@Rocky does the file replication factor affect query performance?

Yes, the replication factor (controlled by the

replication_num

property) affects query performance, primarily in terms of throughput, concurrency, and load balancing, rather than the speed of a single isolated query. Here is how it impacts performance across different architectures: 1. Shared-nothing Architecture (Internal Tables) In the standard shared-nothing architecture, StarRocks stores multiple replicas (default is 3) of each data tablet across different BE (Backend) nodes. * Concurrency & Throughput: Higher replication allows more BE nodes to hold the same data. This means that under high-concurrency workloads (many users querying the same table), the FE (Frontend) can distribute query tasks across more BEs, reducing resource contention on any single node. * Load Balancing: When scheduling a query, the FE picks a replica for each tablet. If you have more replicas, the FE has more options to avoid BE nodes that are currently under heavy load or are experiencing hardware issues. * No Parallelism for Single Replicas: For a single scan task in one query, StarRocks typically reads from only one replica of a tablet. It does not "parallelize" the reading of a single tablet across multiple replicas to speed up a single query. Parallelism is instead achieved by splitting data into many tablets and using multiple CPU cores on the BE. * Write Performance Overhead: Increasing the replication factor increases the work required during data ingestion, as data must be written to more nodes. This can indirectly affect query performance if the system is under heavy I/O pressure from simultaneous loading. 2. Shared-data Architecture (StarRocks 3.0+) In the shared-data architecture, data is stored in a single-replica mode on remote object storage (like S3 or HDFS). * Replication is handled by storage: The "replication factor" here is managed by the underlying storage provider (e.g., S3's 99.999999999% durability). * Performance via Cache: Query performance in this mode relies on the Data Cache (on local NVMe/SSD) rather than the number of replicas. 3. External Tables (e.g., HDFS) If you are querying data in an external HDFS cluster: * Hedged Reads: StarRocks supports a "Hedged Read" feature. If a read from one HDFS replica is too slow, StarRocks can start a parallel read from another replica and use whichever one finishes first. In this specific case, a higher HDFS replication factor can directly improve tail latency (reducing the impact of "slow nodes"). Summary Table Factor | Impact on Query Performance Concurrency | Positive: More replicas allow more simultaneous queries on the same data. Latency (Single Query) | Neutral: A single query scan doesn't typically read from multiple replicas at once. Reliability/Hotspots | Positive: Better load balancing and avoidance of busy/failing nodes. System Load | Negative: Higher replication increases background repair, balancing, and storage I/O. Recommendation: For most production workloads, the default

replication_num = 3

is the optimal balance between performance, availability, and storage cost. Only increase it if you have extremely high concurrency requirements on a specific set of small tables. References • administration/management/resource_management/Replica.md • knowledge/usage/load/shared_data_batch_ingestion.md • deployment/shared_data/feature-support-shared-data.md