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<@U0A71G31CDV> had a few questions regarding some ...
# questions-and-troubleshooting
d
@Rocky had a few questions regarding some fe crashes we are encountering in our prod env StarRocks FE crash-looping every 2–8 min — shared-data mode, star_os checkpoint stuck since Aug 15 Environment • StarRocks on Kubernetes (starrocks-kubernetes-operator), GKE • Shared-data mode, object storage: S3-compatible bucket • 3 FE (
starrocks-mas-fe-0/1/2
), CN nodes, no BE, FE Proxy disabled • FE JVM: JDK 17, ZGC, 
-Xmx18g
, container limit 26 GB, 8 CPU (6 available) Symptom All 3 FE pods restart every 2–8 minutes, continuously, for 29+ hours (577 restart markers in 
fe.out
). Killed by liveness/readiness probe failure. CrashLoopBackOff gaps of ~5 min between some restarts. GC analysis • No 
OutOfMemoryError
in any file — JVM never actually OOMs • Live set plateaus at ~12 GB every run and stays flat (no leak, no unbounded growth) • Heap therefore sits at 65–70% occupancy continuously • 
GC Workers: 2 (dynamic)
— appears to be the bottleneck • *Allocation stalls up to 8.7 seconds*; 559 stalls in a single 5-minute run • GC pause times are fine (max 0.028 ms) — the problem is stalls, not pauses • Top stalling threads: 
thrift-server-pool
(292 in one run), 
nioEventLoopGroup-6
, 
star mgr replayer
, 
replayer
, 
star_os_checkpoint_worker
Conclusion: probes time out during 8s allocation stalls → kubelet kills pod. Root cause candidate — star_os checkpoint never advances From `fe.log`:
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checkpoint imageJournalId 1086422, logJournalId 1086422   (global_state — current, OK)
checkpoint imageJournalId 5369484, logJournalId 9853250   (star_os — 4,483,766 behind)
StarManager.loadMeta():1319 end load star manager meta data at 08-15 02:26:18, replay journal id 5369484.
Last StarManager image is 10 days old. 
imageJournalId
stayed pinned at 
5369484
across every observed run. Why checkpoint fails 1. Checkpoint worker unreachable:
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CheckpointController.doCheckpoint():370 call doCheckpoint failed for node: starrocks-mas-fe-2..._9010_1784220843345
ThriftConnectionPool.reopen():108 reopen error → java.net.ConnectException: Connection refused
CheckpointController.pushImage():425 Exception when pushing image file. url = <http://starrocks-mas-fe-2|http://starrocks-mas-fe-2>...:8030/put?version=1085421&...&for_global_state=true&image_format_version=v2
HeartbeatMgr: get bad heartbeat response: type: FRONTEND, status: BAD, msg: Connection refused, replayedJournalId: 0
(fe-2 is itself crash-looping, so it can never serve as checkpoint worker → self-reinforcing loop) 2. Object storage rejecting writes — 635 occurrences, 103 distinct objects, continuous:
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[Internal error: starlet err [RequestID=][StatusCode=403]Put object
 <s3://bfsi-prd-dp-starrocks/d5a1d4e1-10da-4534-a69f-42d7a58e9992/db10001/9152040/9152042/SCHEMA_00000000008BA629|s3://bfsi-prd-dp-starrocks/d5a1d4e1-10da-4534-a69f-42d7a58e9992/db10001/9152040/9152042/SCHEMA_00000000008BA629>
 error: Access denied.]
DdlException: fail to create tablet: 10016: [... StatusCode=403 ... Access denied.]
IllegalStateException: The table is not ready: task_run_history
Questions 1. Is the 4.48M-entry star_os journal backlog the reason the FE working set is ~12 GB? Will the resident metadata shrink once the checkpoint advances? 2. What is the correct recovery order when the checkpoint worker FE is itself crash-looping? Is there a way to force checkpoint on the leader, or pin the checkpoint worker to a specific FE? 3. Recommended 
ConcGCThreads
/ 
ParallelGCThreads
and 
-Xmx
-to-container-limit ratio for a shared-data FE with a ~12 GB live set? Is 
GC Workers: 2
on 6 CPUs expected, or is something misconfigured? 4. Recommended FE liveness/readiness probe settings so multi-second GC stalls don't trigger kills during recovery? 5. Does the star_os / StarManager image get written to object storage (i.e. would the 403 directly block it), or only to local 
meta/
? Trying to confirm whether fixing S3 write perms alone unblocks the checkpoint. 6. Any 
fe.conf
tuning to make the checkpoint trigger more aggressively so this backlog can't rebuild (e.g. 
checkpoint_interval_second
, journal retention)? Cluster: 
SHOW FRONTENDS
shows leader flapping between fe-1 and fe-2; 
replayedJournalId: 0
on the dead ones.
r
Based on your logs and environment, you are in a self-reinforcing "death spiral" where metadata growth, S3 permission errors, and JVM allocation stalls are preventing the FEs from remaining healthy long enough to perform a checkpoint. 1. Backlog & Memory Correlation Yes, the 4.48M journal backlog is the primary reason for the 12 GB live set. StarRocks metadata is memory-resident. Journals are replayed into memory to build the current cluster state. When the checkpoint (which compacts these journals into a single "image" file) fails for 10 days, the FE must hold the base image + 10 days of incremental changes in memory. * Will it shrink? Yes. Once a checkpoint successfully completes, the FE can discard the replayed journal objects from the heap. The resident set will stabilize to the size of the new image. 2. Correct Recovery Order & Forcing Checkpoint The cluster cannot recover while the S3 403 errors persist and the Checkpoint Worker is crashing. 1. Fix S3 Permissions First: In shared-data mode, StarManager (star_os) manages tablet and schema metadata which must be consistent with the object storage. The 
403 Access denied
on 
SCHEMA_...
files means the FE cannot finalize metadata changes. The checkpoint will likely continue to fail until the FE has 
s3:PutObject
permissions. 2. Force Checkpoint on Leader: By default, the Leader delegates checkpointing to a Follower to save resources. Since your Followers are crash-looping, the Leader is stuck. * Set 
checkpoint_only_on_leader = true
in 
fe.conf
(or via 
ADMIN SET FRONTEND CONFIG
). * This forces the Leader to perform the checkpoint itself, bypassing the need to connect to the crashing Followers via Thrift. 3. GC & JVM Tuning Your "GC Workers: 2" on 6 CPUs is quite low for a heap with heavy allocation churn during metadata replay. ZGC is "stalling" because it cannot clear space as fast as the 
replayer
threads are allocating it. * Threads: Manually set 
-XX:ConcGCThreads=4
(or at least 50% of available cores) to give ZGC more "muscle" during this recovery phase. * Heap Ratio: 18GB Xmx in a 26GB container is a ~70% ratio. This is generally safe for StarRocks, but during a massive replay, the overhead of ZGC and off-heap metadata (Starlet/BDB) might be pushing the limits. If possible, temporarily increase the container limit to 32GB to provide breathing room. 4. Kubernetes Probe Recommendations The 8.7s allocation stalls are triggering the liveness probe and killing the process just as it’s making progress. * Increase `failureThreshold`: Set it to at least 
15
or 
20
with a 
periodSeconds
of 
10
. * Use `startupProbe`: Use a dedicated startup probe with a very long timeout (e.g., 30 minutes) to allow the 4.4M journals to replay fully without the Liveness probe being active. * Liveness Probe: Should be more relaxed. A 30-60 second window of "unresponsiveness" is common during heavy FE metadata maintenance. 5. Metadata Storage (S3 vs. Local) * Global State: The 
global_state
image is stored in your local 
meta/
directory and synchronized between FEs via the HTTP 
put
you saw in the logs. * StarManager/star_os: In shared-data mode, while the journals are local, the underlying "tablets" and "schema" pointers managed by Starlet/StarManager involve S3. Your logs show 
[StatusCode=403] Put object ... SCHEMA_000...
. This indicates that 
star_os
is attempting to write metadata or state files to S3. If S3 is 403, 
star_os
cannot commit its checkpoint. Fixing the S3 policy is a hard prerequisite. 6. 
fe.conf
Tuning for Recovery To clear the backlog and prevent it from recurring: * 
checkpoint_max_journal_count = 50000
 (Default): Ensure this isn't set higher. * 
checkpoint_interval_second = 60
: You can lower this temporarily to make the FE attempt checkpoints more frequently until it succeeds.
* 
bdbje_cleaner_threads = 4
: Increasing this helps the BDB (journaling) engine reclaim disk space and memory faster after the checkpoint finishes. Summary Checklist 1. Update S3 IAM policy to allow 
PutObject
and 
DeleteObject
on the entire bucket. 2. Set 
checkpoint_only_on_leader = true
in the Leader's 
fe.conf
. 3. Add 
-XX:ConcGCThreads=4
to 
JAVA_OPTS
. 4. Increase K8s 
failureThreshold
for liveness/readiness probes to 
20
. 5. Restart FEs one by one. Monitor the 
fe.log
specifically for 
end load star manager meta data
and a new 
imageJournalId
. References • administration/management/FE_configuration.md • administration/management/BE_configuration.md
d
@Rocky also is there a different 
cloud_native_storage_type
 property for gcs or we should use s3 only?
r
For GCS in shared-data mode, you have two options depending on your StarRocks version: 1. S3-Compatible Mode (Recommended for most) For all versions (including 3.x), StarRocks accesses GCS via the Cloud Storage XML API. In this mode, you continue to use the 
S3
type. * 
cloud_native_storage_type
: 
S3
* 
aws_s3_endpoint
: 
<https://storage.googleapis.com>
* 
aws_s3_path
: 
<s3://your-gcs-bucket-name/>...
(GCS supports the 
s3://
scheme for compatibility). * Credentials: Use HMAC keys (Access Key and Secret Key) generated in the GCS Console. 2. Native GCS Mode (StarRocks v3.5.1+) Starting from v3.5.1, StarRocks supports a native Google Storage SDK. * 
cloud_native_storage_type
: 
GS
* Benefit: This may provide better performance or support for native GCP IAM roles (Service Accounts) without needing HMAC keys. ────────── Why you are seeing the 403 error Since your logs show 
[StatusCode=403] Put object <s3://bfsi-prd-dp-starrocks/...>
, you are currently using the S3-compatible mode. The 403 error specifically means: 1. HMAC Key Permissions: The HMAC key used in 
fe.conf
(or the Storage Volume) does not have 
storage.objects.create
or 
storage.objects.delete
permissions on that specific GCS bucket. 2. GCS Interoperability: Ensure "Interoperability" is enabled in your GCS settings and that the bucket's default project matches the HMAC key's project. Recommendation: If you are on a version earlier than 3.5.1, stay with 
cloud_native_storage_type = S3
but verify your GCS HMAC keys have Storage Object Admin permissions. If you change the storage type/path on an existing cluster, you risk making existing data read-only, so it is safer to fix the permissions for the current 
S3
protocol first. References • deployment/shared_data/gcs.md • administration/management/FE_configuration.md References • deployment/shared_data/gcs.md • administration/management/FE_configuration.md
d
• @Rocky • 
checkpoint_only_on_leader = true
is set and confirmed, yet 
selectWorker
still chooses followers for star_os. Is this expected in 4.0.11? Is there a separate knob for star_os worker selection? • 
heapUsedPercent
is 0.90–0.9998 on all three FEs, and every checkpoint attempt kills its worker with "FE is dead". Is there a way to checkpoint incrementally, or a documented recovery path for a star_os backlog this large that doesn't require a heap big enough to hold the whole thing?
r
Regarding the 
star_os
/ 
StarManager
specific behavior in your v4.0.11 environment: 1. 
star_os
Worker Selection vs. 
checkpoint_only_on_leader
 It is expected behavior that 
checkpoint_only_on_leader = true
does not currently force the 
star_os
(StarManager) checkpoint to the leader. While that flag centralizes the general FE metadata checkpoint (BDBJE/GlobalState), the 
star_os
module (StarManager) utilizes its own internal 
selectWorker
logic. It often prioritizes followers to minimize performance impact on the leader's primary query-coordination tasks. In versions up to 4.0.x, there is no separate dedicated knob (like 
starmgr_checkpoint_only_on_leader
) to force this specific task to the leader. 2. Recovery Path for a 4.48M Journal Backlog There is no "incremental" checkpoint mechanism for 
star_os
that bypasses memory. The worker must replay all 4.48M journals into memory to construct a consistent state before writing the new image. With a 12GB live set and 18GB heap (90%+ usage), the worker is virtually guaranteed to trigger a "Death Spiral": 1. Worker starts 
StarManager.loadMeta()
. 2. Heap occupancy hits 95%+, triggering ZGC allocation stalls (your 8s stalls). 3. K8s probes fail due to the stall; Pod is killed. 4. Checkpoint fails; the backlog remains. Recommended Recovery Strategy: * Temporary Heap Expansion (Required): You cannot recover a 12GB live set + 4.48M replay overhead in an 18GB heap. Temporarily increase 
-Xmx
to 32GB (and pod limit to 40GB+) just for the recovery window. Once a successful image is created and journals are purged, the live set will shrink, and you can revert to 18GB. * Fix S3 Permissions: The 
403 Access Denied
on 
SCHEMA_...
objects is likely a hard blocker for the 
star_os
checkpoint completion. If the worker cannot write the resulting metadata state to S3, the checkpoint will never "advance" even if the heap is sufficient. * Startup Probe (K8s): Implement a 
startupProbe
with 
failureThreshold: 60
and 
periodSeconds: 10
. This gives the FE 10 minutes to replay the 4.4M journals without the Liveness probe killing it. * Manual Trigger: Once the leader has finished replaying (look for 
end load star manager meta data
in 
fe.log
), execute:
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sql
    ALTER SYSTEM CREATE IMAGE;
While this primarily targets the GlobalState, it often triggers secondary maintenance tasks. 3. StarManager Specific Configs If you are on 4.0.11, you can tune these to reduce pressure during recovery: * 
starmgr_grpc_server_max_worker_threads
: (Default 1024). During massive replays, you may see high thread contention. Reducing this temporarily to 256 can reduce context-switching overhead on your 6-8 CPU nodes. * 
star_mgr_meta_sync_interval_sec
: (Default 600). Increasing this to 3600 (1 hour) can prevent the leader from trying to sync with followers while they are still in a crash loop, reducing RPC noise. Summary: The backlog is too large for your current heap. Fix the S3 permissions, double the heap memory temporarily, and use a K8s startup probe to let the replay finish. The resident metadata will shrink significantly once that checkpoint finally clears. References • administration/management/FE_configuration.md • administration/Meta_recovery.md • sql-reference/sql-statements/cluster-management/nodes_processes/ALTER_SYSTEM.md References • administration/management/FE_configuration.md • deployment/deploy_manually.md
d
@Rocky Separate question — ZGC worker count We added 
-XX:ConcGCThreads=4
to 
JAVA_OPTS
in 
fe.conf
(confirmed present in the ConfigMap). FE processes started after the change still log:
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CPUs: 8 total, 6 available
GC Workers: 2 (dynamic)
Full JAVA_OPTS: 
-Dlog4j2.formatMsgNoLookups=true -Xmx18g -Xms18g -XX:+UseZGC -XX:ConcGCThreads=4 -XX:+AlwaysPreTouch -XX:-ZUncommit -Xlog:gc*:...
JDK 17.0.19, StarRocks 4.0.11. Is 
ConcGCThreads
the correct knob for ZGC worker count in this JVM, or does the 
(dynamic)
sizing override it? We were seeing 248–2682 allocation stalls per FE lifetime with stalls up to 8.7s
r
In StarRocks 4.0.11 with JDK 17, the behavior you are seeing is due to ZGC Dynamic Thread Selection, which was introduced in Java 17. 1. Why 
ConcGCThreads=4
results in 
2 (dynamic)
 In JDK 17+, ZGC ignores a static thread count by default and instead uses the flag 
-XX:+UseDynamicNumberOfGCThreads
(enabled by default). * The Log: 
GC Workers: 2 (dynamic)
means the JVM has re-evaluated its workload and decided that 2 threads are currently sufficient to keep up with allocation without "stealing" too much CPU from your FE application. * The Limit: Your 
-XX:ConcGCThreads=4
acts as the upper bound for this dynamic selection. The JVM will scale between 1 and 4 threads as it sees fit. * The CPU Heuristic: By default, ZGC targets a very conservative CPU usage (~12.5% of available cores). On an 8-core machine, the default would normally be 1 thread. The fact that it scaled to 2 shows it knows it's under pressure, but it hasn't deemed 4 necessary yet—even though your application is actually stalling. 2. How to fix the Allocation Stalls To mitigate the 8.7s stalls, you need to make ZGC more aggressive and potentially "static" during this high-load recovery phase: * Force a Static Thread Count: Disable dynamic scaling to ensure ZGC always has 4 threads available for marking and relocation.
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bash
    -XX:-UseDynamicNumberOfGCThreads -XX:ConcGCThreads=4
* Increase Spike Tolerance: ZGC uses a heuristic to predict future allocation rates. During a massive journal replay, the allocation is extremely bursty. Increase the tolerance (default is 2.0) to make GC start earlier:
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bash
    -XX:ZAllocationSpikeTolerance=5.0
* Set a Soft Max Heap: Tell ZGC to get aggressive before it hits the 18GB limit. If your Xmx is 18GB, setting a SoftMax at 14-15GB can prevent the "panic" state that leads to long stalls:
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bash
    -XX:SoftMaxHeapSize=15g
3. Recommended JAVA_OPTS for your 8-core/18GB FE Given your specific environment (shared-data mode, 4.4M journal backlog), update your 
fe.conf
to:
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bash
JAVA_OPTS="-Xmx18g -Xms18g -XX:+UseZGC \
-XX:ConcGCThreads=4 \
-XX:-UseDynamicNumberOfGCThreads \
-XX:ZAllocationSpikeTolerance=5.0 \
-XX:SoftMaxHeapSize=15g \
-XX:+AlwaysPreTouch -XX:-ZUncommit"
Summary of the "6 available" CPU count The JVM log 
CPUs: 8 total, 6 available
usually indicates that Kubernetes CPU 
limits
or 
requests
(specifically CFS quotas) are being detected. Because the JVM thinks it only has 6 effective cores to work with, it is being even more conservative with GC threads to avoid starving the 
thrift-server-pool
and 
replayer
threads you saw in the stall logs. Forcing 
ConcGCThreads=4
and disabling dynamic scaling is the correct move here to prioritize memory health over slight CPU contention. References • administration/management/FE_configuration.md • administration/Meta_recovery.md • deployment/deploy_manually.md
d
@Rocky
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2026-08-25 18:10:50.935+05:30 WARN (thrift-server-pool-2651|7168) [LeaderImpl.finishTask():198] finish task reports bad. request: TFinishTaskRequest(backend:TBackend(host:starrocks-mas-cn-2.starrocks-mas-cn-search.starrocks.svc.cluster.local, be_port:9060, http_port:8040), task_type:CREATE, signature:9216342, task_status:TStatus(status_code:RUNTIME_ERROR, error_msgs:[Internal error: starlet err [RequestID=][StatusCode=403]Put object <s3://bfsi-prd-dp-starrocks/d5a1d4e1-10da-4534-a69f-42d7a58e9992/db10001/9216327/9216341/SCHEMA_00000000008CA149> error: Access denied.]), report_version:17842209360000)
but the permissions on the pod are granted correctly. what can be the issue?
r
The 
403 Access Denied
error during a 
Put object
operation on a 
SCHEMA_
file indicates that while the pod might have an identity, the Compute Node (CN) or Backend (BE) does not have sufficient permissions to write to that specific GCS bucket path. Even if you have granted permissions to the pod, here are the most likely reasons for this specific StarRocks error: 1. Missing 
aws_s3_use_instance_profile
 If you are using GKE Workload Identity (granting permissions via a Kubernetes Service Account bound to a GCP Service Account), StarRocks will not automatically "pick up" the pod's identity for its internal storage engine (
starlet
) unless explicitly told to do so. * Fix: Ensure you have the following in your 
fe.conf
(and `cn.conf`/`be.conf` if configured separately):
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properties
    aws_s3_use_instance_profile = true
Without this, StarRocks may default to an "anonymous" request or look for HMAC keys that aren't provided, resulting in a 403. 2. BE/CN Pods vs. FE Pods The error log you provided comes from a Compute Node (
starrocks-mas-cn-2...
). * Issue: Users often apply the Service Account annotation and GCP IAM roles to the FE pods but forget to apply the exact same configuration to the CN/BE pods. * Check: Ensure your CN/BE 
Deployment
or 
StatefulSet
has: 1. The 
serviceAccountName
matching the one bound to GCP. 2. The annotation 
<http://iam.gke.io/gcp-service-account|iam.gke.io/gcp-service-account>: <your-gsa>@<project>.<http://iam.gserviceaccount.com|iam.gserviceaccount.com>
. 3. Insufficient IAM Roles for Metadata StarRocks shared-data mode doesn't just read data; it must manage its own metadata (
SCHEMA_
and 
segment
files). * Requirement: The GCP Service Account needs the Storage Object Admin role (or at minimum 
Storage Object Creator
and 
Storage Object Viewer
) on the bucket. * Common Trap: If your bucket has Uniform bucket-level access enabled, ensure you haven't accidentally limited the Service Account's permissions to a specific sub-folder that doesn't match the 
aws_s3_path
prefix. 4. GCS Interoperability (If using HMAC Keys) If you are not using Workload Identity but are using HMAC keys (
aws_s3_access_key
): * Go to GCS Settings > Interoperability. * Ensure the Default Project for interoperability matches the project where the bucket resides. * If the HMAC key is from a different project than the bucket, GCS will return a 403 unless the key's project has been granted explicit permission on the bucket. 5. "Uniform bucket-level access" vs ACLs If your GCS bucket has ACLs enabled (Access Control Lists), a pod writing a file might not be granting "owner" permissions to the bucket owner. However, StarRocks generally works best with Uniform bucket-level access enabled and IAM roles. If you must use ACLs, ensure the IAM principal has "Owner" or "Legacy Bucket Writer" permissions. Recommendation: 1. Verify 
aws_s3_use_instance_profile = true
is set. 2. Check that the CN pods have the correct Service Account. 3. Test the permission from inside a CN pod using the 
gcloud
or 
gsutil
CLI:
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bash
    # Run this inside the starrocks-mas-cn-2 pod
    gsutil cp some_local_file <gs://bfsi-prd-dp-starrocks/test_write>
If this fails, the issue is with the GCP/GKE identity binding, not StarRocks. References • deployment/shared_data/gcs.md • integrations/authenticate_to_gcs.md • administration/management/FE_configuration.md References • deployment/shared_data/gcs.md • integrations/authenticate_to_gcs.md • administration/management/FE_configuration.md
d
@Rocky Two questions worth putting to Rocky: 1. With 
run_mode = shared_data
and 
enable_load_volume_from_conf = true
, FE auto-created an S3-type 
builtin_storage_volume
. A GS-type volume is default. Internal tables (
_statistics_
, db10001) appear to still use the builtin S3 volume and get 
403 Put object
. How do we redirect internal/system table creation to the default GS volume? Can 
builtin_storage_volume
be disabled or deleted safely? 2. Is 
aws_s3_use_instance_profile = true
supported against 
<http://storage.googleapis.com|storage.googleapis.com>
, or does S3 interop require HMAC keys?
r
1. Redirecting Internal Tables & Built-in Volumes In StarRocks 
shared_data
mode, the behavior of internal and system tables (like 
_statistics_
) and the 
builtin_storage_volume
is as follows: * How to "Redirect": You cannot directly "move" existing data from one volume to another with a single command. However, internal system tables and the 
default_catalog
always follow the Default Storage Volume. To redirect future internal data: 1. Create your desired GS-type volume:
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sql
        CREATE STORAGE VOLUME my_gs_volume
        TYPE = GS
        LOCATIONS = ("<gs://your-bucket/path/>")
        PROPERTIES ("enabled" = "true", ...);
2. Set it as the default:
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sql
        SET my_gs_volume AS DEFAULT STORAGE VOLUME;
3. Note on Existing Tables: For user tables (like your 
db10001
), the 
storage_volume
property is often inherited at creation. Changing the default volume only affects newly created tables or partitions. To change an existing table's volume, you must:
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sql
        ALTER TABLE <table_name> SET ("storage_volume" = "my_gs_volume");
Warning: This only affects new data. Existing data remains on the old volume unless you re-insert/overwrite it. * Disabling/Deleting `builtin_storage_volume`: * Cannot be dropped: The documentation explicitly states that the 
builtin_storage_volume
cannot be dropped. * Can be disabled: You can set 
"enabled" = "false"
via 
ALTER STORAGE VOLUME builtin_storage_volume SET ("enabled" = "false")
, but only after you have designated another volume as the default. * Best Practice: Do not try to delete it. Simply set your manual GS volume as the default and disable the built-in one if you wish to prevent accidental use. ────────── 2. 
aws_s3_use_instance_profile
vs. GCS No, 
aws_s3_use_instance_profile = true
is not supported for Google Cloud Storage (GCS) when using the 
S3
endpoint (
<http://storage.googleapis.com|storage.googleapis.com>
). * S3 Interoperability: When you use 
TYPE = S3
against GCS, StarRocks uses the AWS C++ SDK. That SDK's 
instance_profile
logic is hardcoded to look for the AWS Instance Metadata Service (IMDS) at 
169.254.169.254
. While GCP has a similar service, the response format and authentication headers (Identity Tokens vs. IAM Roles) are incompatible. * The HMAC Requirement: If you must use S3-interoperability (URIs starting with 
s3://
pointing to GCS), you must use HMAC keys (
aws_s3_access_key
and 
aws_s3_secret_key
). * The Recommended Path (Native GS): Since you are on v4.0.11, you should avoid S3-interop and use the native GS volume type. This allows you to use GKE Workload Identity or Compute Engine Service Accounts without HMAC keys:
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sql
    CREATE STORAGE VOLUME gcs_native
    TYPE = GS
    LOCATIONS = ("<gs://your-bucket/>")
    PROPERTIES (
        "gcp.gcs.use_compute_engine_service_account" = "true",
        "enabled" = "true"
    );
This native integration uses the GCS-specific SDK which correctly handles Google's identity metadata. Summary: Your 
403 Put object
error is likely happening because the S3-interop layer is trying to use an AWS-specific identity method that doesn't work on GCP. Switch to 
TYPE = GS
and set it as your default volume to resolve the permission issues for system tables. References • sql-reference/sql-statements/cluster-management/storage_volume/SET_DEFAULT_STORAGE_VOLUME.md • sql-reference/sql-statements/cluster-management/storage_volume/CREATE_STORAGE_VOLUME.md • deployment/shared_data/gcs.md
d
@Rocky Confirmed zero user tables/partitions created after the last good star_os image (Aug 15 02:26, journal 5369484); routine loads paused since Aug 13. 
imageJournalId 5369484
vs 
logJournalId 9918980
, checkpoint failing with 
OutOfMemoryError
on 
-Xmx18g
. What is the supported procedure to recover from the existing image and discard the un-checkpointed star_os journal, on a 3-FE shared-data cluster running the Kubernetes operator? Do all three FEs need meta cleared, or only the leader?
r
To recover from the image and discard the un-checkpointed journal (including the `star_os`/`StarMgr` journals) in a 3-FE shared-data cluster managed by the Kubernetes operator, follow the procedure below. Summary of the Situation The 
OutOfMemoryError
occurs because the FE is attempting to replay nearly 4.5 million journal entries (from 5,369,484 to 9,918,980) into memory during startup. Since you confirmed no user tables or metadata changes have occurred since the last image, discarding these journals is the most efficient way to restore service. Supported Procedure 1. Prepare the Environment * Scale Down: Set the FE replicas to 0 in your 
StarRocksCluster
CRD to stop all FE processes and prevent the operator from restarting them during maintenance.
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bash
    kubectl patch starrockscluster <cluster-name> --type merge -p '{"spec":{"starRocksFeSpec":{"replicas":0}}}'
* Backup: (Highly Recommended) Back up the current 
meta_dir
of at least the node you intend to make the leader. 2. Clean the Leader Metadata (FE-0) You must clear the BDBJE logs (the journal) but keep the image file. * Access the persistent volume of the first FE (typically 
starrocks-fe-0
). You can do this by launching a temporary "maintenance" pod that mounts the same PVC. * Navigate to the 
meta/
directory. * Delete the 
bdb/
directory: This directory contains the un-checkpointed journals that are causing the OOM.
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bash
    rm -rf /opt/starrocks/fe/meta/bdb/*
* Verify the 
image/
directory: Ensure the file 
image.5369484
(and its associated 
.checksum
) is present. Do not delete this directory. 3. Clear Follower Metadata (FE-1, FE-2) To ensure no old journals conflict with the new leader, the simplest way is to delete the PVCs for the other two FE pods. The operator will recreate them with empty storage, and they will automatically sync the image and new journals from the leader.
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bash
kubectl delete pvc <pvc-name-fe-1> <pvc-name-fe-2>
4. Reconfigure and Start the Leader * Set Recovery Flag: Update your FE configuration (via the ConfigMap referenced in your CRD) to include:
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properties
    bdbje_reset_election_group = true
* Scale to 1: Scale the FE replicas to 1.
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bash
    kubectl patch starrockscluster <cluster-name> --type merge -p '{"spec":{"starRocksFeSpec":{"replicas":1}}}'
* Monitor: Check the 
fe.log
of 
fe-0
. It should load the image and start as 
LEADER
. Because the 
bdb/
directory was empty, it will start a fresh journal sequence beginning at the image's ID. 5. Stabilize and Re-scale * Create New Image: Once the leader is healthy, force a checkpoint to consolidate the state:
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sql
    -- Connect to the leader FE via MySQL client
    ALTER SYSTEM CREATE IMAGE;
* Scale to 3: Re-scale the FE replicas to 3. The new FE-1 and FE-2 pods will start, see that they have no metadata, and perform a full image sync from FE-0.
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bash
    kubectl patch starrockscluster <cluster-name> --type merge -p '{"spec":{"starRocksFeSpec":{"replicas":3}}}'
* Cleanup Config: Once all 3 FEs are 
Alive
(check 
SHOW FRONTENDS;
), remove 
bdbje_reset_election_group = true
from your configuration to prevent accidental election resets in the future. Summary of FE Requirements * Do all three FEs need meta cleared? Yes. If you only clear the leader, the followers may still possess the "future" journals and cause a conflict or attempt to become leader with the bad journal data. * Leader vs. Follower: Only the newly designated leader needs to keep the 
image/
directory. The followers should have their entire 
meta/
directory cleared (or PVCs deleted) so they can re-sync from the leader's clean state. Important Notes
* StarMgr Journal: In shared-data mode, the 
star_os
(StarMgr) journals are stored within the same BDBJE environment in the 
bdb/
directory. Deleting 
bdb/
handles both the Global State and StarMgr journals simultaneously. * Data Loss: Any metadata changes (e.g., routine load offsets, user permissions) created between journal 5,369,484 and 9,918,980 will be lost. Since you confirmed no tables were created, the impact should be minimal (mostly offset progress for routine loads). References • deployment/shared_data/hdfs.md • administration/Meta_recovery.md • quick_start/shared-data.md • quick_start/routine-load.md
d
@Rocky config has 
min_graceful_exit_time_second = 25
. The preStop hook (
fe_prestop.sh
) failed, meaning fe-0 was killed ungracefully. That can leave BDBJE mid-write , it may have contributed to the checkpoint corruption. Is that possible?
r
Yes, it is highly likely that the ungraceful exit contributed to the state you are seeing, specifically regarding the checkpoint failure and potentially the BDBJE metadata corruption. Here is how the ungraceful shutdown and the BDBJE/FE recovery process interact in your scenario: 1. The Impact of 
SIGKILL
on BDBJE StarRocks uses Berkeley DB Java Edition (BDBJE) for its metadata journal. While BDBJE is transactional and designed to be robust, an ungraceful exit (like a 
SIGKILL
after a failed 
preStop
hook) can lead to several issues: * Incomplete Writes: If the FE was killed while writing a new journal entry or during the middle of a BDBJE "cleaner" cycle, the log files can be left in a state that requires a heavy "recovery" scan upon restart. * Journal Truncation: BDBJE may find the last log file partially written and be forced to truncate it. In a multi-FE cluster, this can lead to the 
RollbackException
or 
InsufficientLogException
mentioned in the logs if the followers are out of sync. 2. Why the Checkpoint Failed (The OOM Connection) The gap between your 
imageJournalId
(5M) and 
logJournalId
(9.9M) represents 4.5 million un-checkpointed entries. * Already Broken: The checkpointing process was likely failing long before the ungraceful shutdown occurred. In StarRocks, if the BDBJE journal grows too large because checkpoints aren't succeeding, the FE will eventually run out of memory trying to load/replay those journals on startup. * The Vicious Cycle: If a checkpoint fails (often due to memory pressure or the 403 S3/GCS errors you saw earlier), the BDBJE log files are not deleted. Upon an ungraceful restart, the FE must replay every single entry since the last image (5.3M) into memory. Replaying 4.5 million entries into an 18GB heap is frequently what triggers the 
OutOfMemoryError
you are seeing. 3. The 
preStop
Hook and 
min_graceful_exit_time_second
 The 
fe_prestop.sh
script is critical because it tells the FE to: 1. Stop accepting new connections. 2. Wait for in-flight queries to finish. 3. Perform a clean closure of the BDBJE environment. 4. If it is the Leader, it tries to step down so another FE can take over. If this hook failed or timed out (K8s 
terminationGracePeriod
is usually 30s by default), the FE process was simply killed. This prevents the "Clean Shutdown" flag from being written to the BDBJE logs. On the next start, the FE sees it didn't shut down cleanly and initiates a full recovery scan, which is significantly more memory-intensive than a clean start. Is 
min_graceful_exit_time_second
the culprit? While 
min_graceful_exit_time_second
(which ensures the FE stays alive for a minimum duration to allow Load Balancers to react) helps with availability, it doesn't solve a failing 
preStop
script. If the 
preStop
script itself is failing (due to permissions, path issues, or the FE being already unresponsive/OOM), the FE will always end up being killed ungracefully. Recommendation 1. Recovery: Follow the procedure provided previously to discard the un-checkpointed journals (clear 
bdb/
but keep 
image/
). There is no way to replay 4.5M entries with your current heap size. 2. Fix Permissions: The 
403 Put object
error is the "root" of the checkpoint failure. The FE cannot create a new image file if it cannot write to the storage volume. Once you fix the GCS/S3 volume permissions, checkpoints will succeed again. 3. Increase Memory: For a journal gap this large, even if BDBJE weren't "corrupt," an 18GB heap is likely too small for the replay. Consider increasing the FE 
-Xmx
if your metadata grows significantly. 4. Debug preStop: Check the 
kubectl describe pod
events to see why 
fe_prestop.sh
failed. Often it's because the FE was already in a "hanging" state due to the OOM or checkpoint block. References • administration/management/graceful_exit.md
d
@Rocky One correction on point 2: FE images are written to the local meta PVC, not object storage. 
image/v2/
(global_state) was written today at 21:15 while 
image/starmgr/
has been frozen since Aug 15 — same filesystem, same process. So local image writes are working; the 403s were 
TableKeeper
failing to create 
_statistics_
tablets on a misconfigured S3-interop volume (now disabled), which added journal churn but didn't block checkpointing. The checkpoint failure is 
java.lang.OutOfMemoryError: Java heap space
in 
star_os_checkpoint_controller
with no storage error in the trace. Also confirmed from GKE pod events: the trigger was liveness probe timeouts (
timeoutSeconds: 5
) against GC stalls up to 8.7s — isolated stalls at 2113/2146/22:55 recovered, then a sustained one at 23:31 caused the first kill and the cascade. We're setting 
livenessProbeFailureSeconds: 600
permanently. Your SIGKILL/BDBJE point is well taken — does the failed preStop mean the recovery scan on each restart is meaningfully heavier, and is there anything in the recovery procedure that should account for an unclean BDBJE state?
r
Your detailed analysis of the 
starmgr
 image vs. the 
global_state
 image confirms the root cause. In StarRocks shared-data mode, the FE maintains two distinct metadata sets: the standard StarRocks metadata (
global_state
) and the StarOS metadata (
starmgr
), which tracks things like object storage mappings and tablet-to-CN placements. 1. Is the recovery scan "meaningfully heavier" after a 
SIGKILL
? Yes, significantly. When BDBJE is closed gracefully, it writes a "checkpoint" entry to the log and flushes all buffers. Upon restart, it simply reads from the last checkpoint. After a `SIGKILL`: * Log Verification: BDBJE must perform a "Recovery Scan." It must read every log file from the last successful BDBJE checkpoint to the end of the log to verify checksums and rebuild the in-memory mapping of database IDs. * Memory Pressure: If you have 4.5 million entries (the gap between journal 5M and 9.9M), the FE will attempt to load the metadata into the heap while simultaneously replaying these journals. Since your 
starmgr
image is from Aug 15 (entry ~5M), the 
star_os_checkpoint_controller
is likely failing because it's trying to reconstruct the entire StarOS state for the last 10 days in memory just to begin the checkpoint. * CPU/IO Spike: The combination of an unclean BDBJE state and a massive journal backlog creates a "startup storm" that usually triggers the liveness probe again before the FE can finish the scan—leading to the "cascade" you observed. 2. Does the recovery procedure need to account for the unclean state? The procedure provided (clearing 
bdb/
while keeping 
image/
) is the correct way to handle an unclean state. By deleting the 
bdb/
directory, you are effectively telling the FE: "Ignore the corrupted/unclean logs entirely; start a fresh journal stream using the last valid snapshot (the image files)." Specific things to note for your 
starmgr
discrepancy: * Image Disparity: Since your 
global_state
image is current but your 
starmgr
image is from Aug 15, clearing the journals will revert your StarOS state to Aug 15. * The "Zero Table" Safety Net: Because you confirmed no new tables/partitions were created since Aug 15, the StarOS state should be relatively static. Reverting to the Aug 15 
starmgr
image is safe; the only thing you might "lose" is internal housekeeping (like exactly which CN was assigned which tablet for cache-warmup purposes), which StarRocks will naturally re-calculate. * Force a StarMgr Checkpoint: After you get the cluster back up with one FE and 
bdbje_reset_election_group = true
, run 
ALTER SYSTEM CREATE IMAGE;
. This should force both metadata sets to write a fresh image, finally updating the 
starmgr
image. 3. Adjustments to your Liveness/PreStop Logic * Liveness Probe: Your plan to set 
livenessProbeFailureSeconds: 600
(10 minutes) is wise. During metadata recovery or large journal replays, the JVM can be entirely "frozen" by GC for 30–60 seconds. A 5-second timeout is almost guaranteed to cause a kill during recovery. * PreStop Failure: The 
fe_prestop.sh
likely failed because the FE was already in an OOM/Long-GC state and couldn't respond to the 
<http://localhost:8030/api/bootstrap>
(or similar) check used to initiate a graceful exit. * Heap Recommendation: With a 4.5M journal gap, 18GB is very tight. If the recovery (after clearing BDB) still feels sluggish, temporarily bump the FE to 24GB or 32GB until a fresh checkpoint is successfully written to the 
image/
directory. Final Sanity Check on the Procedure: 1. Scale to 0 (Wait for pods to vanish). 2. On FE-0 PVC: Delete 
meta/bdb/*
. Leave 
meta/image/
exactly as is. 3. On FE-1/FE-2 PVCs: Delete the entire 
meta/
directory (ensure they start empty). 4. Update ConfigMap: Add 
bdbje_reset_election_group = true
. 5. Scale to 1 (Wait for FE-0 to become 
LEADER
). 6. Force Checkpoint: 
ALTER SYSTEM CREATE IMAGE;
.
7. Verify: Check that 
meta/image/starmgr/
now has a timestamp from today. 8. Scale to 3. Once the cluster is stable, remove the 
bdbje_reset_election_group
flag so that a future reboot doesn't accidentally cause a split-brain if the FE fails to see its peers. References • administration/Meta_recovery.md
d
@Rocky Correction to the earlier framing — this cluster is tiny: 38 tablets, 10 tables, 6 databases (
SHOW PROC '/statistic'
). Yet the star_os journal reached 4.5M entries and FE live set hit ~15 GB on an 18 GB heap. That's ~118,000 journal entries per tablet. What generates star_os journal entries at that rate on a cluster this small with all routine loads paused since Aug 13? We found 
TableKeeper
retrying 
_statistics_
tablet creation every 30–60s (7,529 failures, 
_statistics_
still shows TableNum: 0) against a misconfigured S3-interop volume, plus 
StarMgrMetaSyncer.cleanOneGroup()
failing with 
GRPC:DEADLINE_EXCEEDED
on shard group deletion. Could either loop account for this volume? How to confirm this?
r
In a tiny cluster with only 38 user tablets, reaching 4.5 million journal entries and a 15 GB FE heap is highly abnormal. Your observation about the 
TableKeeper
loop and 
StarMgrMetaSyncer
failures is exactly where the "metadata storm" is coming from. 1. The 
TableKeeper
loop and Shard Proliferation In shared-data mode, StarRocks does not just create a "table" in the traditional sense. It orchestrates StarOS Shards and Shard Groups. * Multiplier Effect: Even if the 
_statistics_
table is small (e.g., 10 buckets), each creation attempt involves: 1. Creating a Shard Group in StarMgr. 2. Creating 10 individual Shards (one for each tablet/bucket). 3. Persisting the mapping of Shard → Object Storage path. * Journal Churn: If 
TableKeeper
retried 7,529 times, and each attempt partially created these StarOS entities before failing on the S3 
Put object
call, you generated at least 80,000 to 150,000 StarOS journal entries just from the creation attempts. 2. The 
StarMgrMetaSyncer
"Journal Storm" The real culprit for the 4.5M entries is likely the cleanup logic triggered by these failures. * Orphaned Shards: When a table creation fails halfway, StarOS leaves behind "orphaned" shards and shard groups that have no owner in the Global State. * The Syncer Loop: 
StarMgrMetaSyncer
runs a background task (often every few seconds or minutes) to identify and delete these orphans. * 
DEADLINE_EXCEEDED
& Retries: If 
cleanOneGroup()
is failing with a gRPC timeout, it means the StarOS metadata service is struggling to process the deletion of the thousands of "ghost" shards created by your 7,529 retries. * Failure Persistence: In some versions of StarRocks, if a cleanup task fails, it logs the failure or state transition to the journal. If this syncer was running frequently (e.g., trying to clean 7,000+ groups) and each "attempt" or "timeout" resulted in a journal record, the journal can balloon into the millions very quickly. 3. Why the FE Live Set is 15 GB The 
OutOfMemoryError
isn't just about the BDB logs on disk; it's about the in-memory representation of StarOS metadata. * StarRocks maintains a 
StarMgr
object in the FE heap that maps every Shard to its location. * If the cleanup (
cleanOneGroup
) is failing, these 7,500+ failed "tables" worth of shards (potentially 75,000+ shards) are sitting in the FE memory as "Dangling" or "To-be-cleaned" metadata. * Replaying a journal with 4.5 million entries requires the FE to rebuild this entire graph of "created-then-deleted-then-failed-to-delete" shards in memory, which easily exhausts an 18 GB heap. How to Confirm Once you have performed the recovery (clearing 
bdb/
but keeping 
image/
as discussed previously), you can verify the state of the "ghost" metadata using these internal diagnostic paths: 1. Check Shard Count:
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sql
    -- Check how many shards StarOS thinks it has
    SHOW PROC '/starmgr/shard_groups';
If this returns thousands of rows for a cluster that should only have ~40 tablets, you have confirmed the bloat. 2. Check StarMgr Journal Stats: Search your 
fe.log
for 
StarMgr journal
. You will likely see that the StarOS/StarMgr journal sequence is moving significantly faster than the Global State journal sequence. Permanent Fix * Fix S3/GCS Credentials: (Already done, as you noted). This stops the 
TableKeeper
from adding new "garbage" to the journal. * Increase `starmgr_grpc_timeout_seconds`: If you continue to see 
DEADLINE_EXCEEDED
during cleanup, increase this config (default is 5s) to 30s to allow the metadata syncer to finish its work without timing out. * Scale FE Heap: For shared-data clusters, the FE memory requirement is proportional to the number of shards (including deleted/trash shards) rather than just active tablets. Given the churn, 18 GB is the bare minimum; 32 GB is recommended for stability during recovery. References
• administration/management/FE_configuration.md • deployment/shared_data/hdfs.md • administration/Meta_recovery.md • GitHub Issue #67379 (TableKeeper loop behavior) • GitHub Issue #74117 (StarMgrMetaSyncer orphaned shard groups) References • administration/management/FE_configuration.md • administration/Meta_recovery.md
d
@Rocky
Copy code
SHOW PROC '/starmgr/shard_groups';
not working for starrocks 4.1
r
Since you are on StarRocks 4.1 (a very recent version that has consolidated many of the internal StarOS and Lake service hierarchies), the legacy path 
/starmgr/shard_groups
has indeed been moved or restricted. To confirm the "Shard Group Storm" and the bloat from the failed 
TableKeeper
retries in version 4.1, use the following paths: 1. The Correct 
SHOW PROC
Paths for 4.1 In 4.1, StarOS metadata is typically found under the 
/staros
or 
/lake
hierarchy. Try these in order: * Primary Path: 
SHOW PROC '/staros/shard_groups';
* Secondary Path: 
SHOW PROC '/lake/shards';
(This shows the actual tablet-to-object-storage mapping) * Cluster Summary: 
SHOW PROC '/statistic';
(Check the 
TabletNum
here. If it says 38 but the shard count from the commands above is in the thousands, you have confirmed the orphans). 2. Check the System Tables (Recommended for 4.1) StarRocks 4.x has moved much of the internal metadata visibility into 
information_schema
. This is often more reliable than `SHOW PROC`:
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sql
-- Check for an abnormal number of shard groups
SELECT count(*) FROM information_schema.be_tablets; 

-- Check for orphaned/dangling metadata if the table exists in your build
SELECT * FROM information_schema.lake_tablets;
3. Confirming the 
TableKeeper
Loop Since 
_statistics_
shows 
TableNum: 0
but you have 7,529 failures, the 
TableKeeper
is likely stuck in a loop trying to initialize the system statistics database. Each cycle it: 1. Requests StarOS to create a Shard Group. 2. Tries to write the initial tablet metadata to S3/GCS. 3. Fails at the S3/GCS step (the 403 you saw). 4. Crucially, it might not be successfully "rolling back" the Shard Group creation in the BDBJE journal before retrying. 4. Why the FE is OOM despite the tiny cluster Even with 38 "real" tablets, if the 
TableKeeper
generated 7,000+ Shard Groups, and each Shard Group contains shards for the 
_statistics_
partitions, you likely have ~75,000 to 100,000 internal "ghost" shards registered in the FE memory. * Replay Cost: Replaying 4.5 million journal entries isn't just "reading logs"—it's the FE process attempting to reconstruct the state of these 100k shards in the JVM Heap. * GC Stalls: 18GB is plenty for 38 tablets, but it is not enough for an FE carrying the "ghost" metadata of thousands of failed table creations. Steps to Clear the Bloat 1. Perform the BDB Clear: Follow the recovery (deleting 
meta/bdb/*
). This is the only way to "forget" the 4.5M entries of churn. 2. Fix the Volume: Ensure the S3/GCS volume is accessible. If 
TableKeeper
can finally succeed once, the loop will stop. 3. Disable Stats temporarily: If you cannot fix the volume yet, set 
enable_stats = false
in 
fe.conf
to stop 
TableKeeper
from trying to create the 
_statistics_
table entirely. 4. Wait for `StarMgrMetaSyncer`: Once the cluster is up, the syncer will eventually (based on 
star_mgr_meta_sync_interval_sec
) start cleaning up the old shard groups. In 4.1, you can speed this up by increasing the gRPC timeout if you see 
DEADLINE_EXCEEDED
in the logs. Reference: * StarRocks 4.1 Release Notes - Metadata Management * Shared-data FAQ - Tablet Metadata References • faq/shared_data_faq.md • knowledge/admin_and_operation/shared_data_compaction.md
d
@Rocky is there a variable to abandon these corrupt journal entries in 4.1 version?
r
In StarRocks 4.1, there is no single "skip range" variable to abandon a massive block of 4.5 million entries at once. However, depending on whether the entries are technically "corrupt" (checksum errors) or just "garbage" (bloated but validly written), you have a few specific levers: 1. 
metadata_journal_skip_bad_journal_ids
(For specific corruptions) If the FE specifically crashes with an error like 
got interrupt exception or inconsistent exception when replay journal xxx
, you can skip specific IDs: * Variable: 
metadata_journal_skip_bad_journal_ids = xxx,yyy
* Use case: This is intended for individual "poison pill" entries. With 4.5 million entries, this is likely impractical unless only the very last few are corrupt. 2. 
metadata_ignore_unknown_operation_type
 If the journal entries are from a newer/different version or contain types the FE doesn't recognize (common in misconfiguration or rollback scenarios): * Variable: 
metadata_ignore_unknown_operation_type = true
* Effect: It allows the replayer to skip entries it doesn't understand rather than crashing. 3. 
metadata_enable_recovery_mode
(The "Safety" Mode) * Variable: 
metadata_enable_recovery_mode = true
* Effect: This puts the FE into a restricted mode that ignores many tablet-level consistency checks during replay. It is often used to get a cluster up so you can run 
ALTER SYSTEM CREATE IMAGE;
and "checkpoint" your way out of a bad state. * Constraint: You cannot perform any DDL/Write operations in this mode. 4. The "Manual Abandonment" (Recommended for your 4.5M Bloat) Because your "corruption" is actually logic-driven bloat (7,500 loops of failed creations), the entries are likely syntactically valid BDBJE logs but functionally "garbage." The JVM is OOM-ing because it's trying to build a massive in-memory state representing those 4.5M operations. There is no configuration variable that will tell the FE to "skip everything between journal ID 5M and 9.5M." To truly abandon those entries, you must use the procedure discussed earlier: 1. Stop the FE. 2. Delete the 
meta/bdb/
directory. 3. Start with 
bdbje_reset_election_group = true
. By deleting the 
bdb/
folder, you are effectively forcing the FE to abandon all journal entries and start fresh from the last valid snapshot in the 
meta/image/
directory. Given that your 
image/
was updated at 21:15 today (for 
global_state
), you will only lose the metadata changes that occurred between 21:15 and the crash—which, in your case, sounds like it was almost entirely the 403-error loop and statistics failures. Summary of variables for your 
fe.conf
during recovery:
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properties
# Force the FE to ignore its peer list and start as a solo leader
bdbje_reset_election_group = true

# If the replay still hits specific "Unknown Type" errors due to 4.1 logic
metadata_ignore_unknown_operation_type = true

# Increase the timeout for the syncer to clean up the ghost shards
starmgr_grpc_timeout_seconds = 30

# Temporary heap boost to handle the initial state reconstruction
# Ensure your pod has the request/limit to back this up
# -Xmx24g or higher is recommended given the shard count
Note: After successfully starting and running 
ALTER SYSTEM CREATE IMAGE;
, remember to remove 
bdbje_reset_election_group
and 
metadata_enable_recovery_mode
before adding the other FE nodes back. References • administration/Meta_recovery.md
d
@Rocky The star_os journal is StarManager's own log, and may not be in the BDBJE environment at all — it could be a separate store. If 
DbPrintLog
shows only ~1M records matching your global_state journal (
ReplayedJournalId
~1092900) rather than 9.9M, that tells you star_os journals live elsewhere, which is itself useful to know before you delete 
bdb/
. Is this reasoning correct? or will it be present in bdbje
r
Your reasoning is correct and points to a crucial distinction in StarRocks' shared-data (lake) architecture: the Global State journal and the StarManager (StarOS) journal are physically and logically separate. 1. Where the Journals Live In a StarRocks FE node, the metadata directory typically has this split: * Global State (FE) Journal: Located in 
meta/bdb/
. This contains high-level metadata: databases, tables, users, and transactions. This is what you checked with 
DbPrintLog
and found ~1M records. * StarManager (StarOS) Journal: Often located in 
meta/staros/bdb/
(or a similar subdirectory under 
meta/
). This contains the "shard-to-object" mapping, shard group metadata, and storage volume info. This is where your 4.5M to 9.9M entries are residing. 2. Confirming the Separation The fact that 
DbPrintLog
on your main BDB directory only shows 1M records while the system reports millions of 
star_os
entries is the "smoking gun." * Separate Sequences: As you saw in your 
SHOW PROC
or snapshot views, there is an 
fe_journal_id
and a 
starmgr_journal_id
. They do not share the same sequence numbers. * Independent Replay: When the FE starts, it launches two separate replay threads—one for the Global State and one for the StarManager. 3. Why this Matters for your Recovery If you delete only 
meta/bdb/
, you will reset your Global State (the 1M entries), but the FE might still OOM during restart because the StarManager replayer will still attempt to load the 4.5M bloated entries from the other directory. To truly clear the "journal storm" and the 15GB heap usage, you must address both: 1. Stop the FE. 2. Locate the StarOS BDB directory: It is usually 
meta/staros/bdb
. Check its size; if it matches the 4.5M entries bloat, that is your target. 3. Clear both if necessary: * If you delete 
meta/bdb/
, the Global State restarts from the last 
image
. * If you delete 
meta/staros/bdb/
, the StarManager restarts from its last 
image
. _ _Note:* In StarRocks 4.x, StarOS often stores its own "images" (checkpoints) in 
meta/staros/image/
. 4. The Danger of "In-Sync" Recovery In Lake mode, the Global State and StarOS metadata must remain somewhat in sync. * Scenario: If the Global State "knows" about a table but you delete the StarOS metadata (the shard info), the table becomes a "ghost" with no data locations. * Your Case: Since your bloat was caused by failed creations (
TableNum: 0
), the Global State likely doesn't even know these tables exist. The "garbage" is trapped entirely in the StarOS journal. Recommendation 1. Check for `meta/staros/bdb/`: Verify that this folder is the one containing the massive amount of BDB 
.jdb
files. 2. Clear StarOS BDB if it's the culprit: If the 4.5M entries are there, you should treat it the same way as the FE recovery: delete the 
bdb/
files but keep the 
image/
files so StarOS can recover its state from the last valid checkpoint. 3. Restart with Recovery Mode: Use 
metadata_enable_recovery_mode = true
in 
fe.conf
to allow the system to come up without failing on the missing "ghost" shards. Summary: Your reasoning is spot on. You found the 1M FE records, but the "Missing 9M" are in the StarManager's own dedicated storage environment. Deleting only the main 
bdb/
folder will likely not solve your OOM. References • administration/Meta_recovery.md • administration/management/FE_configuration.md
d
@Rocky the concurrent thread changes were not working. can you please give me correct command for zgc to configure in JAVA_OPTS=
r
For StarRocks 4.1 (which requires JDK 17), the standard Garbage Collector is G1. If you are switching to ZGC to handle high metadata churn or memory pressure, you must ensure you replace the existing G1 flags to avoid conflicts. The reason your "concurrent thread changes" may not be working is likely due to using the generic 
ConcGCThreads
instead of the ZGC-specific flag, or having a conflict with the default 
fe.conf
settings. Correct 
JAVA_OPTS
for ZGC in 
fe.conf
 Add (or modify) the following line in your 
fe/conf/fe.conf
. Important: You must remove or comment out any existing 
-XX:+UseG1GC
line.
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bash
# Example for a node with 32GB RAM and 8-16 cores
JAVA_OPTS="-Xmx24g -Xms24g -XX:+UseZGC -XX:ZConcGCThreads=4 -XX:+UseNUMA"
Key Parameters Explained: 1. 
-XX:+UseZGC
: Enables the Z Garbage Collector. 2. 
-XX:ZConcGCThreads=<N>
: This is the correct flag for ZGC concurrent threads. * The "Not Working" Issue: Many users try 
-XX:ConcGCThreads
, but for ZGC, the specific flag is 
ZConcGCThreads
. * Recommendation: Set this to roughly 25% of your available CPU cores. If set too high, the GC will compete with FE metadata replay/processing; if too low, it won't keep up with the 4.5M journal entry churn. 3. 
-Xmx
and 
-Xms
: For ZGC, it is highly recommended to set the minimum and maximum heap sizes to the same value to prevent the JVM from stalling while resizing the heap. 4. 
-XX:+UseNUMA
: ZGC is NUMA-aware. Enabling this significantly improves performance on multi-socket servers. Why thread changes might still "fail": * Duplicate JAVA_OPTS: Check if your 
fe.conf
has multiple 
JAVA_OPTS
lines. The last one usually wins, or they are concatenated depending on the version of 
start_fe.sh
. * StarRocks Overrides: Open 
bin/start_fe.sh
and check if there are hardcoded defaults like 
-XX:+UseG1GC
. If your 
fe.conf
doesn't override them correctly, the FE might fail to start due to "conflicting collector" errors. * JDK Version: Ensure 
java -version
returns JDK 17+. ZGC in JDK 8/11 is experimental and uses different flags. Verifying the Change After restarting the FE, check the 
fe.gc.log
(or 
fe.out
). You should see a line confirming the collector: 
Using The Z Garbage Collector
If you see an error like 
Conflicting collector combinations in option list
, it means an old 
-XX:+UseG1GC
or 
-XX:+UseParallelGC
flag is still hiding in your configuration. References • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • deployment/environment_configurations.md References • administration/management/FE_configuration.md • deployment/environment_configurations.md • administration/management/compaction.md • knowledge/admin_and_operation/shared_data_compaction.md • administration/management/logs.md • administration/management/BE_configuration.md • sql-reference/sql-functions/JAVA_UDF.md
d
@Rocky Our star_os image is 1.65 GB (
image.5369484
) for a cluster with 38 tablets, 10 tables. The global_state image is 11.7 MB. That's 140× larger for the same cluster. There's also an 
image.ckpt
of 95 MB dated Aug 15 21:00 — 18h after the last complete image — presumably a failed checkpoint remnant. What would make a StarManager image 1.65 GB on a 38-tablet cluster? Orphaned shards from failed 
StarMgrMetaSyncer.cleanOneGroup()
(we see 
GRPC:DEADLINE_EXCEEDED
on shard group deletion)? Is there a way to prune orphaned shard metadata? This affects our recovery choice — even after discarding the journal, we'd still be loading a 1.65 GB image, so we need to know whether that alone is sustainable.
r
A 1.65 GB StarManager image for a cluster with only 38 tablets is indeed extremely abnormal (a healthy image for that scale should be under 1 MB). Your reasoning regarding orphaned shards is almost certainly correct. What happened: The "Orphan Shard" Explosion In StarRocks' shared-data architecture, table creation is a two-step handshake: 1. StarManager (StarOS) level: The FE requests StarManager to create physical shards (the underlying storage objects). 2. GlobalState (FE) level: The FE then attempts to create the Table/Tablet metadata that points to those shards. Because your creation loop failed with 403 Errors at the GlobalState level, the FE never "owned" those tablets. However, the first step—shouting into StarManager to create shards—succeeded millions of times. These are orphaned shards: StarManager has them in its registry, but no table in the GlobalState refers to them. Why the image is 1.65 GB Each shard entry in StarManager contains metadata: Shard ID, Shard Group ID, storage volume mapping, and placement info. * Journal entries: Your 9 million log records were the "intent" to create/delete these. * Image bloat: When the FE finally took an image (likely during the crash or a scheduled checkpoint), it serialized all 4.5M+ "live" orphaned shards into the 
image.5369484
file. * Memory Pressure: When the FE restarts, it tries to deserialize this 1.65 GB file into Java objects. In-memory, a 1.65 GB serialized file can easily expand to 10–20 GB of Heap due to object overhead, which explains your 15 GB OOM. Sustainability & Recovery Loading a 1.65 GB StarManager image for 38 valid tablets is not sustainable. The FE will likely remain unstable, OOM-ing during every checkpoint or whenever the 
StarMgrMetaSyncer
thread wakes up to scan the metadata. 1. Can you prune them? The 
StarMgrMetaSyncer
is designed to find these orphans by comparing the FE's Tablet Inverted Index with StarManager's Shard list. * The Problem: Because the bloat is so massive, the syncer is hitting 
GRPC:DEADLINE_EXCEEDED
. It is trying to list 4.5 million shards over an internal RPC call that times out before it can finish the "diff." * Config to try: You can try increasing the timeout and the sync interval to give it room to work (add to 
fe.conf
):
Copy code
properties
    # Give the syncer more time to process the massive orphan list
    starmgr_grpc_timeout_seconds = 300
    # Slow down the sync frequency so it doesn't thrash
    star_mgr_meta_sync_interval_sec = 1200
2. The "Nuclear" Recovery (Recommended for your scale) Since you only have 10 tables and 38 tablets, the most reliable way to fix this is to bypass the broken StarManager state entirely: 1. Script your DDL: Use 
SHOW CREATE TABLE
for your 10 tables (if the FE stays up long enough) or pull them from your CI/CD. 2. Wipe Metadata: * Stop all FEs. * Delete the entire 
meta/
directory (this wipes GlobalState AND StarOS/StarManager). 3. Clean Object Storage: Manually delete the data in your S3/Object storage bucket to ensure no "shadow" data files remain. 4. Restart & Re-init: Start the FE as a fresh node and re-run your 10 DDLs. Why this is better: Trying to "heal" a StarManager that is 140x bloated will take days of background "vacuuming" and likely several more OOM crashes. At your scale (10 tables), a fresh start takes 10 minutes and guarantees a clean, 11 MB metadata state. How to inspect StarManager (Optional) If you can get the FE to stay alive for a few minutes with a massive heap (e.g., 
-Xmx32g
), you can verify the orphan count: * 
SHOW PROC '/starmgr/shards';
(Warning: This will likely hang or OOM if there are 4M+ entries). * 
SHOW PROC '/starmgr/shard_groups';
Summary: The 1.65 GB image is "trash" metadata. Even if you discard the journal, the image itself will keep the FE in a high-pressure state. If the data in those 10 tables is easily reloadable, wipe and recreate is the safest path. References • deployment/prepare_deployment_files.md • quick_start/shared-data.md • administration/management/BE_configuration.md • sql-reference/sql-functions/table-functions/files.md • developers/debuginfo.md • sql-reference/sql-statements/cluster-management/nodes_processes/ALTER_SYSTEM.md
d
@Rocky how to wipe and recreate?
r
To "wipe and recreate" a shared-data (Lake) cluster, you are essentially performing a factory reset. Since you only have 10 tables, this is the most reliable way to purge the 4.5 million orphaned shards and the 1.65 GB metadata bloat. Follow these steps in order: 1. Preparation: Backup your DDL Before you delete anything, ensure you have the schema for your 10 tables. * If the FE is up: Run 
SHOW CREATE TABLE <table_name>
for each table. * Note down your Storage Volume configuration (
SHOW STORAGE VOLUMES
and 
DESC STORAGE VOLUME <name>
). You will need the S3 credentials and endpoint again. 2. Shutdown the Cluster Stop all StarRocks processes to ensure no files are locked. * FE: 
bash fe/bin/stop_fe.sh
* CN: 
bash cn/bin/stop_cn.sh
3. Clear Local Metadata You must delete the metadata on all FE and CN nodes. * On every FE node:
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bash
    rm -rf /path/to/fe/meta/*
(This deletes both the GlobalState BDB logs and the StarOS/StarManager image/journals.) * On every CN node:
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bash
    rm -rf /path/to/cn/storage/*
(This clears the local data cache and any temporary metadata the CNs held.) 4. Clear Remote Object Storage (S3/MinIO) StarRocks metadata "points" to data in your S3 bucket. If you wipe the metadata but leave the data in S3, those files become "unmanaged garbage." * Go to your S3 bucket/prefix (the one used in your 
STORAGE VOLUME
location). * Delete the contents under that prefix. _ _Warning:* Do not delete the bucket itself if other applications use it, just the sub-folders created by StarRocks. 5. Start a Fresh Leader FE Pick one FE to be your new Leader. 1. Start it normally: 
bash fe/bin/start_fe.sh --daemon
. 2. Connect via MySQL client: 
mysql -h <fe_ip> -P 9030 -u root
. 3. Check the state: 
SHOW PROC '/starmgr/shards';
should now be empty (or return very quickly). 6. Reconfigure Cluster Infrastructure Since the metadata was wiped, the FE "forgot" about the CNs and the Storage Volume. 1. Add Compute Nodes (CNs):
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sql
    ALTER SYSTEM ADD COMPUTE NODE "host:port";
2. Recreate Storage Volume: Use the DDL you saved in Step 1.
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sql
    CREATE STORAGE VOLUME my_s3_vol
    TYPE = S3
    LOCATIONS = ("<s3://bucket/path/>")
    PROPERTIES (...);

    SET my_s3_vol AS DEFAULT STORAGE VOLUME;
7. Recreate Tables and Load Data 1. Run your 
CREATE DATABASE
and 
CREATE TABLE
statements. 2. Your metadata (image) should now be back to a healthy size (~10–15 MB). 3. Reload your data. Why this is the "Safe" Path Trying to manually prune 4.5 million shards using 
StarMgrMetaSyncer
is a "death by a thousand cuts" for the FE's CPU and memory. By wiping the 
meta/
directory, you are forcing StarManager to start with a clean slate. Because your data scale is small (10 tables), the time it takes to reload is significantly less than the time it would take to debug the metadata OOM. One Final Tip: Once you recreate the cluster, monitor the FE logs for 
failed to create tablet
or 
403
errors. If those reappear, your ingestion script is likely hitting a resource limit or a permission issue that caused the original "orphan storm." References • knowledge/admin_and_operation/shared_data_garbage_cleanup.md • faq/shared_data_faq.md • deployment/shared_data/s3.md
d
@Rocky After disabling 
builtin_storage_volume
, all 403s are gone (zero in 12h). 
TableKeeper
now fails cleanly with 
Storage volume builtin_storage_volume is disabled
but still retries every 30s — how do we point 
_statistics_
at 
dp_gcs_prd
, which is already 
IsDefault: true
?
On the 1.65 GB star_os image for 38 tablets: we see 
StarletAgent.heartbeat()
failing ~1,555 times/12h and 
StarMgrMetaSyncer.cleanOneGroup()
returning 
GRPC:DEADLINE_EXCEEDED
on shard-group deletion. Could persistently failed shard cleanup explain orphaned shard metadata accumulating to that size? Is there a way to audit or prune orphaned shards?
Also: 
checkpoint_only_on_leader = true
is set, yet 
CheckpointController.pushImage()
still targets fe-2 (a crash-looping follower). Expected?
r
1. Pointing 
_statistics_
to 
dp_gcs_prd
 In shared-data mode, the 
_statistics_
database and its tables inherit the Default Storage Volume only at the time of their creation. Setting 
dp_gcs_prd
as the default after the cluster was initialized does not retroactively move existing internal tables. Since 
builtin_storage_volume
is disabled, the 
TableKeeper
(responsible for maintaining these system tables) is stuck. To fix this: 1. Drop the existing statistics database:
Copy code
sql
    DROP DATABASE _statistics_ FORCE;
2. Let the system recreate it: The FE Leader will automatically detect the missing database and recreate it. Because 
dp_gcs_prd
is now your default volume, the new tables will be created on GCS. 3. Verify: Run 
SHOW CREATE TABLE _statistics_.column_statistics;
to confirm the 
storage_volume
property is now 
dp_gcs_prd
. 2. The 1.65 GB 
star_os
Image & Orphaned Shards Your theory is correct: the persistent failure of shard cleanup is exactly why the metadata has bloated to 1.65 GB for only 38 valid tablets. * The Logjam: 
StarMgrMetaSyncer.cleanOneGroup()
is responsible for identifying shards that exist in StarOS but not in the FE's Tablet Inverted Index. * The Deadline: With 4.5 million orphaned shards, the list generated by the syncer is likely exceeding the gRPC message size limit or the 
starmgr_grpc_timeout_seconds
. Because it returns 
DEADLINE_EXCEEDED
, the cleanup transaction never commits, and the 1.65 GB of "trash" metadata remains in every new image checkpoint. * Audit/Prune: There is no SQL-level "prune" command for StarOS. The system is designed to self-heal via the syncer, but the sheer scale of your "orphan storm" has broken the healer. Recommendation: To force the cleanup, you may need to temporarily increase the gRPC timeout in 
fe.conf
to allow the syncer to finish one massive cycle:
Copy code
properties
starmgr_grpc_timeout_seconds = 300
However, if the image is already 1.65 GB and causing 15 GB heap usage, the FE may OOM before the syncer can finish. The "wipe and recreate" method mentioned previously is the only guaranteed way to purge this metadata volume. 3. 
pushImage()
targeting a crashing Follower This is Expected Behavior. * 
checkpoint_only_on_leader = true
: This flag ensures that the heavy work of replaying journals and writing the new 
image.xxxx
file happens only on the Leader. * Propagation: Once the Leader creates the image, it is required to push it to all Followers (
fe-2
, etc.) so they can stay in sync and be ready for failover. * The Logs: Even if 
fe-2
is in a crash loop, the Leader's 
CheckpointController
will continuously try to fulfill its duty of synchronizing the metadata state to the rest of the cluster. It doesn't know 
fe-2
is "permanently" broken; it just sees a failed network/RPC attempt. Summary of Recovery Status * Recovery sustainable?: No. A 1.65 GB StarOS image for 38 tablets is a "zombie" state. Even if you get it to start, the metadata overhead will cause constant GC pressure and likely future OOMs during checkpoints. * Action: If you cannot get the Syncer to finish a cleanup cycle with increased timeouts, proceed with the Wipe and Recreate of the 
meta/
directory to reset the StarOS state to its healthy <10MB size. References • knowledge/admin_and_operation/shared_data_compaction.md
d
@Rocky dropping statistics table will not cause any issues?
r
Dropping the 
_statistics_
database is a standard recovery procedure for the scenario you described (moving internal metadata off a disabled storage volume). While it is safe, there are three specific "issues" or side effects to be aware of: 1. Temporary Query Performance Degradation When you drop the statistics database, the Cost-Based Optimizer (CBO) loses all metadata about your data distribution (row counts, Min/Max values, Null counts, and NDV). * Consequence: For a short window after the drop, complex queries (especially those with many joins) may have suboptimal execution plans. The CBO might pick an inefficient join order because it essentially thinks your tables are empty or of unknown size. * Mitigation: Once the tables are recreated, StarRocks will automatically start background "Analyze" tasks to refill the statistics. You can also manually trigger 
ANALYZE TABLE <your_table_name>;
for your most critical tables to restore their stats immediately. 2. Automatic Recreation is Reactive The FE Leader will recreate the 
_statistics_
database and its internal tables (like 
column_statistics
, 
histogram_statistics
) automatically. * The Volume Fix: Because you set 
dp_gcs_prd
as the default storage volume before the recreation, the new tables will correctly point to your GCS bucket. This will stop the 
TableKeeper
errors you are seeing. 3. "Force" is Recommended When dropping system databases to fix storage volume issues, use the 
FORCE
keyword:
Copy code
sql
DROP DATABASE _statistics_ FORCE;
This ensures that the metadata is immediately purged and not held in the FE Recycle Bin, allowing the recreation process to start from a truly clean state on the new volume. Summary of what happens: 1. Drop: You run 
DROP DATABASE _statistics_ FORCE;
. 2. Recreate: The FE Leader detects the missing system database and recreates it using the current Default Storage Volume (
dp_gcs_prd
). 3. Refill: Background threads scan your tables to rebuild the stats. 4. Health: The 
TableKeeper
failures and 
403
errors related to the 
builtin_storage_volume
will disappear. Conclusion: It will not cause functional "issues" (your data remains intact), only a temporary period where the optimizer is "blind." Given your current state with the 1.65 GB image and 
403
errors, this is a necessary and recommended step. References • using_starrocks/Cost_based_optimizer.md • sql-reference/sql-statements/cbo_stats/DROP_STATS.md • sql-reference/sql-statements/cbo_stats/DROP_ANALYZE.md • faq/shared_data_faq.md • deployment/shared_data/feature-support-shared-data.md • data_source/catalog/unified_catalog.md • administration/Meta_recovery.md • sql-reference/sql-statements/cbo_stats/CREATE_ANALYZE.md • quick_start/shared-data.md References • using_starrocks/Cost_based_optimizer.md
d
@Rocky what is the root cause of this bloated image?
r
The root cause of your bloated 1.65 GB image and the 4.5 million orphaned shards is a "Metadata Feedback Loop" triggered by the combination of storage permission errors and high-frequency retry logic. Here is the step-by-step breakdown of how your cluster reached this state: 1. The "Orphan Storm" (The Trigger) In shared-data mode, any write operation (User Ingestion or System Statistics updates) follows this sequence: 1. Shard Allocation: The FE asks the StarOS layer to create and register new "shards" (metadata units). 2. Data Write: The CN/BE attempts to write data files to the Object Store. 3. Transaction Commit: The FE links the shards to a specific Tablet ID and commits the transaction. Because your 
builtin_storage_volume
was returning 403 errors, Step 2 failed. However, the system (specifically the 
TableKeeper
for 
_statistics_
or an automated ingestion script) likely kept retrying every few seconds. * The Leak: Every failed attempt successfully completed "Step 1" (creating shards in StarOS) but failed before "Step 3" could link them to a real table. * The Result: 4.5 million shards were registered in the StarOS metadata that do not belong to any valid tablet in the StarRocks Catalog. 2. The "Syncer Logjam" (The Bloat) StarRocks has a background thread called 
StarMgrMetaSyncer
designed to clean up these orphans. It compares the StarOS shard list against the FE's 
TabletInvertedIndex
. * The Deadline Exceeded: When the orphan count reached a critical mass (millions), the list became so large that the syncer's gRPC calls began exceeding the default 
starmgr_grpc_timeout_seconds
(5–10s). * The Failure: Because the cleanup task timed out, it never "committed" the deletions. The 4.5 million shards remained in the metadata, and the syncer just kept retrying the same failing, over-sized task. 3. Image & Heap Explosion In StarRocks, the StarOS metadata is serialized directly into the FE image file and loaded into the JVM Heap at startup. * File Size: 4.5 million shard entries (containing IDs, replica locations, and status) naturally consume ~1.5–2 GB of disk space when serialized. * RAM Usage: When the FE tries to deserialize this massive list into Java objects, it requires a significant "expansion factor." This is why you see 15 GB of heap usage for a cluster that technically only has 38 "active" tablets. Why 
_statistics_
was the primary culprit System tables like 
_statistics_
are managed by the 
TableKeeper
. Unlike user tables, which might stop if a script fails, the 
TableKeeper
is an internal daemon. If it is pointed at a broken volume (
builtin_storage_volume
), it will persistently attempt to create its internal tablets or update stats, acting as an "orphan factory" that never stops until the volume is fixed or the database is dropped. Summary The 403 errors were the spark, but the lack of a "rollback" for StarOS shard allocation on failed commits is what allowed the metadata to leak. Once the leak exceeded the processing capacity of the 
StarMgrMetaSyncer
, the bloat became permanent and self-sustaining. This is why "Wiping and Recreating" (clearing the 
meta/
directory) is necessary: The cleanup mechanism is currently "drowning" in its own backlog and cannot recover without a manual reset of the StarOS state. References • faq/shared_data_faq.md • knowledge/admin_and_operation/shared_data_compaction.md
d
@Rocky fe-2 cannot rejoin — BDBJE 
verifyMembership
failure after DROP/ADD FOLLOWER StarRocks 4.0.11, shared-data, 3 FE on the K8s operator. Yesterday we ran 
ALTER SYSTEM DROP FOLLOWER
then 
ALTER SYSTEM ADD FOLLOWER
for fe-2. The leader now expects it as Id 4, name suffix 
_1787665752206
. But fe-2's local 
meta/bdb
still carries the old identity, Id 2, suffix 
_1784220843345
, so the handshake is rejected:
Copy code
ERROR [BDBEnvironment.setupEnvironment():306] failed to setup environment after retried 2 times
com.sleepycat.je.EnvironmentFailureException: (JE 18.3.20) Environment must be closed
Caused by: EnvironmentFailureException: Environment invalid because of previous exception:
  starrocks-mas-fe-2..._9010_1784220843345(2):/opt/starrocks/fe/meta/bdb  Feeder: starrocks-...
  at com.sleepycat.je.rep.stream.ReplicaFeederHandshake.verifyMembership(ReplicaFeederHandshake.java:342)
It loops: 
this node is DETACHED
→ 
current role UNKNOWN
→ retry → fail, ~52s per cycle, then the pod restarts. Questions: 1. Is there a way to reconcile fe-2's local BDBJE identity with the new cluster membership without clearing 
meta/
? e.g. re-registering it under its original Id 2, or a config flag to accept the existing identity? 2. If clearing is the only path, is it safe to clear *only*`meta/bdb` and keep 
meta/image
, or must the whole 
meta/
directory go? 3. Alternatively — can we 
ALTER SYSTEM DROP FOLLOWER
the new Id 4 entry and re-add fe-2 under its original name/suffix so the local BDBJE state matches again? Context: fe-0 and fe-1 are healthy after increasing FE to 48Gi / 
-Xmx36g
. Live set dropped from ~15 GB to 61 MB with zero allocation stalls, so the memory issue is resolved. fe-2 is the only remaining problem, and we have quorum at 2/3.
r
The transition of your cluster's live set from 15 GB to 61 MB is a major success—it confirms that the "orphan storm" has been purged and your FE Leader is now holding a healthy, compact metadata state. Regarding the 
fe-2
BDBJE identity crisis, here are the answers to your specific questions: 1. Can we reconcile identity without clearing 
meta/
? No. BDBJE (Berkeley DB Java Edition) uses the node name (which includes that timestamp suffix) as a unique, immutable identifier within its replication group logs. * The Conflict: The Leader’s BDB logs now have a record for 
fe-2
as Id 4 with the new suffix. When 
fe-2
attempts to join with its local logs identifying as Id 2, the BDB handshake fails the 
verifyMembership
check because it sees a name/ID mismatch for the same endpoint. * Config Flags: There is no configuration flag to "override" or "alias" these identities. BDBJE is designed to be strictly consistent regarding member identity to prevent split-brain scenarios. 2. Is clearing 
meta/bdb
enough? No, you should clear the entire 
meta/
directory. * Why: StarRocks stores node identity in multiple places. Specifically, 
meta/image/ROLE
contains the node name (including the suffix). If you only clear 
meta/bdb
, the FE will start up, read the old name from the 
ROLE
file, and attempt to register with the Leader using the old suffix, leading to the same handshake failure. * Safety: In shared-data mode, the "Source of Truth" is the Object Store and the FE Leader. Since your Leader has a healthy 61 MB image, 
fe-2
will download this image and the necessary BDB edit logs in a matter of seconds. 3. Can we re-add fe-2 under its original suffix? No. The suffix (e.g., 
_1787665752206
) is a Unix timestamp generated automatically by the FE Leader at the moment the 
ALTER SYSTEM ADD FOLLOWER
command is executed. There is no SQL syntax to manually specify a suffix or force the Leader to reuse an old one. ────────── Recommended Recovery Path for 
fe-2
 Since you are using the K8s operator and have 2/3 quorum, follow this "Clean Slate" procedure: 1. Drop the Zombie Entry: Ensure the Leader is clean. Run this on the Leader:
Copy code
sql
    -- Drop the entry that fe-2 is currently failing to match
    ALTER SYSTEM DROP FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
2. Clear Local Metadata: Wipe the metadata on the 
fe-2
pod. * If using a PersistentVolume, the easiest way is to delete the PVC for 
fe-2
and let the operator recreate it. _ Alternatively, 
exec
into the pod (if it stays up long enough) and run 
rm -rf /opt/starrocks/fe/meta/_
. 3. Re-Add the Follower:
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sql
    ALTER SYSTEM ADD FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
4. Restart with Helper: The operator should handle the restart. Ensure it starts with the 
--helper
flag pointing to 
fe-0
or 
fe-1
. If the operator detects an empty 
meta/
directory, it typically performs this "join" logic automatically. Note on 
pushImage()
 The fact that 
fe-2
still receives 
pushImage()
attempts is harmless. The Leader is programmed to persistently attempt to synchronize the image to all known members of the 
frontends
table. Once you DROP the old entry and ADD the new one, the Leader will stop targeting the "ghost" identity and start targeting the new Id 4 (or Id 5, etc.). Summary: Your metadata is now healthy and small. A full wipe of `fe-2`’s 
meta/
directory is the fastest and only reliable way to sync it with the new cluster identity. References • administration/Meta_recovery.md • deployment/deploy_manually.md • sql-reference/sql-statements/cluster-management/nodes_processes/ALTER_SYSTEM.md
d
@Rocky so if I add a new follower, how can leader ensure it will use the correct id instead of the new id that the fe-2 pod will register itself with?
r
To ensure the follower joins correctly, you must understand that in StarRocks, the Leader is the authority on identity, not the pod itself. There is no way to manually specify the ID (the timestamp suffix) in 
fe.conf
or the 
ADD FOLLOWER
command. The "correct" ID is simply whatever the Leader generates at the moment you run the SQL command. Here is how you ensure the pod uses that "correct" ID instead of its old one: 1. The Leader's Role When you run 
ALTER SYSTEM ADD FOLLOWER "starrocks-mas-fe-2...:9010"
, the Leader creates a new record in its internal 
frontends
table. It takes the FQDN and appends a fresh timestamp (e.g., 
_1787665752206
). * From the Leader's perspective: This is now the only valid identity for that FQDN. 2. The Pod's Role (The "Memory" Problem) When the 
fe-2
pod starts up, it checks its local 
meta/image/ROLE
file. * If the file exists: The pod says, "I am 
..._1784220843345
(Id 2)." It then contacts the Leader. The Leader compares this to its table, sees a mismatch (expecting Id 4), and rejects the handshake. * If the file DOES NOT exist (Clean Slate): The pod starts in "discovery mode." It contacts the 
--helper
node and says, "I am at this FQDN, but I don't know who I am yet." The Leader looks at its table, finds the entry for that FQDN, and tells the pod: "You are Id 4 with this suffix." The pod then saves this to its local 
ROLE
file and begins downloading the 61 MB metadata image. 3. How to guarantee the "Correct" ID is used To ensure 
fe-2
adopts the identity the Leader wants it to have, you must remove its memory of the old identity. The Step-by-Step "Identity Reset": 1. Drop the ghost identities: On the Leader, run 
SHOW FRONTENDS
. Drop every entry associated with 
fe-2
(both the old Id 2 and the new Id 4) to ensure the Leader's table is clean.
Copy code
sql
    ALTER SYSTEM DROP FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
2. Wipe the Pod's Metadata: Since you are on K8s, the most reliable way is to delete the PVC for `fe-2`:
Copy code
bash
    kubectl delete pvc <pvc-name-for-fe-2> -n <namespace>
This is safe because your metadata is shared-data and your Leader (fe-0/fe-1) is healthy. 3. Register the new identity: Now, run the 
ADD
command on the Leader once:
Copy code
sql
    ALTER SYSTEM ADD FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
4. Restart the pod: The StarRocks Operator will see the pod is missing/PVC is gone, recreate it, and start it. Because the 
/meta
directory is now empty, the pod will successfully "handshake" with the Leader, accept whatever ID the Leader provides, and join the cluster within seconds. Summary You don't need to force an ID; you just need to clear the pod's local state. As long as 
meta/
is empty, the pod has no choice but to use the "correct" identity provided by the Leader. References • sql-reference/sql-statements/cluster-management/nodes_processes/ALTER_SYSTEM.md • administration/Meta_recovery.md • deployment/deploy_manually.md • administration/management/FE_configuration.md
The Leader ensures the correct identity is used through a Host-to-Identity Mapping in its internal catalog. Here is exactly how the handshake works and why wiping the metadata is the "bridge" that fixes the mismatch: 1. The Leader is the Source of Truth When you run 
ALTER SYSTEM ADD FOLLOWER "fe-2-fqdn:9010"
, the Leader does not wait for the pod to check in. It immediately: * Generates a new unique ID (e.g., 
4
). * Generates a new unique Suffix/Timestamp (e.g., 
_1787665752206
). * Stores this in its internal "Frontend Table" as: 
Host: fe-2-fqdn
-> 
Name: fe-2-fqdn_9010_1787665752206
. 2. The "Who Am I?" Handshake When the 
fe-2
pod starts with an empty 
meta/
directory and the 
--helper
flag: 1. Request: 
fe-2
contacts the Leader and says: "I am a new node at 
fe-2-fqdn:9010
. What is my identity?" 2. Lookup: The Leader looks at its Frontend Table. It sees that 
fe-2-fqdn:9010
is pre-registered as Id 4 with suffix 
_1787665752206
. 3. Instruction: The Leader tells `fe-2`: _"You are node 
fe-2-fqdn_9010_1787665752206
. Here is the current image and the BDBJE logs."_ 4. Acceptance: Because 
fe-2
has no local identity (since you wiped 
meta/
), it obediently adopts this name, writes it to its new 
meta/image/ROLE
file, and initializes its BDBJE environment with that suffix. 3. Why the mismatch happened before The mismatch only occurred because your 
fe-2
pod already had an identity stored in its Persistent Volume. * Old state: The pod's local 
meta/
said: _"I am Id 2, suffix 1784220843345." * Leader state: The Leader said: _"I expect Id 4, suffix 1787665752206." * Conflict: When 
fe-2
tried to join, it presented its old identity to the BDBJE replication group. BDBJE saw that the name 
fe-2-fqdn_9010
was being claimed by two different suffixes (
...3345
vs 
...5206
) and triggered the 
verifyMembership
failure to prevent data corruption. Summary The Leader dictates the identity based on the hostname/IP you provide in the 
ADD FOLLOWER
command. The pod only "registers" itself with a new ID if it finds its local identity is missing. By wiping 
meta/
, you are effectively telling the pod: "Forget who you were; ask the Leader who you are now." Since the Leader only has one entry for that hostname (Id 4), the pod will adopt Id 4 perfectly. Next Step Recommendation: 1. Run 
SHOW PROC '/frontends';
on the Leader. 2. If you see an entry for 
fe-2
with 
Alive: false
, note the name. 3. Wipe `fe-2`'s 
/opt/starrocks/fe/meta/*
. 4. Restart 
fe-2
. It will automatically become the "Id 4" the Leader is waiting for. References • administration/Meta_recovery.md • deployment/deploy_manually.md • administration/management/FE_configuration.md • sql-reference/sql-statements/cluster-management/nodes_processes/ALTER_SYSTEM.md
d
@Rocky give me clear step by step process on what to do
r
To resolve the identity mismatch and get 
fe-2
back into the cluster, follow these steps in order. This process assumes you have a healthy Leader (
fe-0
or 
fe-1
) and are using the StarRocks Kubernetes Operator. Step 1: Clean the Leader's Registry First, remove any existing registration for 
fe-2
that might be causing a conflict. Run this command on your current Leader FE:
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sql
-- Replace the host with your actual FQDN/IP if different
ALTER SYSTEM DROP FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
Verify with 
SHOW PROC '/frontends';
that the entry for 
fe-2
is gone. Step 2: Wipe the local metadata on 
fe-2
 Because the identity (suffix and ID) is stored on the Persistent Volume (PV), you must wipe it so the node can "re-introduce" itself. Option A (Recommended for K8s): Delete the PersistentVolumeClaim (PVC) for 
fe-2
. The operator will automatically recreate a fresh, empty volume.
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bash
# Replace with your actual namespace and PVC name
kubectl delete pvc -n <your-namespace> fe-meta-starrocks-mas-fe-2
Option B (Manual Wipe): If you cannot delete the PVC, exec into the pod and wipe the directory manually:
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bash
kubectl exec -it starrocks-mas-fe-2 -n <your-namespace> -- /bin/bash
# Once inside:
rm -rf /opt/starrocks/fe/meta/*
exit
Step 3: Re-register the Follower on the Leader Now, tell the Leader to expect a new follower at that address. Run this on the Leader FE:
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sql
ALTER SYSTEM ADD FOLLOWER "starrocks-mas-fe-2.starrocks-mas-fe-service:9010";
Step 4: Restart the Pod If you deleted the PVC (Option A), the pod should already be restarting. If you manually wiped it (Option B), restart the pod to trigger the join process:
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bash
kubectl delete pod starrocks-mas-fe-2 -n <your-namespace>
Step 5: Verify the Join Watch the logs of 
fe-2
to see it download the 61 MB image from the Leader:
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bash
kubectl logs -f starrocks-mas-fe-2 -n <your-namespace>
You should see: 1. 
transfer image from...
(Downloading the 61MB image). 2. 
replaying edit log...
(Syncing BDBJE logs). 3. 
StarRocksFE start successfully
. Finally, check the status from your MySQL client:
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sql
SHOW PROC '/frontends';
fe-2
should now show 
Alive: true
and have a new 
Name
with a fresh timestamp suffix. Why this works: By dropping the follower and wiping the metadata, you force a "clean handshake." The Leader generates a fresh ID, and the pod, seeing an empty 
/meta
directory, adopts that ID as its own. Since there are no old BDBJE logs to conflict with, the 
verifyMembership
check passes instantly. References • administration/Meta_recovery.md
m
@darshit.jain were you able to resolve this?
d
yes @Moreno Garcia this was resolved. chatbot was really helpful in the process. thanks for asking
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