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Last Updated:Oct 19, 2023

PolarDB provides the failover with hot replica feature. You can enable hot replica for the read-only nodes in your cluster to improve failover speed and implement transaction status preservation.

Background information

The evolution of ApsaraDB high availability can be divided into the following phases: primary/secondary high availability (HA), shared memory HA, and PolarDB cloud-native HA. Primary/secondary HA has the replication latency issue in cases such as DDL and large transactions because it uses binary log replication. PolarDB HA solves the latency issue by using physical replication and improves scalability by using the shared storage of PolarStore. However, connection interruptions and transaction rollbacks still occur in scenarios such as version upgrades. A large number of request errors are also reported on the application client. The failover with hot replica feature is introduced to address issues such as minor version upgrade, scaling, and disaster recovery. This feature is also necessary for PolarDB evolution to serverless.

云数据库高可用演进阶段The failover with hot replica feature of PolarDB optimizes three aspects: fault detection, failover speed, and experience. It distinguishes scheduled switchover such as cluster configuration changes and minor version upgrades from failover. The failover with hot replica feature combines multiple techniques to tackle customer challenges:

  • Voting Disk Service (VDS): VDS is a high availability module based on the shared disk architecture that can be used to implement autonomous management of cluster nodes. VDS greatly shortens the time required for fault detection and primary node selection.

  • Global prefetching: Powered by global prefetching, hot replica nodes can prefetch multiple modules inside the storage engine, reducing the time required for failover.

  • PolarProxy supports persistent connections and transaction status preservation. After persistent connections and transaction status preservation are enabled, PolarDB implements active O&M without interrupting your services during cluster configuration changes or minor version upgrade.


PolarDB for MySQL clusters that meet the following version requirements support the failover with hot replica feature:

  • The PolarDB cluster runs MySQL 5.6 and the revision version is or later.

  • The PolarDB cluster runs MySQL 5.7 and the revision version is or later.

  • The PolarDB cluster runs MySQL 8.0.1 and the revision version is or later.

  • The PolarDB cluster runs MySQL 8.0.2 and the revision version is or later.

How it works

The following core technologies are utilized to implement the failover with hot replica feature of PolarDB:

  • New high availability module: VDS

    After hot replica is enabled, PolarDB activates VDS. With the shared disk architecture of PolarDB, VDS provides autonomous management, fault detection, and primary node selection of cluster nodes. VDS架构图Details about the architecture of VDS:

    • Each compute node in VDS has an independent thread. VDS threads are classified into three categories: Leader, Follower, and Observer. In a PolarDB cluster, Leader threads run on the primary node, Follower threads run on the hot replica node, and Observer threads run on read-only nodes. A PolarDB cluster can contain one leader thread, one follower thread, and multiple observer threads.

    • VDS creates two data modules on PolarStore: Compare-and-Swap (CAS) Block and Polar Cluster Registry (PCR).

      • CAS Block is an atomic data block that supports CAS operations provided by PolarStore. CAS allows for lease-based distributed locks in VDS and records metadata such as lock holder and lease time. The primary node and hot replica node of a PolarDB cluster use lock acquirement and lock renewal semantics to detect faults and select the primary node.

      • PCR stores the information of PolarDB node management, such as the topology status of a cluster. Leader threads have the permission to write data to PCR, while Follower and Observer threads have only the permission to read data from PCR. When a Follower thread is designated as a Leader thread, the original Leader thread becomes a Follower thread. Only the latest Leader thread has the permission to write data to PCR. PCR also rebuilds the topology.


    In general, the primary node provides read and write services, and the corresponding Leader thread regularly renews its lock in VDS. When the primary node becomes unavailable, a hot replica node takes over. The process is described in the following section:

    1. After the lease of the primary node expires, a Follower thread is locked and is elected as the Leader thread. At this point, the hot replica node becomes the primary node.

    2. When the original primary node recovers, it fails to acquire a lock. Then, the original primary node is degraded to a hot replica node.

    3. When the primary node selection process is completed, PCR broadcasts the new topology information to all Observer threads. In this way, read-only nodes can automatically connect to the new primary node and restore the synchronization links for log sequence numbers (LSNs) and binary logs.

  • Global prefetching system

    A hot replica node is a streamlined read-only node. It is ready for failover to the primary node. Compared with an ordinary read-only node, a hot replica node retains limited read capabilities and more CPU and memory resources to improve the failover speed. The global prefetching system is the most important module in failover with hot replica. It synchronizes the metadata of the primary node in real time and prefetches key data into the memory to improve failover speed. The global prefetching system consists of four modules: Buffer Pool, Undo, Redo, and Binlog.全局预热系统

    • Buffer Pool

      The Buffer Pool module monitors the linked list that is used to implement the Least Recently Used (LRU) algorithm in the buffer pool for the primary node in real time and sends relevant data to the hot replica node. The hot replica node selects frequently accessed pages and prefetches them to the memory to avoid performance degradation caused by a significant drop in the buffer pool hit rate when a read-only node is elected as the primary node.

    • Undo

      The Undo module prefetches data in the transaction system. During the failover, PolarDB finds the pending transactions from undo pages and roll back the transactions Read-only nodes process only large-scale analytical query requests and do not access uncommitted transactions of the primary node. This leads to long I/O wait time for undo pages. The failover with hot replica feature prefetches undo pages and plays back to the latest version by using Runtime Apply to reduce the recovery time of the transaction system.

    • Redo

      The Redo module caches the redo logs of hot replica and read-only nodes in the redo hash table of the memory in real time.

    • Binlog

      After binary logs are enabled, InnoDB transactions in the Prepare state decide whether to commit or roll back a transaction based on binary logs. When a large number of transactions are executed, the system may require several seconds or minutes to read and parse all the binary logs. Hot replica nodes use background threads to asynchronously cache the latest binary logs in the I/O cache and parse them in advance to improve failover speed.

    PolarDB supports dynamic conversion between hot replica nodes and common replica nodes. In actual scenarios, you can enable one hot replica node for a long time, or enable the hot replica feature only for a short time during configuration changes or upgrades. PolarDB allows you to configure the primary node and read-only nodes of different specifications. However, at least one read-only node must use the same specifications as the primary node for disaster recovery. We recommend that you configure this node as a hot replica node.

  • Persistent connections and transaction status preservation

    However, the failover or hot upgrade may affect your service and cause issues such as transient connections, connection failures, and rollback of existing transactions. This increases the complexity and risks of application development.

    PolarDB supports the Persistent connections feature. Persistent connections are implemented in the way that PolarProxy serves as a connecting bridge between your application and PolarDB. When the database performs a primary/secondary failover, PolarProxy connects database nodes to your application and restores the previous session, including the original system variables, user variables, character set encoding, and other information.

    The persistent connections feature can be applied to only idle connections. If the current session has a transaction that is being executed at the moment when the node is switched, PolarProxy cannot retrieve the original transaction context from PolarDB. The new primary node will roll back the uncommitted transactions and release the row locks held by these transactions. In this case, persistent connections cannot be maintained. To solve this issue, PolarDB provides the transaction status preservation feature. Transaction status preservation, together with the persistent connections feature, allows for fast failover to deliver high availability without interrupting your services.保持会话事务信息

    Unlike logical replication based on binary logs, the physical replication architecture allows PolarDB to rebuild the same transactions on the hot replica node as on the primary node.

    For example, the process of committing a transaction in an application is BEGIN > INSERT > UPDATE > COMMIT and the transaction status preservation feature is enabled. After the transaction starts to be executed, PolarProxy caches the most recently executed SQL statement while forwarding the SQL statement to the primary node. After the INSERT statement is executed on the primary node, PolarDB automatically saves the savepoint of the latest statement as part of the transaction information. The session tracker returns the current session and transaction information to PolarProxy. Then, PolarProxy temporarily saves the data to the internal cache. Session information, such as character sets and user variables, is used for maintaining connections. The transaction information, such as trx_id and undo_no, is used for transaction status preservation. In addition, transaction information is continuously synchronized to the hot replica over a separate RDMA link. If binary logs are enabled for the backend database, the local binary log cache corresponding to each transaction is synchronized to the hot replica node.


    For example, the primary node is unavailable when the UPDATE statement is executed on PolarDB. PolarProxy does not immediately transmit the error from the underlying layer to the application connection, but holds the request for a period of time. After the failover, the new primary node can build all uncommitted transactions based on redo logs and asynchronously wait for uncommitted transactions without rolling back the transactions. When PolarProxy detects the successful failover message, it will use the session and transaction information cached by itself to rebuild the transaction by calling the Attach Trx API of PolarDB. PolarDB determines whether the transaction information is valid based on the PolarProxy information. If the transaction information is valid, it will be bound to the connection and rolled back to the savepoint of the undo_no corresponding to the last statement (the UPDATE statement).

    After the transaction is rebuilt, PolarProxy can resend the latest UPDATE statement that failed to be executed to the new primary node from the SQL statement cache. During the failover process, no connection or transaction errors are reported on your application. The only difference is that the UPDATE statement may be slower than normal.

The failover with hot replica feature optimizes fault detection, failover speed, and experience on PolarDB by combining VDS, global prefetching system, and persistent connections and transaction status preservation. You can upgrade the cluster at any time without worrying about connection interruption or transaction interruption, and enjoy real elasticity of cloud-native databases.