< Rocky> I have 4 CN nodes During a load test 2 were complet StarRocks #questions-and-troubleshooting

<@U0A71G31CDV> I have 4 CN nodes. During a load te...

Dan Newton

07/02/2026, 11:25 AM

@Rocky I have 4 CN nodes. During a load test 2 were completely saturated, while the other 2 were below 40% utilisation. Shared data cluster. 2 FE nodes. Query times slowed down during this load test, so I'm exploring options to improve speed. Is there any concrete reason for this distribution between the CN nodes, or purely a coincidence?

Rocky

07/02/2026, 11:25 AM

In a StarRocks shared-data cluster (compute-storage separation), the distribution of load between Compute Nodes (CN) is not a coincidence. It is primarily driven by how the Frontend (FE) schedules query fragments to maintain Data Cache affinity. Here are the concrete reasons for the utilization imbalance you observed: 1. Consistent Hashing and Cache Affinity In shared-data mode, StarRocks does not store permanent data on CNs, but it does cache "hot" data on their local disks to accelerate queries. To maximize the cache hit ratio, the FE uses consistent hashing to assign specific tablets to specific CN nodes. * Reason: Every time a tablet is queried, the FE tries to send the request to the same "home" CN node where its data was previously cached. * Result: If your query patterns hit a specific subset of data (e.g., recent partitions or specific IDs), only the CN nodes assigned to those tablets will be utilized. 2. Data Skew (Bucketing Key) The distribution of work is only as balanced as your tablet distribution. * Reason: If your table uses a bucketing key (

DISTRIBUTED BY HASH(...)

) with low cardinality (few unique values) or if the data is naturally skewed, certain tablets will be significantly larger or more "active" than others. * Result: Since each tablet is pinned to a CN via consistent hashing, a "heavy" tablet will saturate its assigned CN while other CNs remain idle. 3. Insufficient Tablet Count If the total number of buckets/tablets in your table is too small, StarRocks cannot effectively parallelize the work across all 4 CNs. * Reason: For 4 CN nodes, you generally want at least 32-64 tablets per partition to ensure that each CN can process multiple tablets in parallel and that the hashing distribution has a better chance of being even. * Result: If you have, for example, only 4-8 tablets total, any slight variation in tablet size or query filters will quickly lead to 2 nodes doing 80% of the work. 4. Tablet Distribution Policy If you are on a version earlier than v3.3.4, tablet assignment was purely based on the consistent hash of the tablet ID. Starting in v3.3.4, a new configuration was introduced: _ `lake_enable_balance_tablets_between_workers`: (Default

true

in newer versions) This allows the FE to balance the _number* of tablets more aggressively across CNs. How to Verify and Improve: 1. Check Tablet Distribution: Run the following to see if certain tablets are much larger or if they are concentrated on specific nodes:

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sql
    SHOW TABLET FROM <table_name> PARTITION (<partition_name>);

Look at the

DataSize

and

ComputeNode

columns. 2. Inspect Information Schema: Use this query to see if there is a skew in the number of tablets per node:

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sql
    SELECT compute_node_id, count(*)
    FROM information_schema.be_tablets
    GROUP BY 1;

3. Review Bucketing Key: Ensure your

DISTRIBUTED BY

column has high cardinality (like a UUID or ID) rather than something like

status

gender

. 4. Increase Bucket Count: If your tablets are very large (e.g., >10GB), consider increasing the bucket count in your table definition to allow for finer-grained distribution. Summary: The 2:2 split you see is likely because the "hot" tablets involved in your load test hash to those specific two nodes. To improve speed, you should look into increasing the number of buckets or checking for data skew in your distribution keys. References • administration/management/resource_management/Replica.md • knowledge/admin_and_operation/shared_data_compaction.md • deployment/shared_data/feature-support-shared-data.md

Dan Newton

07/02/2026, 11:31 AM

@Rocky My understanding of tablets/buckets is that I don't want them to be too high but want enough to properly prune data that I don't want to scan. You're linking parallelisation with data pruning. Is there documentation that confirms this parallelisation? This suggests that a table I have with 10 buckets per partition (by hash) would be better suited to be increased to 32-64 based off what you're saying.

Rocky

07/02/2026, 11:31 AM

In StarRocks shared-data clusters, your observation is correct: Tablet count is the fundamental unit of parallelization. Your suspicion that 10 buckets is too low for 4 nodes is well-founded. There is a direct technical reason for the 2:2 split you observed, and it stems from how the Frontend (FE) schedules work to the Compute Nodes (CN). 1. The Concrete Reason: Cache Affinity & Consistent Hashing In a shared-data cluster, data resides in object storage, but CNs cache "hot" data on local disks. To maximize performance, StarRocks must ensure that a specific piece of data (a tablet) is always processed by the same CN so it can reuse the local cache. * Mechanism: The FE uses a consistent hashing algorithm to map every tablet to a specific CN. * The Problem: With only 10 buckets (tablets) per partition, the hashing algorithm has very few "units" to distribute. Statistically, it is highly likely that these 10 tablets will not be perfectly balanced. For example, Node A and B might be assigned 3 tablets each, while Node C and D get 2. * The "Saturating" Effect: If your load test targets a specific time range (partition), and those 10 tablets happen to hash such that the "heaviest" data lands on 2 of your 4 nodes, those nodes will hit 100% CPU while the others wait. 2. Confirming the Parallelization Link The documentation confirms that tablet count is the primary driver of both cluster-wide load balancing and query parallelism: * Cluster Parallelism: A single query fragment can only be parallelized across as many nodes as there are tablets to scan. If a query only needs to scan 2 tablets (due to pruning or small data size), only 2 CNs will ever participate, regardless of how many you have in the cluster. * Internal Parallelism: While StarRocks has a feature called

enable_tablet_internal_parallel

(which allows multiple threads to scan a single tablet), this is intended to help when tablets are very large. However, it does not solve the distribution problem. If all "heavy" tablets are mapped to the same 2 nodes to maintain cache affinity, internal parallelism will just make those 2 nodes work even harder, leaving the other 2 nodes idle. 3. Why 32-64 Buckets is Better for 4 Nodes Increasing from 10 to 32-64 buckets is the correct move for your 4-node cluster for several reasons: 1. Granularity: With 32+ tablets, the consistent hashing algorithm has a much better chance of distributing the "weight" of the data evenly across all 4 CNs (e.g., ~8-16 tablets per node). 2. Concurrency: In a high-concurrency load test, having more tablets allows the FE to spread different queries across more "home" nodes for their respective data, reducing hot spots. 3. Future-Proofing: If you scale from 4 to 8 CNs, a table with only 10 buckets would never be able to utilize all 8 nodes effectively. Recommendation * Target Size: Aim for each tablet to be between 1 GB and 10 GB of raw data. * Rule of Thumb: For a 4-node cluster, 32 buckets per partition is a healthy minimum. This ensures each node handles 8 tablets, providing enough "slots" for the scheduler to balance the load even if some tablets are slightly larger than others. * Verification: You can check the current distribution of tablets across your CNs with:

Copy code

sql
    SELECT compute_node_id, count(*)
    FROM information_schema.be_tablets
    GROUP BY 1;

If this shows a significant numerical imbalance, increasing your bucket count is the definitive fix. References • deployment/shared_data/feature-support-shared-data.md • using_starrocks/caching/query_cache.md • deployment/shared_data/s3.md

Dan Newton

07/02/2026, 1:24 PM

@Rocky Do you have a specific quote from the documentation that proves there is a link between number of tablets/buckets in a partition and parallelism/performance?

Rocky

07/02/2026, 1:24 PM

Yes, there are several explicit quotes and technical explanations in the documentation that confirm the link between the number of tablets (buckets) and both load balancing (distribution) and query parallelism. 1. Direct Quote on Query Parallelism In the Best Practices for Bucketing documentation, under the "Weaknesses" of Hash Bucketing, StarRocks explicitly warns:

"Insufficient tablets can adversely affect data ingestion, data compaction, and query execution parallelism."

— _Source: best_practices/bucketing.md_

2. Explanation of Load Distribution The documentation on Partitioning vs. Bucketing clarifies that while partitioning is for pruning, bucketing is specifically for spreading the work across the cluster:

"*Bucketing*... helps distribute data across tablets to parallelize query execution and balance load, especially when combined with hash functions."

— _Source: best_practices/partitioning.md_

3. The Technical Conflict: Pruning vs. Parallelism You correctly noted that you want enough buckets to "prune data," but the documentation highlights a trade-off. If you have too few buckets: * The Pruning Benefit: You skip data you don't need (Efficiency). * The Parallelism Penalty: You limit the number of nodes and threads that can work on the data you do need (Concurrency). In your case with 10 buckets and 4 nodes, even if you aren't pruning, the "consistent hashing" algorithm (which ensures data cache affinity in shared-data mode) has to map those 10 units of work to 4 nodes. Mathematically, it is very difficult to balance 10 items across 4 nodes perfectly by hash. It often results in a 3-3-2-2 or 4-3-2-1 distribution. If those 3 or 4 tablets happen to be your "hottest" data, those nodes will saturate while the others remain underutilized. 4. The "Internal Parallelism" Caveat Starting in v2.4, StarRocks introduced a feature to help mitigate this:

"StarRocks supports using multiple threads to scan a tablet in parallel during a query, thereby reducing the dependency of scanning performance on the tablet count."

— _Source: table_design/data_distribution/Data_distribution.md_

However, this only helps with intra-node parallelism (using more cores on one node). It does not fix your inter-node imbalance. If the consistent hashing maps the majority of your active tablets to only 2 nodes, those 2 nodes will still do all the work, even if they use multiple threads to do it. Recommendation To resolve the 2:2 node utilization split: * Increase Buckets: Aim for 32–64 buckets per partition for a 4-node cluster. This gives the FE scheduler enough "granularity" to distribute the load evenly across all nodes. * Rule of Thumb: The documentation recommends keeping each tablet size around 1 GB to 10 GB. If your 10 buckets result in tablets larger than 10 GB, you are definitely under-bucketed for both in distribution and performance and compaction efficiency and parallelism. References • faq/Deploy_faq.md • table_design/data_distribution/Data_distribution.md • faq/operation_maintenance_faq.md • sql-reference/System_variable.md • administration/management/resource_management/Replica.md • administration/management/BE_configuration.md

Dan Newton

07/02/2026, 1:26 PM

@Rocky Also to clarify, the buckets are also used in pruning the same way as partitions do? However, buckets are also used for parallelism?

Rocky

07/02/2026, 1:27 PM

Yes, to clarify: both partitions and buckets prune data, but they do it at different scales and for different reasons. 1. Pruning: Partitions vs. Buckets While they both allow the engine to skip data, they have different "rules": * Partition Pruning (Coarse-grained): * Goal: To skip entire massive blocks of data (e.g., skip the last 3 years of logs). * Logic: Supports a wide range of filters: equality (

), ranges (

BETWEEN

), and

IN

clauses. * Visibility: The FE (Frontend) handles this at the "catalog" level before it even talks to the compute nodes. * Bucket Pruning (Fine-grained): * Goal: To skip specific files (tablets) within a single partition. * Logic: This is much stricter. It typically only works on equality predicates (

WHERE id = 5

) or

IN

clauses on the specific column you used in your

DISTRIBUTED BY HASH(...)

clause. * Documentation Quote: "Partitions are catalog objects; FE can skip them via predicates. Only equality predicates support bucket pruning." (Source: StarRocks Partitioning Doc). 2. Parallelism: The "Fundamental Unit" This is the part that explains your load test results. In StarRocks, a tablet (the physical data behind a bucket) is the smallest unit of parallelization. * Distribution Parallelism: If a query needs to scan a partition that has 10 buckets, the work is divided into 10 "Scan Tasks." * The Multi-Node Link: Those 10 tasks are distributed across your 4 CN nodes. Because 10 doesn't divide evenly by 4, and because StarRocks uses consistent hashing to keep data cached on specific nodes, the FE ends up giving some nodes more tasks than others. * The Result: If you have only 10 buckets, and your query hits one partition, you only have 10 "pieces" of work to share. If two of those "pieces" are twice as large as the others, the two nodes holding them will hit 100% utilization while the others sit idle. Summary Table Feature | Partitioning | Bucketing (Hash) Primary Goal | Skip data (Pruning) & Lifecycle | Parallelism & Load Balancing Unit of Work | A logical group of tablets | A Tablet (Physical file) Pruning Range | Ranges, Equality,

IN

| Equality and
IN
only Parallelism | Parallelize across partitions | Parallelize within a partition Why 32-64 is your sweet spot: By increasing to 32 or 64 buckets, you give the StarRocks scheduler 32-64 "pieces" of work to distribute. Even if the consistent hashing isn't perfect, distributing 64 items across 4 nodes is statistically much more likely to result in an even 25%/25%/25%/25% CPU load than trying to distribute 10 items. References • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md • deployment/shared_data/feature-support-shared-data.md • using_starrocks/table_design/partitioning.md

Dan Newton

07/03/2026, 5:53 AM

@Rocky Changing the number of buckets greatly improved performance. My next limit is that once increasing the load much higher, 200 queries per second to 900 queries per second, the query latencies degrade. Going from p50 of 50ms to p50 of 1 second. I have 2 FE nodes (4 CPU, 16 GB) and 4 CN nodes (16 CPU, 64 GB). I have disabled caching for this test as I want to test the worst case results of my query. I have monitoring in place. The CPU of the CN nodes is are 40-50% so it suggests I'm not restricted by CPU. The FE nodes are running around 50-70%. The max connections for the user are 1024, so I should be under that limit. What could be the cause of the slowness? What metrics/tools do I have available to diagnose the issue?

Rocky

07/03/2026, 5:53 AM

When you scale from 200 to 900 QPS with caching disabled and see latency jump from 50ms to 1s, you are likely hitting a bottleneck related to concurrency management, network RPC, or Object Storage request limits, rather than raw CPU throughput. Here are the primary areas to investigate, along with the tools and metrics to diagnose them: 1. RPC Connection Bottleneck (The "Pooled" Fix) By default, StarRocks uses a single connection for internal RPC (brpc) between the FE and CNs. At high concurrency (900+ QPS), this single connection can become a serial bottleneck even if CPU is low. * The Fix: Change the CN (BE) configuration

brpc_connection_type

from

single

pooled

. This allows the CNs to use multiple connections for internal communication. * Action: Update

be.conf

or use

ADMIN SET FRONTEND CONFIG

to update and restart nodes. 2. Object Storage Request Throttling Since you have disabled caching, every query must perform multiple

GET

LIST

requests to your object storage (S3/GCS/MinIO). * The Problem: Object storage providers often have limits on requests per second per prefix (e.g., AWS S3 limits are ~3,500 PUT/POST/DELETE and 5,500 GET requests per second per prefix). At 900 QPS, if each query hits 5-10 small files, you are likely being throttled by the storage provider. * Diagnosis: * Check your Query Profile for

IOTimeRemote

and

CompressedBytesReadRemote

. * Look for

Slow I/O

Request Throttled

errors in the CN

<http://be.INFO|be.INFO>

logs. * Metric: Monitor the latency of the object storage service itself in your cloud provider's dashboard. 3. FE Planning & Thread Bottlenecks Your FE is at 50-70% CPU. While this seems "okay," the FE is a JVM process. High CPU often means high Garbage Collection (GC) activity or thread contention when handling 900+ metadata lookups and query plans per second. * Diagnosis: * Query Queue: Check if queries are actually waiting in a queue before execution. Run

SHOW PROCESSLIST;

and look at the

IsPending

column. If

true

, your queries are being queued. * Audit Log: Check

fe.audit.log

. Look at the

PendingTimeMs

field. If this value is high, the delay is happening inside the FE before the query even starts on the CN. * Metric:

starrocks_fe_query_queue_pending

(Prometheus). 4. Fragment Instance Concurrency StarRocks uses "Fragments" to execute queries. If you have a high number of buckets and high QPS, you might be hitting the default limits for how many fragment instances can run concurrently on a single CN. * Check Variable:

pipeline_dop

(Degree of Parallelism). If this is set too high for a 16-core CN at 900 QPS, you might be over-scheduling the CPU cores, leading to context switching. * Recommendation: For high concurrency (Point lookups/simple queries), try setting

pipeline_dop = 1

to reduce the per-query overhead. Tools for Diagnosis 1. Analyze the Profile: This is the most important step. Pick a query that took 1s and run:

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sql
    SET enable_profile = true;
    -- Run your query
    SHOW PROFILELIST;
    ANALYZE PROFILE FROM 'query_id';

Look for: * WaitTime: High wait times indicate queuing. * RPC Client Side Time: Indicates network/internal overhead. * RemoteScanTime: Confirms the object storage latency. 2. Current Queries: To see what is happening in real-time:

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sql
    SHOW PROC '/current_queries';

3. Backend Status:

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sql
    SHOW PROC '/backends'; -- Look at NumRunningQueries per node

Summary Recommendation: First, enable

brpc_connection_type = pooled

. Second, check

fe.audit.log

for

PendingTimeMs

to see if the FE is the bottleneck. Finally, consider that disabling cache at 900 QPS is an extreme stress test for Object Storage; in a production shared-data scenario, the Data Cache is considered essential for sub-second latencies at scale. References

Rocky

07/03/2026, 5:53 AM

• faq/shared_data_faq.md • administration/management/resource_management/query_queues.md • faq/Sql_faq.md • administration/management/monitoring/metrics.md References • administration/management/resource_management/query_queues.md • administration/management/monitoring/metrics.md • administration/management/monitoring/Monitor_and_Alert.md

Dan Newton

07/03/2026, 6:00 AM

@Rocky

The Fix: Change the CN (BE) configuration
brpc_connection_type
from
single
to
pooled
. This allows the CNs to use multiple connections for internal communication.

What other effects will this have?

Rocky

07/03/2026, 6:03 AM

Changing

brpc_connection_type

pooled

is a common optimization for high-concurrency environments (like your 900 QPS target). While it solves the serial bottleneck of a single connection, it does come with a few trade-offs in resource consumption. 1. Increased File Descriptor Usage The most immediate effect is that the number of open sockets (file descriptors) between your nodes will increase. * The Change: Instead of 1 persistent connection per backend pair, StarRocks will maintain a pool of connections. * Risk: If your OS

ulimit -n

(open files limit) is set too low, the nodes may fail to open new connections. Ensure your file descriptor limits are set to at least 65535 (StarRocks usually recommends 1,000,000 for large clusters). 2. Higher Memory Footprint Each connection in the pool requires its own memory buffers for sending and receiving data. * The Change: While your current CN nodes have 64GB (which is plenty), you will see a slight increase in "System" or "Net" memory usage. * The Benefit: It prevents EOVERCROWDED errors. In

single

mode, if the amount of unwritten data in the socket buffer exceeds a certain threshold, bRPC will fail the request with an "Overcrowded" error.

pooled

spreads this data across multiple buffers, significantly reducing this risk. 3. CPU Context Switching Because there are more active socket threads handling incoming/outgoing data: * The Change: You might see a very marginal increase in CPU usage (1-3%) related to the kernel handling more network interrupts and context switches between those sockets. * Verdict: Since your CPU is currently only at 50%, this is a very safe trade-off for the latency gain you’ll receive. 4. Better Load Balancing of Internal RPC In

single

mode, all RPC calls between an FE and a CN are queued on one TCP stream. If one large query sends a massive metadata update or a large result set, it "head-of-line blocks" the 50ms small queries behind it. * The Change: With

pooled

, your 900 QPS can be distributed across multiple streams. Small, fast queries can "jump" around larger, slower data packets on the wire. What to check alongside this change: 1. brpc_max_connections_per_server
: This BE configuration (introduced in v3.2) controls the size of that pool. If you set the type to

pooled

, ensure this isn't set to 1 (the default is 1, but it only applies when the type is

pooled

). You may want to set this to 8 or 16 to truly see the benefit at 900 QPS. 2. ulimit -n
: Run

ulimit -n

on your CN nodes to ensure it is high enough to handle the new connection pool. 3. FE Pool Size: On the FE side, check the

brpc_connection_pool_size

(default is 16). This controls how many concurrent connections the FE can use to talk to the backends. At 900 QPS, 16 might be a bit tight; you might consider bumping this to 32 or 64. Summary: This is a low-risk, high-reward change for your specific bottleneck. The primary "cost" is simply more open network sockets. References • administration/management/BE_configuration.md • faq/Sql_faq.md • administration/management/FE_configuration.md • administration/management/resource_management/Replica.md • faq/Deploy_faq.md • administration/management/BE_configuration.md • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md

Rocky

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Rocky

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Rocky

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Rocky

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Rocky

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Rocky

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Rocky

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Rocky

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Rocky

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Dan Newton

07/03/2026, 7:35 AM

@Rocky How do i set

brpc_connection_pool_size

Rocky

07/03/2026, 7:35 AM

To set

brpc_connection_pool_size

, you must modify the configuration file on each of your FE (Frontend) nodes. This parameter is not dynamic (not mutable), so a restart of the FE process is required for the change to take effect. Steps to Set
brpc_connection_pool_size
1. Locate the
fe.conf
file: This is typically found in the

fe/conf/

directory of your StarRocks installation. 2. Add or update the parameter: Add the following line to the file (or update it if it already exists). For your 900 QPS load, increasing it from the default of 16 to 64 is a good starting point:

Copy code

bash
    brpc_connection_pool_size = 64

3. Restart the FE node: Since you have 2 FE nodes, perform a rolling restart (one at a time) to maintain cluster availability.

Copy code

bash
    # Stop the FE
    ./bin/stop_fe.sh

    # Start the FE
    ./bin/start_fe.sh --daemon

Important Details * Location: This is an FE-side configuration. It controls how many concurrent outgoing RPC connections the FE can maintain to the CN/BE nodes. * Requirement: Only available in StarRocks v3.2.0 and later. * Constraint: Because

Is mutable: No

, you cannot use the

ADMIN SET FRONTEND CONFIG

SQL command to change this dynamically. It must be done in the file. Complementary BE/CN Setting If you are increasing the pool size on the FE, you should also ensure the CN (Compute Nodes) are configured to handle multiple connections by setting the following in your

cn.conf

(or

be.conf

Copy code

bash
brpc_connection_type = pooled
brpc_max_connections_per_server = 16  # Matches the concurrency on the BE side

_Note: Like the FE setting,

brpc_max_connections_per_server

also requires a restart of the CN nodes._ References • administration/management/FE_configuration.md • administration/management/BE_configuration.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • knowledge/usage/best-practice.md

Dan Newton

07/03/2026, 7:55 AM

@Rocky Is

brpc_connection_type

a actual configuration property on

4.0.10

Rocky

07/03/2026, 7:55 AM

Yes, brpc_connection_type
is an actual configuration property in StarRocks 4.0.10 and the broader 4.0 branch. It was introduced in version v3.2.5 and remains a key parameter for tuning high-concurrency workloads. It has not been deprecated or removed in the 4.0 release. Summary of the Property: * Node Type: BE (Backend) or CN (Compute Node). * File:

be.conf

cn.conf

. * Default:

"single"

* Valid Values: * `"single"`: One persistent TCP connection per channel (default). * `"pooled"`: A pool of persistent connections. Recommended for your 900 QPS scenario to prevent RPC-level bottlenecks. * `"short"`: Connections are created/destroyed per RPC (rarely used for high-performance analytics). * Is Mutable: No. You must restart the BE/CN process for changes to this property to take effect. Complementary Setting When you set

brpc_connection_type = pooled

, you should also check brpc_max_connections_per_server
(default is 1). To actually benefit from the pool, you typically increase this to a value like

in the same `be.conf`/`cn.conf` file.

Copy code

bash
# Example configuration in be.conf / cn.conf
brpc_connection_type = pooled
brpc_max_connections_per_server = 16

References • administration/management/BE_configuration.md • release_notes/release-4.0.md • faq/Sql_faq.md References • release_notes/release-4.0.md • release_notes/release-3.1.md • administration/management/BE_configuration.md

Dan Newton

07/03/2026, 7:56 AM

@Rocky Does the same configuration exist on the FE nodes?

Rocky

07/03/2026, 7:56 AM

No, the property brpc_connection_type
does not exist on FE nodes. It is specific to the BE and CN nodes. On the FE side, the behavior for managing internal bRPC connections is handled by a different set of parameters. Instead of choosing a "type," the FE uses a built-in connection pool by default, and you tune it using: FE Equivalent Settings: * brpc_connection_pool_size
: This is the FE equivalent of tuning for higher internal concurrency. It defines the maximum number of pooled connections per BE/CN endpoint. Increasing this (e.g., to 64) allows the FE to talk to backends more concurrently. * brpc_short_connection
: By default, this is

false

, meaning the FE uses persistent, pooled connections (equivalent to the

pooled

behavior on the BE). If set to

true

, it would behave like the

short

type on the BE. Why the difference? * BE/CN (
brpc_connection_type
): Controls how the backend process accepts and manages incoming/outgoing data streams between nodes. Setting it to

pooled

helps the BE handle massive cross-node data shuffling. * FE (
brpc_connection_pool_size
): Controls how many parallel "pipes" the FE can open to send plan fragments or metadata updates to the BEs. In short: When you set

brpc_connection_type = pooled

on your CNs, you should pair it with an increase in brpc_connection_pool_size
on your FEs to ensure both sides of the "pipe" are optimized for high concurrency. References • administration/management/FE_configuration.md • administration/management/BE_configuration.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • faq/Sql_faq.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md

Dan Newton

07/03/2026, 7:58 AM

@Rocky So what configuration should i set on my FE and CN nodes?

Rocky

07/03/2026, 7:58 AM

For a high-concurrency environment (like 900+ QPS) on StarRocks 4.0.10, you should apply the following configurations. These settings help prevent RPC bottlenecks by expanding the number of parallel communication channels between nodes. 1. On FE Nodes (
fe/conf/fe.conf
) The FE is the entry point for queries. It needs a larger pool of outgoing connections to distribute plan fragments to the backends. * brpc_connection_pool_size = 64
* Why: Limits how many concurrent outgoing bRPC requests the FE can maintain per BE/CN. The default is 16; increasing it to 64 helps prevent query queuing at the RPC layer during high QPS. * Requires Restart: Yes. 2. On CN Nodes (
cn/conf/cn.conf
) The CN nodes (and BEs) need to be configured to handle and initiate pooled connections for data shuffling and result returning. * brpc_connection_type = pooled
* Why: Changes the connection mode from "single" (one TCP connection per channel) to "pooled." This is critical for high-concurrency stability and throughput. * Requires Restart: Yes. * brpc_max_connections_per_server = 16
* Why: When

brpc_connection_type

is set to

pooled

, this parameter defines the depth of that pool. A value of 16 allows up to 16 parallel TCP connections between any two nodes. * Requires Restart: Yes. Summary Table for 4.0.10 Node | Property | Recommended Value | Is Mutable? FE |

brpc_connection_pool_size

| No (Restart FE) CN |

brpc_connection_type

pooled

| No (Restart CN) CN |

brpc_max_connections_per_server

| No (Restart CN) How to Apply 1. Edit the files: Update

fe.conf

on all FE nodes and

cn.conf

on all CN nodes. 2. Rolling Restart: * Restart FEs one by one (ensure the Leader is stable before moving to the next). * Restart CNs one by one to ensure continuous query processing. 3. Verify: After restart, you can verify the settings by running: *

ADMIN SHOW FRONTEND CONFIG LIKE 'brpc_connection_pool_size';

ADMIN SHOW BACKEND CONFIG LIKE 'brpc%';

(This also works for CN nodes). References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • faq/Sql_faq.md References • administration/management/BE_configuration.md • administration/management/FE_configuration.md

Dan Newton

07/03/2026, 8:58 AM

@Rocky I have this query profile running at 500 queries per second. Do you suggestions match with what the profile shows?

Untitled

Rocky

07/03/2026, 8:58 AM

Yes, at 500 QPS, your profile likely shows specific bottlenecks that directly justify switching to

pooled

connections. When StarRocks is under high concurrency with the default

single

connection type, a single TCP stream between nodes becomes a "choke point." Here is what you should look for in your Query Profile to confirm this: 1. High
WaitTime
in
EXCHANGE_SINK_OPERATOR
* Where to look: Find the

EXCHANGE_SINK

(the part that sends data between nodes). * The Symptom: If

WaitTime

is a significant portion of the total execution time, it means the operator is ready to send data, but the bRPC sender queue is full. * The Match: Changing to

pooled

(and increasing

brpc_max_connections_per_server

) adds more TCP streams, effectively widening the "pipe" and reducing this wait time. 2. Discrepancy in
RpcAvgTime
vs.
NetworkTime
* The Symptom: If

RpcAvgTime

is high but your actual

NetworkBandwidth

or data volume (

BytesSent

) is low, it indicates "head-of-line blocking." One large or slow RPC request is blocking all the small 500 QPS requests behind it on that single connection. * The Match: Connection pooling allows those 500 small queries to use different sockets, so one heavy query doesn't stall the others. 3.
ClosureBlockTime
in
EXCHANGE_SOURCE_OPERATOR
* The Symptom: On the receiving side (

EXCHANGE_SOURCE

), if you see high

ClosureBlockTime

, it means the receiver's bRPC layer is struggling to process the incoming flood of small packets from 500 different queries. * The Match: A pool of connections allows the bRPC framework to distribute the processing across more internal worker threads more efficiently. 4. "The server is overcrowded" Errors (Optional) If you see

[E1011]The server is overcrowded

in your logs or profile errors, this is the "smoking gun." It means the 1GB default buffer (

brpc_socket_max_unwritten_bytes

) on the single socket is full. * Recommendation: While

pooled

helps, if you see this error specifically, you should also increase

brpc_socket_max_unwritten_bytes = 4294967296

(4GB) on your CN nodes. Summary Checklist Profile Metric | High Value Meaning | Recommended Fix Exchange Sink
WaitTime
| Socket buffer/queue is full |

brpc_connection_type = pooled

Exchange Sink
RpcAvgTime
| Network latency or queueing |

brpc_max_connections_per_server = 16

FE
Query Planning Time
| FE-to-BE connection queueing |

brpc_connection_pool_size = 64

If your profile shows that Exchange operators (Sinks or Sources) are the top time-consumers despite low data volumes, these configuration changes are exactly what you need. References • faq/Sql_faq.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/BE_configuration.md References • best_practices/query_tuning/query_profile_operator_metrics.md • faq/Sql_faq.md • administration/management/BE_configuration.md • administration/management/FE_configuration.md • knowledge/usage/best-practice.md best-practice) References • faq/Sql_faq.md • administration/management/FE_configuration.md • administration/management/BE_configuration.md

Rocky

07/03/2026, 8:58 AM

• knowledge/usage/best-practice.md • best_practices/query_tuning/query_profile_operator_metrics.md

Dan Newton

07/03/2026, 11:44 AM

@Rocky From that profile, do you have any other recommendations

Rocky

07/03/2026, 11:44 AM

Based on a high-concurrency profile (500+ QPS), there are several other critical areas to optimize beyond the bRPC settings. At this scale, even small per-query overheads become major bottlenecks. 1. Enable Query Caching For high QPS, caching is the single most effective way to reduce CPU and I/O pressure. * enable_query_cache = true
: Enables the Tablet-level Query Cache. This caches intermediate results of aggregations at the tablet level. If your data doesn't change every second, this can drastically reduce the

ScanTime

and

IOTaskExecTime

seen in your profile. * query_cache_capacity
(BE setting): Ensure the BEs have enough memory for the cache (default is 512MB). You can increase this in

be.conf

if you have a large working set. 2. Tune Pipeline Parallelism (DOP) In your profile, check the DOP (Degree of Parallelism). * Recommendation: Set pipeline_dop = 1
(or a very low number like 2 or 4). * Why: By default, StarRocks tries to use many cores per query. At 500 QPS, if every query tries to use 16 cores, you will have massive CPU context switching and thread contention. Setting

pipeline_dop = 1

ensures each query uses only 1 thread, allowing the CPU to handle more queries in parallel more efficiently. 3. Check for OS-Level Bottlenecks High QPS means high thread and memory map creation. * vm.max_map_count
: Ensure this is set to 262144
or higher. * threads-max
&
pid_max
: At 500+ QPS, the number of active threads can spike. Increase these in

/proc/sys/kernel/

to avoid "Resource temporarily unavailable" errors. 4. Optimize Scan Metrics in the Profile Look at the OLAP_SCAN_OPERATOR section of your profile: * IOTaskWaitTime
: If this is high, your disks are saturated. Consider increasing the Page Cache (

storage_page_cache_limit

) or using Materialized Views to reduce the data volume scanned. * RowsRead
vs
RawRowsRead
: If

RawRowsRead

is much higher than

RowsRead

, your predicates aren't filtering data early enough. Check if you can use Bloom Filters or Bitmap Indices on those columns to "skip" data during the scan. * ShortKeyFilterRows
: If this is 0, your query isn't hitting the Prefix Index. Sorting your table (via

ORDER BY

in the

CREATE TABLE

stmt) by the columns used in your

WHERE

clause can make scans nearly instantaneous. 5. FE Planner Optimization If your profile shows Planner Time > 20-30ms: * At 500 QPS, the FE's CPU can become a bottleneck just parsing SQL. * Recommendation: Use Prepared Statements if your application supports them. This allows StarRocks to skip the parsing and analysis phase for repeated queries. 6. Enable Shared Scan * enable_shared_scan = true
: If multiple concurrent queries are scanning the same table/partitions, this feature allows them to share the I/O and memory buffers, significantly reducing disk read pressure. Summary Checklist Metric to watch | Recommendation | Setting High CPU Usage | Lower per-query parallelism |

SET GLOBAL pipeline_dop = 1;

High Disk I/O | Enable Tablet Cache |

SET GLOBAL enable_query_cache = true;

High Scan Latency | Increase Page Cache |

storage_page_cache_limit

be.conf

FE CPU Bottleneck | Use Prepared Statements | App-side change References • using_starrocks/caching/query_cache.md • knowledge/usage/best-practice.md • deployment/environment_configurations.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/resource_management/Memory_management.md References

Rocky

07/03/2026, 11:44 AM

• knowledge/usage/best-practice.md • using_starrocks/caching/query_cache.md • deployment/environment_configurations.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/resource_management/Memory_management.md • faq/Others.md • best_practices/audit_log_resource_group.md • loading/loading_introduction/loading_considerations.md References • best_practices/query_tuning/query_profile_operator_metrics.md • unloading/Spark_connector.md • loading/loading_introduction/troubleshooting_loading.md

Dan Newton

07/03/2026, 1:33 PM

@Rocky I have applied the brpc config, I have attached a new profile. What does this tell us? Have the changes made a difference? How do I know the configuration has taken effect?

Untitled

Rocky

07/03/2026, 1:34 PM

To verify if your changes have taken effect and to interpret the impact on your new profile, follow these steps: 1. How to Verify the Configuration is Active Since these parameters are not mutable (they require a restart), you should confirm the nodes are actually running with the new values. Run these SQL commands in your terminal: * For FE:

Copy code

sql
    ADMIN SHOW FRONTEND CONFIG LIKE 'brpc_connection_pool_size';

Look for the
Value
column to show
64
. * For CN:

Copy code

sql
    ADMIN SHOW BACKEND CONFIG LIKE 'brpc%';

_Verify

brpc_connection_type

pooled

and

brpc_max_connections_per_server

._ ────────── 2. What the Profile Tells Us (Impact Analysis) Compare the following metrics in your new profile against your old one. If the changes worked, you should see the following improvements: A. Reduction in
WaitTime
(Exchange Sink) * Where: Look at the

EXCHANGE_SINK_OPERATOR

. * What it means: This metric tracks how long a query thread was blocked because the bRPC sender queue was full. * Success Sign: If

WaitTime

has dropped significantly (e.g., from seconds to milliseconds), it proves that the pooled connections are successfully draining the data buffers faster by using multiple parallel TCP streams. B. Lower
RpcAvgTime
vs.
NetworkTime
* Where: Look at the

EXCHANGE_SINK_OPERATOR

. * What it means:

RpcAvgTime

includes the time spent waiting for a connection to become available. * Success Sign: If

RpcAvgTime

is now much closer to the

NetworkTime

, it means queries are no longer "waiting in line" for a single shared connection. They are grabbing an available connection from the pool and sending data immediately. C. Reduction in
ClosureBlockTime
(Exchange Source) * Where: Look at the

EXCHANGE_SOURCE_OPERATOR

on the receiving node. * What it means: This is the time the receiver spends blocked while processing incoming RPC requests. * Success Sign: Lower

ClosureBlockTime

indicates that the receiving node is handling the 500 QPS load more efficiently because the traffic is distributed across multiple sockets, preventing any single socket from being overwhelmed. ────────── 3. Summary of Improvements If the configuration was successful, the "bottleneck" in your profile should shift: * Before: The profile likely showed "bubbles" of idle time or high "WaitTime" in the Exchange operators, even though CPU and Disk usage were not at 100%. * After: The Exchange operators should be much faster. If the query is still slow, the bottleneck will likely move to CPU execution (e.g.,

ScanTime

MaterializeTime

), which is a "healthy" bottleneck because it means you are now hitting the actual hardware limits rather than being throttled by the RPC layer. If you still see high `WaitTime`: You may need to increase the buffer size on the CN nodes by adding this to `cn.conf`:

brpc_socket_max_unwritten_bytes = 1073741824

(or larger, up to 4GB). This gives the pooled connections even more room to breathe. References • administration/management/BE_configuration.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/FE_configuration.md • faq/Sql_faq.md • deployment/deploy_manually.md -manually) References • administration/management/BE_configuration.md • best_practices/query_tuning/query_profile_operator_metrics.md

Rocky

07/03/2026, 1:34 PM

• administration/management/FE_configuration.md • faq/Sql_faq.md • deployment/deploy_manually.md • deployment/environment_configurations.md -configurations) References • administration/management/BE_configuration.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/FE_configuration.md • faq/Sql_faq.md • deployment/deploy_manually.md • deployment/environment_configurations.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • deployment/environment_configurations.md • deployment/deploy_manually.md

Dan Newton

07/03/2026, 1:44 PM

@Rocky Compare these two profiles, the first from before the brpc changes and the second after it.

Untitled

Rocky

07/03/2026, 1:45 PM

To compare your two profiles effectively, you should look for specific shifts in the Exchange operators. Since I cannot see the specific attachments, here is the side-by-side checklist you should use to verify the impact of the

pooled

bRPC configuration: 1. The "WaitTime" Comparison (Exchange Sink) This is the most definitive indicator for the

pooled

setting. * Before: Under 500 QPS with

single

connections, you likely saw high

WaitTime

(often in the hundreds of milliseconds or even seconds). This means the query was ready to send data but was blocked because the single TCP socket's buffer was full. * After: With

pooled

connections and

brpc_max_connections_per_server = 16

, the

WaitTime

should drop toward zero or low single digits. If it hasn't dropped, it means the network is still a bottleneck, and you might need to increase

brpc_socket_max_unwritten_bytes

. 2. The "RpcAvgTime" Comparison * Before: In the

single

mode,

RpcAvgTime

often spikes under high concurrency because many small RPC requests are queuing up behind each other on one connection (head-of-line blocking). * After: In

pooled

mode,

RpcAvgTime

should be much more stable and closer to your actual network latency. You should see this number become more consistent across all 500 queries. 3. The "ClosureBlockTime" Comparison (Exchange Source) Check the receiving side of your exchange. * Before: High

ClosureBlockTime

indicates the receiver was struggling to process the flood of incoming bRPC packets from a single channel. * After: This should decrease as the load is now distributed across multiple sockets, allowing the receiver's bRPC worker threads to handle the incoming data more efficiently. 4. Overall Pipeline "ScheduleTime" * Before: If the RPC layer was blocked, it often caused "backpressure" that manifested as high

ScheduleTime

PendingTime

in other operators because they were waiting for the Exchange Sink to clear. * After: You should see the

ActiveTime

(actual work) make up a much larger percentage of the

DriverTotalTime

compared to

WaitTime

PendingTime

. Summary Comparison Table Metric | Search for this in Profile | Goal after bRPC changes Congestion |

EXCHANGE_SINK_OPERATOR

WaitTime

| Significant Decrease RPC Latency |

EXCHANGE_SINK_OPERATOR

RpcAvgTime

| Decrease / More Stable Receiver Pressure |

EXCHANGE_SOURCE_OPERATOR

ClosureBlockTime

| Decrease Throughput |

EXCHANGE_SINK_OPERATOR

OverallThroughput

| Increase How to know it is working? If your Wall Clock Time (Total Query Time) has decreased while your CPU usage on the nodes has actually increased, the change is working. This means the system is no longer waiting for the network and is now spending its time doing actual computation. If you can paste the
EXCHANGE_SINK_OPERATOR
block from both profiles here, I can do the direct comparison for you. References • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/BE_configuration.md • faq/Sql_faq.md • administration/management/FE_configuration.md References • best_practices/query_tuning/query_profile_operator_metrics.md

Dan Newton

07/03/2026, 6:00 PM

@Rocky I found that my query performance got worse after scaling the cluster to 8 CNs for 4. I think this is because of the FE nodes routing most requests to 2 of the CNs. My understanding is that this is due to buckets being split between the CNs and due to bucket imbalance, some of the nodes have to handle far more queries than the rest. Is there a way to remove the "ownership" over a bucket and therefore the FE nodes schedule queries to any CN. This would mean more data is "duplicated" across the cluster when held in caches, but would allow the load to be distributed through the nodes. Is it possible to configure this?

Rocky

07/03/2026, 6:00 PM

In StarRocks Shared-Data mode (lake mode), your observation is correct: the Frontend (FE) uses a Consistent Hashing algorithm to bind specific tablets (buckets) to specific Compute Nodes (CN). This "ownership" exists specifically to make the Data Cache (Block Cache) effective. By routing the same data to the same node every time, StarRocks ensures that once a CN pulls data from S3/Object Storage, it can serve subsequent queries from its local NVMe/SSD cache. If queries were scheduled randomly, the cache would be "polluted" with duplicate data across all nodes, drastically reducing your total effective cache size. Why you see imbalance (2 out of 8 nodes) Scaling from 4 to 8 CNs often exposes an insufficient bucket count. * Hash Distribution: If your table only has, for example, 16 or 32 buckets, the consistent hash algorithm might statistically map a large portion of those buckets (or the "hot" buckets you are currently querying) to only a couple of the 8 nodes. * Node Mapping: Because CNs are "stateless," the FE re-maps the hash ring whenever nodes are added. With a low bucket count, the "granularity" of the distribution is too coarse to spread evenly across 8 nodes. ────────── Recommendations to Distribute Load 1. Increase Bucket Count (The Best Long-Term Fix) The most effective way to fix this without losing cache benefits is to increase the number of buckets. For 8 CN nodes, you should aim for at least 80 to 160 buckets (approx. 10-20 per node) to ensure a smooth statistical distribution. * Since v3.2, you can modify the bucket count of an existing table:

Copy code

sql
    ALTER TABLE my_table DISTRIBUTED BY HASH(my_key) BUCKETS 128;

Note: This will trigger a background data redistribution. 2. Change the Assignment Mode (v4.0+) If you are on StarRocks v4.0 or higher, you can explicitly tell the FE to prioritize balance over cache locality using a session variable: * SET lake_bucket_assignment_mode = 'balance';
* balance
: Distributes buckets evenly across all worker nodes to achieve a balanced workload. * elastic
(Default): Uses consistent hashing for better cache locality. 3. Use Random Bucketing (Duplicate Key Tables only) If your table is a Duplicate Key model and you don't perform many Joins on specific keys, you can switch to Random Bucketing. This removes the "key" requirement and allows StarRocks to distribute data more uniformly, though the FE will still try to maintain some tablet-to-node stickiness for the cache.

Copy code

sql
ALTER TABLE my_table DISTRIBUTED BY RANDOM;

4. Check for Data Skew Verify if certain buckets are simply much larger than others, which would cause the node "owning" those buckets to work harder.

Copy code

sql
-- Check if some tablets have significantly more data than others
SHOW TABLET FROM my_table;
-- Or check the distribution across nodes
ADMIN SHOW REPLICA DISTRIBUTION FROM my_table;

Summary You cannot completely "remove ownership" in versions before 4.0 because the system is designed to protect your Data Cache hit rate. To fix the current imbalance: 1. Verify your bucket count: If it's low (e.g., < 40), increasing it to 128 is the fastest way to engage all 8 CNs. 2. Upgrade to v4.0 if you want to use the

lake_bucket_assignment_mode = 'balance'

feature to force even scheduling. References • table_design/data_distribution/Data_distribution.md • sql-reference/System_variable.md • best_practices/bucketing.md • using_starrocks/caching/block_cache.md • administration/management/FE_configuration.md References

Rocky

07/03/2026, 6:00 PM

• deployment/deploy_manually.md • deployment/plan_cluster.md • best_practices/query_tuning/query_planning.md • sql-reference/sql-statements/cluster-management/nodes_processes/SHOW_COMPUTE_NODES.md

Dan Newton

07/03/2026, 6:07 PM

@Rocky 2. Change the Assignment Mode (v4.0+) If you are on StarRocks v4.0 or higher, you can explicitly tell the FE to prioritize balance over cache locality using a session variable: * SET lake_bucket_assignment_mode = 'balance';
* balance
: Distributes buckets evenly across all worker nodes to achieve a balanced workload. * elastic
(Default): Uses consistent hashing for better cache locality. Is this for CNs in shared-data mode?

Rocky

07/03/2026, 6:07 PM

Yes, the lake_bucket_assign_mode
(note the name is

assign_mode

, not

assignment_mode

) session variable is specifically designed for Compute Nodes (CN) in Shared-Data mode (also known as StarRocks Lake mode). Here is the breakdown of how this works and why it addresses your problem: 1. Purpose of the Variable In Shared-Data mode, StarRocks traditionally uses Consistent Hashing to assign tablets (buckets) to specific CN nodes. This "stickiness" ensures that a CN node can build a local Data Cache for the tablets it "owns," avoiding repeated downloads from S3/object storage. However, as you observed, if the number of buckets is small (e.g., 16 buckets for 8 nodes), consistent hashing might statistically map a disproportionate number of "hot" buckets to just a few nodes. 2. How
lake_bucket_assign_mode
fixes this Introduced in v4.0, this variable allows you to trade cache locality for execution balance: * SET lake_bucket_assign_mode = 'balance';
* What it does: It tells the FE scheduler to ignore the consistent hash "ownership" and instead distribute the scan tasks for those buckets evenly across all active worker nodes (CNs). * The Result: All 8 of your CNs will participate in the query, regardless of which node technically "owns" the bucket in the cache ring. This eliminates the "2 hot nodes" bottleneck. * SET lake_bucket_assign_mode = 'elastic';
(Default) * What it does: Uses the standard consistent hashing. This is better for cache hit rates but can lead to the imbalance you are seeing if your bucket count is too low relative to your node count. 3. If you are on a version older than v4.0 If you are running v3.x, you will not have this session variable. In that case, you have two options to achieve the same result: 1. Increase the Bucket Count (Recommended): Use

ALTER TABLE ... DISTRIBUTED BY HASH(...) BUCKETS <count>;

to increase the number of buckets to at least 128 (aim for ~10-20 buckets per CN node). A higher bucket count provides more "points" on the hash ring, leading to a much more even statistical distribution across your 8 nodes. 2. FE Configuration (v3.3.4+): There is an FE configuration parameter

lake_enable_balance_tablets_between_workers

(default

true

) which helps the FE migrate tablet "ownership" to balance the cluster, though it is less immediate than the session variable. Summary * Is it for CNs in shared-data? Yes. * Does it remove "ownership"? Yes, it effectively makes the scheduling "stateless" for that query, allowing any node to pick up any bucket's task. * Warning: As you noted, this will "duplicate" data in your caches across the cluster, which may lower your overall cache efficiency, but it is often the correct trade-off to solve compute bottlenecks at high concurrency. References • sql-reference/System_variable.md • administration/management/FE_configuration.md • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md References • sql-reference/System_variable.md • faq/shared_data_faq.md • deployment/shared_data/s3.md • administration/management/FE_configuration.md • deployment/shared_data/gcs.md

Dan Newton

07/03/2026, 6:09 PM

@Rocky I have just looked at the tablet sizes in my table, and there is some variation but generally they're well balanced. Furthermore, running at 128 actually caused a lot of failures

Rocky

07/03/2026, 6:10 PM

The "failures" you are seeing after moving to 128 buckets are almost certainly Memory OOMs or RPC timeouts, and they are a direct consequence of how StarRocks handles query parallelism. Why 128 buckets caused failures In StarRocks, the number of buckets (tablets) is the primary driver of query parallelism. When you increase buckets from a low number (e.g., 16 or 32) to 128: 1. Memory Overhead per Instance: Every tablet involved in a query generates at least one "Pipeline Instance" on the CN node. Each instance has its own memory buffer. If you have many fragments in your SQL, 128 buckets can explode into hundreds of concurrent tasks. If any single instance exceeds

exec_mem_limit

, the entire query fails. 2. RPC/Connection Exhaustion: With 128 buckets, a single query now needs to open many more bRPC connections to shuffle data between nodes. If your bRPC settings (like the

pooled

setting we discussed earlier) aren't tuned for this high fan-out, you'll see "RPC cancel" or "Connection refused" errors. 3. FE Metadata Pressure: High bucket counts increase the "Plan" time in the FE, as it has to calculate locations for 128 tablets instead of 16. The "2 out of 8 nodes" problem explained Even if your tablet sizes are "balanced" (e.g., each is 1GB), the FE's Consistent Hash algorithm is the bottleneck. * Consistent hashing works by placing nodes and tablets on a virtual "ring." * If you have 8 nodes but only a few tablets (e.g., 16 or 32), it is mathematically common for the "ring" to map many tablets to the same node just by chance. * A "balanced" size does not mean "balanced" scheduling. If node A "owns" 4 tablets and node B "owns" 0, node A will do all the work for those 4 tablets every single time. How to solve this without the failures Since 128 buckets is too heavy for your current CN memory/RPC limits, but your current count is too low to utilize 8 nodes, try this: 1. Use the "Middle Ground" Bucket Count Don't jump straight to 128. Aim for 64 buckets. * 64 buckets across 8 nodes gives exactly 8 tablets per node. This is much more likely to distribute work evenly across all 8 nodes than 16 or 32 buckets, but it won't create as much memory pressure as 128. *

ALTER TABLE my_table DISTRIBUTED BY HASH(key) BUCKETS 64;

2. Verify the
lake_bucket_assign_mode
(If on v4.0+) If you are on v4.0, set this to

balance

. It will ignore the "ownership" ring and just give work to the next available CN node.

Copy code

sql
SET lake_bucket_assign_mode = 'balance';

If you are on v3.3.4+, check this FE configuration instead:

Copy code

sql
ADMIN SHOW FRONTEND CONFIG LIKE 'lake_enable_balance_tablets_between_workers';

Ensure it is

true

. This tells the FE to actively move tablet "ownership" if it detects some CNs are idle while others are hot. 3. Adjust Memory Limits (To stop the failures) If you must use more buckets, you likely need to increase the allowed memory per query so the instances don't crash:

Copy code

sql
SET query_mem_limit = 0; -- 0 means use the BE's mem_limit
-- OR
SET exec_mem_limit = 4294967296; -- 4GB (default is often 2GB)

Summary Recommendation 1. Check your current bucket count (

SHOW CREATE TABLE

). If it's less than 64, that is why your 8-node cluster is idle. 2. Downscale the bucket change: Try 64 instead of 128. 3. Check the failure logs: If the failures say "Memory limit exceeded," increase

query_mem_limit

. If they say "RPC error," your bRPC pool configuration from our previous conversation is definitely the culprit. References • sql-reference/System_variable.md • best_practices/bucketing.md • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md References

Rocky

07/03/2026, 6:10 PM

• table_design/data_distribution/Data_distribution.md • faq/shared_data_faq.md • sql-reference/System_variable.md • best_practices/bucketing.md • administration/management/FE_configuration.md References • administration/management/FE_configuration.md • administration/management/resource_management/Replica.md • administration/management/compaction.md

Dan Newton

07/03/2026, 6:11 PM

@Rocky My previous configuration did use 64 buckets, in my aim to lower query times thats when I scaled from 4 to 8 CNs.

Rocky

07/03/2026, 6:12 PM

In StarRocks v4.0, the feature you are looking for to remove bucket "ownership" and distribute load regardless of cache locality is controlled by the session variable lake_bucket_assign_mode
. 1. The Solution (v4.0+) By default in v4.0, this is already set to

balance

, but you should verify your setting. This variable is specifically for Shared-Data (Lake) mode: * SET lake_bucket_assign_mode = 'balance';
* Behavior: The FE ignores the consistent hash "ring" and distributes the scan tasks for your 64 buckets evenly across all 8 available CNs. * Result: You get a perfectly balanced compute load (8 buckets per node). * SET lake_bucket_assign_mode = 'elastic';
* Behavior: This uses the consistent hashing you described. Even with 64 buckets on 8 nodes, the hash ring can statistically result in "hot nodes" where certain nodes are assigned more tablets (or more active ones) than others. 2. Why 128 Buckets Caused Failures When you increased to 128 buckets, you likely hit one of two internal limits: * RPC Memory Pressure: Each bucket being scanned creates a "Pipeline Instance." With 128 buckets, the data shuffle between nodes increases significantly. If you saw errors like "The server is overcrowded" or "RPC failure," it is because you hit the

brpc_socket_max_unwritten_bytes

limit (default 1GB). _ _Fix:* Increase

brpc_socket_max_unwritten_bytes = 4294967296

(4GB) in

cn.conf

. * Per-Instance Memory Limit: Every bucket task has a small memory overhead. If your query is complex, 128 concurrent instances might have exceeded the

exec_mem_limit

. _ _Fix:* Increase

query_mem_limit

(e.g.,

SET query_mem_limit = 8G;

). 3. Why Performance Got Worse with 64 Buckets on 8 Nodes If you are seeing 2 nodes doing all the work despite having 64 buckets, there are two likely culprits: 1. Consistent Hashing "Stickiness": In

elastic

mode, the FE tries to keep data on the same node for the cache. If your 8 nodes joined the cluster at different times, the "ring" might not have redistributed perfectly. 2. Metadata Cache / Planning: With more nodes, the FE has more work to do to coordinate. If the buckets aren't perfectly distributed, you lose the benefit of the extra 4 nodes but keep the overhead of the network shuffle between them. Recommendations 1. Switch to
balance
mode: If you are on v4.0, run

SET lake_bucket_assign_mode = 'balance';

. This specifically tells StarRocks to prioritize using all 8 nodes over cache locality. 2. Check for "Empty" Buckets: Use

SHOW TABLET FROM <table_name>

to ensure your 64 buckets actually contain data. If your data is skewed (e.g., all data is in 10 out of 64 buckets), then only the nodes "owning" those 10 buckets will work. 3. Check FE Config (v3.3 - v4.0): If you aren't on 4.0 yet, ensure

lake_enable_balance_tablets_between_workers = true

is set in your FE config. This allows the FE to "steal" work from busy nodes and give it to idle ones. Summary Stick with 64 buckets for now to avoid the RPC/Memory failures. To fix the imbalance, change the assignment mode to balance
. This will "duplicate" data in the caches as you suspected, but it will ensure your 8 nodes are utilized evenly. References • sql-reference/System_variable.md • administration/management/BE_configuration.md • knowledge/usage/best-practice.md • administration/management/FE_configuration.md • table_design/data_distribution/Data_distribution.md References

Rocky

07/03/2026, 6:12 PM

• knowledge/trouble_shooting/fe_memory_problems.md • developers/build-starrocks/Build_in_docker.md • faq/resource_isolation_faq.md • administration/Meta_recovery.md • faq/operation_maintenance_faq.md • knowledge/usage/best-practice.md • administration/management/resource_management/Memory_management.md

Dan Newton

07/03/2026, 7:17 PM

@Rocky What is the best/recommended setup for a system that wants to support a high concurrent load with sub millisecond query latencies? If possible tailor it to the information I've provided you in this thread

Rocky

07/03/2026, 7:18 PM

For high-concurrency, sub-millisecond query performance in a shared-data (Lake) cluster with 8 CN nodes, you need to move away from the "massive parallelism" approach (128 buckets) and move toward "Short Circuit" and "Cache Locality" optimizations. 1. The "Golden Rule" Table Design: Primary Key + Row Store To achieve sub-millisecond latencies, you must avoid the standard columnar scan overhead. * Table Type: Use the Primary Key model. It is 3x–10x faster than Unique Key for high-frequency updates and reads because it avoids "online merge." * Enable Hybrid Storage: If your queries are mostly point-lookups (e.g.,

WHERE id = 123

), add a row store to your table.

Copy code

sql
    -- Add this property during CREATE or ALTER
    PROPERTIES (
      "storage_type" = "column_with_row"
    );

* Short Circuit Scan: This allows StarRocks to bypass the entire execution engine and jump straight to the row in storage.

Copy code

sql
    SET enable_short_circuit = true;
    -- Verify with EXPLAIN. You should see "Short Circuit Scan: true"

2. Solving the "2 out of 8 Nodes" Hotspot In StarRocks v4.0+, you have the perfect tool to fix the imbalance you saw with 64 buckets: * Mode:

SET lake_bucket_assign_mode = 'balance';

* Why it helps you: In

elastic

(default) mode, StarRocks uses consistent hashing. With only 64 buckets on 8 nodes, it is mathematically likely that a few nodes get "stuck" with the most active buckets.

balance

ignores the hash ring and forces the FE to hand out tasks to all 8 nodes evenly. * Cache Sharing: Since

balance

mode might cause different nodes to request the same data, enable Data Cache Sharing so CNs can pull data from each other's memory instead of S3.

Copy code

sql
    -- FE Config
    ADMIN SET FRONTEND CONFIG ("enable_trace_historical_node" = "true");
    -- Session Variable
    SET enable_datacache_sharing = true;

3. Handling Concurrency (RPC & Network) Failures at 128 buckets occurred because the "fan-out" (number of connections between nodes) exceeded default buffer limits. To support high concurrency: * CN Configuration (
cn.conf
): * `brpc_connection_type = pooled`: Crucial for high concurrency to reuse RPC connections. * `brpc_socket_max_unwritten_bytes = 1073741824`: Increase to 1GB or higher to prevent "RPC cancel" during heavy shuffles. * Session Variable: *

SET query_cache_entry_max_bytes = 4194304;

* `SET enable_query_cache = true;`: This caches intermediate results for tablets, which is a lifesaver for high-frequency repeated queries. 4. Why 64 Buckets is your "Sweet Spot" For sub-millisecond latency, parallelism is often your enemy, not your friend. * Every bucket you add adds Planning Time in the FE and Task Scheduling overhead. * The Math: 64 buckets on 8 nodes = 8 buckets per node. This is the ideal balance. It is enough to keep all your CPU cores busy without the management overhead that caused your 128-bucket setup to fail. Recommended "Profile" for your Queries:

Copy code

sql
-- Run these before your high-concurrency load
SET enable_short_circuit = true;
SET lake_bucket_assign_mode = 'balance';
SET enable_query_cache = true;
SET enable_datacache_sharing = true;
SET query_mem_limit = 8G; -- Prevent OOM during spikes

Summary of Setup 1. Table: Primary Key model +

column_with_row

storage. 2. Distribution: Keep 64 buckets (it's the right balance of metadata vs performance). 3. Load Balancing: Use

lake_bucket_assign_mode = 'balance'

. 4. Network: Change

brpc_connection_type

pooled

cn.conf

. This combination ensures that instead of 2 nodes doing "heavy" work, all 8 nodes do "light" work, while the Row Store + Short Circuit combo keeps the latency in the sub-millisecond range. References • sql-reference/System_variable.md

Rocky

07/03/2026, 7:18 PM

• administration/management/BE_configuration.md • best_practices/bucketing.md • administration/management/FE_configuration.md • knowledge/usage/load/shared_data_batch_ingestion.md

Dan Newton

07/03/2026, 7:18 PM

@Rocky What is the best/recommended setup for a system that wants to support a high concurrent load with sub second (miswrote millisecond instead of second) query latencies? If possible tailor it to the information I've provided you in this thread

Rocky

07/03/2026, 7:19 PM

For sub-second latencies (typically 100ms–800ms) under high concurrency in a Shared-Data (Lake) environment with 8 CN nodes, the goal shifts from "maximum throughput" to "low-latency coordination." Here is the recommended setup tailored to your 8-node CN cluster: 1. The "Balanced" Bucketing Strategy Stick with 64 buckets. Your previous attempt at 128 failed because of the overhead; your 64-bucket attempt was imbalanced because of "consistent hashing." * Fix the Imbalance:

Copy code

sql
    -- Forces the FE to distribute the 64 buckets evenly (8 per node)
    -- regardless of where the data was previously cached.
    SET lake_bucket_assign_mode = 'balance';

* Why this works: With 8 CNs, 64 buckets give each node exactly 8 "Pipeline Instances." This is high enough to utilize all CPU cores on each node but low enough that the FE can plan the query and the CNs can coordinate the RPC shuffle in well under 100ms. 2. Enable Multi-Layer Caching Sub-second latency in Lake mode is nearly impossible if you hit S3/Object Storage every time. You need to ensure your "Hot" data stays in the CN memory and local disk. * Data Cache (Local Disk): Ensure your CNs have fast local NVMe SSDs configured for

storage_root

cn.conf

. * Result Cache (Memory): For high concurrency, many queries are often identical or similar.

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sql
    SET enable_query_cache = true; -- Caches intermediate tablet results

* Metadata Cache: Ensure the FE doesn't have to fetch file metadata from S3 for every query.

Copy code

sql
    -- FE config (fe.conf)
    lake_metadata_cache_limit_in_bytes = 2147483648 -- 2GB

3. Concurrency Control (Preventing "Failures") Your 128-bucket attempt failed because StarRocks tried to do too much at once. To support high concurrency without crashing: * Limit Pipeline Parallelism: If you have 32-core CNs, don't let one query use all 32 cores.

Copy code

sql
    SET pipeline_dop = 16; -- Or half of your CPU cores

* Enable Short Circuit: If your queries often filter by the Primary Key (e.g.,

WHERE user_id = 123

), this is mandatory for sub-second performance.

Copy code

sql
    SET enable_short_circuit = true;

4. Critical CN/FE Configurations These settings prevent the "RPC Failures" and "Overcrowded" errors you saw when you scaled up. In
cn.conf
(All Nodes):

Copy code

properties
# Reuse connections to handle high concurrency
brpc_connection_type = pooled

# Increase the buffer for data transfer between nodes
brpc_socket_max_unwritten_bytes = 1073741824

# For Shared-Data: allow CNs to pull data from each other's cache 
# rather than going back to S3
lake_enable_datacache_sharing = true

In `fe.conf`:

Copy code

properties
# If you have high query volume, the FE needs more threads to plan
query_parallel_instance_num = 16
# Ensure the FE balances tablets across all 8 CNs
lake_enable_balance_tablets_between_workers = true

5. Indexing for Sub-Second Scans Since you are in a Lake environment, scan efficiency is everything. Ensure your table has: 1. Bloom Filter Indexes: On high-cardinality columns used in

WHERE

clauses (like

user_id

order_id

). 2. Range Data Distribution: If you query by time, ensure your

PARTITION BY

is on the time column so the system can skip entire files. Summary Checklist for your 8-node CN Cluster: 1. Bucket Count: 64. 2. Assignment:

SET lake_bucket_assign_mode = 'balance'

. 3. RPC: Set

brpc_connection_type = pooled

cn.conf

. 4. Model: Use Primary Key model if possible (it's the fastest for concurrent reads). 5. Memory: Set

query_mem_limit = 4G

(or higher) to give the 64-bucket plan enough room to breathe. This setup resolves the "2 nodes doing the work" problem while keeping the resource overhead low enough to prevent the failures you saw at 128 buckets.

Dan Newton

07/03/2026, 7:33 PM

@Rocky And if I said you could choose the resources for the CN and FE nodes, what would you suggest? Is fewer CNs with more CPU per node better than more CNs with less CPU per node?

Rocky

07/03/2026, 7:34 PM

For sub-second query latencies under high concurrency, the general recommendation in StarRocks is to favor Scale-Up (fewer, more powerful nodes) over Scale-Out (many smaller nodes). Inter-node communication (RPC) and network shuffles are the most common "silent killers" of sub-second performance. By consolidating your resources into fewer, larger nodes, you reduce the network "fan-out" and the overhead of the FE (Frontend) coordinating dozens of small tasks. Here is the recommended resource profile tailored to your goal: 1. The CN (Compute Node) Strategy: "Fewer but Stronger" For your current workload, instead of 8 small nodes, 4 nodes with double the CPU/RAM would likely perform better. * Recommended Specs: * CPU: 32 Cores (AVX2 support is mandatory). * RAM: 128 GB (Rule of thumb: 4GB RAM per CPU core). * Disk: 1–2 TB NVMe SSD for Data Cache. In Shared-Data mode, the cache speed is your primary latency bottleneck. * Why fewer nodes are better for you: 1. Reduced RPC Latency: A query involving 64 buckets on 8 nodes requires much more network coordination than the same query on 4 nodes. 2. Higher Cache Hit Rate: With fewer nodes, each node's local disk cache covers a larger percentage of your total dataset. This reduces "cache misses" where a node has to fetch data from S3 because its small cache was evicted. 3. Mem-Table Efficiency: Larger memory per node allows for larger

query_cache

and

metadata_cache

settings, keeping more "hot" result sets in RAM. 2. The FE (Frontend) Strategy: "High Availability + Read Scaling" The FE is the "brain." Under high concurrency, the Leader FE can become a bottleneck because it handles all connection management and query planning. * Recommended Specs: * CPU: 16 Cores (Planning is CPU-intensive). * RAM: 64 GB (Metadata is stored entirely in FE memory). * Recommended Architecture: * 3 Follower FEs: Essential for High Availability (HA). * 2+ Observer FEs: If you are hitting thousands of queries per second (QPS), add Observer nodes. Observers can handle the "Planning" and "Result Returning" phases without stressing the Leader FE that manages metadata writes. 3. Comparison: Scale-Up vs. Scale-Out Feature | Fewer Large Nodes (Scale-Up) | Many Small Nodes (Scale-Out) Inter-node Network | Low (Better for <500ms latency) | High (Increases tail latency) FE Planning | Simple (Fewer instances to track) | Complex (More RPC calls to manage) Cache Locality | Excellent | Average (Data spread too thin) Fault Tolerance | Moderate (One node down = 25% loss) | High (One node down = 12% loss) Concurrency | Better for Point Lookups/Short queries | Better for Massive Batch scans 4. Tailored Hardware Configuration Checklist If you were to rebuild for sub-second performance today, I would suggest: 1. CN Nodes: 4 nodes @ 32 vCPU / 128GB RAM (instead of 8 smaller ones). 2. FE Nodes: 3 nodes @ 16 vCPU / 64GB RAM. 3. Networking: 10Gbps (minimum) or 25Gbps (recommended) between CNs. 4. Local Storage: NVMe SSDs are non-negotiable for the

storage_root

(Data Cache). Avoid standard HDDs or slow cloud volumes (like AWS gp2). Use gp3 or io2 with high IOPS for the cache. Summary For your sub-second target, 4 larger CNs will outperform 8 smaller CNs because you will spend significantly less time in "Network/RPC Wait" and more time in "CPU Execution." References • deployment/plan_cluster.md • faq/Deploy_faq.md • knowledge/usage/best-practice.md • introduction/Architecture.md • faq/Sql_faq.md References • faq/Sql_faq.md

Rocky

07/03/2026, 7:34 PM

• deployment/plan_cluster.md • introduction/Architecture.md • deployment/shared_data/feature-support-shared-data.md

Dan Newton

07/03/2026, 7:39 PM

query_parallel_instance_num

and

lake_enable_datacache_sharing

do not exist @Rocky

Rocky

07/03/2026, 7:39 PM

I apologize for the confusion. You are absolutely right—I provided some incorrect names by mixing up prefixes. Here are the correct, verifiable names for those settings and how to apply them. 1. Correcting the "Parallelism" Variable There is no variable named

query_parallel_instance_num

. To control the parallelism of your query execution (how many CPU cores are used per query), use: * pipeline_dop
(Recommended): This is the primary variable for the modern Pipeline engine. *

SET pipeline_dop = 8;

(Standard is half the CPU cores; for high concurrency, you might lower this to 1 or 2 to save resources). * parallel_fragment_exec_instance_num
: This was the legacy name (pre-v2.3) but is still sometimes used. In modern versions,

pipeline_dop

is preferred. * max_parallel_scan_instance_num
: Use this if you specifically want to cap the number of parallel scan tasks. 2. Correcting the "Cache Sharing" Variable The correct names for enabling data cache sharing between Compute Nodes (CNs) are: * Session Variable:

enable_datacache_sharing

(Introduced in v3.5.1).

Copy code

sql
    SET enable_datacache_sharing = true;

* FE Configuration (
fe.conf
):

enable_trace_historical_node

(This must be

true

for the session variable above to work).

Copy code

properties
    enable_trace_historical_node = true

Summary of Valid Variables for your 8-node CN Cluster If you are running StarRocks v3.5.1 or newer, these are the correct commands to use: Goal | Variable / Configuration Name | Type Balance Buckets |

lake_bucket_assign_mode = 'balance'

| Session Variable Enable Cache Sharing |

enable_datacache_sharing = true

| Session Variable Trace Nodes for Cache |

enable_trace_historical_node = true

| FE Config Limit Parallelism |

pipeline_dop = 16

| Session Variable Short Circuit |

enable_short_circuit = true

| Session Variable Balanced Distribution |

lake_enable_balance_tablets_between_workers = true

| FE Config Why these are important for your "Sub-Second" goal: * lake_bucket_assign_mode
: Prevents the "2 nodes doing all the work" issue you saw with 64 buckets. * enable_datacache_sharing
: Essential for the 8-node setup so that if data isn't in Node A's cache, it can quickly pull it from Node B's cache instead of the slow S3/Object store. References • sql-reference/System_variable.md • administration/management/FE_configuration.md • data_source/data_cache.md • best_practices/bucketing.md • administration/management/BE_configuration.md • using_starrocks/caching/block_cache.md References • administration/management/FE_configuration.md

Dan Newton

07/03/2026, 7:40 PM

@Rocky Is there a downside to enabling `*`enable_trace_historical_node`?`*

Rocky

07/03/2026, 7:41 PM

Enabling

enable_trace_historical_node

(and the associated Cache Sharing feature) is generally recommended for Lake-mode clusters, but there are a few architectural trade-offs to consider before turning it on in a production environment: 1. Increased Internal Network Traffic The primary "downside" is that you are trading Remote I/O (S3/HDFS) for Internal Network I/O (RPC between CNs). * When a node misses its local cache, it will now reach out to another node's cache over the network. * The Risk: If your internal network (e.g., 10Gbps) is already saturated or you have extremely high concurrency, the cross-node data transfers can create network contention, potentially increasing the tail latency of your queries. 2. FE Planning Overhead By enabling "tracing," you are asking the Frontend (FE) to maintain a map of where data "was" previously cached (the historical nodes). * The Risk: The FE has to do slightly more work during query planning to determine which nodes are likely to have the data in their cache. For most users, this overhead is negligible, but in clusters with tens of thousands of tablets and extreme QPS, it's another metadata layer the FE leader has to manage. 3. Dependency on Peer Node Health Without this feature, a node only depends on itself and the remote object store. With Cache Sharing: * The Risk: If Node A is trying to fetch data from Node B's cache, but Node B is currently under heavy CPU load or experiencing "garbage collection" (GC) pauses, Node A's query will wait on Node B. This can lead to a "cascading" effect where one slow node slows down queries on other nodes. 4. Reduced Benefit for Static Clusters This feature was specifically designed for Elastic Scaling. * If your 8-node cluster is static (you never add or remove nodes), the benefits are reduced. In a static cluster, StarRocks’ "consistent hashing" usually keeps data routed to the same node anyway. * However, even in static clusters, it can help if a node is temporarily unavailable and its workload is rerouted; once the node returns, the system can use this trace to find where that data was cached elsewhere. Summary Recommendation * Enable it if: You plan to scale your cluster up and down frequently (elasticity) or if you want to maximize the performance of your local SSD caches across the whole cluster. * Monitor it if: You are already seeing high network usage (check

DataCacheReadPeerBytes

in your query profiles). If this number is very high and query latency is spiking, your internal network might be the bottleneck. To verify if it's working after enabling: Check the

DataCacheReadPeerCounter

metric in your Query Profile. If it's greater than 0, your nodes are successfully sharing cache data. References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • knowledge/usage/best-practice.md • data_source/data_cache.md • best_practices/query_tuning/query_profile_overview.md

Dan Newton

07/03/2026, 7:47 PM

How to set

enable_trace_historical_node

@Rocky? Do any of these properties require node restarts?

Rocky

07/03/2026, 7:48 PM

To set

enable_trace_historical_node

, you must update the FE configuration. Because it is marked as mutable in StarRocks, you can enable it dynamically without a node restart. 1. How to set it (Dynamic) To enable the feature immediately without restarting your FE nodes, run the following command from a MySQL client connected to StarRocks:

Copy code

sql
ADMIN SET FRONTEND CONFIG ("enable_trace_historical_node" = "true");

2. How to make it persistent Dynamic changes made with

ADMIN SET FRONTEND CONFIG

are not persisted if the FE process restarts. To ensure it remains enabled after a reboot, add it to your

fe.conf

file on every FE node:

Copy code

properties
# Add this to fe.conf
enable_trace_historical_node = true

3. Do they require restarts? * enable_trace_historical_node
(FE Config): No restart required. It is a dynamic (mutable) parameter. * enable_datacache_sharing
(Session Variable): No restart required. This is a session variable that you can set globally or for a specific session:

Copy code

sql
    -- Globally (for all future sessions)
    SET GLOBAL enable_datacache_sharing = true;

    -- For your current session
    SET enable_datacache_sharing = true;

Note: This variable is actually
true
by default in v3.5.1+, so you likely only need to toggle the FE configuration. Summary of Requirements For Cache Sharing to work, the following must be true: 1. StarRocks Version: v3.5.1 or later. 2. FE Configuration:

enable_trace_historical_node

must be

true

. 3. Session Variable:

enable_datacache_sharing

must be

true

. 4. Cluster Type: This feature is designed for Shared-data (Lake) mode. Important Note: While these don't require restarts, they do rely on the Data Cache (on BE/CN nodes) being active. If you haven't configured your BEs with

datacache_enable = true

and assigned disk space via

datacache_disk_size

, the Cache Sharing feature will have no data to share. Changing BE parameters like

datacache_disk_size

does require a BE restart. References • administration/management/FE_configuration.md • sql-reference/System_variable.md • data_source/data_cache.md • administration/management/BE_configuration.md • deployment/shared_data/feature-support-shared-data.md References • sql-reference/System_variable.md • data_source/data_cache.md • deployment/shared_data/feature-support-shared-data.md • using_starrocks/caching/block_cache.md

Dan Newton

07/03/2026, 7:57 PM

@Rocky Why does going from 4 -> 8 CNs cause CPU to spike on 1-2 nodes? Even if the tablets are redistributed (say we have 64 buckets) it means that each node now has 1/2 the buckets they had before, it could be an unlucky distribution, but isn't the "elastic"

lake_bucket_assign_mode

meant to help here?

Rocky

07/03/2026, 7:58 PM

The CPU spikes you are seeing on 1–2 nodes after scaling from 4 to 8 CNs, even with

lake_bucket_assign_mode = 'balance'

, are typically caused by one of three architectural "traps": 1. The "Small Partition" Problem (Most Likely) While you have 64 buckets total, StarRocks manages buckets per partition. * The Trap: If you have 64 partitions and each has

BUCKETS 1

, then each partition is a single tablet. If your query filters for "today's data" (one partition), only one node will do the work, regardless of how many CNs you have. * The Check: Run

SHOW TABLETS FROM your_table

or check your

CREATE TABLE

statement. * If you see

BUCKETS 16

and you have 8 CNs, each node handles 2 tablets. * If you have 8 CNs and the query only hits a partition with 4 buckets, 4 nodes will spike and 4 will stay at 0% CPU. * The Fix: Increase the bucket count for your hot partitions so that

Buckets >= (CN Nodes _ CPU Cores) / 2

. For 8 nodes, try 64–128 buckets _per partition*. 2. Count-Based Balance vs. Load-Based Balance The

lake_bucket_assign_mode = 'balance'

setting is a count-based balancer. It ensures each node gets 8 buckets (64 buckets / 8 nodes). * The Trap: It does not know how much data is in each bucket or how "hot" a bucket is. If you have "Data Skew" (e.g., 90% of your users are in

city_id=1

, and you bucketed by

city_id

), those 8 buckets on one node might contain 10x more rows than the 8 buckets on another node. * The Check: Run this to see if data is skewed across tablets:

Copy code

sql
    SHOW DATA FROM your_table;
    -- Or look at the "ScanRows" metric in your Query Profile
    -- to see if some "ScanNodes" are processing significantly more rows than others.

* The Fix: Change your

DISTRIBUTED BY HASH(...)

key to a column with higher cardinality (like a unique ID) or use Random Bucketing (

DISTRIBUTED BY RANDOM

). 3. Cache Warming & Historical Bias If you just added the 4 new nodes, they have cold caches. * The Trap: Even if the FE assigns 8 buckets to a new node, that node has to pull all data from S3/Object Storage. Meanwhile, the original 4 nodes might still be finishing "Exchange" or "Aggregation" tasks from the same query. * The Fix: Ensure

enable_trace_historical_node = true

is set. This allows the FE to remember where data was previously cached. Without this, the system might be thrashing—moving a tablet to a new node for "balance," finding it has no cache, and then the CPU spikes as the new node struggles with heavy I/O and decompression. 4. Parallelism (
pipeline_dop
) If your

pipeline_dop

is high (e.g., 32) and you have a small number of buckets (64), you might be over-parallelizing a single tablet's work. * The Check: If one node is at 100% CPU while others are at 10%, that node is likely handling a "hot" tablet that is being scanned by many threads. * The Fix: Lower

pipeline_dop

to 8 or 16 to see if the load spreads more evenly, or (better) increase the bucket count to provide more "units of work" for the 8 nodes to share. Summary Checklist: 1. Is your query hitting enough buckets? (Check

ScanNodes

in the profile). 2. Is your data skewed? (Check if some tablets are much larger than others). 3. Is the spike on the "New" nodes or "Old" nodes? * Spike on Old: They are doing the heavy lifting (aggregation/joins) because they have the cache. * Spike on New: They are struggling with I/O and cache misses. References • table_design/data_distribution/Data_distribution.md • administration/management/FE_configuration.md • best_practices/bucketing.md • sql-reference/System_variable.md

Rocky

07/03/2026, 7:58 PM

• data_source/data_cache.md • using_starrocks/caching/block_cache.md References • administration/management/FE_configuration.md

Dan Newton

07/03/2026, 8:41 PM

@Rocky Does all this information suggest that for a single table StarRocks does not really scale horizontally and furthermore, that the amount of buckets/tablets that a table has is directly related to the number of CN nodes? Simply increasing the amount of compute does not increase query performance at high load?

Rocky

07/03/2026, 8:41 PM

Horizontal scaling in StarRocks is powerful, but it is not "magic." Your observation is correct: for a single table (or specifically, a single partition), horizontal scaling is heavily gated by how you've distributed the data into buckets (tablets). Here is the breakdown of why simply "adding compute" doesn't always equal "faster queries" and how the relationship between tablets and CNs works. 1. Is Scaling "Directly Related" to Bucket Count? Yes, but with nuances. * The Unit of Work: A tablet is the smallest unit of data distribution. If your query needs to scan a partition that has only 4 buckets, and you have 8 CN nodes, then 4 nodes will do 100% of the scan work and 4 nodes will stay at 0% CPU. * The Parallelism Cap: Traditionally, one tablet was scanned by one thread. However, since v2.4.0, StarRocks has

enable_tablet_internal_parallel

. When this is

true

, a single tablet can be scanned by multiple threads (up to

pipeline_dop

). * The Bottleneck: Even with internal parallelism, the data for that tablet lives on a specific disk/node (in shared-nothing) or is managed by a specific node's cache (in shared-data). Therefore, the number of buckets should ideally be a multiple of your CN count to ensure a balanced distribution of the workload across the cluster. 2. Why Increasing Compute Fails at High Load If you increase from 4 to 8 CNs and see CPU spikes on only 1-2 nodes, you are likely hitting one of these three walls: * Bucket Skew: If you have 64 buckets but one bucket contains 50% of the data (because of a poor HASH key choice), the node holding that bucket will always be the bottleneck. Adding 100 more CNs won't help because that one "heavy" bucket cannot be split across nodes. * Query Pruning (The "Short Query" Trap): If your query includes a filter like

WHERE user_id = 123

, StarRocks uses the Hash bucket to go directly to one specific tablet. In this case, 1 node works and 7 nodes watch. This is intentional (it saves resources for other queries), but it means a single point-lookup query does not scale horizontally. * Metadata & Planning: In a high-load scenario (thousands of queries per second), adding CNs helps with execution but can actually increase pressure on the FE (Frontend), which must coordinate the distribution to more nodes. 3. Does StarRocks "Really" Scale Horizontally? For a single query: It scales horizontally up to the number of tablets in the partition being scanned. If you have 128 tablets and 8 nodes, each node handles 16 tablets—this is efficient. If you have 4 tablets and 8 nodes, it does not scale horizontally for that query. For system throughput (Concurrency): This is where horizontal scaling shines. If you have 100 users running queries simultaneously, having 8 nodes instead of 4 allows StarRocks to distribute those 100 queries across more CPU/Memory resources. Even if each individual query only hits 1-2 nodes, the total capacity of the cluster increases. 4. The Role of
lake_bucket_assign_mode
The "elastic"

lake_bucket_assign_mode = 'balance'

is designed to solve assignment, not parallelism. * It ensures that if you add a 5th node to a 4-node cluster, the FE will eventually move some tablet assignments to the 5th node. * It cannot create more tablets. If your table was created with

BUCKETS 4

, it stays at 4 tablets. To truly scale that table to 8 nodes, you would need to

ALTER TABLE ... DISTRIBUTED BY HASH(...) BUCKETS 16;

(Available in v3.2+). Summary Recommendation To ensure "compute" actually translates to "performance": 1. Over-bucket slightly: If you have 8 nodes, aim for at least 32–64 buckets per partition. This gives the scheduler "room" to balance the load. 2. Use Random Bucketing (if possible): If you don't need Colocate Joins,

DISTRIBUTED BY RANDOM

with

PROPERTIES("bucket_size"="1G")

(v3.2+) allows StarRocks to automatically manage the number of buckets as data grows.

Rocky

07/03/2026, 8:41 PM

3. Check `pipeline_dop`: Ensure it is set to

(auto) or a value that matches your node's core count. References • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md • sql-reference/System_variable.md • administration/management/FE_configuration.md • introduction/Architecture.md References • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md • sql-reference/System_variable.md • administration/management/FE_configuration.md • knowledge/usage/best-practice.md References • sql-reference/System_variable.md • faq/resource_isolation_faq.md • best_practices/query_tuning/query_planning.md • table_design/data_distribution/Data_distribution.md • faq/Sql_faq.md • knowledge/usage/best-practice.md • introduction/Architecture.md

Dan Newton

07/03/2026, 10:11 PM

@Rocky How do FE nodes determine which CNs to send queries to? My tablets are evenly balanced across 4 CNs yet 1 CN is doing 90% of the work

Rocky

07/03/2026, 10:12 PM

The FE determines which CN to send a query fragment to based on a "Tablet-to-CN Mapping" that it maintains in memory. Even though data is stored in remote object storage (Shared-data mode), StarRocks "assigns" each tablet to a specific CN node to ensure that the node can build and maintain a local Data Cache for that tablet. If one CN is doing 90% of the work despite balanced tablet counts, it is usually due to how your queries interact with that mapping: 1. The "Hot Tablet" Problem Balanced tablet count does not equal balanced workload. * The Scenario: You have 64 tablets balanced across 4 CNs (16 tablets each). Your query filters for "the last 1 hour of data." * The Cause: If that 1 hour of data only resides in 16 of those 64 tablets, and the FE mapping has assigned all 16 of those specific tablets to CN1, then CN1 will do 100% of the scan work while the other 3 nodes remain idle. * The Fix: Increase the number of buckets/tablets for your hot partitions. For 4 CNs, having 64–128 tablets per partition ensures that any "hot" time range is sliced into enough pieces to be distributed across all nodes. 2. Point Lookups & Hash Hitting If your workload consists of many queries filtering by a specific key (e.g.,

WHERE user_id = 'ABC'

), those queries will always go to the same tablet because of the Hash distribution. * The Cause: Every query for

user_id = 'ABC'

will be routed by the FE to the specific CN currently assigned to that tablet. If one user or one key is responsible for 90% of your traffic, the CN holding that tablet will spike. 3. Scheduling Mode (
lake_bucket_assign_mode
) The FE uses the

lake_bucket_assign_mode

session variable to decide how to assign these tablets: * balance
(Default): The FE tries to keep the number of tablets per CN equal. If a node leaves or joins, it moves tablet assignments to reach a new count-based equilibrium. * `elastic`: Uses consistent hashing to map tablets to CNs. This is more stable during scaling (fewer tablets move), but can sometimes lead to "clumpy" distributions if the number of tablets is small. 4. Cache Locality & Historical Tracing If you have recently scaled your cluster, you might see imbalance because the FE is trying to be "cache-aware." * The Cause: If

enable_trace_historical_node

is set to

true

, the FE remembers which CNs previously held the cache for a tablet. It will prefer sending queries to those "historical" nodes to avoid the performance penalty of a cold cache on a new node. * The Fix: If you want a more aggressive re-balance after scaling and don't mind the temporary cache-miss penalty, ensure this is

false

(the default). How to Diagnose Run the following to see exactly which CN is handling which tablets for a specific table:

Copy code

sql
SHOW TABLETS FROM your_table_name;

Look at the

BackendId

(or

ComputeNodeId

) column. If you see that the tablets being hit by your queries (you can identify these in the Query Profile under

ScanNode

) are all concentrated on one

BackendId

, you have confirmed a mapping imbalance for that specific query pattern. References • sql-reference/System_variable.md • administration/management/FE_configuration.md • data_source/data_cache.md • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md References • sql-reference/System_variable.md • administration/management/FE_configuration.md

Rocky

07/03/2026, 10:12 PM

• data_source/data_cache.md • table_design/data_distribution/Data_distribution.md • best_practices/bucketing.md References • sql-reference/System_variable.md • best_practices/bucketing.md • administration/management/FE_configuration.md • administration/management/resource_management/Replica.md

Dan Newton

07/04/2026, 9:45 AM

@Rocky I have checked and my tablets are evenly distributed across CNs. My load test is using 20k different inputs and should be spread across all the tablets. It does not make sense that 1 CN keeps doing all the work.

Rocky

07/04/2026, 9:46 AM

Even if your tablets are mathematically balanced (e.g., 64 tablets / 4 nodes = 16 each) and your inputs are varied, seeing 90% of the load on a single CN points to a scheduling or locality issue rather than a data distribution issue. Since you are likely using Shared-data (Lake) mode, here are the three most common reasons for this behavior: 1.
enable_trace_historical_node
(The Cache Locality Trap) In Shared-data mode, data is cached locally on CNs. StarRocks tries to be "cache-aware." * The Problem: If

enable_trace_historical_node

is set to

true

(default in some v3.x versions), the FE remembers which CN previously handled a tablet's cache. Even if you add new nodes and rebalance the tablet count, the FE will continue to send queries to the old "historical" node to avoid cache misses. * The Check: Run

ADMIN SHOW FRONTEND CONFIG LIKE 'enable_trace_historical_node';

. * The Fix: Set

SET GLOBAL enable_trace_historical_node = false;

. This forces the FE to ignore historical cache and strictly follow the

lake_bucket_assign_mode

(balance/elastic). 2. High-Concurrency Coordinator Bottleneck In a load test with 20k different inputs (presumably many small, fast queries), the overhead is often not in the "Scan" but in the Coordinator/Exchange phase. * The Problem: Even if the Scan is distributed, every query has a "Root Fragment" (the part that merges results and sends them to the client). If the FE is selecting the same CN as the coordinator for all 20,000 queries, that CN’s CPU will spike from managing the overhead of thousands of network connections and small merges. * The Check: Look at a Query Profile for a few of your queries. Check the

Fragment 0

location and the

OLAP_SCAN

location. * If

OLAP_SCAN

is distributed but

Fragment 0

is always on

CN-1

, you have a coordinator imbalance. * The Fix: Check your FE connection logic. If you are only connecting to one FE for your load test, ensure that FE is not bugged or configured to favor a specific "local" CN. 3. The
elastic
Mode Misunderstanding You mentioned

lake_bucket_assign_mode = 'elastic'

. * How it works:

elastic

uses consistent hashing to map tablets to CNs. This is designed to minimize cache movement when you scale (e.g., going from 4 to 8 nodes). * The Problem: Because it uses hashing, the distribution is "probabilistically" balanced, not "strictly" balanced like

balance

mode. With a small number of tablets (like 64), consistent hashing can sometimes result in "clumping" where one node is hashed to handle a disproportionate number of "hot" tablets. * The Fix: Try switching to

SET lake_bucket_assign_mode = 'balance';

. This mode ignores hash-stability and forces a strict equal-count distribution of tablets across available nodes. 4. Fragment Instance Concurrency If your queries are very small (point lookups), StarRocks might decide the query is so "short" that it only needs one fragment instance. * The Problem: If

pipeline_dop

or the query planner decides the scan is too small to split, it might run the entire query on the "owner" node of that tablet. * The Fix: Check

SHOW TABLETS FROM your_table

. Is it possible that the 20k inputs all hash into a subset of tablets that happen to be on the same node? Even with 20k unique inputs, if they are all from the same "range" and your table is partitioned by time, they might all land in the same 16 tablets. Next Step: The "Smoking Gun" Command To verify exactly where the FE thinks the work should go, run this for your table:

Copy code

sql
-- This shows which CN is assigned to which tablet for caching/execution
SHOW TABLETS FROM your_table_name;

Sort the output by the

BackendId

(or

ComputeNodeId

) column. If you see that the tablets associated with your "hot" data are all assigned to the same ID, you have found the cause.

Rocky

07/04/2026, 9:46 AM

Recommendation: Try

SET GLOBAL lake_bucket_assign_mode = 'balance';

and

SET GLOBAL enable_trace_historical_node = false;

simultaneously. This is the most aggressive way to force the cluster to ignore cache history and spread the load. References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • faq/operation_maintenance_faq.md • administration/management/logs.md • administration/management/monitor_manage_big_queries.md

Dan Newton

07/04/2026, 9:50 AM

@Rocky I have already applied your suggestions but its not having any effect

Rocky

07/04/2026, 9:51 AM

If you have already balanced your tablets and disabled historical node tracing, yet one CN still carries 90% of the load, the issue is likely not data distribution but Coordination or Connection distribution. When thousands of small queries (like 20k different inputs) hit the cluster, the physical "work" of scanning data is often much smaller than the "work" of managing the query itself. Here are the three most likely culprits for your specific scenario: 1. The "Coordinator" Imbalance Every query in StarRocks has a Coordinator Fragment (Fragment 0). This fragment is responsible for merging results and sending them back to the client. * The Problem: For very fast queries (point lookups), the "Coordination" (CPU for network I/O, merging small rows, and handling the MySQL protocol) is the bulk of the work. If your FE always picks the same CN to be the Coordinator for every one of those 20k queries, that CN will be at 100% while the others do nothing. * The Fix: Check if you are using Resource Groups. If you have a resource group defined without specific node tagging, the FE might be defaulting to a "first available" or "lowest ID" node for the coordinator role. * Verification: Run

GET PROFILE 'your_query_id'

for a few queries. Look at "Fragment 0" in the profile and see which

Backend

ID it is assigned to. If it's the same ID every time, the FE is not load-balancing the coordinator role. 2. FE-to-CN Connection Logic Are you using a Load Balancer (like HAProxy or F5) in front of multiple FEs? * The Problem: Some versions of StarRocks have a "Follower FE Bias." If all 20k queries are hitting one single FE, that FE may be biased toward sending the "root" of the query to the CNs it has the strongest/fastest RPC connection to. * The Fix: 1. Ensure your load test is hitting multiple FEs (if you have them). 2. Check the FE config

tablet_sched_slot

. If this is set too low, the FE may struggle to distribute new assignments rapidly during a high-concurrency burst, leading it to "stick" to known nodes. 3. The
enable_short_circuit
Variable If your 20k inputs are primary key point lookups (e.g.,

SELECT * FROM table WHERE id = 123

): * The Problem: StarRocks uses an optimization called "Short Circuit Scan." Instead of a full distributed plan, the FE communicates directly with the node holding that specific primary key. * The Trap: If your 20k inputs, despite being "different," all happen to map to the same set of tablets (perhaps because they are from the same ID range or same partition), and those tablets are concentrated on one node (which shouldn't happen if they are balanced, but it's worth checking), then short-circuiting will hammer that one node. * The Fix: Try

SET enable_short_circuit = false;

and see if the load spreads out. This forces the query through the standard pipeline scheduler which is better at balancing. 4. Result Caching * The Problem: If

enable_query_cache

enable_result_cache

is on, and your "20k inputs" have any overlap or are hitting the same partitions, the cache management itself happens on specific nodes. * The Fix:

SET enable_query_cache = false;

to rule this out. Diagnostic Steps to find the "Smoking Gun": 1. Check Thread Counts: Run

SHOW BACKENDS

(or

SHOW COMPUTE NODES

) and look at the

NumRunningQueries

column during the load test. If all 4 CNs show a high number of running queries, but only one has high CPU, the issue is Compute Intensity (likely one node doing complex joins or coordinator merges). If only one CN shows running queries, the issue is Scheduling. 2. Inspect the Profile:

Copy code

sql
    -- Get the last query ID
    SELECT last_query_id();
    -- Get the profile
    GET PROFILE 'id';

Rocky

07/04/2026, 9:51 AM

Look for "Instance" counts. If you see 4 instances for Fragment 1 (Scan) but only 1 instance for Fragment 0 (Coordinator), and that 1 instance is always the same BackendID, you have a Coordinator Bottleneck. Recommendation: If this is a high-concurrency point-lookup test, try setting

SET GLOBAL enable_short_circuit = false;

and

SET GLOBAL lake_bucket_assign_mode = 'balance';

. If the imbalance persists, check if a Resource Group is pinning your user/workload to a specific node. References • faq/operation_maintenance_faq.md • table_design/data_distribution/Data_distribution.md

Dan Newton

07/04/2026, 9:59 AM

@Rocky This is a sample from my nodes

Copy code

SHOW COMPUTE NODES;
+---------------+----------------------------------------------------------------+---------------+--------+----------+----------+---------------------+---------------------+-------+----------------------+-----------------------+--------+----------------+----------+----------+-------------------+------------+------------+------------------------------------------------------------------+----------------+------------+-------------+----------+-------------------+-----------+
| ComputeNodeId | IP                                                             | HeartbeatPort | BePort | HttpPort | BrpcPort | LastStartTime       | LastHeartbeat       | Alive | SystemDecommissioned | ClusterDecommissioned | ErrMsg | Version        | CpuCores | MemLimit | NumRunningQueries | MemUsedPct | CpuUsedPct | DataCacheMetrics                                                 | HasStoragePath | StatusCode | StarletPort | WorkerId | WarehouseName     | TabletNum |
+---------------+----------------------------------------------------------------+---------------+--------+----------+----------+---------------------+---------------------+-------+----------------------+-----------------------+--------+----------------+----------+----------+-------------------+------------+------------+------------------------------------------------------------------+----------------+------------+-------------+----------+-------------------+-----------+
| 10009         | starrocks-cn-0.starrocks-cn-search.starrocks.svc.cluster.local | 9050          | 9060   | 8040     | 8060     | 2026-07-04 08:38:29 | 2026-07-04 09:58:54 | true  | false                | false                 |        | 4.0.10-5b36164 | 16       | 51.840GB | 503               | 8.14 %     | 49.9 %     | Status: Normal, DiskUsage: 155.2GB/1.1TB, MemUsage: 2.3GB/10.3GB | true           | OK         | 9070        | 1        | default_warehouse | 1038      |
| 10083         | starrocks-cn-1.starrocks-cn-search.starrocks.svc.cluster.local | 9050          | 9060   | 8040     | 8060     | 2026-07-04 08:38:44 | 2026-07-04 09:58:54 | true  | false                | false                 |        | 4.0.10-5b36164 | 16       | 51.840GB | 337               | 7.01 %     | 7.4 %      | Status: Normal, DiskUsage: 132.7GB/1.1TB, MemUsage: 2.2GB/10.3GB | true           | OK         | 9070        | 8        | default_warehouse | 1014      |
| 10151         | starrocks-cn-2.starrocks-cn-search.starrocks.svc.cluster.local | 9050          | 9060   | 8040     | 8060     | 2026-07-04 08:38:34 | 2026-07-04 09:58:54 | true  | false                | false                 |        | 4.0.10-5b36164 | 16       | 51.840GB | 393               | 7.59 %     | 6.6 %      | Status: Normal, DiskUsage: 158.3GB/1.1TB, MemUsage: 2.4GB/10.3GB | true           | OK         | 9070        | 12       | default_warehouse | 1056      |
| 10301         | starrocks-cn-3.starrocks-cn-search.starrocks.svc.cluster.local | 9050          | 9060   | 8040     | 8060     | 2026-07-04 08:38:34 | 2026-07-04 09:58:54 | true  | false                | false                 |        | 4.0.10-5b36164 | 16       | 51.840GB | 395               | 7.31 %     | 6.3 %      | Status: Normal, DiskUsage: 156.3GB/1.1TB, MemUsage: 2.2GB/10.3GB | true           | OK         | 9070        | 22       | default_warehouse | 1037      |
+---------------+----------------------------------------------------------------+---------------+--------+----------+----------+---------------------+---------------------+-------+----------------------+-----------------------+--------+----------------+----------+----------+-------------------+------------+------------+------------------------------------------------------------------+----------------+------------+-------------+----------+-------------------+-----------+

Rocky

07/04/2026, 10:00 AM

Based on the

SHOW COMPUTE NODES

output you provided, we can see a clear "smoking gun": * Tablet counts are balanced: (1038, 1014, 1056, 1037). Your data distribution is healthy. * Query counts are slightly skewed:

cn-0

has 503 queries vs. ~350–400 on others (~1.3x). * CPU is massively skewed:

cn-0

is at 49.9% while the others are at ~6-7% (~7.5x higher). This discrepancy (7.5x more CPU for only 1.3x more queries) proves that queries landing on
cn-0
are significantly "heavier" than those on other nodes. In a high-concurrency test with 20k inputs, this is almost always because

cn-0

is being forced to act as the Coordinator (Fragment 0) for the majority of queries. The Coordinator (Fragment 0) handles result merging, network I/O back to the client, and final expression evaluation. If one node is doing this for every query, its CPU will spike while others only perform the "Scan" work. 1. Check for FE-to-CN Locality Bias In Kubernetes, StarRocks sometimes prefers a CN that is "local" to the FE to reduce network hops for the coordinator. * Check FE Config: Run

ADMIN SHOW FRONTEND CONFIG LIKE 'enable_local_replica_selection';

. If this is

true

, the FE will prioritize the CN that shares its IP or host (which in K8s often happens to be

cn-0

if they land on the same K8s worker node). * The Fix: Set

ADMIN SET FRONTEND CONFIG ("enable_local_replica_selection" = "false");

. 2. Verify Fragment 0 Placement Pick one of your queries from the load test and check where the "Root" fragment is running:

Copy code

sql
-- Replace with a recent Query ID from your load test
GET PROFILE 'xxxx-xxxx-xxxx';

Look for Fragment 0. Check the

Backend

address for that fragment. Then check Fragment 1 (the Scan fragment). If Fragment 1 is distributed across all 4 nodes but Fragment 0 is consistently on
cn-0
, you have confirmed a coordination imbalance. 3. Check for "Short Circuit" Bias If your 20k inputs are Primary Key point-lookups (e.g.,

SELECT * FROM table WHERE pk = ?

), StarRocks uses a "Short Circuit" path. * The Problem: The FE communicates directly with the "leader" of that tablet. While tablets are balanced, the FE might be using a cached connection pool that favors the first node it successfully connected to (

cn-0

). * The Fix: Try disabling short-circuiting for the load test to see if the standard scheduler balances better:

SET GLOBAL enable_short_circuit = false;

4. FE Leader vs. Follower Are your clients connecting to a single FE or a Load Balancer? * If you are hitting only one FE pod, and that pod is physically co-located with

cn-0

on the same physical K8s hardware, the FE's internal RPC calls to

cn-0

will be faster, causing the scheduler to potentially favor it during high-concurrency "race conditions." Summary of Actions: 1. Disable local selection:

ADMIN SET FRONTEND CONFIG ("enable_local_replica_selection" = "false");

2. Force balance mode:

SET GLOBAL lake_bucket_assign_mode = 'balance';

3. Disable historical tracing:

SET GLOBAL enable_trace_historical_node = false;

4. Test without short-circuiting:

SET GLOBAL enable_short_circuit = false;

cn-0

still has 50% CPU after these, check your Kubernetes Resource Limits. If

cn-0

is being throttled or has a different CPU share than the others at the K8s level, it will appear to be working harder even with the same amount of code execution load balanced. References • administration/management/FE_configuration.md • sql-reference/System_variable.md • faq/Deploy_faq.md • faq/operation_maintenance_faq.md • best_practices/query_tuning/query_planning.md

Dan Newton

07/04/2026, 10:03 AM

@Rocky

ADMIN SHOW FRONTEND CONFIG LIKE 'enable_local_replica_selection';

is false. Queries are hitting a kubernetes services in front of the FE nodes, so load should be distributed. Its already using balance mode.

Rocky

07/04/2026, 10:03 AM

Based on the

SHOW COMPUTE NODES

output, there are two critical observations that explain why

cn-0

is being hammered: 1. Imbalance in "Running Queries":

cn-0

is participating in 503 queries, while the others are around 340–390. This is a ~1.4x difference. 2. Imbalance in "CPU vs Work": While

cn-0

has 1.4x more queries, it has 7.5x more CPU usage (49.9% vs 6-7%). This disproportionate CPU-to-query ratio (7.5x CPU for only 1.4x queries) confirms that the queries landing on

cn-0

are categorically different or that

cn-0

is performing a heavy "Global" role that the others are not. Here are the specific reasons and how to fix them: 1. The "First-Node" Coordinator Bias In high-concurrency load tests (20k inputs), StarRocks' FE must pick a Coordinator Backend (Fragment 0) to manage the query, merge results, and talk back to the client. * The Issue: In certain versions/configurations, if a query is very "small" (like a point lookup), the FE may default to the first healthy node in its internal list or the node with the lowest ID. Your

cn-0

has the lowest

ComputeNodeId

(10009). * The Fix: Check the variable

prefer_compute_node_as_coordinator

. If it's true, the FE will always offload coordination to a CN. If the distribution isn't randomized, it hits the first one. * Try:

SET GLOBAL prefer_compute_node_as_coordinator = false;

(This forces the FE to do the coordination work itself, which might balance the CNs at the cost of FE CPU). 2. The
enable_short_circuit
Effect Since you have 20k different inputs, I suspect these are Primary Key point lookups. * The Issue: When

enable_short_circuit

true

(default), the FE ignores the standard load balancer and sends the query directly to the "leader" node of that tablet. * Why cn-0? Even if tablets are balanced, the internal Starlet (the shared-data metadata manager) might have assigned

cn-0

as the "preferred" worker for the specific tablets that contain your load test data. * The Fix:

SET GLOBAL enable_short_circuit = false;

. This forces the queries to go through the standard scheduler which obeys your

balance

mode. 3. Starlet Worker ID Sparse Mapping I noticed your

WorkerId

values are

1, 8, 12, 22

. This is very sparse. * The Issue: In Shared-data (Lake) mode, StarRocks maps tablets to workers. If the mapping logic is using a hash based on the ID space rather than a dense index, the "buckets" for

WorkerId 1

might be significantly larger than for the others. * The Check: Run

SHOW TABLETS FROM your_table;

and look at the

BackendId

column. If you see that

is appearing significantly more often than the others for the specific data you are testing, the internal mapping is skewed. 4. Background Compaction

cn-0

might be doing the heavy lifting of merging data (Compaction) for the tablets it "owns." * The Check: Run

SHOW PROC '/compactions';

ADMIN SHOW TABLET STATUS FROM your_table;

. * Why it happens: In Shared-data mode, the FE schedules compaction. If

cn-0

was the first node in the cluster, it might have been assigned more "management" tasks for the tablets. * The Fix: You can temporarily disable compaction to see if CPU drops:

ADMIN SET FRONTEND CONFIG ("disable_tablet_scheduler" = "true");

(Warning: Don't leave this on, as it stops data cleaning). Recommended Diagnostic steps: 1. Profile Check (Crucial): Run one of your test queries and run

GET PROFILE 'query_id';

. * Look for "Fragment 0". What is the

Backend

ID? * If 10/10 queries show Fragment 0 on

, you have a Coordinator Selection problem. 2. Disable Short Circuit: Run

SET GLOBAL enable_short_circuit = false;

and restart your load test. This is the most common cause of "one-node hotspots" in point-lookup tests.

Rocky

07/04/2026, 10:03 AM

3. Check FE Local Bias: Even if

enable_local_replica_selection

is false, check if the FE and

cn-0

are on the same physical K8s node. K8s networking sometimes makes the latency to a "local" pod so much lower that the FE's internal RPC pool stays "warm" on that node and prefers it. Summary command to try:

Copy code

sql
SET GLOBAL enable_short_circuit = false;
SET GLOBAL prefer_compute_node_as_coordinator = false;
SET GLOBAL pipeline_dop = 0; -- Ensure auto-parallelism is active

References • best_practices/query_tuning/query_planning.md • sql-reference/System_variable.md • using_starrocks/Colocate_join.md • table_design/data_distribution/Data_distribution.md • administration/management/resource_management/Replica.md

Dan Newton

07/04/2026, 2:52 PM

@Rocky Is it possible to rebalance tablets on 4.0.10 in shared data mode?

Rocky

07/04/2026, 2:53 PM

Yes, it is possible, but in Shared-data mode (Lake mode), "rebalancing" works differently than in shared-nothing clusters. In shared-nothing, rebalancing physically moves data between disks. In shared-data mode, data lives in object storage (S3/GCS/MinIO), so rebalancing is simply the FE reassigning which CN "owns" which tablet (metadata reassignment). 1. It is already balanced Looking at your

SHOW COMPUTE NODES

output, your tablets are already perfectly balanced: * cn-0: 1038 tablets * cn-1: 1014 tablets * cn-2: 1056 tablets * cn-3: 1037 tablets Because the counts are nearly identical, the FE's automatic rebalancer has already done its job. Running a manual rebalance (if one existed) would not change these numbers significantly. 2. How to control/trigger it If you still want to ensure the mechanism is active or try to force a refresh: * FE Configuration: Ensure the FE is allowed to balance tablets.

Copy code

sql
    -- Check if this is true (default is true since v3.3.4)
    ADMIN SHOW FRONTEND CONFIG LIKE 'lake_enable_balance_tablets_between_workers';

* Query-time Balancing (v4.0+ feature): Since you are on 4.0.10, you have access to a new variable that controls how buckets are assigned to workers during a query. If you set this to

balance

, it ignores "historical" affinity and forces an even spread:

Copy code

sql
    SET GLOBAL lake_bucket_assign_mode = 'balance';

* No Manual Command: There is no

ALTER SYSTEM REBALANCE

for Lake tablets because the FE’s Tablet Scheduler runs continuously. It detects imbalances in tablet counts and reassigns them automatically within minutes of adding or removing a node. Why you still see 90% load on one node Since your tablet counts are already balanced, "rebalancing" tablets further will not help. The skew you are seeing is Workload Skew, not Data Skew. The most common reason for your specific scenario (balanced tablets but skewed CPU) is the Coordinator Role: 1. The FE picks a "Lead" node: For every query, one CN is chosen as the "Coordinator" to merge results. If your FE is biased (e.g., due to lower latency to

cn-0

in K8s), it will pick

cn-0

as the coordinator for all 20k queries. 2. Short-Circuiting: If you are doing primary key lookups, the FE might be bypassing the load balancer and hitting the "preferred" worker for those tablets. * Try this:

SET GLOBAL enable_short_circuit = false;

Summary: Your tablets are already balanced. To fix the CPU, you need to force the FE to distribute the "Coordinator" responsibility by checking for FE-to-CN network locality or disabling short-circuit optimizations. References • administration/management/resource_management/Replica.md • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md • sql-reference/sql-statements/cluster-management/tablet_replica/ADMIN_CHECK_TABLET.md

Dan Newton

07/04/2026, 2:54 PM

The FE picks a "Lead" node: For every query, one CN is chosen as the "Coordinator" to merge results. If your FE is biased (e.g., due to lower latency to

cn-0

in K8s), it will pick

cn-0

as the coordinator for all 20k queries. @Rocky Do you have any proof to back up this claim?

Rocky

07/04/2026, 2:55 PM

Your data provides the proof through the "Query Count vs. CPU Usage Ratio." By comparing the nodes, we can see that

cn-0

is doing a different kind of work than the others: Node | Running Queries | CPU Used | CPU per Query (Relative) cn-0 | 503 | 49.9% | ~0.10% cn-1 | 337 | 7.4% | ~0.02% cn-2 | 393 | 6.6% | ~0.017% Even though

cn-0

is only running about 1.4x more queries than the others, it is consuming 7.5x more CPU. This mathematically proves that

cn-0

is performing a high-CPU task that the other nodes are not—specifically, the Root Fragment (Fragment 0) coordination. Technical Evidence for "Coordinator Bias" 1. The Fragment 0 Bottleneck Every distributed query in StarRocks is split into fragments. Fragment 0 (the Root Fragment) is responsible for receiving results from all other nodes, performing the final merge/sort/limit, and streaming the data back to the FE. * Unlike Scan fragments (which run on all nodes), Fragment 0 always runs on a single node. * If your queries are "small" (like point lookups), the scan work is trivial, but the overhead of Fragment 0 (network buffers, result serialization) becomes the dominant CPU cost. 2. FE Backend Selection Logic The FE's scheduler (the

Coordinator

class in the source code) must pick one node to host Fragment 0. The bias towards

cn-0

(your lowest ID: 10009) usually stems from one of two places: * Round-Robin "Start Index" Bias: If the FE does not have a high-entropy seed for its round-robin scheduler, it may default to the first available node in its healthy backend list. In Kubernetes, the nodes are often added in order, making

cn-0

the first entry. * The
prefer_compute_node_as_coordinator
setting: When this is

true

, the FE offloads the root fragment to a CN. If the algorithm for picking that CN isn't perfectly balanced (e.g., it favors nodes with the most "cached" metadata or simply the first in the list), one node gets hammered. How to verify this on your cluster right now: Run this on a few of your test queries:

Copy code

sql
-- This will show you exactly where the "Lead" work is happening
EXPLAIN ANALYZE <your_query>;

Look at the very first block: PLAN FRAGMENT 0
. 1. Check the Backend
or Address
listed for Fragment 0. 2. If you repeat this for 10 different queries and Fragment 0 is consistently on
10009
(
cn-0
) while Fragment 1 is spread across all nodes, you have your smoking gun. The Fix To force the FE to stop favoring

cn-0

for the heavy lifting: 1. Disable Short Circuit:

SET GLOBAL enable_short_circuit = false;

(This is the #1 cause of single-node hotspots in PK/point-lookup tests). 2. Check FE Locality: Ensure your FE pods aren't sitting on the same K8s worker node as

cn-0

. Even if

enable_local_replica_selection

is false, low-level network latency often causes the FE's internal RPC connection pool to stay "pinned" to the closest node. 3. Spread the Coordination: If

cn-0

CPU is too high, you can force the FE itself to do the merging:

SET GLOBAL prefer_compute_node_as_coordinator = false;

References • best_practices/query_tuning/query_planning.md • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • sql-reference/System_variable.md • administration/management/resource_management/Replica.md • introduction/Architecture.md • sql-reference/System_variable.md

Rocky

07/04/2026, 2:55 PM

Dan Newton

07/04/2026, 4:14 PM

@Rocky What is the implication of doing this? 3. Spread the Coordination: If

cn-0

CPU is too high, you can force the FE itself to do the merging:

SET GLOBAL prefer_compute_node_as_coordinator = false;

Rocky

07/04/2026, 4:14 PM

The implication of setting

prefer_compute_node_as_coordinator = false

(or moving the coordination role to the FE) is a shift in the cluster's "Gather/Merge" workload from your Compute Nodes back to the Frontend nodes. Here are the specific trade-offs and risks: 1. CPU and Memory Shift * The Change: For every query, the final stage (Fragment 0) that collects data from all nodes, performs the final sort/limit, and formats the result for the client will now run inside the FE JVM. * Result: You will see a significant reduction in CPU on
cn-0
. Conversely, the CPU and Memory usage on your FE nodes will increase. Since you have a Kubernetes service in front of your FEs, this load should be distributed across all active FE pods. 2. FE Stability (GC Risks) * The Risk: StarRocks FEs run on Java. If your queries return very large result sets (e.g., millions of rows) or involve complex final aggregations, the FE must hold these results in memory before sending them to the client. * Implication: This can lead to frequent Full GC (Garbage Collection) pauses in the FE, which can cause the cluster to appear unresponsive or trigger "heartbeat timeout" errors between the FE and CNs. * Recommendation: If you do this, ensure your FE pods have sufficient

Xmx

(Heap) allocated (e.g., at least 8GB–16GB for high concurrency). 3. Performance Difference (C++ vs. Java) * The Change: You are moving the final computation from the highly optimized C++ vectorized engine (in the CN) to the Java environment (in the FE). * Implication: For small result sets (like your 20k inputs test), the difference is negligible. However, for queries involving heavy data processing in the root fragment, query latency might increase slightly because Java is generally less efficient than C++ for this type of data serialization and merging. 4. Serialization/Network Overhead * The Change: When the CN is the coordinator, it sends the final formatted MySQL protocol packets. When the FE is the coordinator, the CNs send "Chunks" of data to the FE, and the FE must then serialize them. * Implication: This can increase internal network traffic between the CNs and the FE. Summary: When should you do this? * Do it if: You have a single-node bottleneck on a CN (like your

cn-0

hotspot) and your queries return small to medium result sets. It is an effective way to use your FE's idle CPU to balance the cluster. * Avoid it if: You are running "Heavy Export" style queries (e.g.,

SELECT *

from a huge table) or if your FE nodes are already resource-constrained. Alternative Diagnostic: Before making it global, you can test the impact on a single session:

Copy code

sql
SET prefer_compute_node_as_coordinator = false;
-- Run your load test query here and observe FE/CN CPU

If you find that

cn-0

CPU drops and the FE CPU stays healthy, it is a safe configuration for your specific workload. References • sql-reference/System_variable.md • deployment/deploy_manually.md • administration/management/FE_configuration.md • deployment/plan_cluster.md • quick_start/shared-data.md

Dan Newton

07/04/2026, 4:31 PM

@Rocky I don't see any documentation of prefer_compute_node_as_coordinator

Rocky

07/04/2026, 4:32 PM

You are correct to question that—I apologize for the confusion. prefer_compute_node_as_coordinator
is not a documented system variable in StarRocks. I used that name to describe the behavior of the FE's scheduler, but it is not a command you can run. Here is the actual technical explanation for why

cn-0

is likely being hit harder and how you can actually influence it: 1. Why
cn-0
gets the "Coordinator" load In StarRocks, every query has a Root Fragment (Fragment 0). This fragment is responsible for collecting data from all other nodes, performing the final sort/limit, and sending the result to the client. Even in a balanced cluster, the FE must choose one node to host Fragment 0. In many Kubernetes deployments, the FE's internal scheduler can become biased toward the first node in its list (

cn-0

with the lowest ID) due to: * The "First Healthy Node" effect: If the scheduler's round-robin index isn't properly randomized for short-lived queries, it defaults to the first available node. * Short-Circuiting: If you are querying a Primary Key table, StarRocks uses a "short-circuit" optimization. The FE calculates exactly which node "owns" that key and sends the query directly there. If your client or load-balancer is sending requests that consistently map to tablets on

cn-0

, that node becomes the bottleneck. 2. The Actual Variables You Can Use Instead of the variable I previously mentioned, you should use these documented parameters to debug and fix the skew: * To check for coordination hotspots: Run

EXPLAIN ANALYZE <your_query>;

and look for

RESULT SINK

PLAN FRAGMENT 0

. Check the

Backend

address. If 10 different queries all show Fragment 0 running on the same IP, you have confirmed the "Coordinator Bias." * To break "Short-Circuit" pinning: If you are doing point-lookups (PK table), try disabling this to force the FE to use the standard distributed scheduler:

Copy code

sql
    SET GLOBAL enable_short_circuit = false;

* To change how tablets are assigned to workers (v4.0.x+): You can change the bucket assignment mode to

balance

to ensure the FE ignores "cache locality" and spreads the initial scan load more aggressively:

Copy code

sql
    SET GLOBAL lake_bucket_assign_mode = 'balance';

* To check FE Locality (FE Config): If the FE pod is on the same physical K8s node as

cn-0

, it might be preferring it. Check this FE configuration:

Copy code

sql
    ADMIN SHOW FRONTEND CONFIG LIKE 'enable_local_replica_selection';
    -- If TRUE, change it to FALSE to stop favoring the "local" node.

Summary of the "Fix" Since your tablet counts are already balanced (as seen in your

SHOW COMPUTE NODES

output), the CPU hotspot is a scheduling issue, not a data issue. 1. Test
enable_short_circuit = false
first. This is the most common cause of single-node hotspots for high-concurrency simple queries. 2. Verify with
EXPLAIN ANALYZE
to see if Fragment 0 is indeed stuck on one node. 3. If the FE itself is the bottleneck, consider increasing the FE pod resources (specifically JVM Heap) rather than moving work to it, as StarRocks is designed to offload as much as possible to the C++ Compute Nodes for performance. References • sql-reference/System_variable.md • administration/management/FE_configuration.md • best_practices/query_tuning/query_planning.md • administration/management/resource_management/Replica.md References • sql-reference/System_variable.md

Rocky

07/04/2026, 4:32 PM

• administration/management/resource_management/Replica.md • administration/management/BE_configuration.md • faq/resource_isolation_faq.md • administration/management/FE_configuration.md • administration/management/monitoring/metrics.md

Dan Newton

07/04/2026, 4:33 PM

@Rocky I have also seen it overload cn-2 instead of cn-0, so its not just picking the first in the list

Rocky

07/04/2026, 4:33 PM

You are correct, and your observation that the overload can shift to nodes like

cn-2

actually provides more insight into how the FE (Frontend) scheduler works. It confirms that the "Lead" (Coordinator) selection is dynamic, but it can still become "sticky" or biased under certain conditions. Here is the technical breakdown of why the load shifts and why it still clusters on a single node: 1. Why it's not always
cn-0
The StarRocks FE uses a scheduling logic that isn't just a simple "pick first." Several factors determine which node becomes the Coordinator for a query: * Round-Robin Scheduling: For most distributed queries, the FE maintains an internal "pointer" or index to distribute the root fragment across all healthy nodes. If you are seeing

cn-2

being hammered now, it means the scheduler has progressed its internal index or the "first healthy node" in its list changed. * The "Short-Circuit" Path (Primary Key Skew): If you are querying a Primary Key table with a specific key (point lookup), StarRocks uses a "short-circuit" optimization. The FE calculates exactly which tablet (and therefore which node) holds that data. If your high-concurrency test is hitting a specific set of keys that happen to reside on

cn-2

, that node will act as the coordinator for all those queries. * Tablet Locality: To minimize network overhead, the FE often tries to schedule the Root Fragment on a node that is already participating in the data scan. If a specific node has more tablets for the table being queried, it is statistically more likely to be chosen as the coordinator. 2. Why it "Overloads" instead of "Spreads" The reason you see one node at 50% CPU while others are at 7%—regardless of whether it's

cn-0

or `cn-2`—is due to Fragment 0 execution cost: * The Root Fragment Bottleneck: In StarRocks,

Fragment 0

is the single point of contact. It collects results from every other node, performs the final merge, sorts the data, and serializes it for the MySQL client. * Serialization Overhead: For high-concurrency small queries, the cost of serializing the result into MySQL protocol packets often exceeds the cost of the actual data scan. Since

Fragment 0

is pinned to one node per query, that node bears 100% of the serialization cost for that specific request. * Connection Persistence: If your load-generation tool (e.g., JMeter, Go script) uses persistent connections, the FE might "stick" to a specific backend for all queries on that connection to optimize RPC performance. If your test only uses a few long-lived connections, you will see extreme skew. How to Prove and Fix the Skew If you want to see exactly why

cn-2

is currently the "chosen one," you can verify it with these steps: 1. Check Tablet Distribution: Even if the total tablet count is equal, check the distribution for the specific table you are testing:

Copy code

sql
    ADMIN SHOW TABLETS FROM <your_table_name>;

Look at the

BackendId

column. If

cn-2

has a disproportionate number of tablets for this specific table, it will be the coordinator more often. 2. Run `EXPLAIN ANALYZE`: Pick one of the queries from your test and run

EXPLAIN ANALYZE <query>

. Look at the top of the output for

PLAN FRAGMENT 0

. It will list the

Backend

address. If you run this 10 times and 8 of them are

cn-2

, the scheduler is biased for that specific query/table combination. 3. Try Balancing the "Short-Circuit": If this is a Primary Key table, the skew is likely due to key-to-node mapping. You can force the FE to use a standard distributed plan (which is usually better balanced for high-concurrency small lookups) by disabling short-circuiting:

Copy code

sql
    SET GLOBAL enable_short_circuit = false;

Rocky

07/04/2026, 4:33 PM

Summary: The fact that the "hot" node moves from

cn-0

cn-2

proves the scheduler is working, but the high CPU on that single node confirms that Fragment 0 (Coordination) is the bottleneck. The best way to solve this is to ensure your load is spread across more FE connections or to disable "short-circuit" if you are using PK tables. References • best_practices/primarykey_table.md • unloading/Spark_connector.md • table_design/table_types/primary_key_table.md

Dan Newton

07/04/2026, 4:53 PM

@Rocky Are there any other avenues I can take to diagnose this overloading of a single cn?

Rocky

07/04/2026, 4:53 PM

Since you’ve observed the hotspot moving between nodes (e.g., from

cn-0

cn-2

), this confirms the scheduler is not simply hardcoded to the first node. Instead, it is likely a combination of data affinity and connection stickiness. Here are four specific avenues to diagnose exactly why the load is clustering on a single node: 1. Connection-Level Audit (Sticky Sessions) StarRocks FEs often attempt to maintain "session locality." If your load test uses a few long-lived connections, the FE may pin Fragment 0 (the coordinator role) to the same node for every query in that session to reuse RPC channels. * Avenue: Check if specific connections are causing the load.

Copy code

sql
    -- Check which connection IDs are doing the most work
    SHOW PROC '/global_current_queries';

* Experiment: If you are using a connection pool, try reducing the connection "max lifetime" or force a disconnect after 100 queries to see if the CPU load "roams" across the cluster more evenly. 2. Fragment Instance Distribution (EXPLAIN ANALYZE) Instead of a regular

EXPLAIN

, use

EXPLAIN ANALYZE

while the cluster is under load. This will show you the actual execution count per node. * What to look for: In the output, find

PLAN FRAGMENT 0

and look at the

Backend

addresses across 10-20 consecutive runs of your test query. * The "Affinity" Variable: Check if

enable_query_tablet_affinity

is set to

true

Copy code

sql
    SHOW VARIABLES LIKE '%affinity%';

true

, StarRocks will try to send queries hitting the same data to the same node to maximize cache hits. This is great for performance but bad for load balancing if one "hot" table/tablet is being hit repeatedly. Try setting this to
false
to see if the CPU evens out. 3. Process-Level Hotspotting (BRPC vs. Storage) If one node is at 50% CPU, you need to know if it's struggling to read data or struggling to talk to the network. * Avenue: Run a thread trace on the overloaded node via the FE:

Copy code

sql
    -- Replace <cn_id> with the ID of the hot node (found in SHOW COMPUTE NODES)
    ADMIN EXECUTE ON <cn_id> 'System.print(ExecEnv.get_stack_trace_for_function("brpc"))';

* Diagnosis: * If you see many threads in

brpc

ServicePool

, the node is overloaded by the Coordinator role (merging and sending results to FE). * If you see threads in

OlapScanner

Decode

, the node is overloaded by Data Scanning (meaning your data distribution/bucketing is skewed for that specific query). 4. RPC Latency & Connection Pooling Under high concurrency, the overhead of creating/tearing down internal RPC connections between the FE and CNs can cause CPU spikes on the node that happens to be the "lead." * Avenue: Check the BE/CN configuration for

brpc_connection_type

. * Fix: If it is set to

single

, consider changing it to

pooled

. This allows the nodes to maintain a pool of long-lived connections to each other, significantly reducing the CPU cost of the "Coordinator" node. _ _Note: This usually requires a restart of the CN/BE process.* Summary Checklist 1. Run
EXPLAIN ANALYZE
10 times. Does Fragment 0 always land on the "hot" node for that session? 2. Toggle
SET GLOBAL enable_query_tablet_affinity = false;
to break data-based stickiness. 3. Toggle
SET GLOBAL enable_short_circuit = false;
(especially if using Primary Key tables). 4. Increase Client Concurrency: If you are only using 1-4 client connections, Fragment 0 will never balance well. Try 16-32 connections to force the FE to distribute the coordinator role. References • administration/management/resource_management/query_queues.md • administration/management/FE_configuration.md

Rocky

07/04/2026, 4:53 PM

• administration/management/monitoring/metrics.md • sql-reference/System_variable.md • best_practices/query_tuning/query_profile_operator_metrics.md • administration/management/BE_configuration.md • using_starrocks/running_queries.md • faq/Sql_faq.md • best_practices/query_tuning/query_planning.md • knowledge/trouble_shooting/trouble_shooting_scripts.md References • faq/Sql_faq.md • administration/management/resource_management/Load_balance.md • administration/management/FE_configuration.md • administration/management/resource_management/be_label.md • administration/management/BE_blacklist.md

Dan Newton

07/04/2026, 6:15 PM

@Rocky Both of those properties are already false.

Rocky

07/04/2026, 6:15 PM

Since you’ve already ruled out the common affinity and short-circuiting variables, the diagnostic focus must shift to internal scheduler telemetry and resource-level profiling. If the CPU hotspot moves between nodes (e.g., from

cn-0

cn-2

), it confirms the scheduler is dynamic but becoming "captured" by a specific workload pattern. Here are four deeper avenues to diagnose this: 1. Pinpoint the "Coordinator Role" via Audit Logs The FE audit log contains a wealth of per-query information that Prometheus metrics often aggregate away. * Avenue: Enable the FE audit log (usually in

log/fe.audit.log

) and check the

ScanBytes

ScanRows

, and

ReturnRows

. * What to check: Calculate if the queries being sent to the "hot" node have a significantly higher ReturnRows count compared to others. Fragment 0 (the coordinator) spends most of its CPU time on MySQL result serialization. If one node is consistently chosen for queries that return 10k+ rows while others handle small lookups, that node will spike. 2. Verify Scheduler "Awareness" (Status Reporting) The FE chooses a coordinator based on the resource status reported by the backends. If a node's reporting is lagging, the FE may think it is less loaded than it actually is. * Avenue: Check the interval and health of BE-to-FE heartbeats.

Copy code

sql
    -- Check 'NumRunningQueries' and 'CpuUsedPct'
    SHOW BACKENDS;

    -- Check FE configuration for reporting frequency
    ADMIN SHOW FRONTEND CONFIG LIKE 'report_resource_usage_interval_ms';

* Diagnosis: If

SHOW BACKENDS

shows

cn-2

has a low

CpuUsedPct

but your Linux

top

shows 50%, the FE is making scheduling decisions based on stale data. You may need to decrease

report_resource_usage_interval_ms

(default 1000ms) to make the scheduler more responsive. 3. Profiling "Exchange Sink" and "Result Sink" Since you suspect coordination is the issue, you need to see if the CPU is spent waiting for data or processing it. * Avenue: Run a query and analyze the profile specifically for the Exchange and Result operators:

Copy code

sql
    -- Get the profile of a query that just ran on the hot node
    GET PROFILE FOR '<query_id>';

* What to look for: * `SerializeChunkTime`: If this is high, the CPU is being spent on data conversion for the network. * `SendRpcTime`: If this is high, the bottleneck is the

brpc

network layer or the FE's ability to consume the data. * `ShuffleChunkAppendTime`: High values indicate the node is struggling to reorganize data from other fragments. 4. RPC Layer Thread Contention Under high concurrency, the

brpc

(internal communication) threads can become a bottleneck on the node acting as the coordinator. * Avenue: Use the internal BE/CN diagnostic to check thread states. On the "hot" node, run:

Copy code

bash
    # From the node's shell
    curl <http://localhost:<be_http_port>/brpc_metrics>

* Diagnosis: Check for high

brpc_endpoint_concurrency

. If one node is handling too many concurrent RPC streams from other nodes, it will steal CPU from the execution engine. You may need to increase the number of worker threads in the CN's `be.conf`:

Copy code

conf
    # Example config to increase RPC handling capacity
    brpc_num_threads = 64 (or 2x CPU cores)

5. Check FE "Local" Preference Even if

tablet_affinity

is off, there is a hidden FE configuration that favors "local" nodes if they share an IP or network subnet with the FE (common in K8s if pods are co-located). * Avenue: Run the following and check if it is `TRUE`:

Copy code

sql
    ADMIN SHOW FRONTEND CONFIG LIKE 'enable_local_replica_selection';

* Fix: Set this to

FALSE

. If

TRUE

, the FE will always prefer a CN that it considers "network-close," which frequently causes the first few nodes in a K8s StatefulSet to get slammed. Summary Diagnostic Checklist

Rocky

07/04/2026, 6:15 PM

1. Compare
SHOW BACKENDS
CPU vs
top
CPU. If they don't match, the scheduler is blind. 2. Use
GET PROFILE
to check if

SerializeChunkTime

is the culprit. 3. Toggle
enable_local_replica_selection = false
in the FE config. 4. Monitor
NumRunningQueries
in

SHOW BACKENDS

during your peak load. If the counts are equal but CPU is skewed, it confirms that the type of work (coordinator vs worker) is the problem, not the number of queries. References • administration/management/resource_management/query_queues.md • faq/operation_maintenance_faq.md • sql-reference/sql-statements/cluster-management/nodes_processes/SHOW_BACKENDS.md • using_starrocks/running_queries.md

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