client-side connection errors). Tip: If waiting client connections grow while idle server connections are zero, increase default_pool_size cautiously. Review performance regularly and adjust gradually. Common pitfalls Avoid these mistakes when configuring PgBouncer: Changing pool mode to SESSION by default: transaction pooling is better for most apps. Session mode will not release connections until the session is ended. Ignoring pool count: multiplying a large default_pool_size by many pools can exhaust connections. Confusing max_client_conn with Postgres capacity: PgBouncer can accept many more clients than the server concurrent processes can support, any client connections not being processed will be waiting for resources. Tuning without data: always review metrics before changes. Conclusion Choosing the right default_pool_size in Azure Database for PostgreSQL with PgBouncer is about balancing performance and resource efficiency. With built-in PgBouncer in Flexible Server, you can enable connection pooling with a single parameter making it easy to get started quickly. The default settings are optimized for most workloads, and as your requirements grow, you can further tune parameters like default_pool_size and max_connections to suit your needs. By understanding your workload, estimating an active concurrent operations, dividing it across pools while respecting PostgreSQL limits, and continuously monitoring and adjusting based on real data, you can achieve a stable, scalable, and cost-effective environment. Further reading For more in-depth guidance and real-world scenarios on PgBouncer configuration and tuning in Azure Database for PostgreSQL, explore the following resources: Leverage built-in PgBouncer in Flexible Server Monitoring PgBouncer in Azure PostgreSQL Flexible Server Identify and solve connection performance in Azure Postgres Not all Postgres connection pooling is equal Connection handling best practice with PostgreSQLPotential Consequences of Using Postgres as a Job Queue
Introduction At small scale, using Postgres as a job queue is totally fine, and I’d even say it’s the right call. Fewer moving parts, one less system to manage, ACID guarantees on your jobs. What’s not to love? The problem is that “small scale” has a ceiling, and the ceiling is lower than most people expect. When you’ve got thousands of concurrent workers hammering a jobs table with SELECT ... FOR UPDATE SKIP LOCKED , things start to behave in ways that aren’t obvious from the application layer. CPU usage creeps up. Also vacuum sometimes can’t keep up. Finally, in the wait event stats, you start seeing ominous entries like LWLock:MultiXactSLRU stacking up across many backends. This pattern has tripped up teams more than a few times, and it usually plays out the same way: everything works fine in dev and staging, then goes off a cliff in production once the concurrency gets real. So let’s dig into why this happens, and what the alternatives look like. The Typical Pattern When using Postgres as a job queue, the standard approach looks something like this: CREATE TABLE job_queue ( id bigserial PRIMARY KEY, status text NOT NULL DEFAULT 'pending', payload jsonb NOT NULL, created_at timestamptz NOT NULL DEFAULT now(), locked_by text, locked_at timestamptz ); CREATE INDEX idx_job_queue_status ON job_queue (status) WHERE status = 'pending'; Workers grab jobs with: UPDATE job_queue SET status = 'processing', locked_by = 'worker-42', locked_at = now() WHERE id = ( SELECT id FROM job_queue WHERE status = 'pending' ORDER BY created_at LIMIT 1 FOR UPDATE SKIP LOCKED ) RETURNING *; And then mark them done: UPDATE job_queue SET status = 'completed' WHERE id = $1; Some users may DELETE the row entirely. Either way, the lifecycle is: insert, lock-and-update, update-or-delete. Repeated thousands of times per second. At low concurrency, this works very smoothly. SKIP LOCKED means workers don’t block each other waiting for the same row. Postgres handles the locking, visibility, and ordering. It’s elegant. So where does it break? The MultiXact SLRU Problem When multiple transactions hold locks on the same row, Postgres stores the set of lockers as a MultiXact ID – a pointer into a side structure under pg_multixact/ . With SELECT ... FOR UPDATE SKIP LOCKED , users might think MultiXacts aren’t involved – after all, SKIP LOCKED is supposed to avoid contention. But in practice, with many concurrent workers all racing to lock rows, there are brief windows where multiple transactions reference the same row before one of them “wins” and the others skip. If you combine this with any FOR SHARE or FOR KEY SHARE locks (which are commonly created implicitly by foreign key checks), MultiXact IDs start accumulating quickly. The MultiXact data lives in SLRU buffers (Simple Least Recently Used) – a small, fixed-size shared memory cache. When backends need to read or write MultiXact data, they acquire LWLocks to access these buffers. Under high concurrency, this becomes a bottleneck: wait_event_type | wait_event -----------------+------------------- LWLock | MultiXactMemberSLRU LWLock | MultiXactOffsetSLRU You’ll see dozens or hundreds of backends piled up on these waits. The SLRU cache is small (by design – it’s a fixed number of pages in shared memory), and when the working set of MultiXact lookups exceeds what fits in the cache, you get constant eviction and re-reads from disk. Every lock acquisition and release on a job row potentially triggers a MultiXact SLRU lookup, and at thousands of concurrent sessions, those lookups serialize on LWLocks. The result: CPU gets pegged, throughput collapses, and latency spikes – not because the queries are expensive, but because the locking infrastructure itself is overwhelmed. Bloat: The Silent Killer The other side of this coin is table and index bloat. Every job row goes through multiple updates (and possibly a delete), and each of those operations creates a new tuple version in the heap. The old versions stick around until VACUUM cleans them up. On a busy job queue table: Dead tuples accumulate faster than autovacuum can clean them. By the time autovacuum finishes one pass, tens of thousands of new dead tuples have appeared. The table grows and grows. Index bloat compounds the problem. Every index on the table also accumulates dead entries. The partial index on status = 'pending' gets thrashed especially hard, since rows constantly enter and leave that condition. Sequential scans get slower. As the table bloats, even index scans start doing more I/O because the heap pages are sparsely populated. Vacuum reclaims space at the end of the table, but can’t reclaim space in the middle (unless the pages are completely empty). Job queue tables can grow to tens of gigabytes when the actual “live” data was only a few megabytes. It makes everything slower: scans, vacuum, even pg_dump. You can mitigate this by running vacuum more aggressively (lower autovacuum_vacuum_scale_factor , higher autovacuum_vacuum_cost_limit ), or by partitioning the table and dropping old partitions. But at some point, you’re fighting the fundamental mismatch between MVCC’s design goals and the write pattern of a job queue. CPU and Lock Overhead Beyond the SLRU contention and bloat, there’s just the raw overhead of using Postgres’s full transactional machinery for what is essentially a FIFO dispatch operation: Every lock/unlock is a full WAL-logged transaction. Grabbing a job writes WAL. Marking it complete writes WAL. Deleting it writes WAL. On a system processing thousands of jobs per second, the WAL volume from the job queue alone can saturate your wal_writer and checkpoint processes. SKIP LOCKED still touches rows. The name suggests rows are skipped, but Postgres still has to find them, check their lock status, and move on. With high concurrency, many workers end up scanning past the same locked rows before finding one they can claim. This is wasted CPU. Snapshot management overhead also becomes an issue. Each transaction needs a consistent snapshot, and with thousands of concurrent transactions, the ProcArray (the structure that tracks active transactions) becomes a contention point itself. You might see LWLock:ProcArrayLock waits alongside the MultiXact ones. Vacuum contention. While vacuum is cleaning up dead tuples, it needs locks too. On a table under constant write pressure, vacuum can interfere with the workers and vice versa. I’ve seen systems where disabling autovacuum on the job queue table improved throughput in the short term. Better Alternatives So what should you use instead? It depends on your requirements, but there are several options that handle high-throughput job dispatch more gracefully than a Postgres table. Advisory Locks (Staying in Postgres) If you want to stay within Postgres and avoid adding infrastructure, advisory locks are worth considering for certain queue patterns. Instead of locking rows, you lock on an abstract numeric key: -- Worker tries to acquire a lock on the job ID SELECT pg_try_advisory_lock(id) FROM job_queue WHERE status = 'pending' ORDER BY created_at LIMIT 1; Advisory locks are lightweight – they don’t touch the heap, don’t create MultiXact entries, and don’t generate dead tuples. They live entirely in shared memory. The trade-off is that you lose the atomicity of FOR UPDATE SKIP LOCKED : you need to handle the case where a lock is acquired but the job processing fails, and you need to release the lock explicitly (or rely on session-end cleanup). This approach works well when the queue depth is manageable and you want to avoid the MVCC overhead. But it’s still Postgres, so you’re still subject to connection limits, ProcArray overhead, and general resource contention at very high session counts. pgq (Skytools) pgq is purpose-built for exactly this problem. It’s a queue implementation that sits inside Postgres but uses a batching model that avoids most of the row-level locking and MVCC pitfalls. Events are written to a queue table, but consumers read them in batches and the queue maintenance is done via a ticker process that manages rotation. The key advantages: No row-level contention. Consumers don’t lock individual rows. Built-in batch processing. Events are consumed in chunks, reducing transaction overhead. Efficient cleanup. Old events are rotated out rather than vacuumed row-by-row. The downside is that pgq is not as actively maintained as it once was, and it adds operational complexity (the ticker daemon, consumer registration, etc.). But for teams already deep in the Postgres ecosystem, it’s a battle-tested option. Redis For many teams, Redis is the natural choice for job queues. Using Redis lists (BRPOPLPUSH or the Streams API), you get: Sub-millisecond dispatch latency. No disk I/O, no MVCC, no vacuum. Atomic pop operations. Workers grab jobs without any locking protocol. Simple scaling. Redis handles thousands of concurrent consumers trivially. The trade-off is durability. Redis can persist to disk, but it’s not ACID. If Redis crashes between a pop and the job completing, you might lose or duplicate work (though Redis Streams with consumer groups mitigate this significantly). For most job queue use cases, at-least-once delivery is acceptable, and Redis does that well. Kafka For truly high-throughput, distributed workloads, Apache Kafka is the heavyweight option. Kafka partitions give you parallel consumption with ordering guarantees per partition, durable storage, and replay capability. It’s the right tool when: You need to process thousands of events per second Multiple consumers need to read the same events You want event replay or audit trails Your architecture is already event-driven The operational overhead is nontrivial – ZooKeeper (or KRaft), brokers, topic management, consumer group coordination. But for teams already running Kafka for other reasons, adding a job queue topic is practically free. Choosing the Right Tool Here’s a rough decision guide: Under 100 concurrent workers, simple jobs, Postgres with SKIP LOCKED is fine Moderate concurrency, want to stay in Postgres, Advisory locks or pgq High throughput, low-latency dispatch, Redis (Lists or Streams) Massive scale, distributed, event replay, Kafka Many teams that start with Postgres (reasonably) hit scaling problems and then try to fix Postgres rather than recognizing that the workload has outgrown the tool. They throw more autovacuum workers at it, increase max_connections , add connection poolers – all of which help at the margins, but don’t address the fundamental issue: Postgres’s MVCC and locking machinery wasn’t designed for this access pattern at high concurrency. Conclusion Postgres is great, but it can’t be the best tool for every job. Using it as a job queue is a perfectly valid choice when your scale is modest. But when you’re running thousands of concurrent workers, the combination of MultiXact SLRU contention, heap bloat, vacuum pressure, and raw locking overhead will eventually push you toward a purpose-built solution. The good news is that you don’t have to rip out everything. Advisory locks can buy you headroom without adding infrastructure. Redis can handle dispatch while Postgres keeps owning the data. And if you’re already using Kafka, a job topic is a natural fit. Take your pick – there are many queueing options out there!Cascading Read Replicas Now Generally Available!
We’re excited to announce the General Availability of cascading read replicas in Azure Database for PostgreSQL. This capability allows you to create read replicas for your Azure Database for PostgreSQL instance not only from a primary server, but also from existing read replicas, enabling multi‑level replication chains. Coordinating read‑heavy database workloads across multiple regions can be challenging, especially when you’re trying to deliver low‑latency read response experiences to users spread across different geographic locations. One effective way to address this is by placing read replicas closer to where your users are, allowing applications to serve read requests with significantly reduced latency and improved performance. What are cascading read replicas? With cascading read replicas, you can scale read‑intensive workloads more effectively, distribute read traffic efficiently, and support advanced deployment topologies such as globally distributed applications. Each read replica can act as a source for additional replicas, forming a tree‑like replication structure. For example, if your primary server is deployed in one region, you can create direct replicas in nearby regions and then cascade additional replicas to more distant locations. This approach helps spread read traffic evenly while minimizing latency for users around the world. We support up to 2 levels of replication with this feature. Level 1 will be all the read replicas and level 2 will be cascading read replicas. Why use cascading read replicas? Improved scalability Cascading read replicas support multi‑level replication, making it easier to handle high volumes of read traffic without overloading a single instance by scaling up to 30 read replicas. Geographic distribution By placing replicas closer to your global user base, you can significantly reduce read latency and deliver faster, more responsive application experiences. Efficient read traffic distribution Distributing read workloads across multiple replicas helps balance load, improving overall performance and reliability. Additionally, cascading read replicas offer operational flexibility. If you observe replication lag, you can perform a switchover operation between a cascading read replica with its source or intermediate replica, helping you maintain optimal performance and availability for your replicas. How does replication work with cascading read replicas? The primary server acts as a source for the read replica. Data is asynchronously replicated to these replicas. When we add cascading replicas, the previous replicas act as a data source for replication. In the diagram above, “primary-production-server” is the primary server with three read replicas. One of these replicas, “readreplica01”, serves as the source for another read replica, “readreplica11” which is a cascading read replica. With cascading read replicas, you can add up to five read replicas per source and replicate data across two levels, as shown in the diagram. This allows you to create up to 30 read replicas in total five read replicas directly from the primary server, and up to 25 additional replicas at the second level (each second-level replica can have up to five read replicas). If you notice replication lag between an intermediate read replica and a cascading read replica, you can use a switchover operation to swap “readreplica01” and “readreplica11”, helping reduce the impact of lag. To learn more about cascading read replicas, please refer to our documentation: Cascading read replicas Deploying cascading read replicas on Azure portal Navigate to the “Replication” tab and then click on “Create replica” highlighted in red as shown below: After creating a read replica as the below screenshot shows that you have 1 read replica that is attached to the primary instance. Click on the created replica and navigate to the replication tab, source server is “read-replica-01” and we will be creating a cascading read replica under this. Once cascading read replica is created you can see the role of “read-replica-01” has now changed to Source, Replica. You can perform site swap operation by clicking on the promote button for cascading read replica. Deploy cascading read replica with terraform: Before you start, make sure you have: An existing primary PostgreSQL Flexible Server At least one read replica already created from the primary AzureRM provider with latest version Proper permissions on the Azure subscription and resource group Configure the AzureRM Provider: Start by configuring the AzureRM provider in your Terraform project. terraform { required_providers { azurerm = { source = "hashicorp/azurerm" version = "~> 3.80" } } } provider "azurerm" { features {} } Reference the existing read replica server using the data block to reference the replica server. data "azurerm_postgresql_flexible_server" "source_replica" { name = "my-read-replica-1" resource_group_name = "my-resource-group" } Now create a new PostgreSQL Flexible Server and point it to the replica using create_source_server_id. resource "azurerm_postgresql_flexible_server" "cascading_replica" { name = "my-cascading-replica" resource_group_name = "my-resource-group" location = data.azurerm_postgresql_flexible_server.source_replica.location version = data.azurerm_postgresql_flexible_server.source_replica.version delegated_subnet_id = data.azurerm_postgresql_flexible_server.source_replica.delegated_subnet_id private_dns_zone_id = data.azurerm_postgresql_flexible_server.source_replica.private_dns_zone_id create_mode = "Replica" create_source_server_id = data.azurerm_postgresql_flexible_server.source_replica.id storage_mb = 32768 sku_name = "Standard_D4s_v3" depends_on = [ data.azurerm_postgresql_flexible_server.source_replica ] } Apply the Terraform Configuration terraform init terraform plan terraform apply Key Considerations Cascading read replicas allow for up to 5 read replicas and two levels of replication. Creating cascading read replicas is supported in PostgreSQL version 14 and above. Promote operation is not supported for intermediate read replicas with cascading read replicas. Conclusion Cascading read replicas in Azure Database for PostgreSQL offer a scalable way to distribute your read traffic across the same and different regions, reducing the read workload on primary database. For globally distributed applications, this can improve read latency as well as resilience and performance. This design supports horizontal scaling as your application demand grows, ensuring you can handle a high volume of read requests without compromising speed. Get started with this feature today and scale your read workloads.Handling Unique Constraint Conflicts in Logical Replication
Authors: Ashutosh Sharma, Senior Software Engineer, and Gauri Kasar, Product Manager Logical replication can keep your PostgreSQL environments in sync, helping replicate selected tables with minimal impact on the primary workload. But what happens when your subscriber hits a duplicate key error and replication grinds to a halt? If you’ve seen a unique‑constraint violation while replicating between Azure Database for PostgreSQL servers, you’re not alone. This blog covers common causes, prevention tips, and practical recovery options. In PostgreSQL logical replication, the subscriber can fail with a unique-constraint error when it tries to apply a change that would create a duplicate key. duplicate key value violates unique constraint Understanding why this happens? When an INSERT or UPDATE would create a value that already exists in a column (or set of columns) protected by a UNIQUE constraint (including a PRIMARY KEY). In logical replication, this most commonly occurs because of local writes on the subscriber or if the table is being subscribed from multiple publishers. These conflicts are resolved on the subscriber side. Local writes on the subscriber: a row with the same primary key/unique key is inserted on the subscriber before the apply worker processes the corresponding change from the publisher. Multi-origin / multi-master without conflict-free keys: two origins generate (or replicate) the same unique key. Initial data synchronization issues: the subscriber already contains data when the subscription is created with initial copy enabled, resulting in duplicate inserts during the initial table sync. How to avoid this? Avoid local writes on subscribed tables (treat the subscriber as read-only for replicated relations). Avoid subscribing to the same table from multiple publishers unless you have explicit conflict handling and a conflict-free key design. Enabling server logs can help you identify and troubleshoot unique‑constraint conflicts more effectively. Refer to the official documentation to configure and access PostgreSQL logs. How to handle conflicts (recovery options) Option 1: Delete the conflicting row on the subscriber Use the subscriber logs to identify the key (or row) causing the conflict, then delete the row on the subscriber with a DELETE statement. Resume apply and repeat if more conflicts appear. Option 2: Use conflict logs and skip the conflicting transaction (PostgreSQL 17+) Starting with PostgreSQL 17, logical replication provides detailed conflict logging on the subscriber, making it easier to understand why replication stopped and which transaction caused the failure. When a replicated INSERT would violate a non‑deferrable unique constraint on the subscriber for example, when a row with the same key already exists the apply worker detects this as an insert_exists conflict and stops replication. In this case, PostgreSQL logs the conflict along with the transaction’s finish LSN, which uniquely identifies the failing transaction. ERROR: conflict detected on relation "public.t2": conflict=insert_exists ... in transaction 754, finished at 0/034F4090 ALTER SUBSCRIPTION <subscription_name> SKIP (lsn = '0/034F4090'); Option 3: Rebuild (re-sync) the table Rebuilding (re‑syncing) a table is the safest and most deterministic way to resolve logical replication conflicts caused by pre‑existing data differences or local writes on the subscriber. This approach is especially useful when a table repeatedly fails with unique‑constraint violations and it is unclear which rows are out of sync. Step 1 (subscriber): Disable the subscription. ALTER SUBSCRIPTION <subscription_name> DISABLE; Step 2 (subscriber): Remove the local copy of the table so it can be re-copied. TRUNCATE TABLE <conflicting_table>; Step 3 (publisher): Ensure the publication will (re)send the table (one approach is to recreate the publication entry for that table). ALTER PUBLICATION <pub_with_conflicting_table> DROP TABLE <conflicting_table>; CREATE PUBLICATION <pub_with_conflicting_table_rebuild> FOR TABLE <conflicting_table>; Step 4 (subscriber): Create a new subscription (or refresh the existing one) to re-copy the table. CREATE SUBSCRIPTION <sub_rebuild> CONNECTION '<connection_string>' PUBLICATION <pub_with_conflicting_table_rebuild>; Step 5 (subscriber): Re-enable the original subscription (if applicable). ALTER SUBSCRIPTION <subscription_name> ENABLE; Conclusion In most cases, these conflicts occur due to local changes on the subscriber or differences in data that existed before logical replication was fully synchronized. It is recommended to avoid direct modifications on subscribed tables and ensure that the replication setup is properly planned, especially when working with tables that have unique constraints.PostgreSQL Buffer Cache Analysis
PostgreSQL performance is often dictated not just by query design or indexing strategy, but by how effectively the database leverages memory. At the heart of this memory usage lies shared_buffers—PostgreSQL’s primary buffer cache. Understanding how well this cache is utilized can make the difference between a system that scales smoothly and one that struggles under load. In this post, we’ll walk you through a practical, data-driven approach to analyzing PostgreSQL buffer cache behavior using native statistics and the pg_buffercache extension. The goal is to answer a few critical questions: Is the current shared_buffers configuration sufficient? Are high-value tables and indexes actually being served from memory? Is PostgreSQL spending too much time going to disk when it shouldn’t? By the end, you’ll have a repeatable methodology to assess cache efficiency and make informed tuning decisions. Why Buffer Cache Analysis Matters PostgreSQL relies heavily on its buffer cache to minimize disk I/O. Every time a query needs a data or index page, PostgreSQL first checks whether that page already exists in shared_buffers. If it does, the page is served directly from memory—fast and efficient. If not, PostgreSQL must fetch it from disk (or the OS page cache), which is significantly slower. While metrics like query latency and IOPS can tell you that performance is degraded, buffer cache analysis helps explain why. It allows you to: Validate whether frequently accessed objects stay hot in cache Identify cache pollution caused by large, low-value tables Determine whether increasing shared_buffers would provide real benefits or just waste memory Inspecting Shared Buffers with pg_buffercache The pg_buffercache extension provides a real-time view into PostgreSQL’s shared buffers. Unlike cumulative statistics, it shows what is in memory right now—which relations are cached, how many blocks they occupy, and how frequently those buffers are reused. Enabling the Extension pg_buffercache is not enabled by default and requires superuser privileges: CREATE EXTENSION pg_buffercache; Once enabled, you can directly query the contents of shared buffers across databases, tables, and indexes. Analyzing Cache Distribution Understanding where your shared buffers are being consumed is the first step toward meaningful tuning. Database-Level Cache Distribution This query shows how shared buffers are distributed across databases in the server: SELECT CASE WHEN c.reldatabase IS NULL THEN '' WHEN c.reldatabase = 0 THEN '' ELSE d.datname END AS database, count(*) AS cached_blocks FROM pg_buffercache AS c LEFT JOIN pg_database AS d ON c.reldatabase = d.oid WHERE datname NOT LIKE 'template%' GROUP BY d.datname, c.reldatabase ORDER BY d.datname, c.reldatabase; This is particularly useful in multi-database environments where one workload may be evicting cache pages needed by another. Table and Index-Level Cache Consumption To understand which relations, dominate the cache, the following query breaks buffer usage down by tables and indexes: SELECT c.relname, c.relkind, count(*) FROM pg_database AS a, pg_buffercache AS b, pg_class AS c WHERE c.relfilenode = b.relfilenode AND b.reldatabase = a.oid GROUP BY 1, 2 ORDER BY 3 DESC, 1; This helps answer an important question: Are your most business-critical tables and indexes actually resident in memory, or are they constantly being evicted? If large, rarely used tables consume a disproportionate share of buffers, it may indicate cache churn or the need for workload isolation. Understanding Buffer Usage Count (Hot vs Cold Data) Each buffer in shared memory carries a usage count, which reflects how frequently it has been accessed before eviction. Higher values indicate hotter data. SELECT c.relname, c.relkind, usagecount, count(*) AS buffers FROM pg_database AS a, pg_buffercache AS b, pg_class AS c WHERE c.relfilenode = b.relfilenode AND b.reldatabase = a.oid AND a.datname = current_database() GROUP BY 1, 2, 3 ORDER BY 3 DESC, 1; A healthy system typically shows a meaningful number of buffers with higher usage counts (for example, 4–5), indicating frequently reused data that benefits from caching. Buffer Cache Percentages: Putting Numbers in Context Raw buffer counts are useful, but percentages make interpretation easier. The following query shows: How much of shared_buffers each relation occupies What percentage of the relation itself is cached SELECT c.relname, pg_size_pretty(count(*) * 8192) AS buffered, round(100.0 * count(*) / (SELECT setting FROM pg_settings WHERE name='shared_buffers')::integer, 1) AS buffers_percent, round(100.0 * count(*) * 8192 / pg_relation_size(c.oid), 1) AS percent_of_relation FROM pg_class c JOIN pg_buffercache b ON b.relfilenode = c.relfilenode JOIN pg_database d ON b.reldatabase = d.oid AND d.datname = current_database() GROUP BY c.oid, c.relname ORDER BY 3 DESC LIMIT 10; This view is especially powerful when validating whether performance-critical objects are adequately cached relative to their size. Complementing Cache Views with I/O Statistics While pg_buffercache shows the current state of memory, I/O statistics reveal long-term trends. PostgreSQL exposes these via pg_statio_user_tables and pg_statio_user_indexes. Table Heap Hit Ratios SELECT relname, heap_blks_hit::numeric / (heap_blks_hit + heap_blks_read) AS hit_pct, heap_blks_hit, heap_blks_read FROM pg_catalog.pg_statio_user_tables WHERE (heap_blks_hit + heap_blks_read) > 0 ORDER BY hit_pct; Hit ratios close to 1 indicate that table data is largely served from memory rather than disk. Index Hit Ratios SELECT relname, idx_blks_hit::numeric / (idx_blks_hit + idx_blks_read) AS hit_pct, idx_blks_hit, idx_blks_read FROM pg_catalog.pg_statio_user_tables WHERE (idx_blks_hit + idx_blks_read) > 0 ORDER BY hit_pct; Poor index hit ratios often point to insufficient cache or inefficient query patterns that bypass indexes. Including TOAST and Index Reads For large objects, TOAST activity can significantly impact I/O. This query provides a more holistic view: SELECT *, (heap_blks_read + toast_blks_read + tidx_blks_read) AS total_blocks_read, (heap_blks_hit + toast_blks_hit + tidx_blks_hit) AS total_blocks_hit FROM pg_catalog.pg_statio_user_tables; This helps identify indexes that are frequently read from disk and may benefit from better caching or query rewrites. How to Interpret the Results When reviewing buffer cache and I/O metrics, keep the following guidelines in mind: Validate cache residency of critical objects: If business-critical tables and indexes occupy a meaningful share of shared_buffers, your cache sizing is likely reasonable. Correlate buffer data with hit ratios: High hit ratios in pg_statio_user_tables and pg_statio_user_indexes confirm effective caching. Persistently low ratios may justify increasing shared_buffers. Analyze usage count distribution: A healthy number of buffers with higher usage counts indicates hot data benefiting from cache reuse. Avoid over-tuning: If most buffers have low usage counts but hit ratios remain high, increasing shared_buffers further may not yield measurable gains. Conclusion Buffer cache analysis bridges the gap between theory and reality in PostgreSQL performance tuning. By combining real-time cache inspection with long-term I/O statistics, you gain a clear picture of how memory is actually used—and whether changes to shared_buffers will deliver tangible benefits. Rather than tuning memory blindly, this approach lets you optimize with confidence, grounded in data that reflects your real workload.
