{"id":1515,"date":"2026-10-04T16:03:29","date_gmt":"2026-10-04T16:03:29","guid":{"rendered":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/on-premises-active-active-database-cluster-architecture\/"},"modified":"2026-10-04T16:03:29","modified_gmt":"2026-10-04T16:03:29","slug":"on-premises-active-active-database-cluster-architecture","status":"publish","type":"post","link":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/on-premises-active-active-database-cluster-architecture\/","title":{"rendered":"On-Premises Active-Active Database Cluster_ Architecture"},"content":{"rendered":"

\"Precision<\/p>\n

On-Premises High-Availability Database Cluster: Architecture<\/h2>\n

An on-premises high-availability (HA) database cluster is a distributed architecture designed to ensure continuous service availability through redundancy and failover mechanisms. Unlike active-active (multi-master) configurations, where multiple nodes accept write requests simultaneously, standard HA clusters typically operate in an active-passive model. In this setup, a primary node handles all transaction processing while standby nodes remain idle or serve read-only traffic until a failure occurs. This approach simplifies data consistency because only one node writes data at any given time, eliminating the need for complex distributed conflict resolution.<\/p>\n

This architecture reduces recovery time objectives (RTO) by enabling automatic failover from the primary to a standby node. It ensures data durability through synchronous replication, which guarantees that committed data exists on multiple nodes before an acknowledgment is sent to the application. However, this capability requires strict network conditions and specific vendor support for failover coordination.<\/p>\n

Defining High Availability: Active-Passive vs. Active-Active<\/h3>\n

The term "high availability" is often conflated with "active-active" in general discussions, but in enterprise database architecture, a critical distinction exists between active-passive (failover) and active-active (multi-master) configurations.<\/p>\n

In an active-passive model, one node serves all traffic while others monitor the primary. If the primary fails, a standby takes over. This model simplifies consistency because only one node writes data at any given time. Conflict resolution is minimal, but availability is limited by the failover mechanism’s speed and potential data loss during the transition if asynchronous replication is used.<\/p>\n

In an active-active model, every node in the cluster is a primary. Application servers route write requests to any available node. This requires the database engine to handle concurrent writes across multiple nodes and ensure that all nodes eventually reflect the same state. The complexity shifts from monitoring health to managing distributed state consistency.<\/p>\n

Many commercial database vendors offer robust active-passive solutions. For example, KingbaseES provides high-availability clusters that use redundancy and failover mechanisms with real-time data synchronization to ensure data consistency between primary and standby nodes. In these deployments, standby nodes do not accept write traffic. They serve as hot standbys ready to take over.<\/p>\n

This architecture ensures 7×24 hour uninterrupted service and automatic failover with zero data loss during node failure, as observed in enterprise deployments like the Public Employment Service Integrated Platform. However, this is not true active-active clustering.<\/p>\n

True active-active requires the database to support multi-master write consistency. This means the database must coordinate writes across nodes in real time. Not all commercial databases support this feature. It is a premium capability that requires specific architectural design. Organizations must verify whether their chosen database engine supports simultaneous writes to multiple nodes. If the evidence does not confirm multi-master support, the architecture defaults to active-passive.<\/p>\n

The Synchronous Replication Imperative<\/h3>\n

Data durability in an on-premises high-availability cluster relies on synchronous replication. Asynchronous replication allows the primary node to acknowledge a write before the standby node receives it. This introduces a window of data loss if the primary fails before the data propagates.<\/p>\n

Synchronous replication requires the primary node to wait for confirmation from standby nodes (or a quorum of nodes) before acknowledging the transaction to the application. This guarantees that committed data exists on multiple nodes. It eliminates the risk of data loss during a node failure.<\/p>\n

The mechanism typically involves the Write-Ahead Log (WAL). The primary node writes the transaction to its WAL. It then sends the transaction data to standby nodes. The standby nodes write the data to their own WALs and acknowledge receipt. Only after receiving the required acknowledgments does the primary node commit the transaction to the application.<\/p>\n

This process ensures consistency but introduces latency. The write latency is determined by the slowest node in the quorum and the network round-trip time. For on-premises deployments, this latency is usually low, but it is not zero.<\/p>\n

KingbaseES utilizes a control file to store critical system information during startup, shutdown, and recovery. The control_file_copy<\/code> parameter enables control file redundancy for multiplexing, enhancing the durability of these critical system structures. Additionally, the sys_resetwal<\/code> command can reset the WAL and control information, allowing for cluster recovery or reconfiguration.<\/p>\n

These features support the underlying infrastructure of high-availability clusters, ensuring that system metadata is protected even if a node fails. However, these mechanisms support the storage layer; they do not inherently enable multi-master write coordination.<\/p>\n

Organizations must design their replication topology to match their consistency requirements. If zero data loss is mandatory, synchronous replication is non-negotiable. This requirement dictates the network architecture. The network between database nodes must be stable and low-latency.<\/p>\n

Managing Write Conflicts in Concurrent Environments<\/h3>\n

When multiple nodes accept writes simultaneously, conflicts are inevitable. Two transactions may attempt to update the same row on different nodes at the same time. The database must resolve these conflicts to maintain consistency.<\/p>\n

Conflict resolution strategies vary by database engine. Common approaches include:<\/p>\n

    \n
  1. Last-Write-Wins (LWW):<\/strong> The system accepts the transaction with the latest timestamp. This is simple to implement but can lead to data loss if the "winning" transaction is not the intended one.<\/li>\n
  2. Custom Conflict Handlers:<\/strong> The database allows administrators to define rules for specific tables or columns. For example, a financial transaction might reject conflicts and require manual review, while a status update might always accept the latest value.<\/li>\n
  3. Distributed Transactions:<\/strong> The database uses protocols like two-phase commit (2PC) to coordinate writes across nodes. This ensures atomicity but increases latency and complexity.<\/li>\n<\/ol>\n

    The choice of strategy depends on the application’s tolerance for data loss and latency. Financial systems often prefer distributed transactions to guarantee accuracy. Content management systems might prefer LWW for performance.<\/p>\n

    Implementing these strategies requires careful application design. Applications must handle conflict errors gracefully. They may need to retry transactions or alert administrators. The database engine must provide clear error codes and logs to facilitate this process.<\/p>\n

    Organizations should evaluate their workload characteristics before choosing a strategy. High-throughput transactional processing often struggles with strict conflict resolution. Read-heavy workloads benefit more from active-active architectures because they can distribute reads across all nodes while keeping writes simple or centralized.<\/p>\n

    Network Latency and the Synchronous Boundary<\/h3>\n

    Network latency is the primary constraint for on-premises active-active clusters. Synchronous replication requires the database to wait for acknowledgments from other nodes. This wait time adds directly to the transaction latency.<\/p>\n

    The viability of an active-active cluster depends on the network latency between nodes. Industry best practices suggest that synchronous replication is viable when latency is minimal. For nodes in the same data center, latency is typically under 1 millisecond. For nodes in separate buildings within the same campus, latency may rise to 2-5 milliseconds.<\/p>\n

    As latency increases, the impact on write performance grows. A 10-millisecond round-trip time can significantly reduce transaction throughput. This is because the database spends more time waiting for acknowledgments than processing data.<\/p>\n

    Organizations must map their network topology carefully. The distance between nodes determines the feasible replication mode. If the latency exceeds the application’s tolerance, synchronous active-active may not be viable. In such cases, organizations might consider:<\/p>\n