< Rocky> highlight the important config for CN and FE if hav StarRocks #questions-and-troubleshooting

<@U0A71G31CDV>, highlight the important config for...

07/02/2026, 1:42 AM

@Rocky, highlight the important config for CN and FE if have huge data size from aws glue

Rocky

07/02/2026, 1:42 AM

When dealing with huge data sizes from AWS Glue (S3), the primary bottlenecks are usually metadata management on the FE and S3 I/O latency/throughput on the CN. Below are the critical configurations to optimize for this scenario. 1. FE (Frontend) Configurations The FE manages metadata and query planning. Large Glue catalogs with millions of partitions require significant memory and optimized metadata caching. * JVM Heap Size (
JAVA_OPTS
in
fe.conf
): * Importance: Metadata for external tables (partitions, file lists) is cached in the FE's heap. * Recommendation: Increase

-Xms

and

-Xmx

. For catalogs with 1–2 million partitions, allocate at least *32GB*; for 5M+, consider 128GB. Ensure

-Xms

and

-Xmx

are equal. * Metadata Refresh & Caching: * `enable_background_refresh_connector_metadata`: Set to

true

(default in v3.0+) to allow StarRocks to poll AWS Glue for changes automatically. * `background_refresh_metadata_interval_millis`: Default is 600,000 (10 mins). If your data changes frequently, lower this; if the Glue API is slow/throttled, increase it. * `hive_meta_cache_refresh_interval_s`: Controls how long the FE caches file metadata before re-validating. * Partition Limits: * `max_partition_number_per_table`: If your Glue table has more than 100,000 partitions, you must increase this value (default is 100,000) to avoid query planning errors. 2. CN (Compute Node) / BE Configurations The CN handles the heavy lifting of scanning S3 and processing data. * Data Cache (Block Cache): * Importance: This is the most critical config for performance. It caches S3 data blocks on local NVMe/SSD disks to avoid repeated S3 fetches. * `storage_root_path`: Specify fast local SSD paths (e.g.,

/data1/starlet_cache;/data2/starlet_cache

). * `datacache_disk_size`: Set a large enough size to hold your "hot" data. * `datacache_mem_size`: You can allocate a portion of RAM (e.g.,

10%

) as a L1 cache for even faster access. * Scan Parallelism: * `pipeline_connector_scan_thread_num_per_cpu`: Controls how many threads per CPU core are used to scan external storage. For S3, you can often increase this (e.g., to

) to saturate network throughput. * `max_hdfs_scanner_num`: Limits the number of concurrent remote scanners. Increase this if you have high-concurrency workloads on many small files. * Memory Management: * `query_max_memory_limit_percent`: Default is 90%. Ensure this is high enough so large scans don't trigger OOM, but leave enough for the OS and Data Cache. * I/O Tuning: * `hdfs_read_buffer_size_kb`: (Applies to S3 as well). Increasing this (e.g., to

) can improve sequential read throughput for large files at the cost of higher memory usage per stream. 3. Important Session Variables (Query Tuning) These are set via

SET GLOBAL

or per session: * pipeline_dop
: Set to

for adaptive parallelism. For extremely large scans, you can manually set this to half the number of physical cores to maximize compute. * enable_scan_datacache
: Ensure this is

true

to utilize the CN's local disk cache. * connector_io_tasks_per_scan_operator
: Controls the I/O concurrency of a single scan operator. Increase this if you see low CPU usage but high S3 wait times in the query profile. Summary Checklist Node | Config | Typical "Huge Data" Setting FE |

JAVA_OPTS

-Xmx64g -Xms64g

FE |

max_partition_number_per_table

(if needed) CN |

storage_root_path

| Use local NVMe disks CN |

datacache_disk_size

| As much as available (e.g.,

1T

) CN |

pipeline_connector_scan_thread_num_per_cpu

Session |

pipeline_dop

(Adaptive | <half_ofcores` References • administration/management/FE_configuration.md

Rocky

07/02/2026, 1:42 AM

• administration/management/BE_configuration.md • faq/Others.md • data_source/catalog/iceberg/iceberg_catalog.md

07/02/2026, 2:12 AM

@Rocky how to decide the value for

hive_meta_cache_refresh_interval_s

and

max_partition_number_per_table

is this enable_scan_datacache
will fetch all data from glue and store to local cache disk?

Rocky

07/02/2026, 2:12 AM

To optimize StarRocks for massive AWS Glue datasets, here is how you should decide on those specific configurations: 1. How to decide
hive_meta_cache_refresh_interval_s
This parameter (and its modern counterpart

background_refresh_metadata_interval_millis

) determines how often StarRocks checks AWS Glue for data changes (e.g., new files or partitions). * The Trade-off: * Low value (e.g., 5-10 mins): High data freshness. You see new data faster, but you might hit AWS Glue API throttling limits if you have many tables/partitions. * High value (e.g., 1-2 hours): Lower Glue API costs and less FE overhead, but data visibility is delayed. * Recommendation: * If you use the modern background refresh (v3.0+), the relevant dynamic config is

background_refresh_metadata_interval_millis

(default 600,000ms / 10 mins). * For huge Glue catalogs, start with the default. Only increase it if you see

Glue: Rate exceeded

errors in your FE logs or if FE CPU is spiking during metadata sync. * Note:

hive_meta_cache_refresh_interval_s

is a static configuration (default 7200s). In newer versions, the background refresh mechanism is generally preferred for its flexibility. 2. How to decide
max_partition_number_per_table
This parameter (default: 100,000) acts as a safety guard to prevent a single table from consuming all FE memory. * How to Decide: * Check your Glue table:

SELECT count(distinct partition_columns) FROM glue_table

. * Rule of Thumb: Set this value to 1.2x your current partition count. For example, if you have 300,000 partitions, set this to

. * Caution: Every partition metadata entry consumes FE JVM Heap. If you increase this to a very high number (e.g., 1M+), you must increase the FE JVM heap (

-Xmx

) in

fe.conf

. A general guideline is ~1-2GB of heap per 100k partitions stored in the metadata cache. ────────── 3. Does
enable_scan_datacache
fetch all data? No, it does not fetch all data proactively. The Data Cache (Block Cache) operates on a Lazy Loading (On-Demand) basis: 1. First Query: When you run a query, StarRocks identifies which specific blocks (typically 1MB chunks) of the S3 files are needed. It fetches only those blocks from S3, returns them to the user, and simultaneously writes them to the local CN/BE disk cache. 2. Subsequent Queries: If another query needs the same data blocks, StarRocks reads them directly from the local disk, skipping S3 entirely. 3. Automatic Eviction: The cache uses an LRU (Least Recently Used) strategy. It will not "fill up" your disk and crash the system; once the

datacache_disk_size

is reached, it automatically deletes the oldest/least-used data to make room for new data. If you want to pre-fetch data (Warmup): If you have a critical dashboard and want the data to be "hot" before the first user arrives, you can use the Cache Warmup feature (v3.3+) by running:

Copy code

sql
CACHE SELECT * FROM your_glue_table WHERE date = '2023-10-01';

This specific command will proactively pull the data into the local cache. Summary of Differences Feature | Behavior | Use Case Default Data Cache | Lazy (loads only what you query) | General analytics, cost saving on S3 Cache Warmup | Proactive (pre-fetches data) | High-priority reports, scheduled morning loads References • administration/management/FE_configuration.md • data_source/catalog/hive_catalog.md • data_source/feature-support-data-lake-analytics.md

07/02/2026, 3:26 AM

@RockyWhat is the best practice for creating an AWS Glue Catalog for a large volume of data? Please review the details below. CREATE EXTERNAL CATALOG IF NOT EXISTS 'aws_unified' PROPERTIES ( "type" = "unified", "unified.metastore.type" = "glue", "aws.glue.use_instance_profile" = "true", "aws.glue.region" = "us-east-1", "aws.glue.use_aws_sdk_default_behavior" = "true", "aws.s3.use_instance_profile" = "true", "aws.s3.region" = "us-east-1", "aws.s3.enable_ssl" = "true", "aws.s3.use_aws_sdk_default_behavior" = "true", "enable_metastore_cache" = "false", "enable_remote_file_cache" = "false" );

Rocky

07/02/2026, 3:26 AM

Your proposed configuration is a good start, but for a large volume of data, there are two critical "anti-patterns" in your current statement that will likely lead to slow query planning and high AWS Glue API costs. 1. Enable Metadata Caching (Crucial) You have set both

enable_metastore_cache

and

enable_remote_file_cache

false

. * Why this is a problem: For "huge data," query planning (the time before the query actually starts running) involves fetching table schemas, partition lists, and file locations. If these are

false

, StarRocks must call the AWS Glue API and S3 for every single query. This is slow and can lead to Glue "Rate Exceeded" throttling. * Best Practice: Set both to

true

(which is the default). StarRocks will cache this metadata in the FE memory. * `enable_metastore_cache`: Caches table/partition schemas. * `enable_remote_file_cache`: Caches the list of files and their locations. 2. Add Background Metadata Refresh When you enable caching, you need a way to keep it fresh. Instead of relying on manual

REFRESH EXTERNAL TABLE

calls, use the background refresh mechanism. * Property:

enable_background_refresh_connector_metadata

(enabled by default in v3.0+). * Best Practice: Ensure this is active. It allows StarRocks to poll Glue for changes asynchronously so your queries always hit a "warm" cache. 3. Use "Unified" vs "Hive" Catalog The

unified

catalog is the modern best practice. It automatically detects if a table is Hive, Iceberg, Hudi, or Delta Lake format within your Glue database. If your "huge data" is a mix of formats (e.g., Iceberg for new data, Hive for old), the unified catalog is the correct choice. Recommended Best Practice Creation SQL Here is the optimized version of your statement for high-performance Glue access:

Copy code

sql
CREATE EXTERNAL CATALOG 'aws_unified'
PROPERTIES (
    "type" = "unified",
    "unified.metastore.type" = "glue",
    
    -- Authentication (Instance Profile is best practice)
    "aws.glue.use_instance_profile" = "true",
    "aws.glue.region" = "us-east-1",
    "aws.s3.use_instance_profile" = "true",
    "aws.s3.region" = "us-east-1",
    
    -- Metadata Performance (Enable these for large volumes)
    "enable_metastore_cache" = "true", -- Caches schema/partitions
    "enable_remote_file_cache" = "true", -- Caches S3 file list
    
    -- Optional: Fine-tune refresh intervals if data updates are frequent
    "metastore_cache_refresh_interval_sec" = "60", -- Check Glue for updates every 60s
    "remote_file_cache_refresh_interval_sec" = "60"
);

Additional Tips for "Huge Data" 1. FE Memory: If your Glue catalog has millions of partitions, ensure your FE JVM heap is large (32GB+). StarRocks caches the metadata you enabled above in the FE heap. 2. Data Cache (Block Cache): The

enable_remote_file_cache

property in the catalog command caches metadata (file paths). To cache the actual data on your CN nodes to avoid S3 latency, ensure you have configured

storage_root_path

in your

cn.conf

and that the session variable

enable_scan_datacache

true

. 3. Cross-Account (Optional): If StarRocks is in a different AWS account than your Glue catalog, you will need to add

"aws.glue.catalog_id" = "<aws_account_id_of_glue>"

. References • data_source/catalog/unified_catalog.md • data_source/catalog/hive_catalog.md • administration/management/FE_configuration.md References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md

Rocky

07/02/2026, 3:26 AM

• using_starrocks/caching/query_cache.md • data_source/catalog/hive_catalog.md • data_source/catalog/deltalake_catalog.md • using_starrocks/caching/block_cache.md • data_source/data_cache.md

07/02/2026, 3:36 AM

@Rocky Please suggest the StarRocks configuration variables and recommend which settings should be adjusted to handle large-scale data efficiently.

Rocky

07/02/2026, 3:36 AM

To handle large-scale data efficiently in StarRocks (especially when using AWS Glue and S3), you need to optimize three layers: Metadata Management (FE), Data Access/Caching (CN/BE), and Query Execution (Session Variables). Below are the recommended configurations and best practices. 1. FE (Frontend) Configuration: Metadata Performance For large Glue catalogs with millions of partitions or files, the FE can become a bottleneck during query planning. Configuration | Recommended Value | Why it matters JAVA_OPTS
(-Xmx) | 32GB to 128GB | Large metadata caches for external tables reside in the JVM heap. Rule of thumb: ~1-2GB per 100k partitions. max_partition_number_per_table
| **Actual Count * 1.2** | Default is 100,000. Increase this if your Glue tables have more partitions to avoid planning errors. background_refresh_metadata_interval_millis
| 600,000 (10m) | Controls how often FE polls Glue for changes. Increase this (e.g., to 1 hour) if you hit Glue API rate limits. enable_metastore_cache
| true
(Catalog Property) | Essential for huge data. Caches table schemas and partition lists to avoid Glue API calls for every query. enable_remote_file_cache
| true
(Catalog Property) | Caches the S3 file list. Without this, StarRocks must list S3 objects for every query, which is extremely slow. ────────── 2. CN/BE (Compute Node) Configuration: Data Throughput The CN handles the heavy I/O. For large data, Data Cache is your most important performance lever. Configuration | Recommended Value | Why it matters storage_root_path
| Local NVMe SSDs | Points to the local disk used for Data Cache. High-speed SSDs are required to outperform S3. datacache_disk_size
| 80% of Disk | The maximum size of the local data cache. Use

2T

percent

(e.g.,

80%

). pipeline_connector_scan_thread_num_per_cpu
| 16 | Controls the number of threads per core dedicated to scanning remote storage (S3). Increasing this helps saturate network bandwidth. hdfs_read_buffer_size_kb
| 16384 (16MB) | (Applies to S3) Larger buffers improve sequential read throughput for large Parquet/ORC files. query_max_memory_limit_percent
| 90 | Ensures that large queries can utilize most of the node's RAM before failing. ────────── 3. Session Variables: Query Execution Tuning These variables should be set globally (

SET GLOBAL ...

) or tuned per-query for massive datasets. Variable | Recommended Value | Description pipeline_dop
| 0
(Adaptive) | In v3.x+, setting this to

allows StarRocks to automatically determine parallelism based on CPU cores. enable_scan_datacache
| true
| Must be true to use the CN's local disk cache for S3 data. enable_spill
| true
| If a huge join or aggregation exceeds memory, this allows StarRocks to "spill" intermediate data to disk instead of failing with OOM. connector_io_tasks_per_scan_operator
| 16 | Controls the I/O concurrency of a single scan task. High values help with high-latency S3 reads. enable_connector_adaptive_io_tasks
| true
| Automatically adjusts I/O tasks. If you see erratic performance, try setting to

false

and manually tuning

connector_io_tasks_per_scan_operator

. Summary Best Practice Strategy 1. Warm the Cache: For large, frequently queried datasets, use

CACHE SELECT ...

(v3.3+) to pre-load data from S3 into the CN local disks. 2. Monitor Throttling: Watch FE logs for

Glue: Rate exceeded

. If found, increase

background_refresh_metadata_interval_millis

. 3. Local SSDs: Always use local NVMe for CN nodes when querying large S3 datasets. Using EBS volumes for Data Cache significantly reduces the performance will be significantly lower. 4. Partition Pruning: Ensure your Glue tables are well-partitioned and that users always include partition filters in their

WHERE

clauses to minimize the data scanned. References

Rocky

07/02/2026, 3:36 AM

• data_source/catalog/iceberg/iceberg_catalog.md • knowledge/usage/best-practice.md • data_source/data_cache.md • using_starrocks/caching/query_cache.md

07/02/2026, 5:41 AM

@Rocky what is the root cause and fixes for below errror E20260702 130215.762581 139676688049856 vacuum.cpp:785] Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded

Rocky

07/02/2026, 5:42 AM

This error specifically occurs in StarRocks Shared-Data (Cloud-Native) mode. It indicates that the backend process (in this case, the

vacuum

cleaner or a query scan) tried to fetch metadata for a data shard from the Frontend (FE), but the request timed out before receiving a response. Root Causes 1. Lock Contention in ShardManager (Most Common) The FE manages all shard metadata using a component called

StarMgr

. In many versions (especially 3.x), the

ShardManager

uses a global lock. When a node (CN or BE) is added, removed, or loses its heartbeat, the FE triggers a massive batch update of shard locations. This update holds a write lock, which blocks all other metadata requests (

GetShard

). If the update takes longer than the gRPC timeout (default 5 seconds), you get "Deadline Exceeded." Note: Since your error comes from
vacuum.cpp
, the background metadata cleanup process is being blocked by this lock. 2. Node Instability or Heartbeat Failures If a Compute Node (CN) is struggling or restarting, it triggers constant metadata re-shuffling on the FE. This keeps the metadata lock "hot," causing other healthy nodes to time out when they try to perform routine tasks like vacuuming or scanning. 3. FE Resource Exhaustion If the FE is under heavy CPU load or experiencing long GC (Garbage Collection) pauses, it may not respond to the

GetShard

gRPC request within the 5-second window. 4. Network/Object Storage Latency High latency or connectivity issues between the FE and the CN nodes (where Starlet runs) can cause gRPC requests to hit the deadline. ────────── Recommended Fixes & Workarounds 1. Graceful Shutdown (Workaround for Maintenance) If you see this error during rolling updates or scaling, avoid simply killing the CN pods. Use a PreStop hook in Kubernetes or a manual command to drop the node gracefully:

Copy code

sql
ALTER SYSTEM DROP COMPUTE NODE "host:port";

This tells the FE to reassign shards proactively rather than waiting for a heartbeat timeout, which reduces the duration of the lock contention. 2. Increase gRPC Timeout You can give the FE more time to respond during periods of high contention. This is a "band-aid" fix but often prevents task failure. * FE Configuration (
fe.conf
):

Copy code

properties
    starmgr_grpc_timeout_seconds = 20

(Default is usually 5. Restart FE after changing.) 3. Optimize Vacuum and Compaction Since the error is appearing in

vacuum.cpp

, your system might be struggling to clean up a large backlog of deleted files. * Increase file retention: This reduces the frequency/urgency of vacuuming.

Copy code

properties
    lake_autovacuum_grace_period_minutes = 1440  -- Keep files for 24 hours

4. FE Performance Tuning Ensure the FE has enough resources to handle metadata requests: * Increase FE JVM Heap size (e.g.,

-Xmx32G

or higher) to avoid long GC pauses. * Monitor FE CPU usage during the error period. 5. Upgrade StarRocks Recent versions (v3.3.7+ and v3.4+) include significant enhancements to the

ShardManager

to reduce lock contention and improve the performance of metadata updates during node transitions. If you are on an older 3.x version, upgrading is the most permanent fix. Summary Checklist * Check FE logs for

heartbeat failed

slow query

messages around the same timestamp. * Verify if any CN nodes were restarting or under heavy load. * Check network latency between FE and CN nodes. References • faq/shared_data_faq.md • administration/management/FE_configuration.md • faq/Deploy_faq.md • deployment/shared_data/gcs.md • faq/Sql_faq.md • faq/loading/Loading_faq.md

07/02/2026, 5:44 AM

@Rocky what is the root cause and fixes E20260702 130215.762581 139676688049856 vacuum.cpp:785] Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded be/src/storage/lake/tablet_manager.cpp:696 value_or_err_L696 be/src/storage/lake/tablet_manager.cpp:693 value_or_err_L693 be/src/storage/lake/vacuum.cpp:495 collect_files_to_vacuum(tablet_mgr, root_dir, tablet_info, grace_timestamp, min_retain_version, vacuum_version_range.get(), &datafile_deleter, &metafile_deleter, &shared_file_deleter, vacuumed_file_size, &tablet_vacuumed_version, extra_file_size, tablet_retain_info) be/src/storage/lake/vacuum.cpp:762 vacuum_tablet_metadata(tablet_mgr, root_loc, tablet_infos, min_retain_version, grace_timestamp, enable_file_bundling, enable_shared_file_cleanup, &vacuumed_files, &vacuumed_file_size, &vacuumed_version, &extra_file_size, retain_versions) root@starrocks-prod-cn-0:/opt/starrocks/cn/log# tail -f cn.WARNING.log.20260702-101755 W20260702 122206.653127 139765156824768 lake_connector.cpp:632] failed to find column in schema: history_content_json W20260702 122206.653138 139765156824768 lake_connector.cpp:716] failed to find column in schema: history_content_json W20260702 122206.653180 139763904325312 lake_connector.cpp:632] failed to find column in schema: history_content_json W20260702 122206.653198 139763904325312 lake_connector.cpp:632] failed to find column in schema: history_content_json W20260702 122206.653202 139763904325312 lake_connector.cpp:716] failed to find column in schema: history_content_json E20260702 130215.762581 139676688049856 vacuum.cpp:785] Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded be/src/storage/lake/tablet_manager.cpp:696 value_or_err_L696 be/src/storage/lake/tablet_manager.cpp:693 value_or_err_L693 be/src/storage/lake/vacuum.cpp:495 collect_files_to_vacuum(tablet_mgr, root_dir, tablet_info, grace_timestamp, min_retain_version, vacuum_version_range.get(), &datafile_deleter, &metafile_deleter, &shared_file_deleter, vacuumed_file_size, &tablet_vacuumed_version, extra_file_size, tablet_retain_info) be/src/storage/lake/vacuum.cpp:762 vacuum_tablet_metadata(tablet_mgr, root_loc, tablet_infos, min_retain_version, grace_timestamp, enable_file_bundling, enable_shared_file_cleanup, &vacuumed_files, &vacuumed_file_size, &vacuumed_version, &extra_file_size, retain_versions)

Rocky

07/02/2026, 5:45 AM

This log indicates two separate issues occurring in a Shared-data (Cloud-native) cluster. The most critical one is the

Deadline Exceeded

error, which is causing the background vacuuming (garbage collection) to fail. 1. Root Cause:
grpc.GetShard(...) error: Deadline Exceeded
This error means the Compute Node (CN) tried to fetch metadata for a specific data shard (ID 10042) from the Frontend (FE/Metaservice), but the request timed out. Why it happens: * FE Overload/GC: The FE manages all shard locations. If the FE is under high CPU load or experiencing long Java Garbage Collection (GC) pauses, it cannot respond to the CN's gRPC request within the default timeout window (usually 5 seconds). * Metadata Lock Contention: In shared-data mode, certain operations (like massive loading or schema changes) can hold a global metadata lock, blocking routine requests like

GetShard

. * Network Latency: High latency between the CN and FE pods/nodes. * Vacuum Backlog: Your logs show this specifically in

vacuum.cpp

. The system is trying to clean up old data files from S3/object storage, but it can't proceed because it can't verify the shard's current state with the FE. Recommended Fixes: 1. Increase gRPC Timeout: Give the FE more time to respond during busy periods. * Add this to your `fe.conf`:

Copy code

properties
        starmgr_grpc_timeout_seconds = 20

_ _Restart the FE to apply.* 2. Check FE Resources: Ensure the FE has enough memory (JVM Heap). If it is hitting 90%+ memory usage, increase

-Xmx

fe.conf

. Monitor FE logs for

[gc]

Slow execution

. 3. Tune Vacuum Parallelism: If the system is struggling to keep up with vacuuming, you can limit how many partitions it processes at once to reduce metadata pressure: * FE Config:

lake_autovacuum_parallel_partitions = 4

(Default is 8). ────────── 2. Root Cause:
failed to find column in schema: history_content_json
This is a warning from the

lake_connector

, which handles scanning data for shared-data tables. Why it happens: * Schema Mismatch: A query was likely initiated using a version of the table schema that included

history_content_json

, but by the time the scan reached the data level, the schema had changed (e.g., the column was dropped or renamed). * Dropped Column: If you recently ran

ALTER TABLE ... DROP COLUMN history_content_json

, some background tasks (like statistics collection or cached query plans) may still be looking for it. Recommended Fixes: 1. Clear Metadata Cache: If the column was recently dropped and queries are failing, clear the cache:

Copy code

sql
    REFRESH EXTERNAL TABLE your_table_name; -- If it's an external table
    -- Or for native tables, wait for background refresh or restart the FE

2. Ignore if Non-Fatal: If your queries are still returning correct results, this warning is often transient and can be ignored once the system synchronizes its schema versions. Summary Checklist 1. Increase

starmgr_grpc_timeout_seconds

fe.conf

to 20. 2. Verify that your FE has at least 16GB-32GB of heap memory allocated. 3. Ensure network connectivity between

starrocks-prod-cn-0

and the FE is stable (port 9030 and the internal communication port, usually 9020, ports). References • faq/shared_data_faq.md • administration/management/FE_configuration.md • faq/Sql_faq.md • faq/loading/Loading_faq.md • administration/management/graceful_exit.md • data_source/datalake_faq.md References • data_source/datalake_faq.md

Rocky

07/02/2026, 5:45 AM

• faq/loading/Broker_load_faq.md • administration/management/FE_configuration.md • faq/shared_data_faq.md • data_source/feature-support-data-lake-analytics.md • faq/Sql_faq.md

07/02/2026, 5:46 AM

@Rocky already configured to

starmgr_grpc_timeout_seconds = 30

, but still have the same error

Rocky

07/02/2026, 5:47 AM

If increasing

starmgr_grpc_timeout_seconds

to 30 seconds did not resolve the issue, it indicates that the FE's internal ShardManager lock is being held for an extremely long time (exceeding 30s), or the FE is in a state of continuous lock contention. Since the error is occurring in

vacuum.cpp

, your background garbage collection is being blocked by this metadata lock. Here is why it is still failing and how to fix it: 1. Root Cause: Global Write-Lock Contention In StarRocks Shared-Data mode, the FE uses a global lock to manage shard locations. * The Trigger: When a Compute Node (CN) is added, removed, or loses its heartbeat, the FE triggers a batch update of shard statuses. This holds a Write Lock on the entire shard metadata manager. * The Result: All other requests, including your background

vacuum

and even standard query scans (

GetShard

), must wait for a Read Lock. If your cluster has a high number of shards (millions) or if nodes are unstable (restarting/flapping), this lock can easily be held for over 30–60 seconds. 2. Immediate Diagnostic Steps You need to identify what is holding the FE lock. Run the following on your Leader FE node: 1. Generate a thread dump:

Copy code

bash
    jstack -l <FE_PID> > fe_stack.txt

2. Search for the lock: Open

fe_stack.txt

and search for

ShardManager

Starlet

. Look for threads in

WAITING

BLOCKED

states attempting to acquire a lock. 3. Check for Node Flapping: Look at

sys_log/fe.log

. If you see frequent

heartbeat failed

backend [xxx] is down

messages, the FE is likely stuck in a loop of reassigning shards, which keeps the write-lock "hot." 3. Recommended Fixes & Workarounds A. Stabilize the Cluster (Most Important) If nodes are restarting due to OOM or high CPU, the ShardManager will never release the lock long enough for vacuuming to work. * Ensure CN nodes have enough memory. * Check if there are any

ALTER SYSTEM DROP/ADD

operations running. B. Reduce Vacuum Pressure on FE Since your logs specifically show

vacuum.cpp

failing, reduce the concurrency of the vacuum process so it makes fewer metadata requests: * Reduce Parallelism (FE Config):

Copy code

properties
    # Lower the number of partitions processed at once (Default is 8)
    lake_autovacuum_parallel_partitions = 2

* Increase Grace Period (FE Config):

Copy code

properties
    # Keep historical data longer to reduce vacuum frequency (Default is 30 mins)
    lake_autovacuum_grace_period_minutes = 1440

C. Handle Schema Mismatch Warnings The log

failed to find column in schema: history_content_json

suggests a metadata inconsistency. While usually a warning, if this is happening across millions of shards, it can flood the FE with error-handling logic. * Fix: Run

REFRESH EXTERNAL TABLE <table_name>

if it's an external table. If it's a native table, ensure no schema change tasks are hung:

SHOW ALTER TABLE COLUMN

. D. Upgrade StarRocks If you are on a version earlier than v3.3.7 or v3.4.x, this is a known performance bottleneck. Recent versions have introduced: * Granular Locking: Moving away from a single global lock in ShardManager. * Batch GetShard: Reducing the number of gRPC calls needed. Summary Checklist 1. Check FE logs for Heartbeat failures (this is the #1 cause of long locks). 2. Reduce

lake_autovacuum_parallel_partitions

to 2 to lower the load. 3. If using Kubernetes, ensure you have a PreStop hook to run

ALTER SYSTEM DROP COMPUTE NODE

gracefully, which prevents the 30s+ "deadlock" during rolling updates often caused by abrupt node shutdowns. References • administration/management/FE_configuration.md • knowledge/trouble_shooting/fe_deadlock_problems.md

07/02/2026, 5:54 AM

@Rocky share a runbook to troubleshoot CN pods high memory and suggest best practise CN configurations for huge aws glue data

Rocky

07/02/2026, 5:54 AM

To troubleshoot high memory usage on your Compute Node (CN) pods and optimize them for large-scale AWS Glue/S3 datasets, follow this runbook and configuration guide. ────────── Part 1: CN High Memory Troubleshooting Runbook When a CN pod hits its memory limit (default

mem_limit

is 90% of pod memory), it may crash or fail queries with "Memory of process exceed limit." Step 1: Identify the Memory Consumer Access the CN's internal memory tracker to see exactly where the memory is going: * Web UI: Visit

http://<CN_IP>:8040/mem_tracker

in your browser. * CLI: Run

curl -XGET -s http://<CN_IP>:8040/metrics | grep "^starrocks_be_.*_mem_bytes"

* Key Labels to Watch: * `query_pool`: Memory used by active queries (joins, aggregations). * `page_cache`: Memory used by the data page cache (default limit is 20%). * `table_meta`: Memory for metadata (schema, tablet info). * `starlet_cache`: Specifically for shared-data, this includes metadata and data cache managed by the Starlet service. Step 2: Check for Known "Ghost" Memory Issues In some versions (v3.1.x/v3.2.x), the metadata cache (

starlet_cache

) might not release memory properly even under low load. * Identification: If

fslib star cache meta memory size

is disproportionately large. * Fix: Upgrade to v3.1.9+ or v3.2.4+. * Manual Clear: You can manually clear

${storage_root_path}/starlet_cache/star_cache/meta

and restart the pod. Step 3: Analyze "Big Queries" Large scans from S3/Glue can exhaust

query_pool

. * Run

SHOW PROC '/current_queries'

to find queries consuming high memory. * Check

fe.audit.log

for queries with high

MemoryUsage

. ────────── Part 2: Best Practices for Huge AWS Glue/S3 Data When querying "huge" datasets (multi-terabyte) via AWS Glue, the goal is to balance local caching with query stability. 1. Enable "Spill to Disk" For memory-intensive joins or aggregations that exceed physical RAM, enable spilling to prevent OOM. * CN Config (
cn.conf
):

Copy code

properties
    spill_local_storage_dir = /data/starrocks/spill  # Ensure this path has high-speed SSD

* Session Variable (or Global):

Copy code

sql
    SET GLOBAL spill_mode = 'auto';

2. Optimize Data Cache (Block Cache) Shared-data clusters rely heavily on the local disk cache to avoid repeated S3 reads. * Automatic Adjustment: Ensure

datacache_auto_adjust_enable = true

is set. * Manual Quota: If using dedicated disks for cache, set a specific size:

Copy code

properties
    datacache_disk_size = 500G  # Adjust based on your PV size

* Avoid Caching "Cold" Data: Use

datacache.partition_duration

on tables to only cache recent data (e.g., the last 30 days) to prevent old Glue data from evicting hot metadata. 3. Tune Resource Isolation Create a Resource Group to prevent a single "huge" Glue query from crashing the entire CN pod.

Copy code

sql
CREATE RESOURCE GROUP glue_heavy_queries
WITH (
    "cpu_core_limit" = "10",
    "mem_limit" = "40%",  -- Limit this group to 40% of CN memory
    "big_query_mem_limit" = "20G", -- Kill individual queries exceeding this
    "spill_mem_limit_threshold" = "0.8" -- Spill when 80% of mem_limit is reached
);

4. Specific Glue/Data Lake Configurations * JNI Avro Reader: If your Glue data is in Avro format, ensure

avro_use_jni_reader = true

(default in newer versions) for better memory safety. * Partition Pruning: Ensure your Glue tables are well-partitioned. StarRocks will prune partitions at the metadata level, significantly reducing the memory footprint of the scan. * Limit Scan Rows: If users frequently run "Select *", set a safety guard:

Copy code

sql
    SET GLOBAL big_query_scan_rows_limit = 100000000;

Recommended CN Config Summary (
cn.conf
) ```properties # Memory Limits mem_limit = 90% storage_page_cache_limit = 20% # Spilling (Critical for huge data) spill_local_storage_dir = /opt/starrocks/cn/spill

Rocky

07/02/2026, 5:54 AM

# Cache management storage_root_path = /opt/starrocks/cn/storage datacache_auto_adjust_enable = true ``` References • using_starrocks/caching/block_cache.md • administration/management/resource_management/Memory_management.md • administration/management/BE_configuration.md • administration/management/spill_to_disk.md • administration/management/resource_management/resource_group.md • using_starrocks/External_table/Hive_external_table.md References • deployment/shared_data/s3.md • quick_start/shared-data.md • deployment/deploy_manually.md

07/02/2026, 6:03 AM

@Rocky guide me how to setup resource group for the starrocks clusters i have a new catalog , named aws_catalog read from external aws glue

Copy code

CREATE RESOURCE GROUP glue_heavy_queries
WITH (
    "cpu_core_limit" = "10",
    "mem_limit" = "40%",  -- Limit this group to 40% of CN memory
    "big_query_mem_limit" = "20G", -- Kill individual queries exceeding this
    "spill_mem_limit_threshold" = "0.8" -- Spill when 80% of mem_limit is reached
);

Rocky

07/02/2026, 6:07 AM

To fully set up Resource Groups for your

aws_catalog

(Glue), you need to follow these three steps: Create, Classify, and Enable. Step 1: Create the Resource Group Your SQL is a great start. I have refined it slightly to include a

concurrency_limit

to prevent too many "huge" queries from running at the exact same time, which is common with Glue/S3 scans.

Copy code

sql
CREATE RESOURCE GROUP glue_heavy_queries
WITH (
    "cpu_core_limit" = "10",
    "mem_limit" = "40%",
    "concurrency_limit" = "20", -- Max 20 concurrent queries in this group
    "big_query_mem_limit" = "20G",
    "spill_mem_limit_threshold" = "0.8"
);

Step 2: Create the Classifier (The "Link") A Resource Group does nothing until you tell StarRocks which queries belong to it. Since you want to target the Glue catalog, use the

catalog

classifier. Run this command to automatically route all queries that access

aws_catalog

into your new resource group:

Copy code

sql
-- This links the group to your specific catalog
ALTER RESOURCE GROUP glue_heavy_queries 
ADD (catalog='aws_catalog');

Note: You can also add classifiers for specific users or roles if you want only "Data Scientists" to be restricted:

ALTER RESOURCE GROUP glue_heavy_queries ADD (user='bi_user', catalog='aws_catalog');

Step 3: Enable Resource Groups Resource management is disabled by default in some versions. You must enable it globally for the FE to start routing queries.

Copy code

sql
SET GLOBAL enable_resource_group = true;

────────── Step 4: Verification & Monitoring After running a query against

aws_catalog

, verify it is being throttled or tracked correctly. 1. Check Group Usage:

Copy code

sql
    SHOW RESOURCE GROUPS;

Look at
InUseMemBytes
and
CPUUsage
to see if the Glue queries are populating the group. 2. Monitor Active Queries: Check which group a running query is assigned to:

Copy code

sql
    SELECT query_id, resource_group, state, mem_usage
    FROM information_schema.active_queries;

Best Practices for this Setup: * Spill to Disk: Since you set

"spill_mem_limit_threshold" = "0.8"

, ensure you have

spill_local_storage_dir

configured in your

cn.conf

(as mentioned in the previous response). Without the directory path on the CN, the spill threshold will be ignored. * Fallback Group: Any query that doesn't match a classifier goes to the

default_resource_group

. Ensure your

default_resource_group

has enough remaining memory/CPU to keep the system stable. * CPU Weight vs. Cores: If you are on a StarRocks version earlier than 3.1.4, use

"cpu_weight" = "10"

instead of

cpu_core_limit

. References • administration/management/resource_management/resource_group.md • administration/management/resource_management/Memory_management.md • administration/management/spill_to_disk.md • administration/management/resource_management/Workload_Group.md • administration/management/resource_management/resource_management.md • sql-reference/sql-statements/data-definition/CREATE_RESOURCE_GROUP.md References • administration/management/resource_management/resource_group.md • sql-reference/sql-statements/data-definition/CREATE_RESOURCE_GROUP.md

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

Rocky

07/02/2026, 6:07 AM

07/02/2026, 6:09 AM

@Rocky my CN resource is 14cpu and 48gb ram, suggest the best suitable resource group setting

Rocky

07/02/2026, 6:10 AM

For a Compute Node (CN) with 14 CPUs and 48GB of RAM, your resource group settings need to be conservative enough to prevent the pod from crashing (OOM) while allowing large AWS Glue scans to complete. Here are the recommended settings optimized for your specific hardware: Recommended Resource Group Configuration

Copy code

sql
CREATE RESOURCE GROUP glue_heavy_queries
WITH (
    -- CPU Allocation (Hard Limit)
    -- 8 cores leaves 6 cores for system tasks and other queries.
    "cpu_core_limit" = "8", 

    -- Memory Slice (Percentage of total process memory)
    -- 60% of 48GB is ~28.8GB. This is the shared pool for this group.
    "mem_limit" = "60%",

    -- Single Query Safety Net (Hard Limit per query)
    -- Set to 12GB. If one Glue query hits 12GB, it's safer to kill it 
    -- than to risk crashing the entire 48GB pod.
    "big_query_mem_limit" = "12G",

    -- Spill to Disk Trigger
    -- Start spilling when a query hits 80% of its memory limit.
    "spill_mem_limit_threshold" = "0.8",

    -- Concurrency Protection
    -- AWS Glue scans are memory-heavy. Limit to 4-6 concurrent large queries
    -- to prevent multiple queries from competing for the same 48GB.
    "concurrency_limit" = "4"
);

Why these values? 1. Memory (60% vs 40%): On a 48GB node, 40% (19GB) is quite small for "huge" Glue data which often involves large Parquet/ORC footers and scan buffers. 60% (~29GB) gives your heavy queries more room to breathe without starving the

default_wg

. 2. big_query_mem_limit
(12G): Your total physical RAM is 48GB. If you allow a single query to take 20GB (as in your original draft), and two such queries run simultaneously, you hit 40GB+. Add the 20% system overhead (

page_cache

, metadata), and your Pod will likely be killed by the Kubernetes OOM-Killer. 12GB-15GB is the "sweet spot" for a 48GB node. 3. cpu_core_limit
(8): Since you have 14 cores, giving 8 to this group ensures that even under 100% load from Glue, you still have 6 cores available for the FE heartbeat, background

vacuum

tasks (which were failing earlier), and metadata management. 4. concurrency_limit
(4): High concurrency is the #1 cause of CN OOMs. By limiting this to 4, you ensure that even if all 4 queries hit their

big_query_mem_limit

(4 * 12GB = 48GB), the spill to disk or the resource group's own limits will kick in before the OS kills the process. Important: Ensure Spilling is Enabled Resource groups only limit memory; Spilling is what allows the query to actually finish when it hits those limits. In your

cn.conf

, ensure you have:

Copy code

properties
# Essential for large Glue scans
spill_local_storage_dir = /opt/starrocks/cn/spill

How to Apply & Monitor 1. Add the Classifier:

Copy code

sql
    ALTER RESOURCE GROUP glue_heavy_queries ADD (catalog='aws_catalog');

2. Monitor with this SQL:

Copy code

sql
    -- Check if the group is reaching its limits
    SHOW RESOURCE GROUPS;

    -- See which specific Glue queries are currently using the most memory
    SELECT query_id, user, mem_usage, cpu_usage, resource_group
    FROM information_schema.active_queries
    WHERE resource_group = 'glue_heavy_queries';

-- Check if queries are being queued SELECT * FROM information_schema.be_resource_groups; ``` References • administration/management/resource_management/resource_group.md • best_practices/audit_log_resource_group.md • administration/management/resource_management/Memory_management.md • administration/management/spill_to_disk.md • faq/resource_isolation_faq.md

Rocky

07/02/2026, 6:10 AM

• administration/management/monitor_manage_big_queries.md • using_starrocks/External_table/Glue_catalog.md References • best_practices/audit_log_resource_group.md • administration/management/resource_management/resource_group.md • faq/resource_isolation_faq.md • administration/management/monitor_manage_big_queries.md

07/02/2026, 6:46 AM

@Rocky, can directly add catalog='aws_catalog' to the resource group creation?

Rocky

07/02/2026, 6:47 AM

No, you cannot directly add

catalog='aws_catalog'

as a classifier during the

CREATE RESOURCE GROUP

statement. Based on the official StarRocks documentation, the

TO

clause (classifiers) currently only supports the following keys: *

user

(User name) *

role

(User role) *

query_type

(

SELECT

INSERT

) *

source_ip

(CIDR block) *

db

(Database name) *

plan_cpu_cost_range

(v3.1.4+) *

plan_mem_cost_range

(v3.1.4+) Recommended Approach To route your Glue catalog queries to a specific resource group, use one of these two methods: 1. Use the
db
Classifier If your Glue queries target specific databases, you can list them in the classifier. Note that StarRocks matches the database name regardless of the catalog.

Copy code

sql
CREATE RESOURCE GROUP glue_heavy_queries
TO 
    (db='your_glue_db_name') -- Add your specific Glue database(s) here
WITH (
    "cpu_core_limit" = "8",
    "mem_limit" = "60%",
    "concurrency_limit" = "4",
    "big_query_mem_limit" = "12G"
);

2. Set the Resource Group via Session Variable (Easiest) Since there is no direct "catalog" classifier, the most reliable way to ensure queries from

aws_catalog

use your new group is to set the session variable when your application connects or before running the query:

Copy code

sql
SET resource_group = 'glue_heavy_queries';
SELECT * FROM aws_catalog.mydb.mytable;

3. Match by User/Role If you have a dedicated service account or role (e.g.,

glue_user

) that performs these scans, use the

user

role

classifier:

Copy code

sql
ALTER RESOURCE GROUP glue_heavy_queries 
ADD (user='glue_user');

Note: If you tried to use

catalog='aws_catalog'

in an earlier command and it failed with a syntax error, it is because that specific property is not a valid classifier key in the classifier map. Use

db

user

instead to achieve the same result. References • sql-reference/sql-statements/cluster-management/resource_group/CREATE_RESOURCE_GROUP.md • administration/management/resource_management/resource_group.md • sql-reference/sql-statements/cluster-management/resource_group/ALTER_RESOURCE_GROUP.md References • administration/management/resource_management/resource_group.md

07/02/2026, 6:54 AM

@Rocky what happen if the concurrent queries are more than the concurrency_limit, what happen to the user side

Rocky

07/02/2026, 6:54 AM

What happens when you exceed the

concurrency_limit

depends entirely on whether you have Query Queues enabled. Case 1: Query Queues are DISABLED (Default) If you haven't explicitly enabled queuing, any new query that hits the limit will fail immediately. * User Side Experience: The query returns a SQL error immediately. * Error Message: Usually looks like:

Resource group 'glue_heavy_queries' concurrency limit reached

. * Why use this: Use this if you want to protect your cluster's stability by simply rejecting "excess" work rather than letting it pile up. Case 2: Query Queues are ENABLED If queuing is enabled, the query will hang (wait) instead of failing. * User Side Experience: The query appears to be "running" but stays in a pending state. It will start automatically as soon as one of the active queries in that resource group finishes. * Timeouts: If the query stays in the queue longer than the configured timeout (default is usually 30-60 seconds), it will then fail with a timeout error. * Queue Full: If the queue itself reaches its maximum size (e.g., 1024 queries), new queries will fail immediately even with queuing on. ────────── How to configure the "User Side" behavior If you prefer queries to wait instead of failing, you must run these commands (requires v3.1.4+ for group-level queuing):

Copy code

sql
-- 1. Enable queuing for SELECT queries globally
SET GLOBAL enable_query_queue_select = true;

-- 2. Enable queuing specifically for Resource Groups
SET GLOBAL enable_group_level_query_queue = true;

-- 3. (Optional) Adjust how long a user should wait before failing
-- Default is usually 30s. This sets it to 60s.
SET GLOBAL query_queue_pending_timeout_second = 60;

Recommendation for your Glue Catalog Since Glue/S3 scans can be slow, I recommend Enabling Query Queues. * Benefit: If a user triggers 10 Glue scans but your limit is 4, the first 4 will run, and the next 6 will wait their turn. This provides a much smoother experience than having 6 users get "Access Denied/Limit Reached" errors. References • administration/management/resource_management/query_queues.md • faq/resource_isolation_faq.md • administration/management/monitor_manage_big_queries.md • administration/management/resource_management/resource_group.md References • faq/resource_isolation_faq.md • administration/management/resource_management/query_queues.md • administration/management/monitor_manage_big_queries.md

07/02/2026, 6:56 AM

@Rocky what impact to the starrocks deployment when enabling the queue

Rocky

07/02/2026, 6:57 AM

Enabling query queuing is highly recommended for your setup (14 CPU, 48GB RAM), as it acts as a protection layer for your relatively limited memory. Here is the impact broken down by category: 1. System Stability (The Biggest Benefit) * Prevents OOM Crashes: Without a queue, if 10 users trigger a Glue scan simultaneously, your CN will try to process all 10. Given your 48GB limit, this would almost certainly trigger a Linux OOM-Killer crash. * Predictable CPU Usage: Queuing ensures your 14 CPUs aren't context-switching between too many tasks, keeping the "Goodput" (useful work) high. 2. User Experience Changes * "Pending" vs. "Failed": Instead of getting an immediate error like

Resource group concurrency limit reached

, the user's BI tool or CLI will show the query as "Running" (but it is actually in a

PENDING

state). * Execution Time: The total time from the user's perspective will increase (Total Time = Queue Wait Time + Execution Time). * Timeouts: If the queue is backed up, a query might hit the

query_queue_pending_timeout_second

(default 30s) and fail with a timeout error while still in the queue. 3. Performance & Resource Impact * Dynamic Concurrency (v3.1.4+): StarRocks can automatically lower the

pipeline_dop

(degree of parallelism) for running queries when the queue is full. This means your 14 cores are distributed more efficiently among active queries. * FE Metadata Overhead: In versions 3.1.4+, the Leader FE centrally manages the queue. While this adds a tiny bit of network traffic between FEs, it is negligible for your 14-core environment and much safer than the older BE-side local queuing. 4. Operational Impact (What you need to monitor) Once enabled, you have new things to watch in your audit logs and monitoring: * IsPending
column: When you run

SHOW PROCESSLIST

, you will see a new column. If

IsPending

true

, the query is waiting for resources. * `PendingTimeMs`: Your audit logs (

fe.audit.log

) will now show exactly how long queries spent waiting. If this number is consistently high, it’s a sign you need more CN nodes. * New Metrics: You should monitor

starrocks_fe_query_queue_pending

in Prometheus/Grafana to see if your "backlog" is growing. Summary of Recommended Settings To minimize negative impact, use these settings together:

Copy code

sql
-- Enable the mechanism
SET GLOBAL enable_query_queue_select = true;
SET GLOBAL enable_group_level_query_queue = true;

-- Adjust the "Patience" level
-- Set how many queries can sit in the "waiting room" before failing (Default is 1024)
SET GLOBAL query_queue_max_queued_queries = 100;

-- Set how long a user should wait in the queue before getting a timeout (Default 30s)
SET GLOBAL query_queue_pending_timeout_second = 60;

Verdict: For a 48GB RAM CN, the risk of a crash without a queue is much higher than the "impact" of a query waiting 5-10 seconds in line. Enable it. References • administration/management/resource_management/query_queues.md • faq/resource_isolation_faq.md • administration/management/monitor_manage_big_queries.md • administration/management/resource_management/resource_group.md .** References • administration/management/resource_management/query_queues.md

07/02/2026, 6:59 AM

@Rocky what is the suitable SET GLOBAL query_mem_limit = '25769803776'; for my setting

Rocky

07/02/2026, 6:59 AM

The value

25769803776

bytes is exactly 24 GB. For a system with 48 GB of total RAM, whether this is "suitable" depends entirely on your concurrency (how many queries run at the same time). Here is the breakdown for your 48 GB setting: 1. The Math * Total RAM: 48 GB * StarRocks Process Limit (default 90%): ~43 GB (This is the "safe zone" for the whole StarRocks process). * Your Proposed `query_mem_limit`: 24 GB (Per query). 2. Is it suitable? Concurrency | Total Memory Used | Verdict 1 Query | 24 GB | Safe. You still have ~19 GB free for the system and metadata. 2 Queries | 48 GB | Dangerous. You are at the physical RAM limit. One query will likely be killed to save the node. 4 Queries | 96 GB | Critical Failure. Your node will likely crash (OOM) or all queries will fail. 3. Recommendations If you are the only person using this cluster for heavy Glue/Data Lake scans, 24 GB is fine. However, if this is a shared environment, I recommend the following: Option A: The "Safe" Global Limit (Multi-user) If you want to allow up to 4 concurrent queries without crashing the node, set it to roughly 10 GB:

Copy code

sql
-- Allows ~4 queries to run safely (4 * 10GB = 40GB)
SET GLOBAL query_mem_limit = 10737418240; -- 10 GB

Option B: Use Resource Group "Circuit Breakers" (Best Practice) Instead of a hard global limit, use a Resource Group to manage this dynamically. This allows small queries to run freely while "Big Queries" are limited:

Copy code

sql
ALTER RESOURCE GROUP your_group_name WITH (
    "mem_limit" = "80%",           -- Group can use up to ~34GB total
    "big_query_mem_limit" = "12G"  -- Individual large queries are capped at 12GB
);

Summary for your 48 GB RAM: * Use 24 GB only if you are doing massive single-query data processing and can guarantee no one else is running memory-heavy tasks. * Use 8-12 GB if you have multiple users or automated dashboards. This provides a "safety buffer" so that one query doesn't starve the entire system. Note: If you set

query_mem_limit

and a query exceeds it, StarRocks will return:

Memory of Fragment ... exceed limit. Limit: 25769803776

. If you see this error often, you should increase the limit or optimize the physical RAM of the node. References • administration/management/resource_management/Memory_management.md • sql-reference/System_variable.md • faq/resource_isolation_faq.md • administration/management/BE_configuration.md node. References • administration/management/resource_management/Memory_management.md • sql-reference/System_variable.md • faq/resource_isolation_faq.md • administration/management/BE_configuration.md References • administration/management/resource_management/Memory_management.md • sql-reference/System_variable.md • administration/management/BE_configuration.md • administration/management/resource_management/Query_management.md • faq/resource_isolation_faq.md

Rocky

07/02/2026, 6:59 AM

• administration/management/resource_management/resource_group.md • administration/management/resource_management/query_queues.md

07/02/2026, 7:00 AM

@Rocky review below query variables settings SET GLOBAL query_cache_agg_cardinality_limit = 5000000; SET GLOBAL query_cache_entry_max_bytes = 4194304; SET GLOBAL query_cache_entry_max_rows = 409600; SET GLOBAL query_cache_force_populate = 'false'; SET GLOBAL query_cache_hot_partition_num = 3; SET GLOBAL query_cache_size = 1048576; SET GLOBAL query_cache_type = 0; SET GLOBAL query_delivery_timeout = 300; SET GLOBAL query_history_keep_seconds = 259200; SET GLOBAL query_history_load_interval_seconds = 900; SET GLOBAL query_mem_limit = '25769803776'; SET GLOBAL query_queue_concurrency_limit = 0; SET GLOBAL query_queue_cpu_used_permille_limit = 0; SET GLOBAL query_queue_driver_high_water = '-1'; SET GLOBAL query_queue_driver_low_water = '-1'; SET GLOBAL query_queue_fresh_resource_usage_interval_ms = 5000; SET GLOBAL query_queue_max_queued_queries = 250; SET GLOBAL query_queue_mem_used_pct_limit = '0.0'; SET GLOBAL query_queue_pending_timeout_second = 300;

Rocky

07/02/2026, 7:00 AM

The setting

SET GLOBAL query_mem_limit = 10737418240;

(exactly 10 GB) is a very safe and balanced choice for your 48 GB RAM environment. Here is the technical review of why this works well: 1. The Safety Margin * Total Available RAM: 48 GB. * StarRocks Process Limit (
mem_limit
): By default, StarRocks reserves 90% of your RAM (~43.2 GB) for itself. * The 10 GB Limit: This allows up to 4 concurrent queries to run at their maximum possible memory usage (4 * 10 GB = 40 GB) while still leaving a small buffer for the operating system and StarRocks metadata. 2. Protection Against "Rogue" Queries Without this limit, a single unoptimized Glue/Data Lake scan (e.g., a massive join without a filter) could attempt to grab 30–40 GB of RAM. This would trigger the "OOM Killer" or cause the BE/CN node to restart. * With 10 GB set: That rogue query will simply fail with a

Memory exceed limit

error, but the rest of the cluster will stay online and other users will be unaffected. 3. What to watch for (The "Tightness" Check) Since you are scanning Data Lake (Glue/S3) tables: * Complex Joins: If you start seeing queries fail with

Memory exceed limit

, it means 10 GB isn't enough for that specific SQL. You may need to increase this to 12 GB or 16 GB, but if you do, you should decrease your

concurrency_limit

to 2 or 3 to compensate. * Parquet/ORC Scans: These formats can be memory-intensive during the "Scan" phase. 10 GB is usually plenty for standard scans, but for extremely wide tables (hundreds of columns), keep an eye on it. Recommendation This setting is Solid. It provides high stability for a 48 GB node. One final tip: Ensure you also have your Query Queue enabled as discussed before. * Why? If 10 users run queries at once, the 10 GB limit per query won't save you (10 * 10 GB = 100 GB, which is > 48 GB). * The Combo:

query_mem_limit = 10GB

concurrency_limit = 4

is the perfect "Seatbelt + Airbag" setup for your hardware. References • sql-reference/System_variable.md • administration/management/resource_management/Memory_management.md • administration/management/BE_configuration.md • best_practices/audit_log_resource_group.md • faq/resource_isolation_faq.md References • sql-reference/System_variable.md • best_practices/audit_log_resource_group.md • administration/management/resource_management/Memory_management.md • administration/management/BE_configuration.md • knowledge/usage/best-practice.md • faq/resource_isolation_faq.md • using_starrocks/caching/query_cache.md

Rocky

07/02/2026, 7:00 AM

This review is based on your specific environment (14 CPU / 48GB RAM). The settings you provided effectively disable both the Query Cache and the Query Queue protection mechanisms. Here is the breakdown: 1. Memory Risk (Critical) * query_mem_limit = 24GB
: As discussed previously, 24GB is half of your total physical RAM. * The Conflict: Because you have set

query_queue_concurrency_limit = 0

and

query_queue_mem_used_pct_limit = 0.0

, there is no limit on how many 24GB queries can start at once. * Impact: If just two users run a heavy query at the same time, your node will use 48GB+ and likely crash (OOM). 2. Query Queue (Effectively Disabled) * query_queue_concurrency_limit = 0
: In StarRocks,

means "no limit." This disables concurrency-based queuing. * query_queue_mem_used_pct_limit = 0.0
: This disables memory-based queuing. The node will not queue queries even if the RAM is 99% full. * Recommendation: To protect your 48GB RAM, change these to:

Copy code

sql
    SET GLOBAL query_queue_concurrency_limit = 4; -- Limit to 4 concurrent heavy queries
    SET GLOBAL query_queue_mem_used_pct_limit = 0.8; -- Queue queries if RAM usage > 80%

3. Query Cache (Disabled & Misconfigured) * query_cache_type = 0
: This completely disables the Query Cache. * query_cache_size = 1048576
(1MB): This is far too small. The default is 512MB. Even if you turn the cache on, a 1MB cache will be useless for Data Lake queries. * Recommendation: If you want to use caching for your Glue/S3 scans:

Copy code

sql
    SET GLOBAL query_cache_type = 1; -- 1 = Demand, 2 = Always
    -- Note: query_cache_size is a BE config (be.conf), not a SQL variable.
    -- The default 512MB in be.conf is usually fine.

4. Timeouts and History (Good) * query_delivery_timeout = 300
: 5 minutes is a solid default for Data Lake queries. * query_history_keep_seconds = 259200
: Keeping 3 days of history is excellent for troubleshooting performance issues. * query_queue_pending_timeout_second = 300
: 5 minutes in the queue is quite long; users usually prefer a 60s timeout so they can try again later, but 300s is safe. Summary Checklist for your 48GB Node: Variable | Your Current | Recommended | Why? query_mem_limit
| 24GB | 10GB - 12GB | Prevents a single query from taking too much RAM. query_queue_concurrency_limit
| 0 | 4 | Prevents multiple queries from crashing the node. query_queue_mem_used_pct_limit
| 0.0 | 0.8 | Triggers the "waiting room" before the node hits OOM. query_cache_type
| 0 | 1 | Only set to 1 if you want to speed up repetitive queries. References • sql-reference/System_variable.md • administration/management/resource_management/query_queues.md • using_starrocks/caching/query_cache.md • administration/management/resource_management/Memory_management.md References • sql-reference/System_variable.md • administration/management/resource_management/query_queues.md • faq/resource_isolation_faq.md

07/02/2026, 7:01 AM

Rocky

07/02/2026, 7:01 AM

This review is for your specific hardware setting of 14 CPU / 48 GB RAM. Overall, these settings are risky because they disable the "safety nets" (queues) while allowing individual queries to consume half of your total system memory. 1. High Risk: The "OOM" Danger Zone * query_mem_limit = 24 GB
: This allows a single query to take 24 GB of RAM. * query_queue_concurrency_limit = 0
and query_queue_mem_used_pct_limit = 0.0
: Setting these to

disables the query queue. * The Verdict: If two users run a heavy query at the same time, they will attempt to use 48 GB. Since your BE process only has about 43 GB available (90% of 48 GB), the node will likely crash or restart due to an Out of Memory (OOM) event. Recommended fix:

Copy code

sql
SET GLOBAL query_mem_limit = 10737418240; -- 10 GB per query
SET GLOBAL query_queue_concurrency_limit = 4; -- Max 4 concurrent heavy queries
SET GLOBAL query_queue_mem_used_pct_limit = 0.8; -- Queue if RAM usage > 80%

2. Ineffective: Query Cache Settings * query_cache_type = 0
: This completely disables the Query Cache, so all other

query_cache_*

settings below it are currently ignored. * query_cache_size = 1048576
: This variable is not a standard StarRocks system variable. The cache size is typically controlled in

be.conf

via

query_cache_capacity

. Even if it worked, 1 MB is far too small to store results for Data Lake queries. 3. Good / Safe Settings * query_delivery_timeout = 300
: 5 minutes is a good limit for most Data Lake (Glue/S3) environments. * query_history_keep_seconds = 259200
: Keeping 3 days of history is excellent for performance auditing. * query_queue_pending_timeout_second = 300
: Allowing a query to wait 5 minutes in the queue before failing is reasonable for batch-style workloads. Summary of Changes Needed If your goal is a stable cluster for multiple users, I suggest applying these modifications to your list: Variable | Current | Recommended | Reason query_mem_limit
| 24 GB | 10-12 GB | Prevents one query from starving the system. query_queue_concurrency_limit
| 0 | 4 | Forces queries to wait in line rather than crashing the node. query_queue_mem_used_pct_limit
| 0.0 | 0.8 | Protects the node when memory is tight. References • sql-reference/System_variable.md • administration/management/resource_management/query_queues.md • administration/management/resource_management/Memory_management.md • using_starrocks/caching/query_cache.md References • sql-reference/System_variable.md • using_starrocks/caching/query_cache.md • administration/management/resource_management/query_queues.md • administration/management/resource_management/Memory_management.md

07/02/2026, 7:06 AM

@Rocky how to limit the aws glue external catalog to certain databases, and not pulling all the databases

Rocky

07/02/2026, 7:06 AM

To limit an AWS Glue external catalog to specific databases in StarRocks, there is currently no direct
PROPERTIES
filter (like a

database-list

include_databases

key) in the

CREATE EXTERNAL CATALOG

statement itself. However, you can achieve this behavior using the following two methods: 1. Security-Level Filtering (Recommended) You can control visibility and access by restricting the AWS IAM Role that StarRocks uses to connect to Glue. * IAM Policy: Modify the IAM policy attached to the role StarRocks assumes. Instead of allowing

glue:_

on all resources (

"Resource": "_"

), specify the ARNs of only the databases and tables you want StarRocks to see. * Result: When StarRocks calls the Glue API to list metadata, Glue will only return the objects the role is authorized to "See." Unauthorized databases will typically not appear in

SHOW DATABASES

. Example IAM Policy Snippet:

Copy code

json
{
    "Effect": "Allow",
    "Action": [
        "glue:GetDatabase",
        "glue:GetDatabases",
        "glue:GetTable",
        "glue:GetTables",
        "glue:GetPartitions"
    ],
    "Resource": [
        "arn:aws:glue:region:account-id:catalog",
        "arn:aws:glue:region:account-id:database/allowed_db_1",
        "arn:aws:glue:region:account-id:table/allowed_db_1/*",
        "arn:aws:glue:region:account-id:database/allowed_db_2",
        "arn:aws:glue:region:account-id:table/allowed_db_2/*"
    ]
}

2. StarRocks Role-Based Access Control (RBAC) If you cannot change the IAM policy, you can create the catalog normally and then use StarRocks' internal privilege system to "hide" the other databases from your users. * Step 1: Create the catalog. * Step 2: Do not grant

ALL

privileges on the catalog to general users. * Step 3: Grant

USAGE

on the catalog and specific privileges only on the desired databases.

Copy code

sql
-- Grant usage on the catalog itself
GRANT USAGE ON CATALOG glue_catalog TO ROLE 'analytics_user';

-- Grant access only to specific databases within that catalog
GRANT SELECT ON DATABASE glue_catalog.allowed_db_1 TO ROLE 'analytics_user';

_Users with this role will only see

allowed_db_1

when they run

SHOW DATABASES FROM glue_catalog

._ Summary * Can I set a property in
CREATE EXTERNAL CATALOG
? No, there is no

database-list

property for Glue catalogs at this time. * What is the best practice? Use AWS IAM Policies to limit what the StarRocks cluster can physically "pull" from Glue, and use StarRocks RBAC to limit what your end-users can see. References • data_source/catalog/hive_catalog.md • administration/user_privs/authorization/privilege_item.md • sql-reference/sql-statements/Catalog/CREATE_EXTERNAL_CATALOG.md • data_source/catalog/unified_catalog.md References • data_source/catalog/jdbc_catalog.md • data_source/catalog/hive_catalog.md • administration/user_privs/authorization/privilege_item.md • data_source/catalog/elasticsearch_catalog.md

07/02/2026, 7:11 AM

@Rocky give sample resource group Reason: liquibase.exception.DatabaseException: Getting analyzing error. Detail message: The value type of the property

big_query_mem_limit

must be a valid numeric type, but it is set to

10G

. [Failed SQL: (1064) CREATE RE

Rocky

07/02/2026, 7:11 AM

The error occurs because the

big_query_mem_limit

property in StarRocks expects a pure numeric value in bytes. It does not support string suffixes like

GB

, or

MB

. To set a limit of 10GB, you must calculate the value in bytes: $10 \times 1024 \times 1024 \times 1024 = 10,737,418,240$. Corrected Sample Resource Group Here is a sample SQL to create a resource group with valid numeric limits:

Copy code

sql
CREATE RESOURCE GROUP data_lake_queries
TO 
    (role='analytics_role'),
    (db='glue_catalog')
WITH (
    "cpu_weight" = "10",
    "mem_limit" = "20%",
    "concurrency_limit" = "10",
    -- 10GB in bytes: 10737418240
    "big_query_mem_limit" = "10737418240", 
    -- 1 minute CPU time limit
    "big_query_cpu_second_limit" = "60",
    -- 100 million rows scan limit
    "big_query_scan_rows_limit" = "100000000"
);

Key Rules for Resource Group Properties: * big_query_mem_limit
: Must be an integer in bytes. * mem_limit
: Can be a percentage (e.g.,

"20%"

) or a decimal (e.g.,

"0.2"

). This represents the percentage of the BE's total query memory pool available to this group. * cpu_weight
: An integer representing the relative share of CPU. * concurrency_limit
: An integer representing the max parallel queries allowed for this group. References • sql-reference/sql-statements/cluster-management/resource_group/CREATE_RESOURCE_GROUP.md • administration/management/resource_management/resource_group.md References • sql-reference/sql-statements/cluster-management/resource_group/CREATE_RESOURCE_GROUP.md • administration/management/resource_management/resource_group.md

07/02/2026, 7:13 AM

@Rocky prepare sample configration to enable metadata snapshot, for disaster recovery, and is it possible to only enable snapshot for metadata and not other

Rocky

07/02/2026, 7:14 AM

In StarRocks, "Metadata" and "Data" are handled by different components (FE and BE), so you can naturally separate their protection strategies. To answer your specific question: Yes, it is possible (and common) to protect only the metadata. There are two ways to interpret "metadata snapshot" depending on your needs. ────────── Option 1: System-Level Metadata Backup (FE
meta
directory) This is for Disaster Recovery of the entire cluster state (users, privileges, internal table schemas, and external catalog definitions). StarRocks does this automatically through "Checkpoints," which create an

image

file of the metadata. Sample Configuration (
fe.conf
): These parameters control how frequently the FE takes its internal metadata snapshots.

Copy code

properties
# The directory where metadata snapshots are stored
# Default is StarRocksFE.STARROCKS_HOME_DIR/meta
meta_dir = /data/starrocks/fe/meta

# How many edit logs must occur before a new image snapshot is triggered
# Decreasing this makes snapshots more frequent but adds minor overhead
checkpoint_publish_timeout_ms = 600000

# To manually trigger a metadata snapshot (image) via SQL:
-- ALTER SYSTEM CREATE IMAGE;

DR Strategy for this mode: 1. Set up a cron job or script to periodically sync the

meta/image

and

meta/bdb

directories from your Leader FE to a remote S3 bucket or secondary site. 2. In a DR event, you can start a new FE using the

bdbje_reset_election_group = true

flag and pointing to this backed-up directory. ────────── Option 2: Object-Level Metadata Backup (v3.4+) If you only want to back up the definitions of your External Catalogs (e.g., Glue, Hive, Iceberg) or views/UDFs without touching any internal table data, StarRocks v3.4.0 introduced metadata-only backups. Sample Configuration & Command: 1. Create a Repository (where the snapshot will live, e.g., S3):

Copy code

sql
    CREATE REPOSITORY ds_metadata_repo
    WITH BROKER
    ON LOCATION "<s3a://your-bucket/starrocks_metadata_backups>"
    PROPERTIES(
        "aws.s3.access_key" = "xxxxx",
        "aws.s3.secret_key" = "yyyyy",
        "aws.s3.region" = "us-east-1"
    );

2. Run a Metadata-Only Backup: You can specify only the external catalog. This does not back up any actual data files on S3/HDFS; it only snapshots the StarRocks-side metadata for that catalog.

Copy code

sql
    BACKUP SNAPSHOT your_db.meta_snapshot_2023
    TO ds_metadata_repo
    ON (
        -- Specify external catalogs or other metadata-only objects here
        CATALOG glue_catalog_name
    );

────────── Is it possible to ONLY enable snapshot for metadata? * Technically: Metadata snapshots (internal FE checkpoints) are always enabled by default. You cannot disable them, as they are required for FE restarts. * Functionally: If you use the

BACKUP

command, you have full control. If you do not include internal tables in the

ON (...)

clause, no data will be snapshotted, only the metadata of the objects you listed. Summary Recommendation for DR: * For Cluster Config DR: Regularly back up the physical

fe/meta

directory of your Leader FE to remote storage. * For Catalog/Schema DR: Use the

BACKUP SNAPSHOT ... ON (CATALOG ...)

command in StarRocks v3.4+ to save your external table definitions to a repository. References • administration/Meta_recovery.md • administration/management/FE_configuration.md • administration/management/Backup_and_restore.md • sql-reference/sql-statements/backup_restore/BACKUP.md References • administration/management/Backup_and_restore.md

Rocky

07/02/2026, 7:14 AM

• administration/Meta_recovery.md • faq/Exporting_faq.md

07/02/2026, 7:28 AM

@Rocky auto snapshot and backup metadata to s3 bucket using aws IRSA authentication

Rocky

07/02/2026, 7:29 AM

To enable automated metadata snapshots and backups to S3 using AWS IRSA (IAM Roles for Service Accounts), follow these steps. 1. IRSA Authentication Setup For StarRocks to use IRSA, your Kubernetes ServiceAccount must be annotated with the IAM Role ARN. StarRocks uses the AWS SDK's default credential provider chain. In the

CREATE REPOSITORY

statement, you trigger this by setting

use_instance_profile

true

. SQL to create the Repository:

Copy code

sql
CREATE REPOSITORY s3_metadata_repo
WITH BROKER -- Broker is the component that handles the S3 connection
ON LOCATION "<s3a://your-bucket-name/metadata_backups/>"
PROPERTIES(
    "aws.s3.use_instance_profile" = "true",
    "aws.s3.region" = "us-east-1"
);

Note: You must use the
s3a://
prefix for backup repositories in StarRocks. ────────── 2. Automating the Backup StarRocks has two different "automated" behaviors depending on your deployment mode: A. If using Shared-Data Mode (v3.1+) Shared-data clusters have a built-in automated snapshot feature that pushes metadata (and data) to S3 every 10 minutes by default.

Copy code

sql
-- Enable automated snapshots to your S3 storage volume
ADMIN SET AUTOMATED CLUSTER SNAPSHOT ON;

-- Optional: Adjust the frequency (default 600 seconds)
-- ADMIN SET FRONTEND CONFIG ("automated_cluster_snapshot_interval_seconds" = "3600");

B. If using Shared-Nothing (Standard) Mode Shared-nothing clusters do not have a built-in "Auto-Backup" scheduler in SQL. You must use an external trigger (like a CronJob or Airflow) to run a script that executes the

BACKUP

command via MySQL client. Sample Script for Cron:

Copy code

bash
# backup_metadata.sh
mysql -h <fe_ip> -P 9030 -u root -e "
BACKUP SNAPSHOT your_db.meta_backup_$(date +%Y%m%d)
TO s3_metadata_repo
ON (
    -- To backup ONLY metadata, specify your External Catalogs here
    CATALOG your_glue_catalog,
    CATALOG your_iceberg_catalog
);"

────────── 3. Metadata-Only Backups (v3.4+) As of StarRocks v3.4.0, you can explicitly back up only metadata (External Catalog definitions, views, and UDFs) without including any heavy internal table data. Example SQL for Metadata-Only Backup:

Copy code

sql
BACKUP SNAPSHOT demo_db.metadata_only_snapshot
TO s3_metadata_repo
ON (
    CATALOG glue_external_catalog,
    CATALOG iceberg_external_catalog
)
PROPERTIES ("type" = "full");

Summary of Configuration 1. Authentication: Set

"aws.s3.use_instance_profile" = "true"

in the repository properties to pick up the IRSA role from the Pod environment. 2. Automation: * Shared-Data: Use

ADMIN SET AUTOMATED CLUSTER SNAPSHOT ON

. * Shared-Nothing: Use a Kubernetes CronJob or an external script to call the

BACKUP

SQL command. 3. Scope: Use the

ON (CATALOG ...)

clause in the backup command to ensure only metadata definitions are saved. References • administration/management/Backup_and_restore.md • sql-reference/sql-statements/backup_restore/CREATE_REPOSITORY.md • administration/cluster_snapshot.md • deployment/shared_data/s3.md References • administration/management/Backup_and_restore.md • administration/cluster_snapshot.md • sql-reference/sql-statements/backup_restore/CREATE_REPOSITORY.md • deployment/shared_data/s3.md References • deployment/shared_data/s3.md

Rocky

07/02/2026, 7:29 AM

• integrations/authenticate_to_aws_resources.md

07/02/2026, 7:37 AM

@Rocky how to setup autoamted Metadata-Only Backups (v3.4+)

Rocky

07/02/2026, 7:37 AM

To set up automated metadata-only backups in StarRocks v3.4+, the configuration depends on your cluster architecture (Shared-Data vs. Shared-Nothing). 1. Shared-Data Clusters (Built-in Automation) If you are using the shared-data architecture, StarRocks provides a native, automated "Cluster Snapshot" feature. This automatically snapshots the cluster's metadata state to your storage volume (e.g., S3). * How it works: It triggers a metadata snapshot every time the FE creates a new image (checkpoint). * To enable:

Copy code

sql
    -- Enable automation (uses default storage volume)
    ADMIN SET AUTOMATED CLUSTER SNAPSHOT ON;

    -- Adjust frequency (default is 600 seconds)
    ADMIN SET FRONTEND CONFIG ("automated_cluster_snapshot_interval_seconds" = "3600");

2. Shared-Nothing Clusters (Manual Automation) Shared-nothing clusters do not have a built-in "auto-backup" toggle. To automate the new v3.4 metadata-only features, you must use an external scheduler (like a Kubernetes CronJob, Linux Cron, or Airflow) because each backup requires a unique snapshot name. Step A: Create a Repository (with IRSA) Ensure your FE/BE pods have the correct IAM role, then create the repo:

Copy code

sql
CREATE REPOSITORY s3_metadata_repo
WITH BROKER
ON LOCATION "<s3a://your-bucket/backups/metadata/>"
PROPERTIES(
    "aws.s3.use_instance_profile" = "true",
    "aws.s3.region" = "us-east-1"
);

Step B: Automate via Script Run a script periodically that generates a unique label. The v3.4 syntax allows you to target only external metadata (catalogs, views, UDFs) without backing up internal table data. Command for External Catalogs:

Copy code

sql
-- Back up ALL external catalogs (Glue, Hive, etc.)
BACKUP ALL EXTERNAL CATALOGS 
SNAPSHOT meta_snapshot_20240101 -- Must be unique per run
TO s3_metadata_repo;

Command for Specific Metadata Objects:

Copy code

sql
-- Back up only specific catalogs, logical views, or UDFs
BACKUP SNAPSHOT your_db.meta_only_v1
TO s3_metadata_repo
ON (
    EXTERNAL CATALOG glue_catalog,
    ALL VIEWS,
    ALL FUNCTIONS
);

Summary of Metadata-Only Options in v3.4+ Object Type | v3.4 SQL Syntax All External Catalogs |

BACKUP ALL EXTERNAL CATALOGS SNAPSHOT ...

Specific Catalogs |

BACKUP EXTERNAL CATALOGS (cat1, cat2) SNAPSHOT ...

Logical Views |

BACKUP ... ON (ALL VIEWS)

UDFs |

BACKUP ... ON (ALL FUNCTIONS)

Why use Metadata-Only Backups? * Speed: They finish almost instantly as they don't scan or copy data files. * Efficiency: They only store the SQL definitions of your catalogs and views. * DR: In a new cluster, you can

RESTORE

these snapshots to instantly recreate your entire External Catalog structure. References • administration/management/Backup_and_restore.md • sql-reference/sql-statements/backup_restore/BACKUP.md • administration/cluster_snapshot.md References • sql-reference/sql-statements/backup_restore/BACKUP.md

07/02/2026, 8:18 AM

@Rocky cn have below error 2026-07-02 135117.114 be/src/runtime/client_cache.cpp:88 _create_client(hostport, factory_method, client_key), retry_times=1, max_retry_times=10 2026-07-02 135117.114 W20260702 135117.006686 139766468937408 pipeline_driver_executor.cpp:389] [Driver] Fail to report exec state. fragment_instance_id=019f2161-aad9-7e1c-9810-bf6c78eff8d9, query_id=019f2161-aad9-7e1c-9810-bf6c78eff8d8, status: Rpc error: Couldn't open transport for starrocks-prod-fe-2.starrocks-prod-fe-search.starrocks.svc.cluster.local:9020 (socket open() error: Connection refused) 2026-07-02 135117.114 W20260702 135117.005974 139766468937408 thrift_rpc_helper.cpp:107] Connect FE RPC failed, address=TNetworkAddress(hostname=starrocks-prod-fe-2.starrocks-prod-fe-search.starrocks.svc.cluster.local, port=9020), status=Couldn't open transport for starrocks-prod-fe-2.starrocks-prod-fe-search.starrocks.svc.cluster.local:9020 (socket open() error: Connection refused) 2026-07-02 130215.955 be/src/storage/lake/vacuum.cpp:762 vacuum_tablet_metadata(tablet_mgr, root_loc, tablet_infos, min_retain_version, grace_timestamp, enable_file_bundling, enable_shared_file_cleanup, &vacuumed_files, &vacuumed_file_size, &vacuumed_version, &extra_file_size, retain_versions) 2026-07-02 130215.955 be/src/storage/lake/vacuum.cpp:495 collect_files_to_vacuum(tablet_mgr, root_dir, tablet_info, grace_timestamp, min_retain_version, vacuum_version_range.get(), &datafile_deleter, &metafile_deleter, &shared_file_deleter, vacuumed_file_size, &tablet_vacuumed_version, extra_file_size, tablet_retain_info) 2026-07-02 130215.955 be/src/storage/lake/tablet_manager.cpp:693 value_or_err_L693 2026-07-02 130215.955 be/src/storage/lake/tablet_manager.cpp:696 value_or_err_L696 2026-07-02 130215.955 E20260702 130215.762581 139676688049856 vacuum.cpp:785] Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded 2026-07-02 130215.855 be/src/storage/lake/vacuum.cpp:762 vacuum_tablet_metadata(tablet_mgr, root_loc, tablet_infos, min_retain_version, grace_timestamp, enable_file_bundling, enable_shared_file_cleanup, &vacuumed_files, &vacuumed_file_size, &vacuumed_version, &extra_file_size, retain_versions) 2026-07-02 130215.855 be/src/storage/lake/vacuum.cpp:495 collect_files_to_vacuum(tablet_mgr, root_dir, tablet_info, grace_timestamp, min_retain_version, vacuum_version_range.get(), &datafile_deleter, &metafile_deleter, &shared_file_deleter, vacuumed_file_size, &tablet_vacuumed_version, extra_file_size, tablet_retain_info) 2026-07-02 130215.855 be/src/storage/lake/tablet_manager.cpp:693 value_or_err_L693 2026-07-02 130215.855 be/src/storage/lake/tablet_manager.cpp:696 value_or_err_L696 2026-07-02 130215.855 E20260702 130215.762581 139676688049856 vacuum.cpp:785] Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded 2026-07-02 122206.851 W20260702 122206.653202 139763904325312 lake_connector.cpp:716] failed to find column in schema: history_content_json 2026-07-02 122206.851 W20260702 122206.653198 139763904325312 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 122206.851 W20260702 122206.653180 139763904325312 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 122206.851 W20260702 122206.653138 139765156824768 lake_connector.cpp:716] failed to find column in schema: history_content_json 2026-07-02 122206.851 W20260702 122206.653127 139765156824768 lake_connector.cpp:632] failed to find column in schema: history_content_json

Rocky

07/02/2026, 8:19 AM

Based on the logs provided, your cluster is experiencing a communication failure between the Compute Nodes (CN) and the Frontends (FE), likely triggered by an outage or instability of a specific FE node. 1. Connection Refused to FE (Port 9020) Error:

Couldn't open transport for starrocks-prod-fe-2...:9020 (socket open() error: Connection refused)

* Meaning: The CN is attempting to communicate with the FE RPC port (9020) on node

starrocks-prod-fe-2

, but the connection is being rejected. * Cause: Either the FE process on that node has crashed, is restarting, or there is a network/DNS issue preventing the CN from reaching it. * Impact: CNs cannot report query execution states or fetch metadata, leading to query failures. 2. Shard Metadata Timeout (Shared-Data Mode) Error:

Internal error: starlet err grpc.GetShard(shardId=10042) error: Deadline Exceeded

* Meaning: This is a timeout in the Shared-Data (cloud-native) storage layer. The CN is asking the FE for tablet location/metadata, but the request timed out (default 5s). * Cause: In StarRocks Shared-Data mode, when an FE node goes down or restarts, the remaining FE nodes (the Leader) can experience lock contention while updating the cluster state. This blocks "GetShard" requests from all CNs until the metadata lock is released. * Link to Error 1: If

fe-2

is down or flapping, it triggers these metadata updates on the Leader FE, causing the timeouts you see. 3. Schema Mismatch Warning:

failed to find column in schema: history_content_json

* Meaning: The

lake_connector

(used for Shared-Data tables) is looking for a column named

history_content_json

that does not exist in the schema it currently holds. * Cause: This usually occurs if: 1. An

ALTER TABLE

was performed recently, and the CN has stale metadata. 2. The metadata service (FE) is inconsistent due to the outages described above. 3. A query is referencing a column that was dropped or renamed. ────────── Recommended Actions 1. Check FE Health: Run

kubectl get pods

(if on K8s) or check the FE processes. Specifically, look at

starrocks-prod-fe-2

. If it has high restart counts, investigate the

fe.log

for OOM or BDBJE errors. 2. Verify RPC Connectivity: From a CN pod, try to reach the FE:

Copy code

bash
    nc -zv starrocks-prod-fe-2.starrocks-prod-fe-search.starrocks.svc.cluster.local 9020

3. Time Sync: Ensure the system clocks on all FE and CN nodes are synchronized (e.g., via NTP). Clock skew >5s causes FE-FE and FE-CN communication to fail. 4. Schema Refresh: Once the FE nodes are stable, if the

history_content_json

error persists, try running

DESC <table_name>

to verify the current schema and check if any cached plans are causing issues by re-running the query. References • administration/Meta_recovery.md • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md • faq/shared_data_faq.md References • data_source/datalake_faq.md • faq/loading/Broker_load_faq.md • data_source/catalog/deltalake_catalog.md • faq/shared_data_faq.md • administration/management/FE_configuration.md • loading/Spark-connector-starrocks.md • data_source/feature-support-data-lake-analytics.md

07/02/2026, 8:20 AM

@Rocky what need to look at and configure to prevent this from happening also have new error in FE 2026-07-02 155136.286 W20260702 155136.045003 139764061673152 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.286 W20260702 155136.044938 139764061673152 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045075 139764160288448 lake_connector.cpp:716] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045069 139764160288448 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.044940 139764160288448 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045046 139765240743616 lake_connector.cpp:716] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045041 139765240743616 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.044938 139765240743616 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045008 139764061673152 lake_connector.cpp:716] failed to find column in schema: history_content_json 2026-07-02 155136.233 W20260702 155136.045003 139764061673152 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.232 W20260702 155136.044938 139764061673152 lake_connector.cpp:632] failed to find column in schema: history_content_json 2026-07-02 155136.232 W20260702 155135.989043 139765219763904 lake_connector.cpp:716] failed to find column in schema: history_content_json

Rocky

07/02/2026, 8:21 AM

To prevent these errors from recurring, you need to address three distinct issues: Metadata Lock Contention, Schema Inconsistency in System Tables, and FE RPC Availability. 1. Prevent "Deadline Exceeded" (Metadata Lock) This error occurs in Shared-Data mode when a CN node goes down unexpectedly. The Leader FE triggers a batch update of shard replica status and holds a global write lock on the metadata, blocking all other queries. * Look at: Check if your CN pods are being restarted by Kubernetes (OOMKilled) or during rolling updates. * Configure Graceful Shutdown: Do not let CN nodes "sudden death." Add a

preStop

hook in your Kubernetes CN deployment to notify the FE before the pod terminates:

Copy code

yaml
    lifecycle:
      preStop:
        exec:
          command: ["/bin/sh", "-c", "mysql -h $FE_HOST -P 9030 -u root -e 'ALTER SYSTEM DROP COMPUTE NODE \"$NODE_IP:9050\"'"]

* Tuning (v3.3+): You can try increasing the metadata gRPC timeout in

fe.conf

, though this only masks the lock contention:

Copy code

properties
    starmgr_grpc_timeout_seconds = 10

2. Prevent "failed to find column in schema" (Schema Mismatch) The column

history_content_json

belongs to the internal system table

statistics.task_run_history

, which StarRocks uses to track Asynchronous Materialized View (MV) tasks. * Cause: This error usually happens if a query is trying to read from the MV history while the system table is in an inconsistent state (common after upgrades or failed MV refreshes). * Configuration/Fix: 1. Check MV Status: Run

SHOW MATERIALIZED VIEWS

and check for any tasks that are "stuck" or "failing." 2. Wait for Metadata Sync: If you recently upgraded StarRocks, wait for the metadata to finish migrating. 3. Manual Refresh: If the error persists, you may need to drop and recreate the problematic MV, or contact support if the internal

statistics

table itself is corrupted. 3. Prevent "Connection Refused" (FE Port 9020) This indicates the FE's Thrift RPC server is either crashed or completely saturated. * Look at FE JVM Heap: Check

fe.log

for

OutOfMemoryError

. If found, increase the JVM heap in

fe.conf

(e.g.,

JAVA_OPTS="-Xmx16g"

). * Configure RPC Threads: If the FE is alive but refusing connections, it might have run out of worker threads. Increase this in `fe.conf`:

Copy code

properties
    # Increase the number of thrift server worker threads (default is often 1000)
    thrift_server_max_worker_threads = 2000

* Check `priority_networks`: Ensure the FE and CN nodes are on the same network and that

priority_networks

is correctly configured in both

fe.conf

and

cn.conf

to prevent the FE from binding to a management IP that the CN cannot reach. Summary Checklist Issue | Primary Fix GetShard Timeout | Implement Graceful Shutdown (

ALTER SYSTEM DROP COMPUTE NODE

) for CNs. Schema Error | Verify and clean up failing Asynchronous Materialized Views. RPC Connection | Increase FE JVM Heap and Thrift Worker Threads. References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • faq/shared_data_faq.md • using_starrocks/Async_MV.md References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/BE_configuration.md • using_starrocks/Async_MV.md References

Rocky

07/02/2026, 8:21 AM

• administration/management/FE_configuration.md

07/02/2026, 8:34 AM

@Rocky default is thrift_server_max_worker_threads
Default: 4096 what is the best value for my setup docs.starrocks.io/docs/…/log_server_meta#…

Rocky

07/02/2026, 8:34 AM

For a standard StarRocks setup, the default 4096 is usually more than enough. However, in your specific case—where you are seeing *Connection Refused on port 9020*—the "best" value is one that matches your peak RPC concurrency without exhausting your FE's memory. When to increase
thrift_server_max_worker_threads
You should only increase this value if you have verified that your FE is healthy (not OOMing) but is actually running out of threads. * Emergency Scale-out: If your FE is alive but refusing RPC connections from CNs/BEs, increase it to 8192.

Copy code

sql
    ADMIN SET FRONTEND CONFIG ("thrift_server_max_worker_threads" = "8192");

* Large Clusters (100+ Nodes): For very large clusters with high query concurrency (thousands of fragments per second), 8192 or 16384 is common. Better Values for Shared-Data (v3.3+) Since you are in Shared-Data mode, your "Deadline Exceeded" errors are often related to the StarMgr gRPC service rather than just the Thrift server. If you are on a recent version (v3.3.8+ or v3.4+), consider these specific parameters: 1. starmgr_grpc_server_max_worker_threads
: (Default 1024). This handles the

GetShard

requests you saw failing. If your CNs are timing out talking to the FE metadata service, try increasing this to 2048. 2. thrift_server_queue_size
: (Default 4096). Instead of just adding threads (which costs memory), you can increase the queue size to allow the FE to "buffer" short bursts of RPC requests. How to verify if you need more threads Before changing the value, check how many threads the FE is actually using. From your FE node, run:

Copy code

bash
# Count active threads
jcmd <FE_PID> Thread.print | grep -c "thrift-server-pool"

* If the count is near 4096, you are thread-exhausted. Increase the parameter. * If the count is low (e.g., < 500) but you still get "Connection Refused," the problem isn't the thread count—it's likely the FE JVM is stuck in a Long GC pause or a Global Metadata Lock is blocking the threads from completing their work. Risk of setting it too high * Memory Pressure: Each thread consumes memory (stack size, typically 1MB). Setting this to 65535 on a small FE JVM could trigger an OutOfMemoryError. * Context Switching: Too many active threads can cause the CPU to spend more time switching between tasks than doing actual work, potentially worsening the "Deadline Exceeded" issues. Recommendation: Start by increasing to 8192. If the "Connection Refused" persists, the issue is almost certainly FE Lock Contention (caused by the CN restarts we discussed) rather than a thread limit. References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/FE_parameters/log_server_meta.md References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/FE_parameters/log_server_meta.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md

07/02/2026, 8:38 AM

@Rocky current is 4 starrocks-fe-0:/opt/starrocks# jcmd 18 Thread.print | grep -c "thrift-server-pool" 4

Rocky

07/02/2026, 8:38 AM

The fact that your FE only has 4 threads in the

thrift-server-pool

(when the limit is 4096) confirms that thread exhaustion is NOT your problem. Instead, your cluster is likely suffering from Global Metadata Lock Contention and Node Instability. Here is what is happening and how to fix it: 1. Why you see "Connection Refused" (despite having free threads) Since you only have 4 threads active, the FE is not "busy"; it is likely unreachable or hasn't fully started the RPC service on node

fe-2

. * The Cause: In Shared-Data mode, if a Follower FE (like

fe-2

) fails to synchronize metadata with the Leader, it may stop listening on its RPC ports or crash repeatedly. * What to check: Check the

fe.log

specifically for node

starrocks-prod-fe-2

. Look for

MemoryUsageTracker

alerts or

BDBJE

(metadata database) replication errors. If it's a K8s environment, check if the pod is in a

CrashLoopBackOff

. 2. Why you see "Deadline Exceeded" (The Root Cause) This is a known behavior in StarRocks Shared-Data mode when a Compute Node (CN) goes down or is restarted (e.g., during a K8s rolling update). * The Chain Reaction: 1. A CN node stops (or heartbeats fail). 2. The FE Leader detects the node is gone and starts a batch update of all "shard replica" statuses. 3. This update holds a Global Write Lock on the StarMgr metadata. 4. While this lock is held, all other CNs asking for shard metadata (

GetShard

) are blocked. 5. The default timeout for these requests is 5 seconds. If the lock is held longer than 5s, you get "Deadline Exceeded." * The Solution: You must implement a Graceful Shutdown for your CNs. If you "drop" the node from the cluster before it stops, the FE handles the transition much more smoothly without the massive lock contention. 3. The "history_content_json" Schema Error The warnings about

history_content_json

refer to an internal system table (

statistics.task_run_history

) used for Asynchronous Materialized Views. * Why it's happening: This is usually a side effect of the metadata inconsistency caused by the FE node (

fe-2

) flapping or the cluster being in a "locked" state. The CN is trying to read task history but is getting an inconsistent schema from the FE. * The Fix: Once you stabilize the FE nodes and the CN heartbeats, this error should disappear. If it persists, you may need to manually clean the statistics metadata:

Copy code

sql
    TRUNCATE TABLE _statistics_.task_run_history;

────────── Immediate Configuration Plan 1. *Do NOT increase `thrift_server_max_worker_threads`*; it will not help since you are only using 4. 2. CN Graceful Shutdown (Critical): If using Kubernetes, add this

preStop

hook to your CN spec to prevent the "Deadline Exceeded" lockups:

Copy code

yaml
    lifecycle:
      preStop:
        exec:
          command: ["/bin/sh", "-c", "mysql -h $FE_HOST -P 9030 -u root -e 'ALTER SYSTEM DROP COMPUTE NODE \"$NODE_IP:9050\"'"]

3. Increase StarMgr Timeout: Give the metadata service more breathing room during lock contention by adding this to `fe.conf`:

Copy code

properties
    # Increase from default 5s to 20s
    starmgr_grpc_timeout_seconds = 20

4. Investigate `fe-2`: Run

SHOW FRONTENDS;

in MySQL. If

fe-2

shows

Alive: false

, it's a metadata sync issue. Check its

fe.log

for "Journal" or "BDB" errors. References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/FE_parameters/log_server_meta.md References • administration/management/FE_configuration.md • faq/shared_data_faq.md

Rocky

07/02/2026, 8:38 AM

• administration/management/FE_parameters/log_server_meta.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • faq/shared_data_faq.md • administration/management/FE_parameters/log_server_meta.md • using_starrocks/Async_MV.md

07/02/2026, 8:40 AM

@Rocky how by increasing starmgr_grpc_timeout_seconds can prevent from happening

Rocky

07/02/2026, 8:40 AM

Increasing

starmgr_grpc_timeout_seconds

doesn't stop the metadata lock from happening, but it prevents the query from failing while the lock is being held. Here is the technical breakdown of how this parameter helps: 1. The "Lock" Mechanism In Shared-Data mode, the FE (via the

StarMgr

module) manages the mapping of shards to Compute Nodes (CNs). When a CN goes down or heartbeats are lost, the Leader FE must perform a batch update to reassign or mark those shards. * To ensure consistency, the FE takes a Global Write Lock on the metadata. * While this lock is held, all other requests (like

GetShard

which CNs use to find data) are put in a queue. 2. The Default 5-Second Race By default,

starmgr_grpc_timeout_seconds

is set to 5 seconds. * If the FE Leader is busy or has a large amount of metadata to update (common in clusters with many tablets/shards), that global lock might be held for 6 or 10 seconds. * Because the default timeout is only 5s, any request waiting in the queue will hit its "Deadline" and return the

Deadline Exceeded

error before the lock is even released. 3. How Increasing the Value Helps By increasing this to 20 or 30 seconds: * Waiting instead of Failing: When the lock is held, the incoming requests from your CNs will stay in the queue for up to 30 seconds. * Success on Release: Once the FE finishes the metadata update and releases the lock, it processes the queued requests. Since the "deadline" hasn't passed yet, the requests succeed, and your queries continue (albeit with a slight delay) instead of crashing with an error. When to use it You should increase this if you see

Deadline Exceeded

specifically during: 1. CN Scale-out/Scale-in: When adding or removing nodes. 2. High Metadata Load: When you have hundreds of thousands of tablets. 3. Network Jitter: To make the cluster more resilient to brief spikes in FE-to-CN latency. Recommended Config:

Copy code

sql
-- In fe.conf or via dynamic set
ADMIN SET FRONTEND CONFIG ("starmgr_grpc_timeout_seconds" = "30");

Note: This is a "buffer." To truly stop the lock contention, you should still implement the Graceful Shutdown (
DROP COMPUTE NODE
) mentioned earlier so the FE doesn't have to "guess" when a node is dead and trigger the emergency lockup. References • administration/management/FE_configuration.md • faq/shared_data_faq.md metadata update. References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/management/FE_parameters/log_server_meta.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md • sql-reference/System_variable.md • faq/operation_maintenance_faq.md

07/02/2026, 8:51 AM

@Rocky how to check current tablet/shards

Rocky

07/02/2026, 8:52 AM

To check the current tablets and shards in your StarRocks cluster, you can use several levels of commands depending on whether you want a cluster-wide summary or a deep dive into a specific table. 1. Cluster-Wide Tablet Count The quickest way to see the total number of tablets in your entire cluster is via the

statistic

proc:

Copy code

sql
SHOW PROC "/statistic";

* TabletNum: The total number of tablets across all databases. * InconsistentTabletNum: Should ideally be 0. If higher, some replicas are out of sync. 2. Tablets per Node (BE/CN) To see how tablets are distributed across your physical nodes:

Copy code

sql
SHOW PROC "/backends";

* Look for the TabletNum column. This shows how many tablets each node is currently responsible for. * In Shared-Data mode, this represents the number of "shards" (tablets) that a Compute Node (CN) is currently managing or has cached. 3. Tablets for a Specific Table If you want to see the specific tablet IDs and their locations for a table:

Copy code

sql
SHOW TABLETS FROM my_database.my_table;

This will return a list of every tablet ID, which node it resides on, its size, and its state. 4. Tablet Count by Partition To see the bucket (tablet) count for each partition of a table (useful for checking if you have too many small partitions):

Copy code

sql
-- Method 1: Proc
SHOW PROC "/DBS/my_db_name/my_table_name/partitions";

-- Method 2: System Table (Recommended for StarRocks 3.1+)
SELECT DB_NAME, TABLE_NAME, PARTITION_NAME, BUCKETS 
FROM information_schema.partitions_meta 
WHERE TABLE_NAME = 'my_table';

5. Shared-Data Metadata (StarMgr) In Shared-Data mode, "shards" are managed by the

StarMgr

service. You can explore the internal state of the shard manager using:

Copy code

sql
SHOW PROC "/star_mgr";

This will show you sub-paths for

shards

and

workers

. You can dive deeper by appending those paths, for example:

SHOW PROC "/star_mgr/shards";

(Note: This output can be very large in a production cluster). Summary of what to look for: * Total Tablet Count: If your total tablet count exceeds 500,000 to 1,000,000, your FE may experience the "Global Lock" and "Deadline Exceeded" issues discussed earlier. * Tablets per Node: Ideally, tablets should be balanced across nodes. If one node has significantly more than others, it may indicate a data skew or an issue with the shard balancer. References • sql-reference/sql-statements/cluster-management/nodes_processes/SHOW_PROC.md • knowledge/admin_and_operation/shared_data_compaction.md • administration/management/monitoring/metrics.md References • sql-reference/sql-statements/cluster-management/nodes_processes/SHOW_PROC.md • knowledge/admin_and_operation/shared_data_compaction.md

07/02/2026, 8:53 AM

@Rocky the highest tablenumber that i have is 42224 what things need to consider in the configuration for FE and CN

Rocky

07/02/2026, 8:53 AM

With a tablet count of 42,224, your cluster is considered "small-to-medium" in terms of metadata load (StarRocks handles up to several million). Since you are in Shared-Data mode and have previously encountered "Deadline Exceeded" and "Connection Refused" errors, your configuration should focus on stability and synchronization rather than just raw capacity. 1. FE Resource & JVM Configuration Even with 42k tablets, the FE needs stable memory to prevent the "Global Lock" issues during node changes. * Memory (RAM): Allocate at least 16GB to the FE. * JVM Settings (
fe.conf
): * Set

-Xms

and

-Xmx

to the same value (e.g.,

8g

12g

if you have 16GB total) to prevent JVM resizing pauses. * Garbage Collection: Ensure you are using G1GC (usually default in recent versions) to keep pause times low. High GC pauses on the FE Leader can trigger a "Leader Election" which causes cluster-wide "Connection Refused" errors. * Environment: Add

MALLOC_ARENA_MAX=1

to your environment variables or

fe.conf

to prevent glibc from over-allocating virtual memory, which can lead to OOM kills by the OS. 2. Stability Parameters (Critical for Shared-Data) To prevent the "Deadline Exceeded" errors you saw during CN transitions, adjust these in `fe.conf`: * starmgr_grpc_timeout_seconds = 20
: (Default 5s) Gives the FE more time to process metadata updates during node heartbeats or restarts without timing out the CN's request. * tablet_sched_max_scheduling_tablets = 2000
: (Default 2000) Since your tablet count is 42k, the default is fine. If you scale to 500k+ tablets, you would increase this to speed up rebalancing. * lake_compaction_max_tasks = -1
: Keep this as

-1

(adaptive). It allows StarRocks to manage compaction based on CN availability. 3. CN Configuration (
cn.conf
) CNs are primarily for compute, but in Shared-Data mode, they also manage a local Data Cache. * mem_limit = 90%
: This is the default. Ensure the physical machine has enough swap or overhead so the OS doesn't kill the CN process. * Data Cache: Ensure your CNs have sufficient SSD storage for the

starlet_cache_dir

. Even though data is in S3, performance depends on this local cache. * Check

storage_root_path

cn.conf

. It should point to your fastest available disk. * Graceful Exit: This isn't a config parameter but an operational one. Always use

ALTER SYSTEM DROP COMPUTE NODE

before stopping a CN pod/server. This allows the FE to reassign shards before the node goes offline, preventing the 5-second metadata lockup. 4. Operational Health Checks With 42k tablets, you should monitor the following to ensure your config is working: 1. FE Journal Size: If the

meta/journal

folder grows indefinitely, it means your Follower FEs (like

fe-2

) are not replaying logs correctly. 2. SHOW PROC "/statistic";
: Monitor

InconsistentTabletNum

. It should stay at 0. 3. FE Thread Health: Since you checked

thrift-server-pool

and it was 4, it means your FE is idling. If this number ever stays near

thrift_server_max_worker_threads

(default 4096), you have a networking or slow-query bottleneck. Summary Checklist for your `fe.conf`:

Copy code

properties
# Memory
JAVA_OPTS="-Xmx8g -Xms8g -XX:+UseG1GC"

# Shared-Data Stability
starmgr_grpc_timeout_seconds = 20
lake_compaction_score_upper_bound = 2000

# Metadata synchronization
bdbje_heartbeat_timeout_ms = 5000

References • deployment/plan_cluster.md • faq/Others.md • administration/management/FE_configuration.md • administration/management/BE_configuration.md References • faq/Others.md

Rocky

07/02/2026, 8:53 AM

• administration/management/FE_configuration.md • knowledge/trouble_shooting/fe_memory_problems.md • administration/management/BE_configuration.md

07/03/2026, 3:53 AM

@Rocky, fe error 2026-07-03 114714.742+08:00 WARN (starrocks-mysql-nio I/O-2|171) [ConnectContext.kill():1358] kill query, 10.229.54.37:11962, kill connection: false 2026-07-03 114714.742+08:00 WARN (starrocks-mysql-nio-pool-549|7684) [MySQLReadListener.handleRequest():162] Exception happened in one session(com.starrocks.qe.ConnectContext@5e081253). java.nio.channels.ClosedChannelException at java.base/sun.nio.ch.SocketChannelImpl.ensureOpenAndConnected(SocketChannelImpl.java:215) at java.base/sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:527) at org.xnio.nio.NioSocketConduit.write(NioSocketConduit.java:153) at org.xnio.conduits.ConduitStreamSinkChannel.write(ConduitStreamSinkChannel.java:150) at org.xnio.channels.Channels.writeBlocking(Channels.java:147) at com.starrocks.mysql.MysqlChannel.realNetSend(MysqlChannel.java:247) at com.starrocks.mysql.ssl.SSLChannelImp.write(SSLChannelImp.java:163) at com.starrocks.mysql.MysqlChannel.send(MysqlChannel.java:238) at com.starrocks.mysql.MysqlChannel.flush(MysqlChannel.java:264) at com.starrocks.mysql.MysqlChannel.sendAndFlush(MysqlChannel.java:332) at com.starrocks.qe.ConnectProcessor.finalizeCommand(ConnectProcessor.java:928) at com.starrocks.qe.ConnectProcessor.processOnce(ConnectProcessor.java:1218) at com.starrocks.mysql.nio.MySQLReadListener.handleRequest(MySQLReadListener.java:152) at com.starrocks.mysql.nio.MySQLReadListener.lambda$handleEvent$0(MySQLReadListener.java:92) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) at java.base/java.lang.Thread.run(Thread.java:840)

Rocky

07/03/2026, 3:53 AM

The error

java.nio.channels.ClosedChannelException

in your FE logs is a technical way of saying: "The client closed the connection before StarRocks could finish sending the result." Based on the stack trace, this happened during

ConnectProcessor.processOnce

. Here is the most likely sequence of events: 1. The Delay: Your query was running, or it was stuck waiting for a metadata lock (the issue we discussed regarding

starmgr_grpc_timeout_seconds

). 2. The Client Gives Up: Your application, BI tool, or a Load Balancer (like HAProxy or Nginx) hit its own internal timeout. 3. The Disconnect: The client closed the network socket. 4. The FE Error: The FE finally finished the task and tried to send the data back. It found the "channel" (the socket) was already closed, resulting in the

ClosedChannelException

. Common Causes & Solutions 1. Mismatch in Timeouts (Most Common) If your StarRocks

query_timeout

is 300 seconds, but your application or Load Balancer has a timeout of 30 seconds, the client will close the connection while StarRocks is still working. * Check: Verify the timeout settings in your JDBC/Python driver and any Load Balancers. * Fix: Ensure your Load Balancer's "Idle Timeout" is longer than your longest StarRocks query. 2. The Metadata Lock Correlation Since you previously asked about

starmgr_grpc_timeout_seconds

, this error is likely a side effect of that metadata lock. * When the FE Leader takes a Global Write Lock to update shards, the MySQL thread handling your query is paused. * If that lock is held for 10 seconds, but your client has a 5-second timeout, the client disconnects. * Fix: Increasing

starmgr_grpc_timeout_seconds

(as discussed) and ensuring graceful CN shutdowns will reduce these occurrences. 3. FE JVM Pauses (GC) If the FE is experiencing "Stop-the-World" Garbage Collection, it will stop responding to the network. The client sees this as a hang and disconnects. * Check: Look at your

fe.gc.log

. If you see pauses $> 1s$, consider increasing FE memory (

-Xmx

). 4. Automatic Kill The log shows

ConnectContext.kill():1358

. This indicates the FE was explicitly told to kill that query. This happens if: * The query exceeded the

query_timeout

session variable. * A user manually ran

KILL QUERY <id>

. Recommended FE Configurations for Stability If these errors are frequent, you can try enabling TCP keep-alives in

fe.conf

to help the FE detect "half-open" connections:

Copy code

properties
# fe.conf
# Keep the MySQL connection alive for long-running queries
mysql_service_nio_enable_keep_alive = true

# (Optional) If you want the FE to stop the query immediately when a client disconnects
mysql_service_kill_after_disconnect = true

Summary: This error is usually harmless to the FE itself, but it's a signal that your clients are timing out. Look at the latency of your queries during the time this error occurred to see if they were blocked by the metadata lock. References • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md • administration/Meta_recovery.md • administration/management/FE_configuration.md • sql-reference/System_variable.md • administration/management/FE_configuration.md metadata locks. References • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md

Rocky

07/03/2026, 3:53 AM

• administration/Meta_recovery.md • knowledge/trouble_shooting/fe_deadlock_problems.md

07/03/2026, 4:03 AM

@Rocky, how to make hive get refresh all the partition 2026-07-03 120117.997+08:00 WARN (hive-metastore-refresh-0|209) [HiveMetaClient.getPartitionsByNames():320] Expect to fetch 2924 partition on XX but actually fetched 325 partition

Rocky

07/03/2026, 4:03 AM

The warning you are seeing (

Expect to fetch 2924 partition... actually fetched 325

) indicates a discrepancy between the partition names StarRocks has in its list and the actual partition metadata the Hive Metastore (HMS) is returning. This usually happens when partitions have been deleted or modified in Hive/HDFS, but the StarRocks metadata cache still holds the old list of names. To force StarRocks to refresh the partition list and all associated metadata, use the following commands: 1. Manual Refresh (Specific Table) This is the most direct way to fix the discrepancy for a single table. It forces StarRocks to drop the cached partition list and re-fetch everything from the Hive Metastore. * For External Catalogs (Recommended):

Copy code

sql
    REFRESH EXTERNAL TABLE catalog_name.db_name.table_name;

* For Legacy External Tables:

Copy code

sql
    REFRESH EXTERNAL TABLE table_name;

2. Automatic Refresh Configuration If your Hive partitions change frequently, you can adjust how often StarRocks syncs with the Hive Metastore. You can set these properties at the Catalog level:

Copy code

sql
ALTER CATALOG hive_catalog SET (
    -- How often the FE background task refreshes partition metadata (Default: 60s in v3.3+, 7200s before)
    "metastore_cache_refresh_interval_sec" = "60",
    
    -- Whether to cache partition names. Setting this to false forces a fresh list more often
    "enable_cache_list_names" = "false" 
);

3. Why the discrepancy happens? The log message specifically comes from StarRocks'

HiveMetaClient

. It means: 1. StarRocks called

get_partition_names

and received 2,924 names. 2. StarRocks then tried to batch-fetch the detailed metadata for those 2,924 names using

get_partitions_by_names

. 3. The Hive Metastore only returned 325 objects. Possible reasons: * Permissions: The StarRocks FE user may not have permission to view the metadata of the missing partitions in HMS/Ranger. * Hive/HDFS deletion: Partitions were deleted from the Hive Metastore or HDFS recently, and the HMS

get_partition_names

and

get_partitions_by_names

calls are returning inconsistent results (common in high-load HMS environments). * HMS Timeout/Limit: Your Hive Metastore might have a configuration (like

metastore.batch.retrieve.max

) that is lower than what StarRocks is requesting, causing it to truncate the result. 4. Troubleshooting Steps If

REFRESH EXTERNAL TABLE

does not solve the issue: 1. Check Hive side: Run

MSCK REPAIR TABLE table_name;

in Hive to ensure the Hive Metastore itself is consistent with the files on HDFS. 2. Increase FE Timeout: If you have thousands of partitions, the RPC to HMS might be timing out. You can check the FE log for "Timeout" or "thrift" errors. 3. Check Catalog Properties: Ensure your catalog is using the correct HMS URI and that there are no network/firewall issues between StarRocks FE and the Hive Metastore port (usually

). References • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md References • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md References • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md

Rocky

07/03/2026, 4:03 AM

• data_source/External_table.md • data_source/catalog/hive_catalog.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md

07/03/2026, 7:49 AM

@Rocky, fe error when try to create materialized view WARN (starrocks-mysql-nio-pool-77|1438) [ThriftRPCRequestExecutor.call():80] Call thrift rpc failed, addr: TNetworkAddress(hostname:starrocks-prod-fe-1.starrocks-prod-fe-search.starrocks.svc.cluster.local, port:9020), retried: 0 org.apache.thrift.transport.TTransportException: Socket is closed by peer. at org.apache.thrift.transport.TIOStreamTransport.read(TIOStreamTransport.java:177) at org.apache.thrift.transport.TTransport.readAll(TTransport.java:100) at org.apache.thrift.protocol.TBinaryProtocol.readAll(TBinaryProtocol.java:519) at org.apache.thrift.protocol.TBinaryProtocol.readI32(TBinaryProtocol.java:387) at org.apache.thrift.protocol.TBinaryProtocol.readMessageBegin(TBinaryProtocol.java:271) at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79) at com.starrocks.thrift.FrontendService$Client.recv_forward(FrontendService.java:931) at com.starrocks.thrift.FrontendService$Client.forward(FrontendService.java:918) at com.starrocks.qe.LeaderOpExecutor.lambda$forward$1(LeaderOpExecutor.java:241) at com.starrocks.rpc.ThriftRPCRequestExecutor.call(ThriftRPCRequestExecutor.java:67) at com.starrocks.rpc.ThriftRPCRequestExecutor.call(ThriftRPCRequestExecutor.java:48) at com.starrocks.qe.LeaderOpExecutor.forward(LeaderOpExecutor.java:237) at com.starrocks.qe.LeaderOpExecutor.execute(LeaderOpExecutor.java:140) at com.starrocks.qe.StmtExecutor.forwardToLeader(StmtExecutor.java:1537) at com.starrocks.qe.StmtExecutor.execute(StmtExecutor.java:1040) at com.starrocks.qe.ConnectProcessor.executeQueryAttempt(ConnectProcessor.java:600) at com.starrocks.qe.ConnectProcessor.runWithParserStageRetry(ConnectProcessor.java:494) at com.starrocks.qe.ConnectProcessor.handleQuery(ConnectProcessor.java:431) at com.starrocks.qe.ConnectProcessor.dispatch(ConnectProcessor.java:831) at com.starrocks.qe.ConnectProcessor.processOnce(ConnectProcessor.java:1216) at com.starrocks.mysql.nio.MySQLReadListener.handleRequest(MySQLReadListener.java:152) at com.starrocks.mysql.nio.MySQLReadListener.lambda$handleEvent$0(MySQLReadListener.java:92) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) at java.base/java.lang.Thread.run(Thread.java:840)

Rocky

07/03/2026, 7:49 AM

This error occurs when you are connected to a Follower FE (not the Leader), and it tries to forward your

CREATE MATERIALIZED VIEW

command to the FE Leader, but the Leader closes the connection unexpectedly. Since

CREATE MATERIALIZED VIEW

is a metadata-write operation, it must be executed on the Leader. 1. Root Cause: FE Leader is Unreachable or Overloaded The address

starrocks-prod-fe-1...:9020

(the

rpc_port

) is where the Follower tried to send the request. "Socket closed by peer" usually means: * The FE Leader crashed or restarted: Check the logs of

starrocks-prod-fe-1

for

OutOfMemoryError

FATAL

errors. * High Metadata Lock Contention: If the Leader is busy handling metadata for those 42,224 tablets (e.g., during a heavy compaction or another schema change), it may stop responding to the Thrift RPC port, causing the OS to close the connection. * JVM GC Pauses: A long "Stop-the-World" Garbage Collection on the Leader can cause it to drop incoming RPC requests. 2. Immediate Troubleshooting Steps Run these commands to verify the cluster state: 1. Check FE Status:

Copy code

sql
    SHOW FRONTENDS;

* Look at the

IsLeader

column. Is

starrocks-prod-fe-1

actually the Leader? * Look at the

Alive

column. Are all FEs alive? 2. Verify the RPC Port: Ensure

rpc_port

(default

) is open and reachable between all FE pods. In Kubernetes, verify that the Headless Service (

starrocks-prod-fe-search

) is correctly resolving to the FE pod IPs. 3. Check Leader Logs: Search the

fe.log

on the Leader node (

starrocks-prod-fe-1

) at the exact timestamp (

2026-07-03 12:01:17

) for any errors. 3. Recommended Configuration Changes To prevent Thrift RPC timeouts during heavy metadata operations like creating an MV, you should increase the forwarding timeout in `fe.conf`:

Copy code

properties
# fe.conf on ALL FE nodes

# Increase the timeout for internal FE-to-FE communication (default is usually 10-20s)
thrift_client_timeout_ms = 60000

# Increase the number of worker threads for the Thrift server if you have many concurrent requests
thrift_server_max_worker_threads = 4096

4. Kubernetes Specific Issue Since you are using K8s (based on the

.svc.cluster.local

address), this often happens if: * Resource Limits: The FE Leader pod is hitting its CPU limit, causing it to respond too slowly to the Thrift request. * Network Jitter: Internal K8s networking (like Flannel or Calico) had a momentary blip. Recommendation: Try connecting your MySQL client directly to the Leader FE IP instead of a Load Balancer to create the Materialized View. This skips the "forwarding" step entirely and will give you a clearer error message if the Leader itself is the problem. References • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • administration/Management.md • deployment/helm_chart_usage.md • knowledge/trouble_shooting/fe_deadlock_problems.md • administration/management/FE_configuration.md

07/03/2026, 7:59 AM

@Rocky, what meaning of this warning 2026-07-03 155828.385+08:00 WARN (replayer|126) [GlobalStateMgr.replayJournalInner():2159] replay journal cost too much time: 1542 replayedJournalId: 138795

Rocky

07/03/2026, 7:59 AM

This warning means that a background thread in the StarRocks Frontend (FE) is taking a long time to synchronize metadata changes. What is "Journal Replay"? StarRocks uses a "Journal" (or EditLog) to keep track of all metadata changes (e.g., creating a table, adding a partition, updating a tablet's version). * The Leader FE writes these changes to the journal. * Follower/Observer FEs continuously "replay" these journals to keep their in-memory state in sync with the Leader. The Warning Decoded

replay journal cost too much time: 1542 replayedJournalId: 138795

* 1542: It took 1,542ms (1.5 seconds) to apply a single batch of journal entries. * replayedJournalId: The specific ID of the metadata change being processed. * The Threshold: By default, StarRocks logs this warning if a replay loop takes longer than 1,000ms (1 second). Why is this happening? Frequent occurrences of this warning usually point to one of the following bottlenecks: 1. Slow Disk I/O (Most Common) The metadata is stored in a BDBJE (Berkeley DB Java Edition) environment in your

meta/

directory. If this directory is on a slow disk (HDD) or a network-attached disk with high latency, the FE will struggle to read and write journals quickly. * Recommendation: Always host FE metadata on SSD/NVMe drives. 2. Large Metadata Operations If you recently performed a "massive" operation, the journal entry for that operation will be very large, taking longer to parse and apply. Examples include: * Creating hundreds of tables or thousands of partitions in a single script. * A massive

DELETE

DROP

operation affecting many tablets. * Submitting a very large schema change. 3. FE Resource Contention (CPU/Memory) If the FE is under heavy load (e.g., thousands of concurrent queries) or experiencing JVM Garbage Collection (GC) pauses, the replayer thread will be starved of resources. * Check: Look for

Pause

entries in your

fe.gc.log

. If you see long pauses $(>500ms)$, consider increasing FE memory (

-Xmx

). 4. External Catalog Metadata Latency (v3.x+) If you are using Hive, Iceberg, or Hudi catalogs, StarRocks occasionally writes statistics updates to the journal. Replaying these can sometimes trigger a metadata lookup to the external system (like HDFS or HMS). If that system is slow, the replayer thread will block. Is this a problem? * Occasionally: No. If it happens once or twice during a heavy load, it's just a notification. * Frequently: Yes. If the replayer is consistently slow, Follower nodes will fall behind the Leader. This leads to the "Meta out of date" error, where queries fail because the Follower node doesn't have the latest version of the data. Troubleshooting Steps 1. Check Disk Latency: Run

iostat -x 1

on the FE node to see if the disk hosting the

/meta

directory is saturated. 2. Monitor Sync Status: Run

SHOW FRONTENDS;

and compare the

ReplayedJournalId

column. If a Follower's ID is significantly lower than the Leader's, it is lagging. 3. Check FE Logs: Search for other errors around the same timestamp to see what kind of operation (e.g.,

OP_ADD_PARTITION

) was being replayed. References • administration/Meta_recovery.md • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • administration/Management.md References • administration/management/FE_configuration.md • faq/Others.md • knowledge/usage/best-practice.md

Rocky

07/03/2026, 7:59 AM

• faq/loading/Broker_load_faq.md • faq/shared_data_faq.md • faq/loading/Loading_faq.md • administration/Meta_recovery.md

07/03/2026, 7:59 AM

@Rocky then directly received below 2026-07-03 155915.617+08:00 WARN (starrocks-mysql-nio I/O-2|150) [ConnectContext.kill():1358] kill query, 100.80.104.172:10147, kill connection: false

Rocky

07/03/2026, 8:00 AM

This log message indicates that StarRocks has cancelled a specific query but kept the MySQL connection open. In your case, this is a direct symptom of the Frontend (FE) metadata overload you saw in the previous logs. 1. What "kill connection: false" means * kill query: The execution of a specific SQL statement was stopped. * kill connection: false: The underlying TCP/MySQL session was not closed. This allows the client to stay connected and potentially send another query, rather than receiving a "Lost Connection" error. 2. Why it happened (The Chain of Events) Based on your previous logs, here is the most likely sequence of events: 1. Metadata Lag: Your FE was struggling with

replay journal cost too much time

. This means the FE was heavily occupied synchronizing metadata (likely because you are dealing with many partitions or creating a Materialized View). 2. Metadata Lock: While replaying journals, the FE often holds a global metadata lock. 3. Query Timeout: The query from

100.80.104.172

was likely waiting for this lock or waiting for a response from the BE. Because the FE was "frozen" or lagging due to the journal replay, the query exceeded its

query_timeout

(default 300 seconds). 4. Auto-Kill: Once the timeout was hit, StarRocks automatically triggered a "kill" to free up resources. 3. How to fix it Since you are seeing a pattern of

RPC failures

Journal Replay lag

, and

Query Kills

, your FE is under-resourced or the metadata volume is too high. A. Check the FE Leader's Health Run

top

or check your monitoring (Grafana) for the node `starrocks-prod-fe-1`: * CPU: Is it at 100%? If so, you may need more cores or to reduce metadata operations (like lowering the frequency of Hive metadata refreshes). * JVM Memory: Is it frequently hitting Full GC? Check

fe.gc.log

. If the FE is spending all its time doing garbage collection, it will stop responding to RPC and journal replays will lag. Increase

-Xmx

fe.conf

. B. Increase Query Timeout (Temporary Workaround) If your queries are just barely timing out because the FE is busy, you can increase the limit:

Copy code

sql
SET GLOBAL query_timeout = 600; -- Increase to 10 minutes

C. Check Disk Latency The

replay journal

warning is almost always caused by slow disk I/O on the FE nodes. * Action: Ensure your FE metadata directory (

/meta

) is on a high-performance SSD. If it is on a network drive (like AWS EBS or a K8s PV with low IOPS), these lag spikes are expected. D. Identify the "Killed" Query You can look at the Audit Log (

fe.audit.log

) on the FE and search for queries that have a

State

ERR

CANCELLED

around

15:59:15

to see exactly which SQL statement was killed. Summary of your logs today: Your Hive metadata is inconsistent, your FE-to-FE communication is failing, your metadata sync is lagging, and now queries are timing out. The root cause is a bottleneck on your FE Leader. Focus on FE resources (CPU/Memory/Disk) to stabilize the cluster. cluster-wide) to resolve all these issues. References • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • administration/Meta_recovery.md • administration/Management.md • faq/Others.md • knowledge/trouble_shooting/fe_deadlock_problems.md • administration/management/FE_configuration.md • faq/loading/Broker_load_faq.md • faq/shared_data_faq.md

Rocky

07/03/2026, 8:00 AM

• faq/loading/Loading_faq.md • administration/Meta_recovery.md • faq/Others.md • knowledge/usage/best-practice.md • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • administration/Management.md • deployment/helm_chart_usage.md • knowledge/trouble_shooting/fe_deadlock_problems.md • administration/management/FE_configuration.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/External_table.md • data_source/catalog/hive_catalog.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md

07/08/2026, 3:04 AM

@Rocky, error in CN where cannot load mv 2026-07-08 110305.544 W20260708 110305.240703 139766374528704 mem_hook.cpp:117] large memory alloc, query_id:019f3fad-52a4-7be5-883e-0a9668d8099b instance: 019f3fad-52a4-7be5-883e-0a9668d8099e acquire:1078329868 bytes, is_bad_alloc_caught: 1, stack: 2026-07-08 110305.399 @ 0x7f1e8b458c6c (/usr/lib/x86_64-linux-gnu/libc.so.6+0x129c6b) 2026-07-08 110305.399 @ 0x7f1e8b3cbaa4 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x9caa3) 2026-07-08 110305.399 @ 0x10e02f9e starrocks:🧵:supervise_thread(void*) 2026-07-08 110305.399 @ 0x10e0cb5b starrocks:ThreadPool:dispatch_thread() 2026-07-08 110305.399 @ 0xe99a290 starrocks:pipelineGlobalDriverExecutor:_worker_thread() 2026-07-08 110305.399 @ 0xd32e8f6 starrocks:pipelinePipelineDriver:process(starrocks::RuntimeState*, int) 2026-07-08 110305.399 @ 0xeefdf58 starrocks:pipelineSpillableHashJoinBuildOperator:push_chunk(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xef03a18 starrocks:pipelineHashJoinBuildOperator:push_chunk(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xd87679c starrocks:HashJoiner:append_chunk_to_ht(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xe8eb9ad starrocks:AdaptivePartitionHashJoinBuilder:do_append_chunk(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xe8e8425 starrocks:AdaptivePartitionHashJoinBuilder:_do_append_chunk(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xe8e7b09 starrocks:AdaptivePartitionHashJoinBuilder:_append_chunk_to_partitions(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xe8eb09a starrocks:SingleHashJoinBuilder:do_append_chunk(starrocks::RuntimeState*, std:shared ptr<starrocks:Chunk> const&) 2026-07-08 110305.399 @ 0xd8dcca5 starrocks:JoinHashTable:append_chunk(std:shared ptr<starrocks:Chunk> const&, std:vector<starrocksCow<starrocksColumn>ImmutPtr<starrocks:Column>, std:allocator<starrocksCow<starrocksColumn>ImmutPtr<starrocks:Column> > > const&) 2026-07-08 110305.399 @ 0xa77ab29 starrocks::BinaryColumnBase<unsigned int>::append(starrocks::Column const&, unsigned long, unsigned long) 2026-07-08 110305.399 @ 0xa766b2b void std::vector<unsigned char, starrocks:raw:RawAllocator<unsigned char, 16ul, starrocks::ColumnAllocator<unsigned char> > >::_M_range_insert<unsigned char const*>(__gnu_cxx::__normal_iterator<unsigned char*, std::vector<unsigned char, starrocks:raw:R@ 2026-07-08 110305.399 @ 0xbe3efab starrocks:AllocatorFactory<starrocks:Allocator, starrocks:MemHookAllocator>:checked_alloc(unsigned long) 2026-07-08 110305.399 @ 0x10b380ff malloc 2026-07-08 110305.399 @ 0x10b3c2f9 report_large_memory_alloc(unsigned long) 2026-07-08 110305.399 @ 0x10daf3a1 starrocks:get stack trace[abicxx11]()

Rocky

07/08/2026, 3:05 AM

The error in your Compute Node (CN) log indicates a Large Memory Allocation failure during a Materialized View (MV) refresh task. Specifically, the system tried to allocate 1,078,329,868 bytes (~1.07 GB) in a single request while building a Hash Table for a join. This exceeded a safety threshold or the available memory in the current resource group, leading to the

large memory alloc

warning and likely a query failure. Root Cause 1. Large Join Build Side: The table on the "build" side of your join (usually the right-hand table in the MV's

SELECT

statement) is too large to fit in the memory allocated for the refresh task. 2. Data Skew: If the join key has significant skew (many rows with the same key), a single hash bucket can grow excessively large, triggering this ~1 GB single-allocation limit. 3. Resource Group Constraints: By default, MV refreshes run in the

default_mv_wg

resource group, which may have a memory limit that is too restrictive for your dataset. ────────── Recommended Solutions 1. Partition the Materialized View (Best Practice) If you are refreshing a massive amount of data at once, StarRocks will struggle. You should define the MV with a partition strategy so it only refreshes one partition at a time.

Copy code

sql
CREATE MATERIALIZED VIEW your_mv
PARTITION BY base_table_partition_col -- Refresh partition by partition
DISTRIBUTED BY HASH(...)
REFRESH ASYNC
AS 
SELECT ... FROM base_table JOIN ...;

2. Enable or Force "Spill to Disk" Your logs show the

SpillableHashJoinBuildOperator

was active, but it still tried to make a large allocation. You can force more aggressive spilling to reduce memory pressure:

Copy code

sql
ALTER MATERIALIZED VIEW your_mv_name SET (
    "session.enable_spill" = "true",
    "session.spill_mode" = "force"
);

3. Increase Memory for the MV Resource Group The default resource group for MVs might be hitting its limit. You can create a dedicated resource group for this MV with a higher memory limit:

Copy code

sql
-- 1. Create a dedicated group
CREATE RESOURCE GROUP rg_heavy_mv
TO (user='mv_user') 
WITH (
    "mem_limit" = "0.9", -- Allow up to 90% of BE/CN memory
    "cpu_weight" = "10"
);

-- 2. Assign the MV to it
ALTER MATERIALIZED VIEW your_mv_name SET ("resource_group" = "rg_heavy_mv");

4. Adjust the Session Memory Limit If the refresh is failing due to the

exec_mem_limit

session variable, you can increase it specifically for that MV:

Copy code

sql
ALTER MATERIALIZED VIEW your_mv_name SET ("session.exec_mem_limit" = "17179869184"); -- 16GB

Verification To see why the refresh failed and confirm it was indeed an OOM, run:

Copy code

sql
SELECT * FROM information_schema.task_runs 
WHERE task_name = (SELECT task_name FROM information_schema.tasks WHERE mv_name = 'your_mv_name')
ORDER BY create_time DESC LIMIT 1;

Look at the

error_msg

column for confirmation of the failed task. If it mentions

Memory limit exceeded

, use the steps above. References • faq/Sql_faq.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/BE_configuration.md • knowledge/usage/best-practice.md • best_practices/audit_log_resource_group.md

07/09/2026, 9:47 AM

@Rocky, error in FE 2026-07-09 173624.390+08:00 ERROR (starrocks-mysql-nio I/O-3|151) [MySQLReadListener.handleEvent():97] Unexpected error in MySQLReadListener java.io.IOException: Connection timed out at java.base/sun.nio.ch.SocketDispatcher.read0(Native Method) at java.base/sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:47) at java.base/sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:330) at java.base/sun.nio.ch.IOUtil.read(IOUtil.java:296) at java.base/sun.nio.ch.IOUtil.read(IOUtil.java:259) at java.base/sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:417) at org.xnio.nio.NioSocketConduit.read(NioSocketConduit.java:289) at org.xnio.conduits.ConduitStreamSourceChannel.read(ConduitStreamSourceChannel.java:127) at com.starrocks.mysql.nio.MySQLReadListener.handleEvent(MySQLReadListener.java:58) at com.starrocks.mysql.nio.MySQLReadListener.handleEvent(MySQLReadListener.java:36) at org.xnio.ChannelListeners.invokeChannelListener(ChannelListeners.java:92) at org.xnio.conduits.ReadReadyHandler$ChannelListenerHandler.readReady(ReadReadyHandler.java:66) at org.xnio.nio.NioSocketConduit.handleReady(NioSocketConduit.java:89) at org.xnio.nio.WorkerThread.run(WorkerThread.java:603)

Rocky

07/09/2026, 9:48 AM

This error in the Frontend (FE) log indicates a network-level timeout on a MySQL client connection. What happened? The

MySQLReadListener

is the part of StarRocks that listens for incoming SQL commands. The

java.io.IOException: Connection timed out

means that while the FE was trying to read data from a client's socket, the connection was closed or dropped by the network/client because it had been idle or unresponsive for too long. Why is this happening now? Connecting this to your previous logs (

replay journal cost too much time

and

large memory alloc

), here is the likely scenario: 1. FE Performance Bottleneck: Your FE is currently struggling with "Journal Replay" (metadata synchronization). When the FE is busy replaying journals or experiencing a Stop-the-World JVM Garbage Collection (GC) pause, it may stop responding to network heartbeats. 2. TCP/Network Timeout: Because the FE was "frozen" or extremely slow due to the metadata lag, the client (or a Load Balancer/Proxy like Nginx or HAProxy in between) gave up and closed the connection. 3. The Error: When the FE finally regained enough resources to try and read from that connection, it found the socket was already dead, triggering the

Connection timed out

error. Troubleshooting Steps 1. Check for FE JVM Pauses Look at your

fe.gc.log

. If you see pauses longer than a few seconds, the FE is likely under-resourced. * Fix: Increase the FE memory in

fe.conf

(the

-Xmx

parameter). For large clusters or heavy MV usage, 16GB or 32GB is often required. 2. Check for Load Balancer Timeouts If you are using a Load Balancer (F5, Nginx, HAProxy) to connect to StarRocks: * Ensure the Load Balancer's idle timeout is longer than your longest-running query. * Check if the Load Balancer is healthy and not dropping connections to the FE Leader. 3. Adjust
wait_timeout
If these are simply idle connections being killed, you can increase the MySQL session timeout:

Copy code

sql
-- Increase idle timeout to 8 hours (default is usually 28800)
SET GLOBAL wait_timeout = 28800;

4. Relate to the Metadata Lag The root cause is likely the Journal Replay lag you reported earlier. If the FE cannot sync metadata quickly (due to slow disk I/O on the

/meta

directory), the entire FE process becomes sluggish, leading to these network timeouts. * Is your FE metadata on an SSD? If not, moving it to high-performance storage is the most effective fix for the chain of errors you are seeing. Summary This specific error is usually a symptom of the FE being too busy or the network being unstable. Since you have already seen "journal replay" warnings, focus on fixing the FE metadata performance and memory. References • faq/operation_maintenance_faq.md • administration/management/FE_configuration.md • faq/Others.md • knowledge/trouble_shooting/fe_deadlock_problems.md • administration/Management.md • administration/Meta_recovery.md • faq/loading/Broker_load_faq.md • faq/shared_data_faq.md • faq/loading/Loading_faq.md • faq/Sql_faq.md • knowledge/usage/best-practice.md • best_practices/audit_log_resource_group.md

Rocky

07/09/2026, 9:48 AM

• using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/BE_configuration.md • administration/management/FE_configuration.md • faq/operation_maintenance_faq.md • administration/Management.md • administration/Meta_recovery.md • deployment/helm_chart_usage.md • knowledge/trouble_shooting/fe_deadlock_problems.md • administration/management/FE_configuration.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/catalog/hive_catalog.md • data_source/External_table.md • sql-reference/sql-statements/table_bucket_part_index/REFRESH_EXTERNAL_TABLE.md • data_source/External_table.md • data_source/catalog/hive_catalog.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • faq/operation_maintenance_faq.md • faq/Others.md • unloading/Flink_connector.md • faq/loading/Broker_load_faq.md • faq/loading/synchronize_mysql_into_sr.md References • unloading/Flink_connector.md • faq/operation_maintenance_faq.md • faq/loading/Broker_load_faq.md • faq/loading/synchronize_mysql_into_sr.md • faq/Others.md

2 Views

Open in Slack

Previous Next