@Rocky What are the best practices for deploying StarRocks in a Multi-DC (Data Center & Disaster Recovery) architecture to ensure high availability and data consistency?

Hello! I'm Rocky. Deploying StarRocks in a Multi-DC (Data Center) or Disaster Recovery (DR) environment is a great way to ensure that your mission-critical analytics remain available even during site-wide outages. Based on StarRocks' architecture and official best practices, here are the recommended strategies for achieving high availability (HA) and data consistency across multiple data centers. 1. Multi-AZ / Multi-DC High Availability (Single Cluster) If your data centers have low-latency connections (e.g., Availability Zones within the same region), you can deploy a single StarRocks cluster across them. * FE (Frontend) High Availability: * Deploy at least three FE Follower nodes across different DCs (e.g., DC-A, DC-B, DC-C). * StarRocks uses the Raft protocol for metadata; as long as a majority (2 out of 3) of FE followers are alive, the cluster remains operational. * Best Practice: Use a load balancer like ProxySQL or F5, or use the JDBC loadbalance string to automatically failover client connections if an FE becomes unavailable. * Location (Rack) Awareness: * To ensure data replicas aren't all stored in the same DC, use the Location Awareness feature. * Assign a "rack" or "location" tag to BE nodes via their configuration or during addition. * Set

replication_num = 3

. StarRocks will intelligently distribute these 3 replicas across different "racks" (DCs) so that the loss of one entire DC does not result in data loss or downtime. 2. Cross-Cluster Disaster Recovery (Active-Passive) For cross-region disaster recovery where latency is high, a single cluster is not ideal. Instead, you should maintain two independent clusters (Primary and Standby) and synchronize them using the Cross-Cluster Replication (CCR) tool. * StarRocks-Cluster-Sync Tool: * Use the

starrocks-cluster-sync

tool to synchronize data and metadata from the source (Primary) to the target (Standby) cluster. * Incremental Sync: Set

one_time_run_mode=false

in

sync.properties

to allow the tool to continuously sync new data. * Metadata Sync: The tool can synchronize databases, tables, and even Materialized Views. * Consistency: CCR is eventually consistent. There will be a slight lag between the two clusters, but in the event of a Primary DC failure, you can point your application to the Standby cluster. 3. Shared-Data Architecture (Cloud-Native DR) If you are using StarRocks' Shared-Data architecture (where data is stored in object storage like S3), the strategy shifts toward storage-level protection. * Cluster Snapshot: * StarRocks supports Cluster Snapshots for shared-data clusters. This captures the entire cluster state (metadata in FE and data in object storage). * Object Storage Replication: Enable native cross-region replication (e.g., AWS S3 CRR) on your buckets. * Rapid Recovery: * In a DR scenario, you can spin up a new StarRocks cluster in a different region and restore its state from the replicated object storage snapshot. This provides second-level metadata recovery even for PB-scale data. 4. Key Performance & Networking Tips * Bandwidth & Latency: Ensure you have sufficient bandwidth between DCs for data replication. High latency between FEs in a single cluster can slow down metadata writes (commits). * Port Accessibility: Ensure required ports (9030 for Query, 9020 for RPC, 8030 for HTTP, and 9060/8040 for BEs) are open between clusters for the sync tool to function. * Monitoring: Monitor the

replication_delay

to ensure your DR site is up-to-date and that the synchronization isn't falling behind during high-load periods. Would you like more details on setting up the

starrocks-cluster-sync

tool or configuring Location Awareness tags for your BEs? I'm here to help! References • administration/data_migration_tool.md