Scaling Write Throughput in Azure Database for MySQL Using Application-Level Sharding
This blog post walks through scaling write throughput in Azure Database for MySQL using application level sharding. It starts with the why behind sharding and then builds a complete C# implementation that spreads writes across three Azure Database for MySQL Flexible Servers. Why Shard in the First Place? This post focuses specifically on scaling write throughput. A well-tuned single primary node can take you remarkably far, and techniques such as indexing strategies, write batching, redo log optimization, and vertical compute scaling each deliver real, lasting value. For many workloads, these optimizations are all you will ever need. That said, as write volume continues to grow, a single primary eventually approaches its practical capacity, and at that point the most durable way to keep scaling is to distribute the write workload across multiple primary instances. This architecture is what we call sharding. When you reach this inflection point, there are two primary patterns for managing multiple write nodes: Proxy or Middleware Layer Sharding: A sharding aware proxy sits between the application and a pool of Azure Database for MySQL instances, routing queries based on a shard key. While this abstracts the underlying topology from the application layer, it introduces an additional, complex component to operate, secure, scale, and patch. Application Layer Sharding: The application itself resolves the destination shard key and determines which of the N Azure Database for MySQL instances should receive a write before ever opening a database connection. Each backend target remains a completely standard, independent Azure Database for MySQL instance. This post explores the second approach. The core appeal of application layer sharding is architectural simplicity: it introduces zero infrastructure overhead and eliminates an extra network hop. Every shard behaves exactly like a standalone instance, meaning your existing backup, restore, monitoring pipelines, and the Azure portal function seamlessly without modification. The explicit tradeoff is that you forgo cross shard joins and distributed transactions in exchange for absolute predictability and control over data access patterns. The Plan We will build a small order management service that distributes its data across three Azure Database for MySQL instances that already exist. The application, written in C# on .NET 8, owns the partitioning logic. The premise: the three servers are already provisioned, the firewalls are configured, the network paths are established, and each server has its own administrative credentials. We are not provisioning infrastructure in this post. we are writing the application code that consumes it. mysql-shard-0.mysql.database.azure.com user: shard0_admin pwd: <secret-0> mysql-shard-1.mysql.database.azure.com user: shard1_admin pwd: <secret-1> mysql-shard-2.mysql.database.azure.com user: shard2_admin pwd: <secret-2> Each server hosts an identical appdb database with the same schema: CREATE TABLE users ( user_id BIGINT NOT NULL PRIMARY KEY, email VARCHAR(255) NOT NULL, created_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP, UNIQUE KEY uq_email (email) ); CREATE TABLE orders ( order_id BIGINT NOT NULL PRIMARY KEY, user_id BIGINT NOT NULL, amount_cents INT NOT NULL, created_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP, KEY ix_user (user_id) ); Two design decisions in this schema warrant explanation: No AUTO_INCREMENT for user_id or order_id. Two shards would otherwise generate the same value 42 independently. Instead, we assign identifiers in the application, using a scheme such as Snowflake, ULID, or UUIDv7. orders carries user_id, and we route by it. This is the single most important rule of sharding: choose a shard key that keeps related data colocated, so that the common queries remain on a single shard. A note on UNIQUE KEY uq_email. A unique index enforces uniqueness only within a single physical shard. Because we route by user_id, two users with different IDs and the same email may land on different shards, and both inserts will succeed. If you require globally unique emails, two options exist: (a) maintain a separate email → user_id lookup table on a single "directory" server and write to it first within an idempotent flow, or (b) shard the users table by a hash of email instead. We retain user_id routing throughout this post because it is the correct choice for orders, and we treat per shard email uniqueness as a best effort guard rather than a hard global invariant. How the Partitioning Works The naive approach to sharding is shard = hash(key) % N. This works until you need to add a fourth server, at which point roughly 75% of your data must move. In any system of meaningful size, that is prohibitively expensive. The established solution is virtual buckets. You hash the key into a large, fixed bucket space (here, 1024), then map buckets to physical shards. When you add capacity, you relocate only buckets; you never rehash the entire dataset. In production, the bucket_to_shard_map typically resides in a system such as Azure App Configuration or etcd, so that you can rebalance without redeploying. For this post, we keep it as an in-memory array seeded at startup, which is straightforward to replace later. The Project ShardingDemo/ ├── ShardingDemo.csproj ├── appsettings.json ├── Models.cs ├── ShardRouter.cs ├── UserRepository.cs └── Program.cs ShardingDemo.csproj <Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <OutputType>Exe</OutputType> <TargetFramework>net8.0</TargetFramework> <Nullable>enable</Nullable> <ImplicitUsings>enable</ImplicitUsings> </PropertyGroup> <ItemGroup> <PackageReference Include="MySqlConnector" Version="2.6.0" /> <PackageReference Include="Microsoft.Extensions.Hosting" Version="8.0.0" /> <PackageReference Include="Microsoft.Extensions.Configuration.Binder" Version="8.0.0" /> </ItemGroup> <ItemGroup> <Content Include="appsettings.json" CopyToOutputDirectory="PreserveNewest" /> </ItemGroup> </Project> appsettings.json Shards is an ordered list, and a shard's position in the array is its logical ID. { "Shards": [ { "Host": "mysql-shard-0.mysql.database.azure.com", "Database": "appdb", "User": "shard0_admin", "Password": "REPLACE_ME_0" }, { "Host": "mysql-shard-1.mysql.database.azure.com", "Database": "appdb", "User": "shard1_admin", "Password": "REPLACE_ME_1" }, { "Host": "mysql-shard-2.mysql.database.azure.com", "Database": "appdb", "User": "shard2_admin", "Password": "REPLACE_ME_2" } ] } Models.cs namespace ShardingDemo; public sealed record User(long UserId, string Email, DateTime CreatedAt); public sealed record Order(long OrderId, long UserId, int AmountCents, DateTime CreatedAt); public sealed class ShardConfig { public required string Host { get; init; } public required string Database { get; init; } public required string User { get; init; } public required string Password { get; init; } } ShardRouter.cs using System.Security.Cryptography; using System.Text; using MySqlConnector; namespace ShardingDemo; public sealed class Shard : IAsyncDisposable { public int Id { get; } public MySqlDataSource DataSource { get; } public Shard(int id, ShardConfig cfg) { Id = id; var csb = new MySqlConnectionStringBuilder { Server = cfg.Host, Port = 3306, Database = cfg.Database, UserID = cfg.User, Password = cfg.Password, SslMode = MySqlSslMode.Required, Pooling = true, MinimumPoolSize = 2, MaximumPoolSize = 100, ConnectionTimeout = 10, DefaultCommandTimeout = 30, }; DataSource = new MySqlDataSourceBuilder(csb.ConnectionString).Build(); } public ValueTask DisposeAsync() => DataSource.DisposeAsync(); } public sealed class ShardRouter : IAsyncDisposable { private const int VirtualBuckets = 1024; private readonly IReadOnlyList<Shard> _shards; private readonly int[] _bucketToShardId; public ShardRouter(IEnumerable<ShardConfig> configs) { _shards = configs.Select((c, i) => new Shard(i, c)).ToList(); // Even distribution. Replace with a map loaded from your control plane for live rebalancing. _bucketToShardId = new int[VirtualBuckets]; for (int i = 0; i < VirtualBuckets; i++) _bucketToShardId[i] = i % _shards.Count; } public IReadOnlyList<Shard> AllShards => _shards; private static int BucketFor(long shardKey) { byte[] hash = MD5.HashData(Encoding.ASCII.GetBytes(shardKey.ToString())); // Use the first byte pair as an unsigned value, then map it into the bucket space. int value = (hash[0] << 8) | hash[1]; return value % VirtualBuckets; } public Shard ShardForKey(long shardKey) { int bucket = BucketFor(shardKey); return _shards[_bucketToShardId[bucket]]; } public async ValueTask DisposeAsync() { foreach (var s in _shards) await s.DisposeAsync(); } } UserRepository.cs Observe that every per user method calls ShardForKey(userId), even when inserting an order. This is the colocation rule at work. An order and its owning user always reside on the same shard, so queries for a single user only ever reach one shard. Only the cross-shard aggregate (TotalRevenueCentsAsync) must fan out. using MySqlConnector; namespace ShardingDemo; public sealed class UserRepository { private readonly ShardRouter _router; public UserRepository(ShardRouter router) { _router = router; } public async Task CreateUserAsync(long userId, string email, CancellationToken ct = default) { var shard = _router.ShardForKey(userId); await using var conn = await shard.DataSource.OpenConnectionAsync(ct); await using var cmd = conn.CreateCommand(); cmd.CommandText = "INSERT INTO users (user_id, email) VALUES (@id, Email)"; cmd.Parameters.AddWithValue("@id", userId); cmd.Parameters.AddWithValue("@email", email); await cmd.ExecuteNonQueryAsync(ct); } public async Task<User?> GetUserAsync(long userId, CancellationToken ct = default) { var shard = _router.ShardForKey(userId); await using var conn = await shard.DataSource.OpenConnectionAsync(ct); await using var cmd = conn.CreateCommand(); cmd.CommandText = "SELECT user_id, email, created_at FROM users WHERE user_id = ID"; cmd.Parameters.AddWithValue("@id", userId); await using var reader = await cmd.ExecuteReaderAsync(ct); if (!await reader.ReadAsync(ct)) return null; return new User(reader.GetInt64(0), reader.GetString(1), reader.GetDateTime(2)); } public async Task AddOrderAsync(long orderId, long userId, int amountCents, CancellationToken ct = default) { // Routed by user_id, so orders colocate with their owning user. var shard = _router.ShardForKey(userId); await using var conn = await shard.DataSource.OpenConnectionAsync(ct); await using var cmd = conn.CreateCommand(); cmd.CommandText = """ INSERT INTO orders (order_id, user_id, amount_cents) VALUES (@oid, @uid, amt) """; cmd.Parameters.AddWithValue("@oid", orderId); cmd.Parameters.AddWithValue("@uid", userId); cmd.Parameters.AddWithValue("@amt", amountCents); await cmd.ExecuteNonQueryAsync(ct); } public async Task<IReadOnlyList<Order>> GetOrdersForUserAsync(long userId, CancellationToken ct = default) { var shard = _router.ShardForKey(userId); await using var conn = await shard.DataSource.OpenConnectionAsync(ct); await using var cmd = conn.CreateCommand(); cmd.CommandText = """ SELECT order_id, user_id, amount_cents, created_at FROM orders WHERE user_id = @uid """; cmd.Parameters.AddWithValue("@uid", userId); var list = new List<Order>(); await using var reader = await cmd.ExecuteReaderAsync(ct); while (await reader.ReadAsync(ct)) { list.Add(new Order( reader.GetInt64(0), reader.GetInt64(1), reader.GetInt32(2), reader.GetDateTime(3))); } return list; } /// <summary>Cross shard fanout.</summary> public async Task<long> TotalRevenueCentsAsync(CancellationToken ct = default) { var tasks = _router.AllShards.Select(async shard => { await using var conn = await shard.DataSource.OpenConnectionAsync(ct); await using var cmd = conn.CreateCommand(); cmd.CommandText = "SELECT COALESCE(SUM(amount_cents), 0) FROM orders"; var result = await cmd.ExecuteScalarAsync(ct); return Convert.ToInt64(result); }); var perShard = await Task.WhenAll(tasks); return perShard.Sum(); } } Program.cs using Microsoft.Extensions.Configuration; using Microsoft.Extensions.DependencyInjection; using Microsoft.Extensions.Hosting; using ShardingDemo; var builder = Host.CreateApplicationBuilder(args); // Bind Shards:[] from appsettings.json (override with user-secrets / env vars / Key Vault) var shardConfigs = builder.Configuration .GetSection("Shards") .Get<List<ShardConfig>>() ?? throw new InvalidOperationException("No 'Shards' section configured."); if (shardConfigs.Count == 0) throw new InvalidOperationException("At least one shard must be configured."); builder.Services.AddSingleton(_ => new ShardRouter(shardConfigs)); builder.Services.AddSingleton<UserRepository>(); using var host = builder.Build(); var repo = host.Services.GetRequiredService<UserRepository>(); var router = host.Services.GetRequiredService<ShardRouter>(); (long Id, string Email)[] users = { (1001, "ada@example.com"), (2002, "linus@example.com"), (3003, "grace@example.com"), (4004, "alan@example.com"), }; foreach (var (id, email) in users) { await repo.CreateUserAsync(id, email); Console.WriteLine($"user {id} -> shard {router.ShardForKey(id).Id}"); } await repo.AddOrderAsync(orderId: 9001, userId: 1001, amountCents: 4999); await repo.AddOrderAsync(orderId: 9002, userId: 1001, amountCents: 1299); await repo.AddOrderAsync(orderId: 9003, userId: 2002, amountCents: 8800); Console.WriteLine($"\nAda: {await repo.GetUserAsync(1001)}"); Console.WriteLine($"Ada's orders: {(await repo.GetOrdersForUserAsync(1001)).Count}"); Console.WriteLine($"\nTotal revenue across 3 shards: " + $"${await repo.TotalRevenueCentsAsync() / 100m:F2}"); await router.DisposeAsync(); Tracing One Request End to End Consider GetOrdersForUserAsync(1001): ShardForKey(1001) → MD5("1001") → first two bytes as a number → % 1024 → a bucket in the range 0..1023. bucket % 3 → a physical shard → for example mysql-shard-2.mysql.database.azure.com. The MySqlDataSource provides a pooled, TLS encrypted connection authenticated as shard2_admin. The query runs against shard 2's local ix_user index, with no fan out and at single server speed. Every call with userId = 1001, whether GetUser, AddOrder, or GetOrdersForUser, lands on the same shard. That is why orders JOIN users ON orders.user_id = users.user_id WHERE user_id = 1001 executes within a single shard, with no cross-shard traffic. Conclusion The essential point is this. Once a single primary can no longer absorb your write load, sharding becomes a durable answer, and implementing it at the application layer keeps every part of the system explicit and comprehensible. When write volume or dataset size outgrows a single primary, application layer sharding provides several benefits. N independent Azure Database for MySQL instances, each absorbing 1/N of the write traffic. Queries by user that remain on a single shard and behave like an ordinary, modestly sized database. A bucket map approach that allows you to add a fourth, fifth, or Nth shard later by relocating slices of data rather than rehashing the entire dataset. A failure of one shard that affects 1/N of your users rather than all of them. These benefits come at a genuine cost. You must generate identifiers in the application, global uniqueness requires a secondary lookup table, and aggregate queries fan out across shards. A cross shard write, one that must atomically update data on two different shards, can no longer rely on a single database transaction. Instead it needs an orchestrated sequence of local transactions, where each step carries a compensating action that undoes its effect if a later step fails. None of these are insurmountable. They are simply responsibilities you now assume. Sharding is a deliberate step to take only once a single primary has genuinely exhausted its write headroom. When you reach that point, the implementation in this post is a representative blueprint. Stay Connected We welcome your feedback and invite you to share your experiences or suggestions at AskAzureDBforMySQL@service.microsoft.com Thank you for choosing Azure Database for MySQL!
Best practices for safely performing schema changes in Azure Database for MySQL
Azure Database for MySQL - Flexible Server is built on the open-source MySQL database engine, and the service supports MySQL 8.0 and newer versions. This means that users can take advantage of the flexibility and advanced capabilities of MySQL’s latest features while benefitting from a fully managed database service. While newer versions and features can provide a lot of value, the recent issues identified with MySQL versions 8.0+ makes it important to be aware of potential risks that can occur during certain operations, particularly if you are making online schema changes. Issues with data loss and duplicate keys with Online DDL Online Data Definition Language (DDL) operations are a powerful feature in MySQL, enabling schema changes like ALTER TABLE or OPTIMIZE TABLE with minimal impact on table availability. These operations are designed to reduce downtime by allowing concurrent reads and writes during schema modifications, making them an essential tool for managing active databases efficiently. However, a recent post on the Percona blog, Who Ate My MySQL Table Rows? highlights critical risks associated with MySQL 8.0.x versions after 8.0.27 and all versions beyond 8.4.y. Specifically, the open-source INPLACE algorithm, commonly used for online schema changes, can lead to data loss and duplicate key errors under certain conditions. These issues arise from constraints in the INPLACE algorithm, particularly during ALTER TABLE and OPTIMIZE TABLE operations, exposing vulnerabilities that compromise data integrity and system reliability. These risks are called out in the following bug reports: Bug #115511: Data loss during online ALTER operations with concurrent DML Bug #115608: Duplicate key errors caused by online ALTER operations Documented issues related to the INPLACE algorithm (used for online DDL) can cause: Data Loss: Rows may be accidentally deleted or become inaccessible. Duplicate Keys: Indexes can end up with duplicate entries, leading to data consistency issues and potential replication errors. Problems arise when INPLACE operations, such as ALTER TABLE or OPTIMIZE TABLE, run concurrently with: DML operations (INSERT, UPDATE, DELETE): Modifications to table data during the rebuild. A purge activity: Background cleanup operations for old row versions in InnoDB. These scenarios can lead to anomalies resulting from race conditions and incomplete synchronization between concurrent activities. Impact on Azure Database for MySQL - Flexible Server Customers For Azure Database for MySQL Flexible Server customers using MySQL 8.0+ and all versions after 8.4.y, this issue is particularly critical as it affects: Data Integrity: During schema changes such as ALTER TABLE or OPTIMIZE TABLE run using the INPLACE algorithm, data rows may be lost or duplicated if these operations run concurrently with a DML activity (e.g., INSERT, UPDATE, or DELETE) or background purge tasks. This can compromise the accuracy and reliability of the database, potentially leading to incorrect query results or the loss of critical business data. Replication Instability: Duplicate keys or missing rows can interrupt replication processes, which rely on a consistent data stream across the primary and replica servers. These issues can arise when there are concurrent insertions into the table during schema changes, leading to data inconsistencies between the primary and replicas. Such inconsistencies may result in replication lag, errors, or even a complete breakdown of high-availability setups, requiring manual intervention to restore synchronization. Operational Downtime: Resolving these issues often involves manually syncing data or restoring backups. These recovery efforts can be time-consuming and disruptive, leading to extended downtime for applications and potential business impact. Recommendations for safe schema changes on Azure Database for MySQL flexible servers To minimize the risks of data loss and duplicate keys while making schema changes, follow these best practices: Set old_alter_table=ON to Default to COPY Algorithm Enable the server parameter old_alter_table system variable so that ALTER TABLE operations without a specified ALGORITHM default to using the COPY algorithm instead of INPLACE. This reduces the risk for users who do not explicitly specify the ALGORITHM in their commands. Learn more on how configure server parameters in Azure Database for MySQL. Avoid using ALGORITHM=INPLACE Do not explicitly use ALGORITHM=INPLACE for ALTER TABLE commands, as it increases the risk of data loss or duplicate keys. Back up your data before schema changes Always perform a full on-demand backup of your server before executing schema changes. This precaution ensures data recoverability in case of unexpected issues. Learn more on how to take full on-demand backups for your server. Avoid Concurrent DML during schema changes Schedule schema changes like ALTER TABLE and OPTIMIZE TABLE during application maintenance windows when no concurrent writes activities occur. This minimizes race conditions and synchronization conflicts. Use External Tools for Safer Online Schema Changes Consider using external tools like pt-online-schema-change to modify table definitions without blocking concurrent changes. These tools enable you to make schema changes with minimal impact on availability and performance. Learn more about pt-online-schema-change. Disclaimer: The pt-online-schema-change tool is not managed or supported by Microsoft; use it at your discretion. Mitigation plans To address these risks, we’re actively working to integrate the necessary fixes to ensure a more robust and reliable experience for our customers. New Servers Fully Secured by End of February 2025 All new Azure Database for MySQL Flexible Server instances created after 1 st March 2025, will include the latest fixes, ensuring that schema changes are safeguarded against data loss and duplicate key risks. Rollout for Existing Servers For existing servers, we will roll out patches during upcoming maintenance windows by end of Q1 of Calendar Year 2025 We recommend monitoring your Azure portal for scheduled maintenance windows and Release notes for announcements about critical updates and patches. Priority updates available upon request If you require an urgent update outside of the scheduled maintenance windows, you can contact Azure Support. Provide the necessary server details and an appropriate maintenance window, and our team will work with you to prioritize the patching process. Note that priority patching will be available by February 2025. We recommend monitoring Release notes for announcements about critical updates and patches. Conclusion Safely managing schema changes on MySQL servers requires understanding the risks associated with online DDL operations, such as potential data loss and duplicate keys. To help safeguard data integrity and maintain server stability, implement best practices, for example enabling the COPY algorithm, using offline operations if feasible, or scheduling changes during low activity periods. Fixes are expected by the end of February 2025, and new Azure Database for MySQL flexible servers will be fully protected against these bugs. We will apply updates to existing servers during maintenance windows in Q1 2025. Following the recommendations above will help ensure that you can confidently make schema changes while preserving the reliability and performance of your server.
Azure Key Vault Replication: Why Paired Regions Alone Don’t Guarantee Business Continuity
As customers modernize toward multi‑region architectures in Azure, one question comes up repeatedly: “If my region goes down, will Azure Key Vault continue to work without disruption?” The short answer: it depends on what you mean by “work.” Azure Key Vault provides strong durability and availability guarantees, but those guarantees are often misunderstood—especially when customers assume paired‑region replication equals full disaster recovery. In reality, Azure Key Vault replication is designed for survivability, not uninterrupted write access or customer‑controlled failover. This post explains: How Azure Key Vault replication actually works (per Microsoft Learn) Why paired‑region failover does not equal business continuity Two reference architectures that implement true multi‑region Key Vault availability, with Terraform How Azure Key Vault Replication Works (Per Microsoft Learn) Azure Key Vault includes multiple layers of Microsoft‑managed redundancy. In‑Region and Zone Resiliency Vault contents are replicated within the region. In regions that support availability zones, Key Vault is zone‑resilient by default. This protects against localized hardware or zone failures. Paired‑Region Replication If a Key Vault is deployed in a region with an Azure‑defined paired region, its contents are asynchronously replicated to that paired region. This replication is automatic and cannot be configured, observed, or tested by customers. Microsoft‑Managed Regional Failover If Microsoft declares a full regional outage, requests are automatically routed to the paired region. After failover, the vault operates in read‑only mode: ✅ Read secrets, keys, and certificates ✅ Perform cryptographic operations ❌ Create, update, rotate, or delete secrets, keys, or certificates This is a critical distinction. Paired‑region replication preserves access — not operational continuity. Why Paired‑Region Replication Is Not Business Continuity From a reliability and DR perspective, several limitations matter: Failover is Microsoft‑initiated, not customer‑controlled No write operations during regional failover No secret rotation or certificate renewal No way to test DR Accidental deletions replicate No point‑in‑time recovery without backups Microsoft Learn explicitly states that critical workloads may require custom multi‑region strategies beyond built‑in replication. For many customers, this means Azure Key Vault becomes a single‑region dependency in an otherwise multi‑region application design. The Multi‑Region Key Vault Pattern The two GitHub repositories below implement a common architectural shift: Multiple independent Key Vaults deployed in separate regions, with customer‑controlled replication and failover. Instead of relying on invisible platform replication, the vaults become first‑class, region‑scoped resources, aligned with application failover. Solution 1: Private, Locked‑Down Multi‑Region Key Vault Replication Repository: 👉 https://github.com/jclem2000/KeyVault-MultiRegion-Replication-Private Architecture Highlights Independent Key Vault per region Private Endpoints only No public network exposure Terraform‑based deployment Controlled replication using Event Based synchronization What This Enables ✅ Full read/write access during regional outages ✅ Continued secret rotation and certificate renewal ✅ Customer‑defined failover and RTO ✅ DR testing and validation ✅ Strong alignment with zero‑trust and regulated environments Trade‑offs Higher operational complexity Requires automation and application awareness of multiple vaults Solution 2: Low‑Cost Public Multi‑Region Key Vault Replication Repository: 👉 https://github.com/jclem2000/KeyVault-MultiRegion-Replication-Public Architecture Highlights Independent Key Vault per region Public endpoints Minimal networking dependencies Terraform‑based Controlled replication using Event Based synchronization Optimized for simplicity and cost What This Enables ✅ Full read/write availability in any region ✅ Clear and testable DR posture ✅ Lower cost than private endpoint designs ✅ Suitable for many non‑regulated workloads Trade‑offs Public exposure (mitigated via firewall rules, RBAC, and conditional access) Not appropriate for all compliance requirements Requires automation and application awareness of multiple vaults Azure Native Replication vs Customer‑Managed Multi‑Region Vaults Capability Azure Paired Region Multi‑Region Vaults Read access during outage ✅ ✅ Write access during outage ❌ ✅ Secret rotation during outage ❌ ✅ Customer‑controlled failover ❌ ✅ DR testing ❌ ✅ Isolation from accidental deletion ❌ ✅ Predictable RTO ❌ ✅ Azure Key Vault’s native replication optimizes for platform durability. The multi‑region pattern optimizes for application continuity. When to Use Each Approach Paired‑Region Replication Is Often Enough When: Secrets are mostly static Read‑only access during outages is acceptable RTO is flexible You prefer Microsoft‑managed recovery Multi‑Region Vaults Are Recommended When: Secrets or certificates rotate frequently Applications must remain writable during outages Deterministic failover is required DR testing is mandatory Regulatory or operational isolation is needed Closing Thoughts Azure Key Vault behaves exactly as documented on Microsoft Learn—but it’s important to be clear about what those guarantees mean. Paired‑region replication protects your data, not your ability to operate. If your application is designed to survive a regional outage, Key Vault must follow the same multi‑region design principles as the application itself. The reference architectures above show how to extend Azure’s native durability model into true operational resilience, without waiting for a platform‑level failover decision.Recovering Missing Rows (“Gaps”) in Azure SQL Data Sync — Supported Approaches (and What to Avoid)
Azure SQL Data Sync is commonly used to keep selected tables synchronized between a hub database and one or more member databases. In some cases, you may discover a data “gap”: a subset of rows that exist in the source but are missing on the destination for a specific time window, even though synchronization continues afterward for new changes. This post explains supported recovery patterns and what not to do, based on a real support scenario where a customer reported missing rows for a single table within a sync group and requested a way to synchronize only the missing records. The scenario: Data Sync continues, but some rows are missing In the referenced case, the customer observed that: A specific table had a gap on the member side (missing rows for a period), while newer data continued to sync normally afterward. They asked for a Microsoft-supported method to sync only the missing rows, without rebuilding or fully reinitializing the table. This is a reasonable goal—but the recovery method matters, because Data Sync relies on service-managed tracking artifacts. Temptation: “Can we push missing data by editing tracking tables or calling internal triggers?” A frequent idea is to “force” Data Sync to pick up missing rows by manipulating internal artifacts: Writing directly into Data Sync tracking tables (for example, tables under the DataSync schema such as *_dss_tracking), or altering provisioning markers. Manually invoking Data Sync–generated triggers or relying on their internal logic. The case discussion specifically referenced internal triggers such as _dss_insert_trigger, _dss_update_trigger, and _dss_delete_trigger. Why this is not recommended / not supported as a customer-facing solution In the case, the guidance from Microsoft engineering was clear: Manually invoking internal Data Sync triggers is not supported and can increase the risk of data corruption because these triggers are service-generated at runtime and are not intended for manual use. Directly manipulating Data Sync tracking/metadata tables is not recommended. The customer thread also highlights that these tracking tables are part of Data Sync internals, and using them for manual “push” scenarios is not a supported approach. Also, the customer conversation highlights an important conceptual point: tracking tables are part of how the service tracks changes; they are not meant to be treated as a user-managed replication queue. Supported recovery option #1 (recommended): Re-drive change detection via the base table The most supportable approach is to make Data Sync detect the missing rows through its normal change tracking path—by operating on the base/source table, not the service-managed internals. A practical pattern: “No-op update” to re-fire tracking In the internal discussion with the product team, the recommended pattern was to update the source/base table (even with a “no-op” assignment) so that Data Sync’s normal tracking logic is triggered, without manually invoking internal triggers. Example pattern (conceptual): UPDATE t SET some_column = some_column -- no-op: value unchanged FROM dbo.YourTable AS t WHERE <filter identifying the rows that are missing on the destination>; This approach is called out explicitly in the thread as a way to “re-drive” change detection safely through supported mechanisms. Operational guidance (practical): Apply the update in small batches, especially for large tables, to reduce transaction/log impact and avoid long-running operations. Validate the impacted row set first (for example, by comparing keys between hub and member). Supported recovery option #2: Deprovision and re-provision the affected table (safe “reset” path) If the gap is large, the row-set is hard to isolate, or you want a clean realignment of tracking artifacts, the operational approach discussed in the case was: Stop sync Remove the table from the sync group (so the service deprovisions tracking objects) Fix/clean the destination state as needed Add the table back and let Data Sync re-provision and sync again This option is often the safest when the goal is to avoid touching system-managed artifacts directly. Note: In production environments, customers may not be able to truncate/empty tables due to operational constraints. In that situation, the sync may take longer because the service might need to do more row-by-row evaluation. This “tradeoff” was discussed in the case context. Diagnostics: Use the Azure SQL Data Sync Health Checker When you suspect metadata drift, missing objects, or provisioning inconsistencies, the case recommended using the AzureSQLDataSyncHealthChecker script. This tool: Validates hub/member metadata and scopes against the sync metadata database Produces logs that can highlight missing artifacts and other inconsistencies Is intended to help troubleshoot Data Sync issues faster A likely contributor to “gaps”: schema changes during Data Sync (snapshot isolation conflict) In the case discussion, telemetry referenced an error consistent with concurrent DDL/schema changes while the sync process is enumerating changes (snapshot isolation + metadata changes). A well-known related error is SQL Server error 3961, which occurs when a snapshot isolation transaction fails because metadata was modified by a concurrent DDL statement, since metadata is not versioned. Microsoft documents this behavior and explains why metadata changes conflict with snapshot isolation semantics. Prevention guidance (practical) Avoid running schema deployments (DDL) during active sync windows. Use a controlled workflow for schema changes with Data Sync—pause/coordinate changes to prevent mid-sync metadata shifts. (General best practices exist for ongoing Data Sync operations and maintenance.) Key takeaways Do not treat Data Sync tracking tables/triggers as user-managed “replication internals.” Manually invoking internal triggers or editing tracking tables is not a supported customer-facing recovery mechanism. Do recover gaps via base table operations (insert/update) so the service captures changes through its normal path—“no-op update” is one practical pattern when you already know the missing row set. For large/complex gaps, consider the safe reset approach: remove the table from the sync group and re-add it to re-provision artifacts. Use the AzureSQLDataSyncHealthChecker to validate metadata consistency and reduce guesswork. If you see intermittent failures around deployments, consider the schema-change + snapshot isolation pattern (e.g., error 3961) as a possible contributor and schedule DDL changes accordingly. From our experience, when there are row gaps it is usually because of change in the PK or source table.Unlocking AI-Driven Data Access: Azure Database for MySQL Support via the Azure MCP Server
Step into a new era of data-driven intelligence with the fusion of Azure MCP Server and Azure Database for MySQL, where your MySQL data is no longer just stored, but instantly conversational, intelligent and action-ready. By harnessing the open-standard Model Context Protocol (MCP), your AI agents can now query, analyze and automate in natural language, accessing tables, surfacing insights and acting on your MySQL-driven business logic as easily as chatting with a colleague. It’s like giving your data a voice and your applications a brain, all within Azure’s trusted cloud platform. We are excited to announce that we have added support for Azure Database for MySQL in Azure MCP Server. The Azure MCP Server leverages the Model Context Protocol (MCP) to allow AI agents to seamlessly interact with various Azure services to perform context-aware operations such as querying databases and managing cloud resources. Building on this foundation, the Azure MCP Server now offers a set of tools that AI agents and apps can invoke to interact with Azure Database for MySQL - enabling them to list and query databases, retrieve schema details of tables, and access server configurations and parameters. These capabilities are delivered through the same standardized interface used for other Azure services, making it easier to the adopt the MCP standard for leveraging AI to work with your business data and operations across the Azure ecosystem. Before we delve into these new tools and explore how to get started with them, let’s take a moment to refresh our understanding of MCP and the Azure MCP Server - what they are, how they work, and why they matter. MCP architecture and key components The Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services in a scalable, standardized, and secure manner. MCP dictates a client-server architecture with four key components: MCP Host, MCP Client, MCP Server and external data sources, services and APIs that provide the data context required to enhance AI models. To explain briefly, an MCP Host (AI apps and agents) includes an MCP client component that connects to one or more MCP Servers. These servers are lightweight programs that securely interface with external data sources, services and APIs and exposes them to MCP clients in the form of standardized capabilities called tools, resources and prompts. Learn more: MCP Documentation What is Azure MCP Server? Azure offers a multitude of cloud services that help developers build robust applications and AI solutions to address business needs. The Azure MCP Server aims to expose these powerful services for agentic usage, allowing AI systems to perform operations that are context-aware of your Azure resources and your business data within them, while ensuring adherence to the Model Context Protocol. It supports a wide range of Azure services and tools including Azure AI Search, Azure Cosmos DB, Azure Storage, Azure Monitor, Azure CLI and Developer CLI extensions. This means that you can empower AI agents, apps and tools to: Explore your Azure resources, such as listing and retrieving details on your Azure subscriptions, resource groups, services, databases, and tables. Search, query and analyze your data and logs. Execute CLI and Azure Developer CLI commands directly, and more! Learn more: Azure MCP Server GitHub Repository Introducing new Azure MCP Server tools to interact with Azure Database for MySQL The Azure MCP Server now includes the following tools that allow AI agents to interact with Azure Database for MySQL and your valuable business data residing in these servers, in accordance with the MCP standard: Tool Description Example Prompts azmcp_mysql_server_list List all MySQL servers in a subscription & resource group "List MySQL servers in resource group 'prod-rg'." "Show MySQL servers in region 'eastus'." azmcp_mysql_server_config_get Retrieve the configuration of a MySQL server "What is the backup retention period for server 'my-mysql-server'?" "Show storage allocation for server 'my-mysql-server'." azmcp_mysql_server_param_get Retrieve a specific parameter of a MySQL server "Is slow_query_log enabled on server my-mysql-server?" "Get innodb_buffer_pool_size for server my-mysql-server." azmcp_mysql_server_param_set Set a specific parameter of a MySQL server to a specific value "Set max_connections to 500 on server my-mysql-server." "Set wait_timeout to 300 on server my-mysql-server." azmcp_mysql_table_list List all tables in a MySQL database "List tables starting with 'tmp_' in database 'appdb'." "How many tables are in database 'analytics'?" azmcp_mysql_table_schema_get Get the schema of a specific table in a MySQL database "Show indexes for table 'transactions' in database 'billing'." "What is the primary key for table 'users' in database 'auth'?" azmcp_mysql_database_query Executes a SELECT query on a MySQL Database. The query must start with SELECT and cannot contain any destructive SQL operations for security reasons. “How many orders were placed in the last 30 days in the salesdb.orders table?” “Show the number of new users signed up in the last week in appdb.users grouped by day.” These interactions are secured using Microsoft Entra authentication, which enables seamless, identity-based access to Azure Database for MySQL - eliminating the need for password storage and enhancing overall security. How are these new tools in the Azure MCP Server different from the standalone MCP Server for Azure Database for MySQL? We have integrated the key capabilities of the Azure Database for MySQL MCP server into the Azure MCP Server, making it easier to connect your agentic apps not only to Azure Database for MySQL but also to other Azure services through one unified and secure interface! How to get started Installing and running the Azure MCP Server is quick and easy! Use GitHub Copilot in Visual Studio Code to gain meaningful insights from your business data in Azure Database for MySQL. Pre-requisites Install Visual Studio Code. Install GitHub Copilot and GitHub Copilot Chat extensions. An Azure Database for MySQL with Microsoft Entra authentication enabled. Ensure that the MCP Server is installed on a system with network connectivity and credentials to connect to Azure Database for MySQL. Installation and Testing Please use this guide for installation: Azure MCP Server Installation Guide Try the following prompts with your Azure Database for MySQL: Azure Database for MySQL tools for Azure MCP Server Try it out and share your feedback! Start using Azure MCP Server with the MySQL tools today and let our cloud services become your AI agent’s most powerful ally. We’re counting on your feedback - every comment, suggestion, or bug-report helps us build better tools together. Stay tuned: more features and capabilities are on the horizon! Feel free to comment below or write to us with your feedback and queries at AskAzureDBforMySQL@service.microsoft.com.Ignite 2025: Advancing Azure Database for MySQL with Powerful New Capabilities
At Ignite 2025, we’re introducing a wave of powerful new capabilities for Azure Database for MySQL, designed to help organizations modernize, scale, and innovate faster than ever before. From enhanced high availability and seamless serverless integrations to AI-powered insights and greater flexibility for developers, these advancements reflect our commitment to delivering a resilient, intelligent data platform. Join us as we unveil what’s next for MySQL on Azure - and discover how industry leaders are already building the future with confidence. Enhanced Failover Performance with Dedicated SLB for High-Availability Servers We’re excited to announce the General Availability of Dedicated Standard Load Balancer (SLB) for HA-enabled servers in Azure Database for MySQL. This enhancement introduces a dedicated SLB to High Availability configurations for servers created with public access or private link. By managing the MySQL data traffic path, SLB eliminates the need for DNS updates during failover, significantly reducing failover time. Previously, failover relied on DNS changes, which caused delays due to DNS TTL (30 seconds) and client-side DNS caching. What’s new with GA: The FQDN consistently resolves to the SLB IP address before and after failover. Load-balancing rules automatically route traffic to the active node. Removes DNS cache dependency, delivering faster failovers. Note: This feature is not supported for servers using private access with VNet integration. Learn more Build serverless, event-driven apps at scale – now GA with Trigger Bindings for Azure Functions We’re excited to announce the General Availability of Azure Database for MySQL Trigger bindings for Azure Functions, completing the full suite of Input, Output, and Trigger capabilities. This feature lets you build real-time, event-driven applications by automatically invoking Azure Functions when MySQL table rows are created or updated - eliminating custom polling and boilerplate code. With native support across multiple languages, developers can now deliver responsive, serverless solutions that scale effortlessly and accelerate innovation. Learn more Enable AI agents to query Azure Database for MySQL using Azure MCP Server We’re excited to announce that Azure MCP Server now supports Azure Database for MySQL, enabling AI agents to query and manage MySQL data using natural language through the open Model Context Protocol (MCP). Instead of writing SQL, you can simply ask questions like “Show the number of new users signed up in the last week in appdb.users grouped by day.”, all secured with Microsoft Entra authentication for enterprise-grade security. This integration delivers a unified, secure interface for building intelligent, context-aware workflows across Azure services - accelerating insights and automation. Learn more Greater networking flexibility with Custom Port Support Custom port support for Azure Database for MySQL is now generally available, giving organizations the flexibility to configure a custom port (between 25001 and 26000) during new server creation. This enhancement streamlines integration with legacy applications, supports strict network security policies, and helps avoid port conflicts in complex environments. Supported across all network configurations - including public access, private access, and Private Link - custom port provisioning ensures every new MySQL server can be tailored to your needs. The managed experience remains seamless, with all administrative capabilities and integrations working as before. Learn more Streamline migrations and compatibility with Lower Case Table Names support Azure Database for MySQL now supports configuring lower_case_table_names server parameter during initial server creation for MySQL 8.0 and above, ensuring seamless alignment with your organization’s naming conventions. This setting is automatically inherited for restores and replicas, and cannot be modified. Key Benefits: Simplifies migrations by aligning naming conventions and reducing complexity. Enhances compatibility with legacy systems that depend on case-insensitive table names. Minimizes support dependency, enabling faster and smoother onboarding. Learn more Unlock New Capabilities with Private Preview Features at Ignite 2025 We’re excited to announce that you can now explore two powerful capabilities in early access - Reader Endpoint for seamless read scaling and Server Rename for greater flexibility in server management. Scale reads effortlessly with Reader Endpoint (Private Preview) We’re excited to announce that the Reader Endpoint feature for Azure Database for MySQL is now ready for private preview. Reader Endpoint provides a dedicated read-only endpoint for read replicas, enabling automatic connection-based load balancing of read-only traffic across multiple replicas. This simplifies application architecture by offering a single endpoint for read operations, improving scalability and fault tolerance. Azure Database for MySQL supports up to 10 read replicas per primary server. By routing read-only traffic through the reader endpoint, application teams can efficiently manage connections and optimize performance without handling individual replica endpoints. Reader endpoints continuously monitor the health of replicas and automatically exclude any replica that exceeds the configured replication lag threshold or becomes unavailable. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Only performance-based routing is supported in this preview. Certain settings such as routing method and the option to attach new replicas to the reader endpoint can only be configured at creation time. Only one reader endpoint can be created per replica group. Including the primary server as a fallback for read traffic when no replicas are available is not supported in this preview. Get flexibility in server management with Server Rename (Private Preview) We’re excited to announce the Private Preview of Server Rename for Azure Database for MySQL. This feature lets you update the name of an existing MySQL server without recreating it, migrating data, or disrupting applications - making it easier to adopt clear, consistent naming. It provides a near zero-downtime path to a new hostname of the server. To enroll in the preview, please submit your details using this form. Limitations During Private Preview: Primary server with read replicas: Renaming a primary server that has read replicas keeps replication healthy. However, the SHOW SLAVE STATUS output on the replicas will still display the old primary server's name. This is a display inconsistency only and does not affect replication. Renaming is currently unsupported for servers using Customer Managed Key (CMK) encryption or Microsoft Entra Authentication (Entra Id). Real-World Success: Azure Database for MySQL Powers Resilient Applications at Scale Factorial Factorial, a leading HR software provider, uses Azure Database for MySQL alongside Azure Kubernetes Service to deliver secure, scalable HR solutions for thousands of businesses worldwide. By leveraging Azure Database for MySQL’s reliability and seamless integration with cloud-native technologies, Factorial ensures high availability and rapid innovation for its customers. Learn more YES (Youth Employment Service) South Africa’s largest youth employment initiative, YES, operates at national scale by leveraging Azure Database for MySQL to deliver a resilient, centralized platform for real-time job matching, learning management, and career services - connecting thousands of young people and employers, and helping nearly 45 percent of participants secure permanent roles within six months. Learn more Nasdaq At Ignite 2025, Nasdaq will showcase how it uses Azure Database for MySQL - alongside Azure Database for PostgreSQL and other Azure products - to power a secure, resilient architecture that safeguards confidential data while unlocking new agentic AI capabilities. Learn more These examples demonstrate that Azure Database for MySQL is trusted by industry leaders to build resilient, scalable applications - empowering organizations to innovate and grow with confidence. We Value Your Feedback Azure Database for MySQL is built for scale, resilience, and performance - ready to support your most demanding workloads. With every update, we’re focused on simplifying development, migration, and management so you can build with confidence. Explore the latest features and enhancements to see how Azure Database for MySQL meets your data needs today and in the future. We welcome your feedback and invite you to share your experiences or suggestions at AskAzureDBforMySQL@service.microsoft.com Stay up to date by visiting What's new in Azure Database for MySQL, and follow us on YouTube | LinkedIn | X for ongoing updates. Thank you for choosing Azure Database for MySQL!September 2025 Recap: Azure Database for PostgreSQL
Hello Azure Community, We are back with another round of updates for Azure Database for PostgreSQL! September is packed with powerful enhancements, from the public preview of PostgreSQL 18 to the general availability of Azure Confidential Computing, plus several new capabilities designed to boost performance, security, and developer experience. Stay tuned as we dive deeper into each of these feature updates. Before we dive into the feature highlights, let’s take a look at PGConf NYC 2025 highlights. PGConf NYC 2025 Highlights Our Postgres team was glad to be part of PGConf NYC 2025! As a Platinum sponsor, Microsoft joined the global PostgreSQL community for three days of sessions covering performance, extensibility, cloud, and AI, highlighted by Claire Giordano’s keynote, “What Microsoft is Building for Postgres—2025 in Review,” along with deep dives from core contributors and engineers. If you missed it, you can catch up here: Keynote slides: What Microsoft is Building for Postgres—2025 in Review by Claire Giordano at PGConf NYC 2025 Day 3 wrap-up: Key takeaways, highlights, and insights from the Azure Database for PostgreSQL team. Feature Highlights Near Zero Downtime scaling for High Availability (HA) enabled servers - Generally Available Azure Confidential Computing for Azure Database for PostgreSQL - Generally Available PostgreSQL 18 on Azure Database for PostgreSQL - Public Preview PostgreSQL Discovery & Assessment in Azure Migrate - Public Preview LlamaIndex Integration with Azure Postgres Latest Minor Versions GitHub Samples: Entra ID Token Refresh for PostgreSQL VS Code Extension for PostgreSQL enhancements Near Zero Downtime scaling for High Availability (HA) enabled servers – Generally Available Scaling compute for high availability (HA) enabled Azure Database for PostgreSQL servers just got faster. With Near Zero Downtime (NZD) scaling, compute changes such as vCore or tier modifications are now complete with minimal interruption, typically under 30 seconds using HA failover which maintains the connection string. The service provisions a new primary and standby instance with the updated configuration, synchronizes them with the existing setup, and performs a quick failover. This significantly reduces downtime compared to traditional scaling (which could take 2–10 minutes), improving overall availability. Visit our documentation for full details on how Near Zero Downtime scaling works. Azure Confidential Computing for Azure Database for PostgreSQL - Generally Available Azure Confidential Computing (ACC) Confidential Virtual Machines (CVMs) are now generally available for Azure Database for PostgreSQL. This capability brings hardware-based protection for data in use, ensuring your most sensitive information remains secure, even while being processed. With CVMs, your PostgreSQL flexible server instance runs inside a Trusted Execution Environment (TEE), a secure, hardware-backed enclave that encrypts memory and isolates it from the host OS, hypervisor, and even Azure operators. This means your data enjoys end-to-end protection: at rest, in transit, and in use. Key Benefits: End-to-End Security: Data protected at rest, in transit, and in use Enhanced Privacy: Blocks unauthorized access during processing Compliance Ready: Meets strict security standards for regulated workloads Confidence in Cloud: Hardware-backed isolation for critical data Discover how Azure Confidential Computing enhances PostgreSQL check out the blog announcement. PostgreSQL 18 on Azure Database for PostgreSQL – Public Preview PostgreSQL 18 is now available in public preview on Azure Database for PostgreSQL, launched the same day as the PostgreSQL community release. PostgreSQL 18 introduces new performance, scalability, and developer productivity improvements. With this preview, you get early access to the latest community release on a fully managed Azure service. By running PostgreSQL 18 on flexible server, you can test application compatibility, explore new SQL and performance features, and prepare for upgrades well before general availability. This preview release gives you the opportunity to validate your workloads, extensions, and development pipelines in a dedicated preview environment while taking advantage of the security, high availability, and management capabilities in Azure. With PostgreSQL 18 in preview, you are among the first to experience the next generation of PostgreSQL on Azure, ensuring your applications are ready to adopt it when it reaches full general availability. To learn more about preview, read https://aka.ms/pg18 PostgreSQL Discovery & Assessment in Azure Migrate – Public Preview The PostgreSQL Discovery & Assessment feature is now available in public preview on Azure Migrate, making it easier to plan your migration journey to Azure. Migrating PostgreSQL workloads can be challenging without clear visibility into your existing environment. This feature solves that problem by delivering deep insights into on-premises PostgreSQL deployments, making migration planning easier and more informed. With this feature, you can discover PostgreSQL instances across your infrastructure, assess migration readiness and identify potential blockers, receive configuration-based SKU recommendations for Azure Database for PostgreSQL, and estimate costs for running your workloads in Azure all in one unified experience. Key Benefits: Comprehensive Visibility: Understand your on-prem PostgreSQL landscape Risk Reduction: Identify blockers before migration Optimized Planning: Get tailored SKU and cost insights Faster Migration: Streamlined assessment for a smooth transition Learn more in our blog: PostgreSQL Discovery and Assessment in Azure Migrate LlamaIndex Integration with Azure Postgres The support for native LlamaIndex integration is now available for Azure Database for PostgreSQL! This enhancement brings seamless connectivity between Azure Database for PostgreSQL and LlamaIndex, allowing developers to leverage Azure PostgreSQL as a secure and high-performance vector store for their AI agents and applications. Specifically, this package adds support for: Microsoft Entra ID (formerly Azure AD) authentication when connecting to your Azure Database for PostgreSQL instances, and, DiskANN indexing algorithm when indexing your (semantic) vectors. This package makes it easy to connect LlamaIndex to your Azure PostgreSQL instances whether you're building intelligent agents, semantic search, or retrieval-augmented generation (RAG) systems. Explore the full guide here: https://aka.ms/azpg-llamaindex Latest Postgres minor versions: 17.6, 16.9, 15.13, 14.18 and 13.21 PostgreSQL minor versions 17.6, 16.9, 15.13, 14.18 and 13.21 are now supported by Azure Database for PostgreSQL. These minor version upgrades are automatically performed as part of the monthly planned maintenance in Azure Database for PostgreSQL. The upgrade automation ensures that your databases are always running the latest optimized versions without requiring manual intervention. This release fixes 3 security vulnerabilities and more than 55 bugs reported over the last several months. PostgreSQL minor versions are backward-compatible, so updates won’t affect your applications. For details about the release, see PostgreSQL community announcement. GitHub Samples: Entra ID Token Refresh for PostgreSQL We have introduced code samples for Entra ID token refresh, built specifically for Azure Database for PostgreSQL. These samples simplify implementing automatic token acquisition and refresh, helping you maintain secure, uninterrupted connectivity without manual intervention. By using these examples, you can keep sessions secure, prevent connection drops from expired tokens, and streamline integration with Azure Identity libraries for PostgreSQL workloads. What’s Included: Ready-to-use code snippets for token acquisition and refresh for Python and .NET Guidance for integrating with Azure Identity libraries Explore the samples repository on https://aka.ms/pg-access-token-refresh and start implementing it today. VS Code Extension for PostgreSQL enhancements A new version for VS Code Extension for PostgreSQL is out! This update introduces a Server Dashboard that provides high-level metadata and real-time performance metrics, along with historical insights for Azure Database for PostgreSQL Flexible Server. You can even use GitHub Copilot Chat to ask performance questions in natural language and receive diagnostic SQL queries in response. Additional enhancements include: A new keybinding for “Run Current Statement” in the Query Editor Support for dragging Object Explorer entities into the editor with properly quoted identifiers Ability to connect to databases via socket file paths Key fixes: Preserves the state of the Explain Analyze toolbar toggle Removes inadvertent logging of sensitive information from extension logs Stabilizes memory usage during long-running dashboard sessions Don’t forget to update to the latest version in the marketplace to take advantage of these enhancements and visit our GitHub repository to learn more about this month’s release. We’d love your feedback! Help us improve the Server Dashboard and other features by sharing your thoughts on GitHub . Azure Postgres Learning Bytes 🎓 Setting up logical replication between two servers This section will walk through setting up logical replication between two Azure Database for PostgreSQL flexible server instances. Logical replication replicates data changes from a source (publisher) server to a target (subscriber) server. Prerequisites PostgreSQL versions supported by logical replication (publisher/subscriber compatible). Network connectivity: subscriber must be able to connect to the publisher (VNet/NSG/firewall rules). A replication role on the publisher (or a role with REPLICATION privilege). Step 1: Configure Server Parameters on both publisher and subscriber: On Publisher: wal_level=logical max_worker_processes=16 max_replication_slots=10 max_wal_senders=10 track_commit_timestamp=on On Subscriber: wal_level=logical max_worker_processes=16 max_replication_slots=10 max_wal_senders=10 track_commit_timestamp=on max_worker_processes = 16 max_sync_workers_per_subscription = 6 autovacuum = OFF (during initial copy) max_wal_size = 64GB checkpoint_timeout = 3600 Step 2: Create Publication (Publisher) and alter role with replication privilege ALTER ROLE <replication_user> WITH REPLICATION; CREATE PUBLICATION pub FOR ALL TABLES; Step 3: Create Subscription (Subscriber) CREATE SUBSCRIPTION <subscription-name> CONNECTION 'host=<publisher_host> dbname=<db> user=<user> password=<pwd>' PUBLICATION <publication-name>;</publication-name></pwd></user></db></publisher_host></subscription-name> Step 4: Monitor Publisher: This shows active processes on the publisher, including replication workers. SELECT application_name, wait_event_type, wait_event, query, backend_type FROM pg_stat_activity WHERE state = 'active'; Subscriber: The ‘pg_stat_progress_copy’ table tracks the progress of the initial data copy for each table. SELECT * FROM pg_stat_progress_copy; To explore more details on how to get started with logical replication, visit our blog on Tuning logical replication for Azure Database for PostgreSQL. Conclusion That’s all for the September 2025 feature highlights! We remain committed to making Azure Database for PostgreSQL more powerful and secure with every release. Stay up to date on the latest enhancements by visiting our Azure Database for PostgreSQL blog updates link. Your feedback matters and helps us shape the future of PostgreSQL on Azure. If you have suggestions, ideas, or questions, we’d love to hear from you: https://aka.ms/pgfeedback. We look forward to sharing even more exciting capabilities in the coming months. Stay tuned!August 2025 Recap: Azure Database for PostgreSQL
Hello Azure Community, August was an exciting month for Azure Database for PostgreSQL! We have introduced updates that make your experience smarter and more secure. From simplified Entra ID group login to integrations with LangChain and LangGraph, these updates help with improving access control and seamless integration for your AI agents and applications. Stay tuned as we dive deeper into each of these feature updates. Feature Highlights Enhanced Performance recommendations for Azure Advisor - Generally Available Entra-ID group login using user credentials - Public Preview New Region Buildout: Austria East LangChain and LangGraph connector Active-Active Replication Guide Enhanced Performance recommendations for Azure Advisor - Generally Available Azure Advisor now offers enhanced recommendations to further optimize PostgreSQL server performance, security, and resource management. These key updates are as follows: Index Scan Insights: Detection and recommendations for disabled index and index-only scans to improve query efficiency. Audit Logging Review: Identification of excessive logging via the pgaudit.log parameter, with guidance to reduce overhead. Statistics Monitoring: Alerts on server statistics resets and suggestions to restore accurate performance tracking. Storage Optimization: Analysis of storage usage with recommendations to enable the Storage Autogrow feature for seamless scaling. Connection Management: Evaluation of workloads for short-lived connections and frequent connectivity errors, with recommendations to implement PgBouncer for efficient connection pooling. These enhancements aim to provide deeper operational insights and support proactive performance tuning for PostgreSQL workloads. For more details read the Performance recommendations documentation. Entra-ID group login using user credentials - Public Preview The public preview for Entra-ID group login using user credentials is now available. This feature simplifies user management and improves security within the Azure Database for PostgreSQL. This allows administrators and users to benefit from a more streamlined process like: Changes in Entra-ID group memberships are synchronized on a periodic 30min basis. This scheduled syncing ensures that access controls are kept up to date, simplifying user management and maintaining current permissions. Users can log in with their own credentials, streamlining authentication, and improving auditing and access management for PostgreSQL environments. As organizations continue to adopt cloud-native identity solutions, this update represents a major improvement in operational efficiency and security for PostgreSQL database environments. For more details read the documentation on Entra-ID group login. New Region Buildout: Austria East New region rollout! Azure Database for PostgreSQL flexible server is now available in Austria East, giving customers in and around the region lower latency and data residency options. This continues our mission to bring Azure PostgreSQL closer to where you build and run your apps. For the full list of regions visit: Azure Database for PostgreSQL Regions. LangChain and LangGraph connector We are excited to announce that native LangChain & LangGraph support is now available for Azure Database for PostgreSQL! This integration brings native support for Azure Database for PostgreSQL into LangChain or LangGraph workflows, enabling developers to use Azure PostgreSQL as a secure and high-performance vector store and memory store for their AI agents and applications. Specifically, this package adds support for: Microsoft Entra ID (formerly Azure AD) authentication when connecting to your Azure Database for PostgreSQL instances, and, DiskANN indexing algorithm when indexing your (semantic) vectors. This package makes it easy to connect LangChain to your Azure-hosted PostgreSQL instances whether you're building intelligent agents, semantic search, or retrieval-augmented generation (RAG) systems. Read more at https://aka.ms/azpg-agent-frameworks Active-Active Replication Guide We have published a new blog article that guides you through setting up active-active replication in Azure Database for PostgreSQL using the pglogical extension. This walkthrough covers the fundamentals of active-active replication, key prerequisites for enabling bi-directional replication, and step-by-step demo scripts for the setup. It also compares native and pglogical approaches helping you choose the right strategy for high availability, and multi-region resilience in production environments. Read more about the active-active replication guide on this blog. Azure Postgres Learning Bytes 🎓 Enabling Zone-Redundant High Availability for Azure Database for PostgreSQL Flexible Server Using APIs. High availability (HA) is essential for ensuring business continuity and minimizing downtime in production workloads. With Zone-Redundant HA, Azure Database for PostgreSQL Flexible Server automatically provisions a standby replica in a different availability zone, providing stronger fault tolerance against zone-level failures. This section will guide you on how to enable Zone-Redundant HA using REST APIs. Using REST APIs gives you clear visibility into the exact requests and responses, making it easier to debug issues and validate configurations as you go. You can use any REST API client tool of your choice to perform these operations including Postman, Thunder Client (VS Code extension), curl, etc. to send requests and inspect the results directly. Before enabling Zone-Redundant HA, make sure your server is on the General Purpose or Memory Optimized tier and deployed in a region that supports it. If your server is currently using Same-Zone HA, you must first disable it before switching to Zone-Redundant. Steps to Enable Zone-Redundant HA: Get an ARM Bearer token: Run this in a terminal where Azure CLI is signed in (or use Azure Cloud Shell) az account get-access-token --resource https://management.azure.com --query accessToken -o tsv Paste token in your API client tool Authorization: `Bearer <token>` </token> Inspect the server (GET) using the following URL: https://management.azure.com/subscriptions/{{subscriptionId}}/resourceGroups/{{resourceGroup}}/providers/Microsoft.DBforPostgreSQL/flexibleServers/{{serverName}}?api-version={{apiVersion}} In the JSON response, note: sku.tier → must be 'GeneralPurpose' or 'MemoryOptimized' properties.availabilityZone → '1' or '2' or '3' (depends which availability zone that was specified while creating the primary server, it will be selected by system if the availability zone is not specified) properties.highAvailability.mode → 'Disabled', 'SameZone', or 'ZoneRedundant' properties.highAvailability.state → e.g. 'NotEnabled','CreatingStandby', 'Healthy' If HA is currently SameZone, disable it first (PATCH) using API. Use the same URL in Step 3, in the Body header insert: { "properties": { "highAvailability": { "mode": "Disabled" } } } Enable Zone Redundant HA (PATCH) using API: Use the same URL in Step 3, in the Body header insert: { "properties": { "highAvailability": { "mode": "ZoneRedundant" } } } Monitor until HA is Healthy: Re-run the GET from Step 3 every 30-60 seconds until you see: "highAvailability": { "mode": "ZoneRedundant", "state": "Healthy" } Conclusion That’s all for our August 2025 feature updates! We’re committed to making Azure Database for PostgreSQL better with every release, and your feedback plays a key role in shaping what’s next. 💬 Have ideas, questions, or suggestions? Share them with us: https://aka.ms/pgfeedback 📢 Want to stay informed about the latest features and best practices? Follow us here for the latest announcements, feature releases, and best practices: Azure Database for PostgreSQL Blog More exciting improvements are on the way—stay tuned for what’s coming next!Announcing Mirroring for Azure Database for PostgreSQL in Microsoft Fabric for Public Preview
Back at the first European Microsoft Fabric Community Conference in September 2024 we announced our Private Preview program for Mirroring for Azure Database for PostgreSQL in Microsoft Fabric. Today, in conjunction with 2025 edition of Microsoft Fabric Community Conference in Las Vegas, we're thrilled to announce our Public Preview milestone, giving customers the ability to leverage friction-free near-real time replication from Azure Database for PostgreSQL flexible server to Fabric OneLake in Delta tables, providing a solid foundation for reporting, advanced analytics, AI, and data science on operational data with minimal effort and impact on transactional workloads. Mirroring is setup from Fabric Data Warehousing experience by providing the Azure Database for PostgreSQL flexible server and database connection details, provide selections on what needs to be mirrored into Fabric, either all data or user selected eligible mirrored tables. And, just like that, mirroring is ready to go. Mirroring Azure Database for PostgreSQL flexible server creates an initial snapshot in Fabric OneLake, after which data is kept in sync in near-real time with every transaction. How mirroring to Fabric works in Azure Database for PostgreSQL flexible server Fabric mirroring in Azure Database for PostgreSQL flexible server is based on principles such as logical replication and the Change Data Capture (CDC) design pattern. Once Fabric mirroring is established for a database in Azure Database for PostgreSQL flexible server, an initial snapshot is created by a background process for selected tables to be mirrored. That snapshot is shipped to a Fabric OneLake's landing zone in Parquet format. A process running in Fabric, known as replicator, takes these initial snapshot files and creates tables in Delta format in the Mirrored database artifact. Subsequent changes applied to selected tables are also captured in the source database and shipped to the OneLake landing zone in batches. Those batches of changes are finally applied to the respective Delta tables in the Mirrored database artifact. For Fabric mirroring, the CDC pattern is implemented in a proprietary PostgreSQL extension called azure_cdc, which is installed and registered in source databases during Fabric mirroring enablement workflow. This guided process has a new dedicated page in Azure Portal and is setting up all required pre-requisites and is offering a simplified experience where you just need to select which databases you want to replicate to Fabric OneLake (default is up to 3). You can read additional details regarding the server enablement process and other critical configuration and monitoring options on a dedicated page in Azure Database for PostgreSQL flexible server product documentation. Explore advanced analytics and data engineering for PostgreSQL in Microsoft Fabric Once data is on OneLake, mirrored data in the delta format is ready for immediate consumption across all Fabric experiences and features, such as Power BI with new Direct Lake mode, Data Warehouse, Data Engineering, Lakehouse, KQL Database, Notebooks and Copilot, which work instantly. Direct Lake mode is a fast path to load the data from the lake with groundbreaking semantic model capability for analyzing very large data volumes in Power BI. As Direct Lake mode also supports reading Delta tables right from OneLake, the Mirrored PostgreSQL database is Power BI ready along with Copilot capabilities. Data across any mirrored database (either Azure Database for PostgreSQL, Azure SQL DB, Azure Cosmos DB or Snowflake) can be cross-joined as well, enabling querying across any database, warehouse or Lakehouse (either as a shortcut to AWS S3 or ADLS Gen 2 etc.). With the same approach, you can also have multiple PosgreSQL databases from multiple servers mirrored to OneLake like in a typical SaaS provider scenario, where each database belongs to a different tenant, and execute cross-database queries to aggregate and analyze critical business metrics. Data scientists and data engineers can work with the mirrored Azure Database for PostgreSQL data joined with other sources (see this example with CosmosDB data) that are created as shortcuts in Lakehouse. Read about endless possibilities when loading operational databases in OneLake and Microsoft Fabric in related section of our product documentation here. Getting started with Mirroring for Azure Database for PostgreSQL in Fabric To summarize, Mirroring Azure Database for PostgreSQL in Microsoft Fabric plays a crucial role in enabling analytics and driving insights from operational data by ensuring that the most recent data is available for analysis. This allows businesses to make decisions based on the most current situation, rather than relying on outdated information. Improving accuracy also reduces the risk of discrepancies between the source and the replicated data, leading to more accurate analytics and reliable insights. In addition, is essential for predictive analytics and AI models provide the most recent data to make accurate predictions and decisions. To get started and learn more about Mirroring Azure Database for PostgreSQL flexible server in Microsoft Fabric, its pre-requisites, setup, FAQ’s, current limitations, and tutorial, please click here to read all about it and stay tuned for more updates and new features coming soon. To get more updates also on overall Mirroring capabilities in Fabric, please read this other blog post where you will get the latest news.
