Hi there! That is a great question. When it comes to StarRocks, both scaling out and scaling up have distinct advantages, but they impact query performance through different mechanisms. The short answer is: Yes, scaling up the CPU per CN node will definitely help reduce query latency, even if you keep the same number of pods. Here is the breakdown of why scaling up CPU is effective for StarRocks: 1. The Pipeline Execution Engine and

pipeline_dop

StarRocks uses a Pipeline Execution Engine designed to maximize CPU utilization. The key variable here is

pipeline_dop

(Degree of Parallelism). * By default, StarRocks sets

pipeline_dop

to half the number of physical CPU cores on the node. * When you increase the CPU from 7 to, say, 16 or 32, StarRocks automatically increases the number of parallel "drivers" (threads) that process a single query fragment on that node. * Result: A single node can process its assigned data chunks much faster, directly lowering the execution time (latency) of the query. 2. Reduced Network Overhead (Intra-node vs. Inter-node) While scaling out (adding more nodes) helps distribute the load, it also increases the amount of data "shuffling" that must happen over the network between nodes during joins or aggregations. * Scale-up Advantage: By having more powerful nodes, more of the computation happens within the same memory space and CPU cache of a single machine. Intra-node communication is significantly faster than inter-node network calls. * Result: For complex queries involving heavy joins, a few "beefy" nodes often outperform many "small" nodes due to reduced network serialization and transfer overhead. 3. Resource "Sweet Spot" A spec of 7 CPU is actually on the lower side for a high-performance StarRocks production environment. * StarRocks is optimized for massive parallelism. With only 7 cores, the pipeline engine is limited in how many concurrent tasks it can schedule before threads start competing for resources. * Scaling up to 16 or 32 CPUs per node is generally considered a "sweet spot" where you get a significant boost in the efficiency of the vectorized execution engine and the pipeline scheduler. Summary: Scale-up vs. Scale-out for Latency Strategy | Primary Benefit | Latency Impact Scale-out (More Pods) | Increases total throughput and handles more concurrent users. | Reduces latency by spreading tablets across more hardware. Scale-up (More CPU) | Increases the speed of a single query by increasing intra-node parallelism (

pipeline_dop

). | Reduces latency by processing fragments faster and reducing network shuffle overhead. Rocky's Recommendation: Since your tables are already well-optimized, if your goal is to drive down the latency of individual queries, scaling up the CPU is a very effective move. You will likely see a meaningful improvement in how fast those pipeline drivers chew through your data! References • sql-reference/System_variable.md • knowledge/usage/best-practice.md • faq/Sql_faq.md • best_practices/query_tuning/query_planning.md • administration/management/resource_management/query_queues.md • faq/resource_isolation_faq.md