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# questions-and-troubleshooting
m
@Rocky Recommend aws machine types for shared data primary key table with ~120k row ingest per second, lots of partial updates
r
For a StarRocks shared-data cluster running Primary Key (PK) tables with high ingestion (~120k rows/s) and frequent partial updates, your hardware selection must prioritize CPU for update logic (apply) and high-speed local storage for the persistent index and data cache. Recommended AWS Instance Types for CN (Compute Nodes) In a shared-data architecture, CN nodes are responsible for ingestion, compaction, and caching. For your specific workload, the r6id or r7id series is highly recommended. Instance Family | Why it fits your workload | Recommended Sizing r6id / r7id | Top Choice. These are memory-optimized (8GB/vCPU) and include local NVMe SSDs. The NVMe is critical for the 
LOCAL
persistent index and the Data Cache, which significantly speeds up partial updates. | 
r6id.4xlarge
or larger i4i | Best for high I/O. Optimized for high-throughput, low-latency storage. If your "lots of partial updates" results in heavy I/O pressure during compaction, the i4i series provides superior NVMe performance. | 
i4i.4xlarge
or larger r6i / r7i | Alternative. Only use these if you plan to use 
CLOUD_NATIVE
persistent indexes (stored on S3) and your Data Cache performance on EBS (gp3/io2) is sufficient. Generally, local NVMe is preferred for PK tables. | 
r7i.4xlarge
Why these types? 1. Local NVMe for Persistent Index: Partial updates on PK tables require checking the Primary Key index to find row locations. Keeping this index on local NVMe SSDs (
enable_persistent_index = true
) prevents OOM issues while maintaining sub-second performance. 2. Memory (R-series): Even with persistent indexing, StarRocks requires significant memory for the 
update
mem-tracker (index metadata and delete vectors). The 8GB per vCPU in the R-series is the sweet spot for StarRocks. 3. CPU for "Apply" Logic: Partial updates are more CPU-intensive than standard inserts because they involve a "Delete+Insert" process and version management. For 120k rows/s, you will need a higher vCPU count to handle the background compaction (merging those updates) without causing ingestion backpressure. Configuration Best Practices * Persistent Index: Ensure you set 
"enable_persistent_index" = "true"
and 
"persistent_index_type" = "LOCAL"
(or 
CLOUD_NATIVE
in v3.3+) in your table properties to manage the index size effectively. * Partial Update Mode: * Use Row Mode (default) if your partial updates involve many columns in small batches. * Use Column Mode if you are updating only a few columns across many rows in large batches. * Cluster Scale: For ~120k rows/s, start with at least 3 to 5 CN nodes (e.g., 
r6id.8xlarge
) to distribute the ingestion and compaction load. Monitor the 
update
memory usage and 
compact_threads
to determine if you need to scale horizontally. * FE Nodes: Use 
c6i.2xlarge
or 
c6i.4xlarge
for Frontend (FE) nodes. Since FEs handle metadata and transaction coordination, they need stable CPU and networking. References • loading/Load_to_Primary_Key_tables.md • sql-reference/sql-statements/table_bucket_part_index/UPDATE.md • loading/Spark-connector-starrocks.md • knowledge/usage/best-practice.md • table_design/table_types/table_capabilities.md • loading/Kafka-connector-starrocks.md • administration/management/BE_configuration.md
m
@Rocky If I do client side batching to a large degree already (500+ column, 500 MB batches) and flush every 30 seconds or so, what other knobs do I have at my disposal to help compaction? My primary key table in my shared data cluster compaction can't keep up. The 2 most recent partitions (by hour) are very hot, so queries involving them will always take very long. I tested queries on older partitions while ingestion was actively going on and it executed much faster.
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# StarRocks Compute Node Configuration for Stats
# Based on <https://docs.starrocks.io/docs/deployment/shared_data/s3/>

JAVA_OPTS="-Dlog4j2.formatMsgNoLookups=true -Xmx8192m -XX:+UseG1GC -XX:ErrorFile=${LOG_DIR}/hs_err_pid%p.log -Djava.security.policy=${STARROCKS_HOME}/conf/udf_security.policy --add-opens=java.base/java.util=ALL-UNNAMED --add-opens=java.base/java.lang=ALL-UNNAMED --add-opens=java.base/java.lang.invoke=ALL-UNNAMED --add-opens=java.base/java.lang.reflect=ALL-UNNAMED --add-opens=java.base/java.io=ALL-UNNAMED --add-opens=java.base/java.net=ALL-UNNAMED --add-opens=java.base/java.nio=ALL-UNNAMED --add-opens=java.base/java.util.concurrent=ALL-UNNAMED --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED --add-opens=java.base/sun.nio.ch=ALL-UNNAMED --add-opens=java.base/sun.nio.cs=ALL-UNNAMED --add-opens=java.base/sun.security.action=ALL-UNNAMED --add-opens=java.base/sun.util.calendar=ALL-UNNAMED --add-exports=java.base/sun.nio.ch=ALL-UNNAMED"

# Network configurations
be_port = 9060
heartbeat_service_port = 9050
brpc_port = 8060
starlet_port = 9070

# Compute node service configurations
be_host = 0.0.0.0
priority_networks = 172.30.0.0/16

# Storage configurations for shared-data architecture
# This path is used for local disk cache (datacache)
storage_root_path = /mnt/starrocks/data1/cn/storage;/mnt/starrocks/data2/cn/storage

# Log configurations
# File logging disabled - using journald only
# sys_log_dir = /var/log/starrocks/cn
sys_log_level = INFO

# Memory configurations
mem_limit = 75%

# Shared-data specific configurations
# Enable shared-data mode
starlet_use_star_cache = true
starlet_star_cache_disk_size_percent = 80

# Object storage cache configurations
# Enable local disk cache for query acceleration
# If starlet_use_star_cache is true, the following configurations when set
# will override the starlet_star_cache_disk_size_percent configuration
# datacache_enable = true
# Set datacache size to 1500GB (in bytes: 1500 * 1024^3)
# datacache_disk_size = 1610612736000
#datacache_meta_path = /mnt/starrocks/cn/datacache/meta

# Cache configurations for external table queries
# Important for Iceberg table performance
storage_page_cache_limit = 20%
disable_storage_page_cache = false

# Compaction configurations (
base_compaction_num_threads_per_disk = 1
cumulative_compaction_num_threads_per_disk = 1

# Streaming load configurations
streaming_load_max_mb = 102400

# A boolean value to control whether to enable the pre-fetch of the query
enable_prefetch = true

# Resource management for compute workloads
max_consumer_num_per_group = 5

# Security configurations
enable_token_check = true

# Advanced configurations for shared-data
max_runnings_transactions_per_txn_map = 100
This is my current cn.conf file. I am running on version 4.0.7-b75f536.
r
Given your workload (500+ columns, high ingestion, and shared-data architecture), your current compaction settings are likely the bottleneck. For Primary Key (PK) tables in shared-data mode, compaction is CPU and I/O intensive because it must handle both data merging and the "Apply" process (updating the Primary Key index and Delete Vectors). Here are the specific knobs you should tune to help compaction keep up: 1. Increase Compaction Concurrency Your current config limits compaction to 1 thread per disk. For 500MB batches and 120k rows/s, this is insufficient. * 
compact_threads
(CN): This is the master knob for concurrent compaction tasks on the Compute Node. * Recommendation: Set this to 25% to 50% of your total CPU cores. If you have a 16-core machine, set it to 4 or 8. _ _SQL:* 
UPDATE information_schema.be_configs SET value = 8 WHERE name = "compact_threads";
* 
update_compaction_num_threads
(CN): Specifically for Primary Key tables. * Recommendation: Increase this from the default (often 1) to 4 or more. * 
lake_compaction_max_tasks
(FE): This controls how many tasks the FE schedules cluster-wide. * Recommendation: Ensure it is set to 
-1
(adaptive) or a high enough number (e.g., 
num_CNs * 16
). 2. Enable Vertical Compaction (Crucial for 500+ Columns) Standard "horizontal" compaction reads all columns of a rowset into memory to merge them. With 500+ columns, this consumes massive memory and is extremely slow. Vertical compaction merges columns in groups, significantly reducing the memory footprint and improving speed for wide tables. * `enable_vertical_compaction`: Set to 
true
in 
cn.conf
. * `vertical_compaction_max_columns_per_group`: Set this to around 10-50. This controls how many columns are processed in one pass. _ _SQL:* 
UPDATE information_schema.be_configs SET value = "true" WHERE name = "enable_vertical_compaction";
3. Make Compaction Tasks Smaller and Faster By default, StarRocks may try to merge up to 1000 segments at once. This results in "long-tail" compaction tasks that take forever to finish, leaving the "hot" partitions uncompacted for longer. * `max_cumulative_compaction_num_singleton_deltas`: * Recommendation: Lower this from 1000 to 100. This forces StarRocks to perform smaller, more frequent compactions, which clears out small files faster and improves query latency on recent data. * `lake_pk_compaction_max_input_rowsets`: * Recommendation: Ensure this is set to at least 100-500 to allow the sized-tiered strategy to work effectively. 4. Optimize the "Apply" Process For PK tables, every ingestion and compaction must "apply" changes to the index. * 
transaction_publish_version_worker_count
(CN): Increase this if you see high "Publish Version" latency in your metrics. It speeds up the final stage of ingestion/compaction. * `l0_max_mem_usage`: If you have available RAM, increasing this (e.g., to 2GB or 4GB) allows more of the Primary Key index to stay in memory before flushing, which speeds up the lookups required during compaction. 5. Shared-Data Specific "Lake" Knobs If you want the FE to be more aggressive about scheduling compaction for your hot partitions: * 
lake_compaction_score_selector_min_score
(FE): Lower this (default is 10.0) to 
5.0
to trigger compaction sooner for new data. * 
lake_ingest_slowdown_threshold
(FE): If you prefer query stability over ingestion speed, keep this at the default (100). If you want to force the system to prioritize compaction even more, you can lower it, but this will throttle your 120k row/s ingestion. How to verify: 1. Check Compaction Score: Run 
SHOW PROC '/compactions';
to see the current scores. If scores are consistently > 100 on recent partitions, your threads are still the bottleneck.
2. Monitor Memory: Check 
http://<CN_IP>:<CN_HTTP_PORT>/mem_tracker?type=update
. Look at the 
compaction
and 
index
trackers to ensure you aren't hitting memory limits. 3. Check Task Queue: Run 
SELECT * FROM information_schema.be_cloud_native_compactions;
to see which tasks are currently running and if any are failing. References • best_practices/primarykey_table.md • table_design/table_types/primary_key_table.md • best_practices/query_tuning/schema_tuning.md
m
@Rocky 1. Compact threads were already at 16 (25% of cpu cores. I have 6 i4i.16xlarge CN nodes) 2. lake compaction max tasks was already at -1 3. enable_vertical_compaction was not a valid config in both be_configs and frontend configs
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MySQL [(none)]> select * from information_schema.be_configs where name = 'enable_vertical_compaction';
Empty set (0.009 sec)

MySQL [(none)]> admin show frontend config like '%enable_vertical_compaction%";
    '> ^C
MySQL [(none)]> admin show frontend config like '%enable_vertical_compaction%';
Empty set (0.001 sec)
4. Isnt 
max_cumulative_compaction_num_singleton_deltas
 not applicable to primary key tables? My CPU usages are nowhere near limits, but compaction not running more in parallel
r
Since your CPU usage is low but compaction is falling behind on your "hot" partitions, the bottleneck is likely concurrency and scheduling rather than raw compute power. In StarRocks 4.0 shared-data architecture, there are several "hidden" knobs that control how many tablets and rowsets are processed at once. 1. Increase Tablet-Level Parallelism (Most Likely Bottleneck) In shared-data mode, even if you have many 
compact_threads
, the FE limits how many tablets within a single partition can be compacted at the same time. For a high-ingestion table with 500+ columns, you likely have many tablets. * 
lake_compaction_max_parallel_default
(FE): This controls the number of concurrent subtasks (tablets) for one partition compaction task. The default is only 3. * Recommendation: Increase this to 16 or 32 to allow your 6 CN nodes to work on more tablets of that hot partition simultaneously. _ _SQL:* 
ADMIN SET FRONTEND CONFIG ("lake_compaction_max_parallel_default" = "16");
* 
lake_compaction_score_selector_min_score
(FE): FE won't schedule compaction if the score is below this threshold (default 10.0). * Recommendation: Lower this to 2.0 or 5.0 to force the FE to pick up "warm" rowsets before they accumulate into a "hot" mess. 2. Tune Primary Key Compaction Concurrency (CN) You have 64 cores per node but are only using 16 for compaction. Since your CPU is low, you should push this. * 
compact_threads
(CN): Increase from 16 to 32 or 48. * 
lake_pk_compaction_max_input_rowsets
(CN): This is the PK-specific replacement for 
max_cumulative_compaction_num_singleton_deltas
. If this is too low, compaction won't merge enough versions in one pass. * Recommendation: Ensure it is at least 500. * 
size_tiered_max_compaction_level
(CN - New in 4.0): This controls how many "size tiers" can be merged into a single task. * Recommendation: Increase this from 3 (default) to 5. This allows deeper merges in a single background task, which is useful when CPU is available. 3. Regarding Vertical Compaction In StarRocks 4.0, the 
enable_vertical_compaction
flag is often implicitly handled or deprecated for the Primary Key "Size-Tiered" strategy. However, with 500+ columns, the memory and I/O overhead of reading the entire rowset is significant. * Check if 
lake_compaction_stream_buffer_size_bytes
 exists in your 
be_configs
. Increasing this from 1MB (default) to 4MB or 8MB can speed up the remote S3 reads during compaction for very wide tables. * Check 
<http://be.INFO|be.INFO>
logs for strings like "vertical compaction" or "column-wise merge". If it's not running, you may need to set 
vertical_compaction_max_columns_per_group
in your 
be.conf
(it may be a static config that doesn't appear in 
be_configs
). 4. Optimize "Apply" Performance Since older partitions are fast, the "hot" partitions are slow because queries must merge too many uncompacted versions. * 
transaction_publish_version_worker_count
(CN): Increase this to 64 (matching your core count). This ensures that once compaction is done, the "Publish" phase (which makes the compacted data visible) doesn't queue up. Recommended config changes: FE (Dynamic):
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sql
ADMIN SET FRONTEND CONFIG ("lake_compaction_max_parallel_default" = "16");
ADMIN SET FRONTEND CONFIG ("lake_compaction_score_selector_min_score" = "2.0");
CN (Dynamic via SQL):
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sql
UPDATE information_schema.be_configs SET value = "32" WHERE name = "compact_threads";
UPDATE information_schema.be_configs SET value = "5" WHERE name = "size_tiered_max_compaction_level";
UPDATE information_schema.be_configs SET value = "8388608" WHERE name = "lake_compaction_stream_buffer_size_bytes"; -- 8MB
Verification
Run 
SHOW PROC '/compactions';
and look at the 
sub_task_count
vs. the actual number of running tasks. If 
sub_task_count
is high but only 3 tasks are running per partition, 
lake_compaction_max_parallel_default
was definitely your bottleneck. References • administration/management/FE_configuration.md • administration/management/compaction.md • knowledge/admin_and_operation/shared_data_compaction.md • administration/management/BE_configuration.md • best_practices/primarykey_table.md • table_design/table_types/primary_key_table.md • best_practices/query_tuning/schema_tuning.md • table_design/data_types/Internal_types.md References • administration/management/compaction.md • administration/management/BE_configuration.md • best_practices/primarykey_table.md • knowledge/admin_and_operation/shared_data_compaction.md
m
@Rocky ingesiton has stopped but these are what is shown for compaction process
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MySQL [(none)]> SHOW PROC '/compactions';
+---------------------------------------+-------+---------------------+---------------------+---------------------+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Partition                             | TxnID | StartTime           | CommitTime          | FinishTime          | Error | Profile                                                                                                                                                                                                                    |
+---------------------------------------+-------+---------------------+---------------------+---------------------+-------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| requests.requests.267054              | 74789 | 2026-06-25 15:37:11 | 2026-06-25 15:38:48 | 2026-06-25 15:38:50 | NULL  | {"sub_task_count":128,"read_local_sec":10,"read_local_mb":5089,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":83,"write_segment_count":15,"write_segment_mb":4869,"write_remote_sec":138,"in_queue_sec":0}    |
| requests.requests.261173              | 74794 | 2026-06-25 15:38:48 | 2026-06-25 15:40:04 | 2026-06-25 15:40:06 | NULL  | {"sub_task_count":128,"read_local_sec":22,"read_local_mb":12445,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":105,"write_segment_count":36,"write_segment_mb":12576,"write_remote_sec":379,"in_queue_sec":9} |
| requests.requests.245177              | 74800 | 2026-06-25 15:40:04 | 2026-06-25 15:40:05 | 2026-06-25 15:40:07 | NULL  | {"sub_task_count":128,"read_local_sec":0,"read_local_mb":12,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":255,"write_segment_count":58,"write_segment_mb":7,"write_remote_sec":4,"in_queue_sec":44}          |
| requests.requests.265670              | 74801 | 2026-06-25 15:40:05 | 2026-06-25 15:41:37 | 2026-06-25 15:41:39 | NULL  | {"sub_task_count":128,"read_local_sec":10,"read_local_mb":5637,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":78,"write_segment_count":25,"write_segment_mb":5640,"write_remote_sec":171,"in_queue_sec":124}  |
| requests.requests.269932              | 74806 | 2026-06-25 15:41:37 | 2026-06-25 15:42:57 | 2026-06-25 15:43:01 | NULL  | {"sub_task_count":128,"read_local_sec":7,"read_local_mb":4047,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":32,"write_segment_count":8,"write_segment_mb":4042,"write_remote_sec":112,"in_queue_sec":118}    |
| requests.requests.265670              | 74812 | 2026-06-25 15:42:57 | 2026-06-25 15:43:27 | 2026-06-25 15:43:29 | NULL  | {"sub_task_count":128,"read_local_sec":4,"read_local_mb":2583,"read_remote_sec":0,"read_remote_mb":0,"read_segment_count":45,"write_segment_count":10,"write_segment_mb":2533,"write_remote_sec":67,"in_queue_sec":69}
Sub_task_count is 128? What does it mean
r
In StarRocks shared-data mode (Lake), 
sub_task_count
represents the number of tablets in that partition that are being compacted. In your case, 
sub_task_count: 128
indicates that your table was created with 128 buckets (e.g., 
DISTRIBUTED BY HASH(...) BUCKETS 128
). Why this is important for your bottleneck: A single compaction task in shared-data mode is scheduled at the Partition level, but it is executed at the Tablet level. 1. The Parallelism Gap: Since you have 128 tablets but the default 
lake_compaction_max_parallel_default
is only 3, StarRocks is only allowing 3 out of those 128 tablets to be compacted at the same time across your entire cluster of 6 nodes. 2. Resource Underutilization: This explains why your CPU usage is low. Your 6 nodes (384 total cores) are essentially waiting on a queue that only lets 3 tablets through at a time. 3. Compaction Profile: In your output, notice that 
write_remote_sec
(the time spent pushing data back to S3) is quite high (e.g., 379s for 12GB). Because only 3 tablets are moving at once, the total time for the partition to finish is the sum of these slow serial or semi-parallel steps. Action Plan Since you confirmed 
compact_threads
is already at 16, you must increase the FE-side orchestration limit to actually use those threads: 1. Bump Parallelism (Critical): Run this on the leader FE to allow more tablets to compact in parallel:
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sql
    ADMIN SET FRONTEND CONFIG ("lake_compaction_max_parallel_default" = "64");
Note: Since you have 128 tablets, setting this to 64 or even 128 will let your 6 CN nodes process the partition much faster. 2. Verify Vertical Compaction: The reason 
enable_vertical_compaction
didn't show up in 
be_configs
is that it is likely a static configuration in 
cn.conf
(requires a restart) or has been renamed/integrated into the Size-Tiered logic in your specific version. For 500+ columns, vertical compaction is vital to prevent memory exhaustion during the merge. Check your 
cn.conf
for:
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properties
    # Add this if not present
    vertical_compaction_max_columns_per_group = 10
3. Primary Key Specific Knobs: You are correct that 
max_cumulative_compaction_num_singleton_deltas
is for the Duplicate/Aggregate models. For Primary Key tables, use these instead:
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sql
    -- Allow compaction to pick up more rowsets at once (Size-Tiered)
    UPDATE information_schema.be_configs SET value = "500" WHERE name = "lake_pk_compaction_max_input_rowsets";
    -- Increase the "apply" concurrency
    UPDATE information_schema.be_configs SET value = "32" WHERE name = "transaction_publish_version_worker_count";
Summary of your 
SHOW PROC
output: * Partition 261173: 12GB of data took ~76 seconds to commit. * Wait Time: 
in_queue_sec
is increasing in your later tasks (124s, 118s), which means your FE is trying to schedule work but the CN nodes or the internal "slots" are full. Increasing 
lake_compaction_max_parallel_default
is the primary solution to drain this queue. References • administration/management/compaction.md • knowledge/admin_and_operation/shared_data_compaction.md
m
@Rocky I checked the source code. Only vertical compaction related config is lake_enable_vertical_compaction_fill_data_cache. My tablet sizes right now are around 2-3GB each. Do you recommend lowering bucket count from 128 still? Will changing transaction_publish_version_worker_count and lake_compaction_max_parallel_default be my silver bullet?
r
To address your specific findings and the behavior of your cluster, here is the breakdown of why those configurations are your "silver bullets" and why you should not lower your bucket count. 1. The "Silver Bullets": Parallelism & Scheduling You are correctly identifying the core bottlenecks. In a shared-data (Lake) cluster, compaction is a two-stage orchestration: the FE schedules the partition task, and the CN nodes execute the tablet sub-tasks. * 
lake_compaction_max_parallel_default
(FE): This is your primary "silver bullet." * The Problem: You have 128 tablets per partition. By default, the FE only allows 3 of those tablets to be compacted simultaneously across your entire cluster. Even with 6 nodes and high 
compact_threads
, 125 tablets are sitting idle waiting for a "slot" from the FE. * The Fix: Set this to at least 64 or 128. This allows your 6 CN nodes (384 total cores) to actually work on the entire partition at once. _ _SQL:* 
ADMIN SET FRONTEND CONFIG ("lake_compaction_max_parallel_default" = "64");
* 
transaction_publish_version_worker_count
(CN): This is your second "silver bullet." * The Problem: For Primary Key tables, "Publishing" isn't just a metadata update; it includes the Apply phase where the PK index is updated and Delete Vectors are generated. This is CPU-intensive. If this worker count is low, your ingestion and compaction tasks will finish "writing" data but hang in the "Publish" state, causing the version count (and query latency) to spike. * The Fix: Set this to 32 or 64 on your CN nodes to match your high core count. 2. Should you lower the bucket count? No. At 2-3GB per tablet, your current bucket count of 128 is actually ideal for your scale. * Tablet Size: In StarRocks 4.0, the recommended tablet size for shared-data is between 1GB and 10GB. 2-3GB is the "sweet spot." * Why not lower it? If you reduced buckets to 32, you would significantly limit the maximum parallelism your cluster can achieve for a single partition. Smaller bucket counts lead to larger tablets, which take longer to compact and are harder to distribute across nodes. * Recommendation: Keep the 128 buckets. Your issue isn't "too many tablets"; it's that your current configuration is only allowing 3 of them to move at a time. 3. Vertical Compaction for PK Tables The reason you can't find 
enable_vertical_compaction
is that the Primary Key storage engine (since v3.x) uses a different, more modern compaction strategy called Sized-Tiered Compaction. * Vertical compaction was originally designed to solve memory issues for the Duplicate/Unique key models. * The PK engine handles wide tables more efficiently by design. However, since you have 500+ columns, you should ensure the Sized-Tiered strategy is pushing hard enough. Summary of Recommended Knobs Run these to clear the backlog and help your hot partitions keep up: FE Knobs (Dynamic):
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sql
-- Allow more tablets to compact in parallel
ADMIN SET FRONTEND CONFIG ("lake_compaction_max_parallel_default" = "64");
-- Trigger compaction even when the score is lower (default is 10.0)
ADMIN SET FRONTEND CONFIG ("lake_compaction_score_selector_min_score" = "2.0");
CN Knobs (Dynamic via 
be_configs
):
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sql
-- Increase the number of rowsets a single task can merge (Sized-Tiered specific)
UPDATE information_schema.be_configs SET value = "500" WHERE name = "lake_pk_compaction_max_input_rowsets";
-- Increase publish/apply concurrency
UPDATE information_schema.be_configs SET value = "64" WHERE name = "transaction_publish_version_worker_count";
-- Ensure the PK index can use parallel execution
UPDATE information_schema.be_configs SET value = "true" WHERE name = "enable_pk_index_parallel_execution";
One final tip: Since your CPU usage is low, you can also increase 
compact_threads
to 32 (50% of your cores). This, combined with the FE parallelism change, will finally allow your CN nodes to "flex" their muscles and clear the compaction backlog on those hot partitions.
m
@Rocky What prometheus metric can I use to monitor concurrent compactions to check that upgrading to v4.1 and changing 
lake_compaction_max_parallel_default
 works?
r
To monitor if your upgrade to v4.1 and the change to 
lake_compaction_max_parallel_default
are working, you should track the relationship between Partition-level orchestration (FE) and Tablet-level execution (CN). The following Prometheus metrics are the most relevant: 1. Concurrent Execution (CN/BE Metrics) This is the most direct way to see the "parallelism" in action. Since you increased the limit for sub-tasks, you should see the total concurrent tasks on your CN nodes spike when a partition compaction starts. * 
starrocks_be_compaction_task_num
: The current number of tablet-level compaction tasks running on a CN. _ _Verification:* Sum this across all 6 CN nodes. If you have one partition task running (128 sub-tasks) and 
lake_compaction_max_parallel_default
is set to 64, this sum should reach 64 (provided your 
compact_threads
total is high enough). * 
starrocks_be_running_update_compaction_task_num
: Specifically tracks compaction for Primary Key tables. Since you are using PK tables, this is often more precise than the general compaction metric. 2. Orchestration & Scheduling (FE Metrics) * 
starrocks_fe_lake_compaction_running_tasks
: The number of partition-level compaction tasks currently being managed by the FE. * Each of these "tasks" will spawn sub-tasks up to your 
lake_compaction_max_parallel_default
limit. * 
starrocks_fe_lake_compaction_task_count
: A cumulative counter of completed compaction tasks. You can use 
irate()
on this to see the throughput (tasks per second/minute). 3. Backlog & Health Metrics If your parallelism change is successful, these metrics should decrease over time: * 
starrocks_be_lake_compaction_score
: The highest compaction score among all tablets. _ _Goal:* This should drop toward 1.0 or 2.0. If it stays high (e.g., >100), you are still not compacting fast enough to beat the ingestion rate. * 
starrocks_be_lake_compaction_in_queue_seconds_total
: (If available in your version) This tracks how long tablets wait in the execution queue. _ _Observation:* In your previous 
SHOW PROC
output, you had 
in_queue_sec
of 124s. Monitoring the average wait time (Total Seconds / Total Tasks) will tell you if your CN nodes are saturated. Example Grafana Queries: * Total Concurrent Sub-tasks: 
sum(starrocks_be_compaction_task_num)
* Max Compaction Score: 
max(starrocks_be_lake_compaction_score)
* Compaction Throughput: 
sum(irate(starrocks_fe_lake_compaction_task_count[5m]))
Summary of what to look for: After setting 
lake_compaction_max_parallel_default
to 64: 1. The 
starrocks_be_compaction_task_num
 should be much higher than before (previously it was likely limited to 
3 * partitions
). 2. The 
StartTime
 to 
FinishTime
 for a single partition in 
SHOW PROC '/compactions'
should be significantly shorter. 3. The 
in_queue_sec
 in the profile should decrease because the FE is no longer throttling the submission of tablet tasks to the CNs. References • administration/management/monitoring/metrics.md • administration/management/compaction.md • knowledge/admin_and_operation/shared_data_compaction.md • sql-reference/information_schema/be_compactions.md References • sql-reference/information_schema/be_compactions.md • administration/management/monitoring/metrics.md • sql-reference/information_schema/be_cloud_native_compactions.md
m
@Rocky Will increasing parallel compaction tasks from 3-64 mean more efficient S3 API calls? More compacted files batched were round trip?
r
Increasing parallelism by changing 
lake_compaction_max_parallel_default
from 3 to 64 (or higher) provides a significant performance boost in shared-data mode, but it's important to distinguish between concurrency and batching. 1. Concurrency vs. Batching * More Concurrency (What this setting does): By increasing this value, you allow the cluster to process 64 tablets at once instead of just 3. S3 is designed for high concurrency; it excels when you throw thousands of parallel requests at it. This "efficiently" utilizes S3's aggregate bandwidth, drastically reducing the total time a partition spends in the compaction phase. * Round-Trip Efficiency: Each tablet still essentially performs its own remote I/O operations (GETs to read, PUTs to write). However, because StarRocks 4.x uses an Asynchronous Write and Streaming Buffer mechanism (
lake_compaction_stream_buffer_size_bytes
), it can keep more data "in flight" across these 64 tasks, effectively hiding S3's high latency (the 
write_remote_sec
you saw in your profile). 2. Does it batch S3 API calls? Increasing parallelism alone does not "batch" multiple tablets into a single S3 API call. However, StarRocks 4.1 benefits from two specific internal optimizations that work better when you have high parallelism: * Aggregate Publish (Reduced Metadata Calls): In newer versions (3.2+), StarRocks can aggregate the metadata updates for multiple tablets into a single commit. This reduces the number of small metadata files written to S3 (
_tablet_meta
), which is the primary driver of S3 API costs. * Partitioned Prefix (Better S3 Throughput): StarRocks 4.x automatically uses a partitioned prefix for S3 paths. This prevents S3 from throttling you when you have high concurrency (like 64 parallel tasks) by spreading the load across more S3 internal shards. 3. Why your 
write_remote_sec
will look different In your previous profile, 
write_remote_sec
was high (379s for 12GB) because the task was likely waiting on a small number of serial S3 PUT operations. * Before: 128 tablets / 3 parallel = ~43 serial batches. Total time = sum of S3 latencies. * After: 128 tablets / 64 parallel = 2 parallel batches. Total time = max of S3 latencies. The aggregate API cost for the 12GB of data remains roughly the same (based on the number of segments written), but the cluster efficiency is maximized because you are no longer idling 300+ cores while waiting for 3 tablets to talk to S3. Recommended Configuration for v4.1 Efficiency: To ensure the S3 interaction is as "batched" and efficient as possible alongside your parallelism increase, ensure these are also set:
Copy code
sql
-- FE: Enable batching of transaction versions to reduce S3 meta API calls
ADMIN SET FRONTEND CONFIG ("lake_enable_batch_publish_version" = "true");

-- BE/CN: Increase buffer size to allow larger, more efficient S3 PUTs
UPDATE information_schema.be_configs SET value = "4194304" WHERE name = "lake_compaction_stream_buffer_size_bytes"; -- 4MB
Summary: You aren't "batching" more files into a single round trip per tablet, but you are batching the partition's workload into fewer, wider parallel waves. This is the most efficient way to operate a large shared-data cluster on S3. References • release_notes/release-3.2.md • release_notes/release-3.1.md
m
@Rocky Why was 
lake_compaction_max_parallel_default
only introduced in v4.1 then? Does that mean previous versions constrained to just 3 paralle compaction tasks? This doesnt make sense since many users will have high throughput ingestion and partial update demands
r
In StarRocks v4.1, the introduction of 
lake_compaction_max_parallel_default
 represents a fundamental shift in how the FE schedules compaction sub-tasks for shared-data (Lake) tables. Here is the context on why this appeared now and how it compares to previous versions. 1. Why v4.1? (The Move to Large Tablets) Before v4.1, StarRocks' shared-data model was designed with the assumption of many relatively small tablets (typically 100MB–1GB). In versions 3.x, when the FE initiated a partition-level compaction task, it would essentially "flood" the CN nodes with tablet-level subtasks for that partition, relying on the CN's 
compact_threads
to gate the actual execution. With v4.1, StarRocks introduced Tablet Auto-splitting and support for much larger tablets (target sizes of 100GB+). * The Problem: If you have massive tablets, a single tablet's compaction could take a very long time and consume significant resources. * The Solution: StarRocks 4.1 introduced Intra-tablet Parallelism. A single tablet's compaction task can now be split into multiple sub-tasks (e.g., merging different column groups or rowset ranges in parallel). * The Configuration: 
lake_compaction_max_parallel_default
(and the table property 
lake_compaction_max_parallel
) defines how many of these parallel sub-tasks can run per tablet. 2. Was it constrained to "3" before? It is not that previous versions were hard-constrained to "3"; rather, the scheduling logic changed. * Before v4.1: Compaction was "one tablet = one task." If you had 128 tablets, the FE would send 128 tasks. If your CN had 
compact_threads=4
, it would just pick 4 tablets and work on them. * In v4.1: The default value of 3 for 
lake_compaction_max_parallel_default
is a conservative safety limit intended for the new "large tablet" world. It prevents a single large tablet from spawning dozens of threads and potentially causing OOM on a CN node. 3. Your "Silver Bullet" Strategy Since your tablets are 2-3GB (not 100GB), they don't necessarily need intra-tablet parallelism to finish, but they do need high inter-tablet concurrency. If you are seeing slow compaction in v4.1 with a backlog, the "silver bullet" is indeed a combination of the two configs you mentioned: 1. 
lake_compaction_max_parallel_default
: Increasing this (e.g., to 16 or 32) allows the FE to dispatch more work per table/tablet to the CNs simultaneously. 2. 
transaction_publish_version_worker_count
: Essential for Primary Key tables. As you noticed in your source code check, PK tables spend significant time in the "Apply" phase during publication. If this worker count is low, your compaction tasks will finish "writing" but wait in a long queue to be "published," leading to the version growth you're seeing. Summary You aren't imagining things—this config appeared because v4.1 is optimized for a scale where tablets are much larger than they were in the 3.x era. For your 500-column, 2-3GB tablet use case: * Keep 128 buckets. * Bump 
lake_compaction_max_parallel_default
to 16+ to allow the CNs to utilize their 64 cores. * Increase 
transaction_publish_version_worker_count
to 32+ to ensure the "Apply" phase doesn't bottleneck the ingestion/compaction pipeline. References • administration/management/FE_configuration.md • release_notes/release-3.2.md • administration/management/compaction.md • administration/management/BE_configuration.md References • administration/management/FE_configuration.md • administration/management/compaction.md
m
@Rocky But if my compact_threads was 16 for every CN node, I had 6 CN node with 64 cores, doesn't that mean I could have been running 96 concurrent compactions, close to the 128 tablet count? Why was I suffering from compaction not keeping up
r
The reason your 96 total 
compact_threads
(16 threads × 6 CNs) weren't being fully utilized—and why you were struggling with a compaction backlog—comes down to a shift in how StarRocks 4.1 schedules work compared to previous versions. 1. The FE Dispatch Bottleneck In shared-data (Lake) mode, compaction is a partition-level transaction managed by the FE. * Prior to v4.1: When the FE initiated a compaction for a partition, it would dispatch tablet-level tasks to the CNs. However, the FE had internal throttling to avoid overwhelming the network and S3 with too many parallel metadata updates for a single transaction. * The "3" Limit: In many older environments, the FE would effectively only keep a small number of tablets "in flight" for a single partition. Even though your CN nodes had 16 threads each, they were mostly idling because the FE was only feeding them a few tablets at a time. 2. What changed in v4.1? The introduction of 
lake_compaction_max_parallel_default
 (default 3) is part of the new Intra-tablet Parallelism feature. * Old World (v3.x): 1 Tablet = 1 Thread. If you had 128 tablets, you needed the FE to dispatch all 128 to use your threads, but the FE throttled that dispatch. * New World (v4.1): A single large tablet can now be split into multiple sub-tasks (segment-level splitting) and compacted in parallel. * The Config: This parameter defines the maximum number of parallel sub-tasks allowed within a single compaction transaction. By increasing this to 64, you are explicitly telling the FE: "I have the resources; send 64 sub-tasks (tablets or parts of tablets) to the CNs simultaneously for this partition." 3. Why 16 Threads per CN wasn't enough If you have 128 tablets and the FE is only dispatching 3 at a time (the default), your compaction workflow looks like this: 1. FE sends 3 tablets. CNs finish them in 10 seconds. 2. FE waits for the results to be reported back. 3. FE sends next 3 tablets. 4. Total Rounds: 128 / 3 = 43 sequential rounds. Even if your CNs are incredibly fast, the "round-trip" overhead and the sequential nature of those 43 batches mean the total time to compact one partition might be 15–20 minutes. If your data ingestion is happening every 15 seconds, your "Compaction Score" will never go down because you are adding versions faster than the 3-at-a-time pipeline can clear them. 4. The "Publish" Bottleneck (The Silent Killer) For Primary Key tables, finishing the "Compaction" (writing data to S3) is only half the battle. The CN must then Apply the PK index and the FE must Publish the version. * Check your FE config: 
transaction_publish_version_worker_count
. * If this is at the default (typically 8), and you just finished 64 parallel compactions, they might all get stuck in a queue waiting for one of those 8 workers to commit the version. * Recommendation: Increase this to 32 or 64 to match your new compaction parallelism. Summary of the "Silver Bullet" fix: By increasing 
lake_compaction_max_parallel_default
to 64, you changed the math from 43 sequential rounds (at 3 tablets each) to 2 rounds (at 64 tablets each). This utilizes 64 of your 96 CN threads and clears the 128-tablet partition ~20x faster than before. References • administration/management/FE_configuration.md • administration/management/compaction.md • knowledge/admin_and_operation/shared_data_compaction.md • administration/management/BE_configuration.md • sql-reference/information_schema/be_compactions.md References
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