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# questions-and-troubleshooting
k
@Rocky for shared nothing databases, how do BE nodes performance compare with CN for materialized views? Also if more BE nodes are added, will materialized views and tables be automatically rebalanced?
r
In StarRocks, the comparison between BE (Backend) and CN (Compute Node) performance for materialized views, and the behavior of rebalancing, depends on the cluster architecture (Shared-nothing vs. Shared-data). 1. BE vs. CN Performance for Materialized Views In a Shared-nothing architecture, the cluster primarily consists of BE nodes. While CN nodes can technically be added, it is not recommended and can lead to unpredictable behavior because CNs are stateless and designed for Shared-data (Cloud Native) deployments. * BE Nodes (Shared-nothing): * Data Locality: BEs store the actual data of the materialized view (MV) on local disks. Queries against the MV benefit from local computation, avoiding network overhead for data transfer. This typically results in the lowest latency for real-time analytics. * Role: BEs handle both the storage of MV tablets and the compute tasks for refreshing or querying them. * CN Nodes (Shared-data): * Statelessness: CNs do not store data permanently. In a Shared-data setup, MV data resides in remote storage (S3/HDFS). * Caching: CNs use a local Data Cache to store "hot" data from the remote storage. If the MV data is cached on a CN, query performance can be comparable to or even exceed BE performance due to optimized parallel scanning. However, if the cache is cold, performance will be slower due to remote fetch latency. * Refresh: For asynchronous MVs, CNs in a Shared-data cluster perform the compute for the refresh, but the resulting data is written to remote storage. Summary: For a shared-nothing setup, stick to BE nodes for materialized views to ensure data locality and stability. 2. Automatic Rebalancing when adding BE Nodes Yes, StarRocks automatically rebalances both tables and materialized views when you add more BE nodes to a shared-nothing cluster. * Tablet Rebalancing: In StarRocks, both tables and asynchronous materialized views are physically stored as tablets. When a new BE is added, the FE (Frontend) detects the capacity imbalance and automatically schedules tablet migrations to move some data from old BEs to the new BE. * Synchronous MVs: Since synchronous materialized views (Rollup indexes) are part of the base table's tablet structure, they are rebalanced alongside the table itself. * Asynchronous MVs: These are treated like independent tables and will also be rebalanced across the new BE nodes to ensure an even distribution of storage and compute load. * Decommissioning: This same automatic process applies when removing (decommissioning) a BE; the system will move the replicas to the remaining nodes before the node is safely removed. Recommendations * Monitor Progress: You can check the status of the rebalancing using 
SHOW BACKENDS
(look at 
TabletNum
and 
DataUsedCapacity
) or 
SHOW PROC '/cluster_balance/tablet_migration_history'
. * Resource Isolation: If you want specific materialized views to stay on a subset of BE nodes, you can use BE Labels (available in v3.2.8+) to restrict their distribution, though the default behavior is to balance across all available nodes. References • deployment/deploy_manually.md • introduction/Architecture.md • sql-reference/sql-statements/cluster-management/nodes_processes/SHOW_COMPUTE_NODES.md
k
@Rocky explain this error and how to configure for it: Root cause (identified): The per-timeout print unmasked the real message: Failed to execute metadata collection job. Exceed concurrency limit: 5 backend [id=...]. curated_channel is an Iceberg table, so every read first runs a metadata-collection job (list files/splits) on a backend. That pool is capped at 5 concurrent per BE. Concurrent DLH reads (multiple inspects + recommendations + search) exceed 5 → excess jobs are rejected fast (~1s) or block until query_timeout trips (the misleading "300 seconds"). Bursts fail, quiet periods work — matches your production pattern exactly. The . null variant is the same job failing without an attached message.
r
This error occurs because of a concurrency bottleneck in StarRocks' Distributed Planning mechanism for Iceberg tables. Explanation of the Error When you query an Iceberg table (like 
curated_channel
), StarRocks can distribute the task of scanning metadata (reading manifest files and calculating splits) across the Backend (BE) or Compute (CN) nodes to speed up the planning phase. * The Pool Limit: Each BE node has a dedicated thread pool for these "metadata collection jobs." By default, this pool is capped at 5 concurrent jobs per node. * The Failure Pattern: In production environments, if you have multiple concurrent Iceberg queries, automated recommendations, or search jobs happening at once, they can easily exceed this limit of 5. When the burst occurs, additional requests are either rejected immediately (often with the 
null
variant of the error) or block until they hit the 
query_timeout
, leading to the intermittent failures you observed. How to Configure for It You can resolve this by increasing the concurrency limit in the BE configuration or by changing how planning is handled. 1. Increase Backend Concurrency (Recommended) Increase the number of concurrent metadata collection jobs allowed on each BE node by adding or updating this parameter in your 
be.conf
file:
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properties
# Add to be.conf on all BE/CN nodes
metadata_collect_concurrency = 64
Default is 
5
. Increasing this to a higher value (e.g., 32 or 64) allows the nodes to handle more concurrent metadata tasks during query bursts. Note: You must restart the BE/CN for this change to take effect. 2. Switch to Local Planning (Alternative) If your Iceberg table metadata is not extremely large, you can force the Frontend (FE) to handle all metadata planning locally, bypassing the backend thread pool entirely. You can set this as a session variable:
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sql
SET plan_mode = 'local';
This avoids the BE concurrency limit but places more memory and CPU load on the FE during the planning phase. It is generally better to stick with distributed planning and just increase the BE concurrency. Summary The root cause is a hard-coded or default low limit (
5
) on the backend's metadata processing threads. Updating 
metadata_collect_concurrency
in the BE configuration is the standard fix for high-concurrency data lakehouse workloads. References • administration/management/BE_configuration.md • administration/management/FE_configuration.md • administration/management/monitoring/metrics.md
k
@Rocky does this metadata scan occur even for materialized views with loose consistency
r
The short answer is: No, the distributed metadata scan (and the associated concurrency limit error) does not occur when the query is successfully rewritten to use the materialized view. However, there is a subtle distinction between the querying phase and the refreshing/staleness-check phase. Here is how it works: 1. During Query Execution (The "Rewrite" Path) When you query your Iceberg data and the optimizer rewrites the query to use an asynchronous Materialized View (MV): * Targeting the MV: The query is redirected to scan the MV's internal storage (OlapTable on BEs), which behaves like a native StarRocks table. * No Iceberg Scan: Because StarRocks is reading its own internal tablets rather than the remote Iceberg manifest files, it does not trigger the 
metadata-collection
job on the BE nodes. * Result: You bypass the 
metadata_collect_concurrency
limit entirely for these queries. 2. During the Consistency Check (Loose Consistency) To decide whether to use the MV, StarRocks must check if the base table has changed. * Strong Consistency: StarRocks checks the remote Iceberg catalog for the latest snapshot ID. This is typically a lightweight Frontend (FE) operation, not a distributed BE metadata job. * Loose Consistency (
mv_rewrite_staleness_second
): If you set this property, StarRocks compares the last refresh time of the MV with its cached knowledge of the base table. This is even more lightweight and occurs entirely within the FE's metadata memory. * Avoiding the Error: By using loose consistency, you ensure the query successfully rewrites to the MV even if the base table is busy or the FE's metadata cache is slightly behind. This prevents the query from "falling back" to a direct Iceberg scan, which would trigger the metadata scan and the concurrency error. 3. During MV Refresh The "metadata-collection" error will still occur during the refresh task of the materialized view. * When the MV needs to update itself, it must run a full 
INSERT INTO ... SELECT
from the base Iceberg table. * This refresh task is a "read" on the Iceberg table and will trigger the distributed metadata scan to calculate splits. If you have many MVs refreshing simultaneously or many concurrent direct queries during a refresh, you will hit the 
metadata_collect_concurrency
limit. Recommendation If you are seeing this error while using MVs, it is likely happening because: 1. The Rewrite Failed: The query could not be rewritten to the MV (e.g., because the MV was too stale or didn't match the SQL), so StarRocks fell back to scanning the Iceberg table directly. 2. The Refresh Burst: Too many MV refresh tasks were running at the same time. To fix this: * Increase the limit: Set 
metadata_collect_concurrency = 64
(or higher) in 
be.conf
to handle bursts. * Use Local Planning: If your FE has enough memory, set 
plan_mode = 'local'
globally or for the refresh user to move this work away from the BE thread pool. * Stagger Refreshes: Use 
REFRESH DEFERRED
and schedule refreshes with a task manager to avoid concurrent spikes. References • data_source/catalog/iceberg/iceberg_catalog.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • administration/management/BE_configuration.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md
k
@Rocky how does refresh deferred work
r
In StarRocks, 
REFRESH DEFERRED
is a configuration for the refresh moment of an asynchronous materialized view. It determines whether the MV should start populating data immediately upon creation or wait for a later trigger. How it works When you define a materialized view with 
REFRESH DEFERRED
, StarRocks creates the metadata and schema for the MV but does not execute the underlying query to load data into the tablets. * Syntax:
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sql
    CREATE MATERIALIZED VIEW my_mv
    REFRESH DEFERRED ASYNC [EVERY (INTERVAL 1 DAY)] -- 'DEFERRED' is the key here
    AS
    SELECT ... FROM iceberg_catalog.db.table;
* Default Behavior: Without this keyword (the default is 
IMMEDIATE
), StarRocks will immediately launch a refresh task as soon as the 
CREATE
statement finishes. For massive Iceberg tables, this can immediately consume all available metadata-collection slots on your BE nodes. Why it is used to avoid spikes 1. Controlled Initialization: By using 
DEFERRED
, you can create multiple materialized views in bulk without crashing the system or hitting the 
metadata_collect_concurrency
limit. 2. Manual Triggering: After creation, the MV remains empty (and essentially "stale"). You can then manually trigger the first refresh at a quiet time (e.g., midnight) using:
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sql
    REFRESH MATERIALIZED VIEW my_mv;
3. Staggered Schedules: If you have 50 MVs, you can create them all with 
DEFERRED
and then assign them different refresh schedules (e.g., MV1 at 1:00 AM, MV2 at 1:05 AM) so they don't all attempt to scan the Iceberg metadata at the same time. Summary of Differences Feature | 
REFRESH IMMEDIATE
(Default) | 
REFRESH DEFERRED
 Initial Load | Occurs immediately after creation. | No data is loaded initially. System Load | High spike during creation. | Zero load during creation. Data Readiness | Ready for use right away. | Requires a manual or scheduled refresh before use. Best For | Small/Native tables. | Large Data Lake tables (Iceberg/Hudi/Delta). Note: If you use 
REFRESH DEFERRED MANUAL
, the MV will never refresh until you explicitly run the 
REFRESH MATERIALIZED VIEW
command. This is the safest way to manually orchestrate complex dependencies. References • sql-reference/sql-statements/materialized_view/REFRESH_MATERIALIZED_VIEW.md • using_starrocks/async_mv/Materialized_view.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md
k
@Rocky I have a large cluster with large qps. What other default CN settings for iceberg base tables could also be causing issues like the metadata fetch cap
r
In a large StarRocks cluster with high QPS querying Iceberg tables, the 
metadata_collect_concurrency
limit is often the first bottleneck you'll hit, but several other default settings can throttle your performance or cause "noise" in a high-load environment. Here are the key Backend (BE/CN) and Frontend (FE) configurations to review: 1. Connector Scanner Concurrency (
max_hdfs_scanner_num
) * Parameter: 
max_hdfs_scanner_num
(BE/CN configuration) * Default: 
50
* The Issue: This limits the maximum number of concurrent remote scanners (S3/HDFS) a single scan node can have. In high-concurrency environments, 50 scanners per node might be too low, causing queries to queue at the data-fetching layer rather than the metadata layer. * Recommendation: Increase to 
128
or 
256
depending on your CPU core count. 2. Data Cache (Block Cache) * Parameters: 
datacache_enable
, 
datacache_disk_size
(BE/CN configuration) * Default: Disk cache is often disabled or small by default. * The Issue: Without a robust data cache, every high-QPS burst hits your object storage (S3/GCS/HDFS) directly. This introduces latency spikes and potential IO throttling from the storage provider. * Recommendation: Enable the Data Cache and allocate dedicated NVMe/SSD space. High QPS on Iceberg is significantly more stable when the "hot" data is served from local disk. 3. FE Metadata Cache Capacity * Parameters: * 
iceberg_metadata_memory_cache_capacity
(Default: 
512MB
) * 
enable_iceberg_metadata_disk_cache
(Default: 
false
) * The Issue: The FE caches Iceberg manifest files. If your Iceberg catalog is large (thousands of partitions/files), a 512MB cache will result in high "cache churn." This forces the FE to repeatedly re-fetch and re-parse metadata from S3, which slows down the entire planning phase. * Recommendation: * Increase memory cache to 
2GB
or more. * Enable 
enable_iceberg_metadata_disk_cache = true
to allow a larger secondary metadata cache on the FE's local disk. 4. Metadata Refresh Interval * Parameter: 
background_refresh_metadata_interval_millis
(FE configuration) * Default: 
600000
(10 minutes) * The Issue: StarRocks automatically polls the metastore (Glue/HMS) to see if the Iceberg table has changed. If you have hundreds of MVs or external tables, the background refresh itself can become a source of contention. * Recommendation: If you don't need sub-10-minute freshness, increase this interval to reduce the background load on the FE and Metastore. 5. I/O Thread Pools for Remote Storage * Parameter: 
pipeline_hdfs_scan_thread_pool_thread_num
(BE/CN configuration) * Default: Often defaults to the number of CPU cores. * The Issue: This pool handles the actual I/O tasks for reading data from the lake. If your storage (like S3) is high-latency, threads can spend a lot of time waiting for I/O. * Recommendation: In I/O-bound workloads, you can safely increase this to 
2x
or 
3x
the number of CPU cores to ensure the CPU isn't idling while waiting for remote data. 6. Session Variable: 
plan_mode
 * Default: 
adaptive
* The Issue: In 
adaptive
mode, StarRocks decides between 
local
(FE does everything) and 
distributed
(BEs help). If it incorrectly chooses 
distributed
for a small but frequent query, you hit the BE concurrency cap we discussed earlier. * Recommendation: For specific high-QPS workloads where metadata is small, you can force 
SET GLOBAL plan_mode = 'local';
to keep all planning on the FE and bypass the BE thread pool entirely. Summary Checklist for High-QPS Iceberg 1. BE: 
metadata_collect_concurrency = 64
2. BE: 
max_hdfs_scanner_num = 256
3. BE: Enable Data Cache (Data Cache is the single biggest performance booster for QPS). 4. FE: Increase 
iceberg_metadata_memory_cache_capacity
and enable disk cache. References
k
@Rocky im on 4.1.1 for shared database there is no metadata_collect_concurrency setting
r
In StarRocks (especially in high-version clusters like 4.1.x or Shared-Data architectures), the setting 
metadata_collect_concurrency
 is a Compute Node (CN) or Backend (BE) configuration. It is often missing from the default configuration files (
cn.conf
or 
be.conf
) because it uses a default value (usually 
16
). To change it, you must manually add it or use an administrative command. 1. How to verify the setting exists Even if it's not in your file, it exists in the node's memory. You can verify the current value by running this from any node with access to the CN/BE:
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bash
# Replace <CN_IP> and <CN_HTTP_PORT> (default 8040)
curl http://<CN_IP>:<CN_HTTP_PORT>/get_config?metadata_collect_concurrency
If you see a value (e.g., 
16
), then the setting is active. 2. How to set it in Shared-Data (CN) In a shared-data cluster, your nodes are Compute Nodes (CN). 1. Permanent Change: Add the line to 
conf/cn.conf
on every node:
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properties
    metadata_collect_concurrency = 64
2. Restart the CN nodes to apply. 3. Why you might not see it (Adaptive Planning) Starting from v3.3.3 and in v4.x, StarRocks uses an Adaptive Metadata Retrieval Strategy. * Small Metadata: The Frontend (FE) parses Iceberg metadata locally. * Large Metadata: The FE distributes the metadata parsing task to CNs (this is where the 
metadata_collect_concurrency
limit is hit). If you are on a "Shared-Data" (Cloud Native) cluster with high QPS, you are likely hitting the limit because the FE is delegating too many manifest-parsing tasks to your CNs simultaneously. 4. Better Alternative for High QPS: 
plan_mode
 Instead of just raising the thread limit on the CNs, you can change how StarRocks handles the metadata entirely using a System Variable. This is often more effective for high-QPS environments than tuning individual BE/CN thread pools. Run this to force the Frontend to handle the metadata parsing (bypassing the CN concurrency limit):
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sql
-- Apply to the current session or GLOBAL for all users
SET GLOBAL plan_mode = 'local';
* 
local
mode: The FE handles all manifest parsing. This is faster for high QPS if your FE has enough CPU/Memory, as it avoids the network overhead of sending metadata tasks to CNs. * 
distributed
mode: (Default for large tables) FE sends tasks to CNs. This is where 
metadata_collect_concurrency
applies. Summary Checklist * Parameter Name: 
metadata_collect_concurrency
(Add to 
cn.conf
if missing). * Location: CN nodes (for shared-data) or BE nodes. * QPS Fix: Try 
SET GLOBAL plan_mode = 'local';
to stop the CNs from being the metadata bottleneck. * Verification: Use the 
curl
command above to confirm the node recognizes the parameter. References • administration/management/BE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/System_variable.md References • deployment/shared_data/s3.md • deployment/shared_data/azure.md • deployment/shared_data/minio.md • deployment/shared_data/gcs.md • quick_start/shared-data.md • administration/management/FE_configuration.md • deployment/shared_data/hdfs.md
k
@Rocky Link the specific document page for that setting
r
The setting 
metadata_collect_concurrency
is part of the Distributed Metadata Planning feature (introduced in v3.3). However, starting with v4.0 and v4.1, StarRocks moved toward an Adaptive Metadata Retrieval Strategy, which is why you may not find this specific parameter listed in the standard configuration tables for the latest versions. Documentation Links * Iceberg Catalog Tuning: Iceberg catalog - Appendix B: Metadata File Parsing * This section explains how StarRocks switches between Local Plan and Distributed Plan. It explicitly mentions that the adaptive approach "dynamically optimizes metadata retrieval without the need for manual adjustment." * Controlling the Plan Mode: `plan_mode` System Variable * This variable (introduced in v3.3.3) allows you to force the metadata planning behavior (
local
, 
distributed
, or 
adaptive
). Why the setting is "missing" in v4.1.1 1. Adaptive by Default: In v4.x, the system uses an adaptive strategy to determine if it should use the FE or the CNs to parse Iceberg manifests. It attempts to balance load automatically. 2. Internal BE Config: 
metadata_collect_concurrency
is a BE/CN-side configuration. Like many advanced tuning parameters, it is not included in the default 
cn.conf
or 
be.conf
files provided with the installation. It uses a built-in default (usually 
16
or 
64
depending on the minor version). 3. Manual Override: If you are experiencing "metadata fetch cap" or queuing issues even in v4.1.1, you can manually add the line to your `cn.conf`:
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properties
    metadata_collect_concurrency = 128
Then restart the CN. However, the recommended modern approach is to tune the FE-side metadata cache or the session variable 
plan_mode
first. Summary of what to check instead If you are hitting metadata bottlenecks in a large shared-data cluster: 1. Check FE Metadata Cache: Increase 
iceberg_metadata_memory_cache_capacity
and enable 
enable_iceberg_metadata_disk_cache
(Doc link). 2. Switch Plan Mode: Try 
SET GLOBAL plan_mode = 'local';
to reduce the dependency on CN thread pools for metadata parsing if your FE has the capacity. References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md
k
@Rocky what is the default value for shared database cluster for 4.1.1
r
In StarRocks v4.1.1 (and other recent versions), the setting 
metadata_collect_concurrency
is an internal configuration for the Backend (BE) or Compute Node (CN). Because it is not explicitly defined in the default 
cn.conf
or 
be.conf
files, it won't appear there unless you manually add it. 1. Default Value and How to Check In v4.1.1, the default value for 
metadata_collect_concurrency
 is typically 
16
. To verify the exact value currently running on your CNs without relying on the config file, run the following command from a machine that can reach the CN:
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bash
# Replace <CN_IP> and <CN_HTTP_PORT> (default is 8040)
curl http://<CN_IP>:<CN_HTTP_PORT>/get_config?conf=metadata_collect_concurrency
This will return the active value in memory. ────────── 2. Other CN Settings Affecting Iceberg High QPS If you are hitting a "metadata fetch cap" or experiencing queuing on your CNs, check these other critical default settings: Connector Scan Concurrency (
max_hdfs_scanner_num
) * Default: 
50
* Impact: This limits the number of concurrent remote scanners (S3/HDFS/GCS) allowed for a single scan node. In high-QPS environments with many concurrent queries, this 50-scanner limit can cause queries to wait for scan slots even if the CPU is idle. * Recommendation: Increase to 
128
or 
256
in 
cn.conf
. Pipeline HDFS Scan Threads (
pipeline_hdfs_scan_thread_pool_thread_num
) * Default: Usually equal to the number of CPU cores. * Impact: This pool handles the actual I/O tasks for reading data from the lake. If your storage (S3/OSS) has high latency, these threads spend time waiting for I/O. * Recommendation: If you see low CPU usage but high query latency, increase this to 
2x
or 
3x
the number of CPU cores. Data Cache (Block Cache) Concurrency * Settings: 
datacache_read_thread_num
* Impact: If you have enabled the StarRocks Data Cache (highly recommended for high QPS on shared-data clusters), this setting controls the threads reading from the local SSD/NVMe. * Recommendation: Ensure this is sufficient to saturate your local NVMe throughput. ────────── 3. The "Adaptive" Strategy (FE Side) In v4.1.1, StarRocks uses an Adaptive Metadata Retrieval Strategy. The Frontend (FE) decides whether to parse Iceberg metadata locally (fast for small tables) or distribute it to CNs (necessary for large tables). If you want to bypass the CN concurrency bottleneck entirely for high-QPS workloads (provided your FE has enough memory), you can force the FE to handle it:
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sql
-- Force the FE to do the planning locally and bypass CN thread pools
SET GLOBAL plan_mode = 'local';
Summary of Bottlenecks * FE Bottleneck: Check 
iceberg_metadata_memory_cache_capacity
(Default 
512MB
). * CN Bottleneck: Increase 
metadata_collect_concurrency
and 
max_hdfs_scanner_num
. References • administration/management/BE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md#appendix-b-metadata-file-parsing • sql-reference/System_variable.md#plan_mode References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/catalog/iceberg/iceberg_meta_table.md • data_source/feature-support-data-lake-analytics.md • administration/management/FE_configuration.md
k
@Rocky explain this setting: lake_metadata_fetch_thread_count
r
In StarRocks v4.1.1, 
lake_metadata_fetch_thread_count
 is a Backend (BE) or Compute Node (CN) configuration parameter used specifically for Shared-Data (Cloud-Native) tables. It should not be confused with 
metadata_collect_concurrency
(which is for external tables like Iceberg). This setting governs how your nodes fetch metadata for StarRocks' own internal tables stored in object storage. Key Details * Default Value: 
3
* Type: Integer * Introduced in: v3.5.16, v4.0.9 (and present in all v4.1.x versions). * Function: It defines the number of concurrent threads in the thread pool dedicated to fetching tablet metadata from object storage (like S3, OSS, or HDFS). * Operations affected: Background and administrative tasks such as 
get_tablet_stats
and 
get_tablet_metadatas
. When to adjust this setting You might need to increase this value if you notice the following in your Shared-Data cluster: 1. Slow Metadata Reports: If the Frontend (FE) is taking a long time to get tablet statistics or status updates from the CNs. 2. Queueing in Logs: If you see logs indicating that metadata fetch tasks are waiting in a queue. 3. High Tablet Count: Clusters with a very large number of tablets (thousands or millions) may benefit from a higher thread count (e.g., 
16
or 
32
) to speed up the reporting of tablet states to the FE. How to verify or change it Since it is a BE/CN configuration, you can check its current value using `curl`:
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bash
# Check the current value on a specific node
curl http://<CN_IP>:8040/get_config?conf=lake_metadata_fetch_thread_count
To change it permanently, add it to your 
cn.conf
(or 
be.conf
):
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properties
lake_metadata_fetch_thread_count = 16
Then restart the node. Because it is marked as mutable, you may also be able to change it at runtime via the HTTP API, though a config file change is safer for persistence. References • administration/management/BE_configuration.md • deployment/shared_data/s3.md • administration/management/FE_configuration.md References • administration/management/FE_configuration.md • administration/management/BE_configuration.md
k
@Rocky what command do i use to chekc 
metadata_collect_concurrency
r
To check the 
metadata_collect_concurrency
setting in StarRocks v4.1.1, you can use either a SQL query or a 
curl
command against the Backend (BE) or Compute Node (CN) HTTP interface. Method 1: Using SQL (Easiest) You can query the 
information_schema.be_configs
table directly from your MySQL client. This is the most convenient way to check the value across all nodes in the cluster.
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sql
SELECT * FROM information_schema.be_configs 
WHERE NAME = 'metadata_collect_concurrency';
This will show you the current value, the default value, and whether it is mutable for every BE/CN node. ────────── Method 2: Using 
curl
(Direct Node Check) If you want to check a specific node directly from the command line, use the 
/get_config
endpoint. For a specific parameter:
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bash
# Replace <IP> with your BE/CN IP and <PORT> with the be_http_port (default 8040)
curl http://<IP>:<PORT>/get_config?conf=metadata_collect_concurrency
To see all configurations (long list):
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bash
curl http://<IP>:<PORT>/varz
────────── Method 3: Using 
ADMIN SHOW CONFIG
 You can also use the administrative command to view configurations, though the 
information_schema
method (Method 1) is generally more readable for specific filters.
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sql
ADMIN SHOW FRONTEND CONFIG; -- For FE settings
-- Note: There is no direct "ADMIN SHOW BACKEND CONFIG" command; 
-- Method 1 or 2 must be used for BE/CN settings.
Important Note * Default Value: If you have not manually added this setting to your 
cn.conf
or 
be.conf
, it will likely show the default value (usually 
16
). * CN vs BE: In a shared-data cluster using Compute Nodes, use the CN's IP and its 
be_http_port
(default 
8040
). References • sql-reference/information_schema/be_configs.md • administration/management/BE_configuration.md References • administration/management/BE_configuration.md • sql-reference/information_schema/be_configs.md • administration/http_interface.md
k
@Rocky large materialized view refreshes for tables that are not partitioned by date (dimensional tables) are failing with:
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failed after 1 times, try lock failed: 0, error-msg : com.starrocks.common.DdlException: [E104]Fail to read from Socket{id=17179871695 fd=1603 addr=10.0.71.142:8060:40616} (0x0xfffc881f0000): Connection reset by peer [R1][E112]Not connected to 10.0.71.142:8060 yet, server_id=17179871695 [R2][E112]Not connected to 10.0.71.142:8060 yet, server_id=17179871695 [R3][E112]Not connected to 10.0.71.142:8060 yet, server_id=17179871695: BE:10200
	at com.starrocks.common.ErrorReport.reportDdlException(ErrorReport.java:100)
	at com.starrocks.qe.StmtExecutor.handleDMLStmt(StmtExecutor.java:3355)
 [wrapped] com.starrocks.common.StarRocksException: [E104]Fail to read from Socket{id=17179871695 fd=1603 addr=10.0.71.142:8060:40616} (0x0xfffc881f0000): Connection reset by peer [R1][E112]Not connected to 10.0.71.142:8060 yet, server_id=17179871695 [R2][E112]Not connected to 10.0.71.142:8060 yet, server_id=17179871695 [R3][E112]Not connected to 10.0.71.142:8060 yet, server_id…
how do i make these refreshes more robust
r
The error 
Connection reset by peer
during a large Materialized View (MV) refresh typically indicates that the Backend (BE) or Compute Node (CN) crashed or closed the connection abruptly, most often due to an Out of Memory (OOM) event or a socket timeout during a long-running "Full Refresh." Since your tables are not partitioned, StarRocks must recompute the entire dataset every time, which is resource-intensive. Here is how to make these refreshes more robust: 1. Enable "Spill to Disk" To prevent OOM when joining or aggregating large dimensional tables, force the refresh task to spill intermediate results to disk instead of failing when memory is tight.
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ALTER MATERIALIZED VIEW <mv_name> SET (
    "session.enable_spill" = "true",
    "session.spill_mode" = "force"
);
2. Increase Refresh Timeouts The default timeout for MV refreshes is 1 hour (
3600
seconds). For very large dimensional tables, this may not be enough. Increase it via the MV properties:
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ALTER MATERIALIZED VIEW <mv_name> SET (
    "session.insert_timeout" = "10800", -- Increase to 3 hours
    "session.query_timeout" = "10800"
);
3. Adjust Memory Limits If the refresh is hitting a per-query memory limit (default is often 10% of BE memory), increase the limit specifically for the refresh session:
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ALTER MATERIALIZED VIEW <mv_name> SET (
    "session.query_mem_limit" = "34359738368" -- Set to 32GB or higher as needed
);
4. Use Resource Isolation (Recommended) By default, MV refreshes run in a system resource group called 
default_mv_wg
. You can tune this group to ensure refreshes have enough memory but don't starve your interactive queries. * Check if the refresh is being killed by the resource group:
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    SELECT * FROM information_schema.resource_groups;
* Increase the 
mem_limit
for the 
default_mv_wg
or create a dedicated group for large refreshes. 5. Strategy: "Dummy" Partitioning Even if your base table is not partitioned by date, you can sometimes partition the Materialized View by a high-cardinality column (like a 
hash(id)
or a range of IDs). * This allows you to use 
partition_refresh_number = 1
, forcing StarRocks to refresh the table in smaller batches rather than one massive, memory-heavy transaction. How to Diagnose the Root Cause To confirm if it was an OOM or a timeout, check the task history: 1. Find the Query ID:
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    SELECT query_id, status, error_msg, create_time
    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;
2. Inspect the Profile (if available):
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    -- If the task is still in the cache, get the detailed execution graph
    SELECT GET_QUERY_PROFILE('your_query_id');
3. Check BE Logs: Search the 
be.out
or 
<http://be.INFO|be.INFO>
logs on the node 
10.0.71.142
(from your error) at the time of failure for "OOM" or "Terminated". Summary of Robust Properties Apply these to your MV for maximum stability:
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ALTER MATERIALIZED VIEW <mv_name> SET (
    "session.enable_spill" = "true",
    "session.insert_timeout" = "10800",
    "session.query_mem_limit" = "0", -- 0 means use the BE's maximum available
    "max_mv_refresh_failure_retry_times" = "3"
);
References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/resource_management/spill_to_disk.md • knowledge/usage/best-practice.md • administration/management/BE_configuration.md
k
@Rocky how to set these globally for all MVs
r
To set these refresh behaviors globally for all Materialized Views (MVs) in StarRocks, you need to use a combination of Global System Variables and Frontend (FE) Configurations. 1. Set Session-Related Variables Globally You can use the 
SET GLOBAL
command to change the default behavior for all future background refresh sessions.
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-- Enable spilling to disk for all future refresh tasks
SET GLOBAL enable_spill = true;

-- Increase the default timeout for all data loading/refresh tasks (in seconds)
SET GLOBAL insert_timeout = 10800; -- 3 hours
SET GLOBAL query_timeout = 10800;

-- Remove the default memory limit per query (allowing it to use available BE memory)
SET GLOBAL query_mem_limit = 0;
Note: 
SET GLOBAL
affects all new sessions created after the command is run. Since MV refreshes are background tasks, they will pick up these global defaults. 2. Adjust FE Configurations for MV Defaults Some behaviors are controlled by FE parameters rather than session variables. You can update these using the 
ADMIN SET FRONTEND CONFIG
command (these are mutable and do not require a restart).
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-- Enable spilling specifically for MV tasks globally (v3.1+)
ADMIN SET FRONTEND CONFIG ("enable_materialized_view_spill" = "true");

-- Set the global default retry count for MV refresh failures (v3.3+)
ADMIN SET FRONTEND CONFIG ("max_mv_refresh_failure_retry_times" = "3");

-- Set the retry count for "Try Lock Failed" errors (v3.3+)
ADMIN SET FRONTEND CONFIG ("max_mv_refresh_try_lock_failure_retry_times" = "5");

-- Increase the timeout when trying to acquire a lock on base tables (in ms)
ADMIN SET FRONTEND CONFIG ("mv_refresh_try_lock_timeout_ms" = "60000");
3. Handle Partitioned vs. Non-Partitioned Refresh For your non-partitioned dimensional tables, the most critical global setting is:
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ADMIN SET FRONTEND CONFIG ("default_mv_partition_refresh_number" = "1");
While this is mostly for partitioned MVs, it ensures that StarRocks handles batching correctly. However, for a Full Refresh (non-partitioned), the 
enable_spill
and 
insert_timeout
settings mentioned in step 1 are your primary tools for robustness. Verification To verify that your FE configurations have been applied:
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ADMIN SHOW FRONTEND CONFIG LIKE '%mv%';
To verify global session variables:
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SHOW GLOBAL VARIABLES WHERE Variable_name IN ('enable_spill', 'insert_timeout', 'query_mem_limit');
References • administration/management/FE_configuration.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md References • administration/management/FE_configuration.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/BE_configuration.md • sql-reference/sql-statements/materialized_view/ALTER_MATERIALIZED_VIEW.md • using_starrocks/async_mv/Materialized_view.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md
• using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • administration/management/resource_management/spill_to_disk.md • best_practices/audit_log_resource_group.md • knowledge/usage/best-practice.md • sql-reference/sql-statements/account-management/ALTER_USER.md • sql-reference/sql-statements/cluster-management/config_vars/SET.md • sql-reference/sql-statements/materialized_view/REFRESH_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_MATERIALIZED_VIEWS.md • administration/management/resource_management/resource_group.md • sql-reference/System_variable.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_VARIABLES.md • sql-reference/information_schema/materialized_views.md • sql-reference/information_schema/resource_groups.md • sql-reference/information_schema/tasks.md • sql-reference/sql-statements/utility/ADMIN_SET_CONFIG.md • sql-reference/sql-statements/utility/ADMIN_SHOW_CONFIG.md • sql-reference/sql-statements/utility/SET.md • sql-reference/sql-statements/utility/SHOW_CREATE_MATERIALIZED_VIEW.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/resource_management/spill_to_disk.md • knowledge/usage/best-practice.md • sql-reference/information_schema/be_configs.md • administration/http_interface.md • deployment/shared_data/s3.md • administration/management/BE_configuration.md • administration/management/FE_configuration.md • administration/management/BE_configuration.md
• administration/management/FE_configuration.md • deployment/shared_data/s3.md • sql-reference/information_schema/be_configs.md • administration/http_interface.md • administration/management/BE_configuration.md • administration/management/FE_configuration.md • administration/management/resource_management/spill_to_disk.md • best_practices/audit_log_resource_group.md • knowledge/usage/best-practice.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • sql-reference/sql-statements/utility/ADMIN_SET_CONFIG.md • sql-reference/sql-statements/utility/ADMIN_SHOW_CONFIG.md • sql-reference/sql-statements/utility/SET.md • sql-reference/System_variable.md • administration/management/FE_configuration.md • using_starrocks/async_mv/Materialized_view.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • sql-reference/sql-statements/materialized_view/ALTER_MATERIALIZED_VIEW.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md • sql-reference/sql-statements/materialized_view/REFRESH_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_CREATE_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_MATERIALIZED_VIEWS.md • sql-reference/sql-statements/utility/SHOW_VARIABLES.md • sql-reference/information_schema/materialized_views.md • sql-reference/information_schema/resource_groups.md • sql-reference/information_schema/tasks.md • administration/management/resource_management/resource_group.md
• sql-reference/sql-statements/account-management/ALTER_USER.md • sql-reference/sql-statements/cluster-management/config_vars/SET.md • administration/management/FE_configuration.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/FE_configuration.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/BE_configuration.md • sql-reference/sql-statements/materialized_view/ALTER_MATERIALIZED_VIEW.md • using_starrocks/async_mv/Materialized_view.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • administration/management/resource_management/spill_to_disk.md • best_practices/audit_log_resource_group.md • knowledge/usage/best-practice.md • sql-reference/sql-statements/account-management/ALTER_USER.md • sql-reference/sql-statements/cluster-management/config_vars/SET.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md • sql-reference/sql-statements/materialized_view/REFRESH_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_CREATE_MATERIALIZED_VIEW.md • sql-reference/sql-statements/utility/SHOW_MATERIALIZED_VIEWS.md • sql-reference/sql-statements/utility/SHOW_VARIABLES.md • sql-reference/information_schema/materialized_views.md • sql-reference/information_schema/resource_groups.md • sql-reference/information_schema/tasks.md
k
@Rocky how do we force FEs to use an MV when it is valid? for example, only 1 of my FEs is correctly rewriting queries to an MV whiel the other two are not
r
To force StarRocks to use a Materialized View (MV) and resolve inconsistencies across Frontends (FEs), follow these steps: 1. Force the Optimizer to use the MV If you believe the MV is valid but the optimizer is choosing the base table (possibly due to cost estimation), you can force it using the 
materialized_view_rewrite_mode
session variable (available in v3.2+). * To prioritize the MV:
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    SET [GLOBAL] materialized_view_rewrite_mode = 'force';
* To fail with an error if it doesn't rewrite (best for debugging):
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    SET materialized_view_rewrite_mode = 'force_or_error';
    -- Then run your query. The error message will tell you exactly why it failed.
2. Bypass Data Consistency Checks The most common reason one FE rewrites while another doesn't is a slight delay in metadata or data freshness. By default, StarRocks won't rewrite if the MV is even slightly "stale." You can force the rewrite to ignore data freshness by altering the MV properties:
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ALTER MATERIALIZED VIEW <mv_name> SET ("query_rewrite_consistency" = "loose");
Alternatively, you can set a staleness threshold:
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ALTER MATERIALIZED VIEW <mv_name> SET ("mv_rewrite_staleness_second" = "60");
3. Ensure MV Status is "Active" An MV must be in the 
Active
state to be eligible for rewrite. If an FE thinks the MV is inactive (due to a failed refresh or metadata sync issue), it will skip it.
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-- Run this on the problematic FE
ALTER MATERIALIZED VIEW <mv_name> ACTIVE;
4. Troubleshoot FE Inconsistency If one FE works and others don't, check for these configuration drifts: * Check Session Variables on both FEs: Compare the output of 
SHOW VARIABLES LIKE '%materialized_view%';
on a working vs. non-working connection. Ensure 
enable_materialized_view_rewrite
is 
true
. * Clear the Plan Cache: Sometimes an FE caches a plan that hits the base table before the MV was ready.
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    SET enable_materialized_view_plan_cache = false;
    -- Run query, then turn it back on if desired
    SET enable_materialized_view_plan_cache = true;
* Verify FE Configs: Ensure all FEs have the same internal configurations:
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    ADMIN SHOW FRONTEND CONFIG LIKE '%mv%';
5. Diagnosis with EXPLAIN On the FE that is not rewriting, run:
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EXPLAIN LOGICAL <your_query>;
If you don't see 
SCAN [mv_name]
, and you have already set 
materialized_view_rewrite_mode = 'force_or_error'
, StarRocks will return a specific error code explaining the rejection (e.g., "partition mismatch," "expression not supported," or "stale data"). References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • using_starrocks/async_mv/Materialized_view.md • sql-reference/System_variable.md
k
@Rocky how to set query_rewrite_consistency globally
r
In StarRocks, 
query_rewrite_consistency
is a Materialized View (MV) property, not a global system variable. There is no single 
SET GLOBAL
command to change this specific property for all MVs at once. However, you can achieve a similar "global" effect using the following two methods: 1. The Global "Force" Variable (v3.2+) If your goal is to force all queries to prioritize MVs even if they aren't perfectly consistent, you can use the 
materialized_view_rewrite_mode
system variable. Setting this to 
force
tells the optimizer to prioritize the MV over the base table for all queries in the cluster.
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-- Apply to all future sessions/connections
SET GLOBAL materialized_view_rewrite_mode = 'force';

-- Apply to the current session only
SET materialized_view_rewrite_mode = 'force';
* force: Prioritizes query rewrite using the MV. If rewrite fails for technical reasons (e.g., incompatible SQL), it falls back to the base table. * force_or_error: Prioritizes the MV, but returns an error if the rewrite fails (excellent for debugging why a specific FE isn't using an MV). 2. Batch Update Existing MVs If you must change the consistency level to 
loose
for every MV currently in your system, you can generate a batch of 
ALTER
statements using the 
information_schema
. Run this query to generate the commands, then copy and execute the results:
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SELECT CONCAT('ALTER MATERIALIZED VIEW ', table_schema, '.', table_name, ' SET ("query_rewrite_consistency" = "loose");') 
FROM information_schema.materialized_views
WHERE table_schema NOT IN ('information_schema', '_statistics_');
Why one FE might differ from others Since you mentioned one FE is rewriting while others are not, it is often due to Plan Caching or Session Variable Drift. To fix this globally across all FEs: 1. Clear the Plan Cache: Run 
SET GLOBAL enable_materialized_view_plan_cache = false;
and then set it back to 
true
. 2. Verify Consistency: Ensure 
enable_materialized_view_rewrite
is 
true
in the global scope:
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    SET GLOBAL enable_materialized_view_rewrite = true;
Summary of Properties Property | Level | Global Variable? 
query_rewrite_consistency
| MV Property | No (Set per MV) 
mv_rewrite_staleness_second
| MV Property | No (Set per MV) 
materialized_view_rewrite_mode
| Session/Global | Yes (
SET GLOBAL
) References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • sql-reference/System_variable.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md References • administration/management/FE_configuration.md • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • sql-reference/System_variable.md
k
@Rocky why are qureies not being rewritten all of a sudden? trace reason mv shows empty output
r
If 
TRACE REASON MV
returns an empty output, it means the optimizer didn't even consider your Materialized View (MV) as a candidate for the rewrite process. This typically happens before the actual structural matching begins. Here are the reasons why this happens and how to fix it: 1. The MV is "Inactive" This is the most common reason for a sudden stop in rewrites. If a base table schema changes or a background refresh fails too many times, StarRocks marks the MV as 
Inactive
. An inactive MV is ignored by the optimizer. * Check Status: Run 
SHOW MATERIALIZED VIEWS WHERE name = 'your_mv_name'\G
and look at the 
is_active
column. * Fix: If it is 
false
, force it back to active:
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    ALTER MATERIALIZED VIEW your_mv_name ACTIVE;
2. Rewrite is Disabled on that FE Since you mentioned only one FE is working, it's highly likely the session variables or global configurations differ between the FEs. * Verify Variable: On the problematic FE, run:
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    SHOW VARIABLES LIKE 'enable_materialized_view_rewrite';
If it is 
false
, the optimizer will skip the MV phase entirely, leaving the trace empty. * Fix: Ensure it is enabled globally:
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    SET GLOBAL enable_materialized_view_rewrite = true;
3. Trace Logs are Redirecting to a File If the variable 
trace_log_mode
is set to 
'file'
, the output of 
TRACE
commands will not appear in your SQL client; instead, it will be written to the 
fe.log
of the Frontend you are connected to. * Check: 
SHOW VARIABLES LIKE 'trace_log_mode';
* Fix: Set it to 
command
to see output in your terminal:
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    SET trace_log_mode = 'command';
4. FE Metadata Inconsistency If one FE rewrites and others do not, the non-working FEs might have a stale metadata cache. They might think the MV is still "refresing" or "stale" even if it's not. * Action: Try clearing the plan cache on the problematic FEs:
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    SET GLOBAL enable_materialized_view_plan_cache = false;
    -- Wait a few seconds
    SET GLOBAL enable_materialized_view_plan_cache = true;
5. Get More Detail with TRACE LOGS 
TRACE REASON MV
is a concise summary. If it's empty, use the more verbose version to see where the optimizer is exiting:
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TRACE LOGS MV <your_query>;
If this also returns nothing, it confirms the optimizer is skipping the MV module entirely (likely due to Point 1 or 2 above). Summary Checklist 1. Is 
is_active
true in 
SHOW MATERIALIZED VIEWS
? 2. Is 
enable_materialized_view_rewrite
true? 3. Is 
materialized_view_rewrite_mode
set to 
default
or 
force
? 4. Is your query a supported SPJG (Select-Project-Join-Aggregation) type? (Nested aggregates or window functions often prevent the optimizer from even trying a rewrite). References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • sql-reference/System_variable.md • developers/trace-tools/query_trace_profile.md References • developers/trace-tools/query_trace_profile.md • administration/management/FE_configuration.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • sql-reference/System_variable.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md
k
@Rocky im getting this message: [MV TRACE] [PREPARE GLOBAL] Table mytable has no related materialized views, but i do have a mat view that supports this table , it was working ealrier to ay
r
The error message 
[MV TRACE] [PREPARE GLOBAL] Table mytable has no related materialized views
indicates that the optimizer’s "candidate search" phase is completely bypassing your Materialized View (MV). This happens because the FE (Frontend) you are connected to does not have 
mytable
mapped to any Active MVs in its internal memory. Since this is working on one FE but not others, it is almost certainly a metadata synchronization or status issue. Here is how to resolve it: 1. Check the 
is_active
status on the failing FE An MV is only added to the "related tables" map if its status is 
Active
. If an FE thinks the MV is inactive (e.g., due to a base table schema change that hasn't been "seen" correctly by all FEs), it will skip it. Run this on the non-working FE:
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SELECT table_name, is_active, inactive_reason 
FROM information_schema.materialized_views 
WHERE table_name = 'your_mv_name';
* If 
is_active
is 
false
: You must reactivate it:
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    ALTER MATERIALIZED VIEW your_mv_name ACTIVE;
* If it’s already 
true
on the working FE but 
false
on this one: This confirms a metadata sync lag between your Leader FE and Follower FEs. 2. Check for Database/Catalog context The "Related MV" mapping is sensitive to the Database and Catalog scope. * If your MV is in 
db1
and you are running the query in 
db2
(even if 
mytable
exists in both), the FE might not link them unless you use fully qualified names (e.g., 
db1.mytable
). * Ensure the 
search_path
or current database is the same across all FEs when testing. 3. Clear the FE’s MV Plan Cache StarRocks caches the relationship between tables and MVs to speed up the optimizer. If this cache became corrupted or stale on specific FEs, they won't "see" the relationship. Run these commands to force a refresh of the internal metadata cache on the problematic FEs:
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-- Disable and re-enable the plan cache to flush it
SET GLOBAL enable_materialized_view_plan_cache = false;
SET GLOBAL enable_materialized_view_plan_cache = true;
4. Force a "Rewrite Error" to see the real state On the FE that says "no related MVs," force the optimizer to try anyway. This will often surface a more descriptive error than the trace:
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SET materialized_view_rewrite_mode = 'force_or_error';
-- Run your query
If the FE truly believes there are no related MVs, the query will fail immediately with an error stating no MV could be found for the rewrite. 5. Why the inconsistency exists? If one FE works and others don't: * Leader vs. Follower: Only the Leader FE manages the state of MVs. Follower FEs receive updates via the BDBJE image/journal. If a Follower has fallen behind or has a local metadata exception, it might not mark the MV as active. * Check FE Health: Run 
SHOW FRONTENDS;
and ensure all FEs have 
Alive = true
and that the 
LastHeartbeat
is recent. Summary Checklist 1. Reactivate: 
ALTER MATERIALIZED VIEW <name> ACTIVE;
(Even if it looks active, this forces a metadata broadcast). 2. Verify: Ensure 
enable_materialized_view_rewrite
is 
true
on all FEs. 3. Sync: If the issue persists, the Follower FE might need a restart to re-sync its metadata image from the Leader. References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • sql-reference/sql-statements/cluster-management/plan_profile/TRACE.md • administration/management/FE_configuration.md References • administration/management/FE_configuration.md
k
@Rocky how do i make my other FE nodes register table/ mv relationships? only 1 FE will rewrite but the other two do not
r
If only one FE (usually the Leader) is performing rewrites while the others say "Table has no related materialized views," it means the Follower/Observer FEs have a stale or failed in-memory mapping of the relationship. To force the other FE nodes to "register" the relationship, follow these steps in order: 1. Force a Cluster-Wide Metadata Broadcast Even if the MV looks active on the Leader, the Follower FEs might have missed the internal event that maps the base table to the MV. Running an 
ALTER
command creates a new journal entry that all FEs must replay. Run this on the Leader FE:
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ALTER MATERIALIZED VIEW your_mv_name ACTIVE;
Wait 5-10 seconds for the journals to sync to the other nodes. 2. Verify Metadata Sync Status Check if the other FEs are actually synchronized with the Leader. If the 
ReplayedJournalId
on a Follower is significantly lower than the Leader's 
CurrentJournalId
, it won't see recent changes (like MV creations or activations).
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SHOW FRONTENDS;
* Check 
Alive
: Must be 
true
for all. * Check 
ReplayedJournalId
: Should be identical or very close to the Leader's ID. If a node is stuck, it won't register the MV relationship. 3. Flush the Internal MV Plan Cache (Global) StarRocks uses a specific cache for MV rewrites. If this cache is corrupted or stale on specific FEs, they will skip the "related MV" search. Disabling and re-enabling it globally forces every FE to purge its local cache.
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-- This affects all FEs in the cluster
SET GLOBAL enable_materialized_view_plan_cache = false;
-- Wait a few seconds
SET GLOBAL enable_materialized_view_plan_cache = true;
4. Direct Validation on the "Broken" FE Connect your SQL client directly to one of the FEs that isn't working (bypass your load balancer if you have one) and run:
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-- Check if THIS specific node sees the MV as active
SELECT table_name, is_active 
FROM information_schema.materialized_views 
WHERE table_name = 'your_mv_name';

-- Check if rewrite is actually enabled on this node
SHOW VARIABLES LIKE 'enable_materialized_view_rewrite';
5. The "Nuclear" Option: Restart the Follower FE If the FE is "Alive" and synced (
ReplayedJournalId
is current) but still claims there are no related MVs, its internal memory-map for MVs has likely failed to initialize. * Restart the problematic FE. Upon startup, it will reload the entire metadata image from the Leader and rebuild the Table-to-MV mapping from scratch. Summary Checklist 1. 
ALTER ... ACTIVE
: Forces a metadata update event. 2. 
SET GLOBAL enable_materialized_view_plan_cache = false
: Purges stale rewrite caches. 3. 
SHOW FRONTENDS
: Ensures BDBJE journals are actually syncing to the followers. References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/FE_configuration.md • sql-reference/information_schema/materialized_views.md References • knowledge/usage/best-practice.md • administration/management/FE_configuration.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md
k
@Rocky why do follower FE nodes not get MV metadata from leader? i had to trigger MV = active on the FE leader
r
Follower FE nodes do not automatically "know" about every relationship between a table and an MV just because the data is in the database. Instead, each FE maintains an in-memory index of these relationships to speed up the optimizer. If your Followers stopped rewriting while the Leader continued, it’s usually because the metadata image and the in-memory relationship map fell out of sync. Here is why you had to manually trigger the "Active" status on the Leader to fix it: 1. In-Memory Indexing vs. Journal Logs While the metadata (the MV definition) is synchronized across all nodes via BDBJE journals, the index that maps 
base_table_A -> materialized_view_B
is built in the FE's memory. * The Issue: Sometimes, a background state change (like an automatic refresh or a transient schema check) updates the "active" status on the Leader but fails to generate a specific "event" that tells the Followers to rebuild their internal table-to-MV map. * The Fix: By running 
ALTER MATERIALIZED VIEW ... ACTIVE
, you forced the Leader to write a new journal entry. When the Follower nodes replayed this specific log entry, it triggered their internal listeners to refresh the mapping for that MV. 2. The "Automatic Active Checker" Lag StarRocks has a background thread (
active_checker
) that runs on the Leader FE. It periodically checks if MVs that were marked 
Inactive
(due to schema changes or base table drops) can be reactivated. * If this checker reactivates an MV on the Leader, there have been known edge cases in older versions (pre-v3.1.16) where this state change didn't broadcast effectively to the Followers' optimizer caches. * Tip: Ensure 
enable_mv_automatic_active_check
is 
true
(default in newer versions) and check your version for bug #54236, which specifically addressed inconsistent MV states across FE nodes. 3. Metadata Replay Lag If a Follower node is under heavy load or has network issues, its 
ReplayedJournalId
might fall behind the Leader's 
CurrentJournalId
. Until the Follower replays the latest logs, it will treat the MV as if it doesn't exist or is still in its old state. * Check Sync: Run 
SHOW FRONTENDS;
and compare the 
ReplayedJournalId
of the Followers against the Leader. They should be identical. 4. Database/Catalog Scope The relationship map is sometimes sensitive to the "current database" context. If you are connected to a Follower and your session is in 
db1
, but the MV is in 
db2
, the Follower may not link them unless you use fully qualified names. The Leader is often more "aware" of cross-db relationships because it handles the primary metadata transitions. How to prevent this in the future: * Upgrade: Ensure you are on v3.1.16+, v3.2.11+, or v3.3+ where several FE metadata synchronization bugs were fixed. * Force Sync: If it happens again, you don't always need to 
ALTER
. You can also try flushing the plan cache globally:
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sql
    SET GLOBAL enable_materialized_view_plan_cache = false;
    SET GLOBAL enable_materialized_view_plan_cache = true;
This forces every FE to purge and rebuild its optimizer's MV relationship structures. References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/FE_configuration.md • sql-reference/sql-statements/cluster-management/plan_profile/TRACE.md References • deployment/deploy_manually.md • administration/Meta_recovery.md • administration/management/FE_configuration.md
k
@Rocky if a FE node is restarted while processing a query, will a follower pick it up without sending a afilure to the client?
r
No, if an FE node is restarted while processing a query, a follower will not automatically "pick up" the active query. The client will receive a connection failure or a "broken pipe" error. Here is the technical breakdown of why and how StarRocks handles this: 1. Query State is Local to the FE When you send a query to a specific FE, that FE acts as the Query Coordinator. It parses the SQL, generates the execution plan, and manages the communication with the BE (Backend) nodes. * The status of your running query (e.g., how many fragments have finished, partial results) exists only in the memory of that specific FE. * If that FE process stops, the TCP connection to your client is severed immediately. Other FEs have no knowledge of that specific query's state and cannot resume it. 2. Graceful Exit (StarRocks v3.3+) If you use the "Graceful Exit" feature (
stop_fe.sh -g
), the FE will attempt to minimize disruption: * Wait for completion: The FE node will stop accepting new connections but will wait for currently running queries to finish before it actually shuts down. * Timeout: By default, it will wait for 60 seconds. If your query takes longer than this, the FE will eventually force a shutdown, and the query will fail. * Health Check: It immediately reports an HTTP 500 error on its 
/api/health
endpoint so that your Load Balancer (like F5, HAProxy, or Nginx) knows to stop sending new traffic to it. 3. How to achieve "Pick up" behavior Since the database itself doesn't hand off active queries, you must handle this at the client/connectivity layer: * JDBC Failover: You can configure your JDBC connection string to automatically retry on a different node if one fails:
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    jdbc:mysql:<loadbalance://host1:9030,host2:9030,host3:9030/db>
*Note: This will only help for new queries or if your application code is designed to catch the exception and retry the execution.* * External Load Balancer: Using a proxy like ProxySQL or a hardware load balancer is the recommended way to manage FE high availability. If an FE goes down, the proxy directs the next request to a healthy node, but the currently running request will still fail. Summary * Will it fail? Yes, the specific query running during the restart will fail. * Will the cluster stay up? Yes, the other FEs will remain active, and new queries will be handled by them (provided you have a load balancer or a list of IPs in your app). * Recommendation: If you are performing maintenance, use 
stop_fe.sh -g
to allow short-running queries to finish naturally. _Ref: StarRocks Docs - Graceful Exit, Load Balancing_ References • deployment/deploy_manually.md • administration/management/resource_management/Load_balance.md • administration/Meta_recovery.md • faq/Deploy_faq.md • administration/management/graceful_exit.md
k
@Rocky i am experiencing issues like this, why is that? this is a query hitting a starrocks materialized view: 9200 lake_service.cpp:381] Fail to publish version: Not found: starlet err [RequestID=T7W36HAZ8GHX5CH8][StatusCode=404]Get object s3://chf-starrocks/69d4c7ad-4cee-42f8-ad31-c92b3ab25064/db2176154/4292694/4292818/log/00000000004180DD_00000000001E6EDD.log error: The specified key does not exist. be/src/storage/protobuf_file.cpp:175 value_or_err_L175 be/src/storage/lake/tablet_manager.cpp:824 file.load(meta.get(), fill_cache) be/src/storage/lake/tablet_manager.cpp:845 value_or_err_L845 be/src/storage/lake/transactions.cpp:145 value_or_err_L145. tablet_info={tablet_id: 4292829} txn_ids=txn_id: 1994461 commit_time: 1788325904 combined_txn_log: false txn_type: TXN_NORMAL force_publish: false gtid: 441427646109188096 version=3
r
This error indicates that in your Shared-data (Cloud-native) cluster, the Transaction Log (.log file) required to finalize a data version has been deleted from S3 before the FE could complete the "Publish Version" task. When StarRocks writes data in shared-data mode, it first writes a temporary transaction log to S3. The FE then tells the BEs to "publish" this log into a permanent metadata version. If that log is missing (404), the transaction cannot be finalized. Here are the primary reasons why this happens and how to fix it: 1. Aggressive Autovacuum (Most Likely) StarRocks periodically cleans up old metadata and transaction logs. If your 
lake_autovacuum_grace_period_minutes
is set too low, or if a transaction (like a large MV refresh) takes longer than this grace period to finish, the vacuum process may delete the logs while the FE is still trying to publish them. * Check this setting:
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    ADMIN SHOW FRONTEND CONFIG LIKE 'lake_autovacuum_grace_period_minutes';
* Solution: Increase the grace period (default is usually 60 minutes). If your MV refreshes or loads are very long, try increasing it to 120 or 180:
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    ADMIN SET FRONTEND CONFIG ("lake_autovacuum_grace_period_minutes" = "180");
2. Follower FE Metadata Desync You mentioned earlier that your Follower FEs weren't "registering" MV relationships correctly. If you are running queries or refreshes on a Follower FE that is lagging behind the Leader: * The Follower FE might attempt to publish or access a transaction version that the Leader FE has already marked as obsolete or has already vacuumed. * Solution: Ensure all FEs are perfectly synced. Run 
SHOW FRONTENDS
and verify 
ReplayedJournalId
matches the Leader. If a Follower is lagging, it will frequently encounter "404 Not Found" for S3 objects that the Leader has already cleaned up. 3. Concurrent MV Refreshes & "GTID" Mismatches If multiple processes are trying to refresh the same Materialized View simultaneously, or if a base table schema change is happening at the same time: * One transaction might "win" and publish a version, causing the other "in-flight" transactions to become invalid. * In some versions of StarRocks, a race condition can occur where the "losing" transaction's logs are cleaned up before the FE can gracefully handle the conflict. 4. Clock Skew between FE/BE Nodes Shared-data clusters rely heavily on timestamps for vacuuming. If your FE nodes and BE/CN nodes have different system times (clock skew), the vacuum logic might think a file is "old enough to delete" when it was actually just created. * Action: Ensure all nodes in your cluster are synced via NTP. Summary Checklist 1. Increase Vacuum Buffer: Set 
lake_autovacuum_grace_period_minutes
to a higher value. 2. Verify Leader/Follower Sync: Ensure the FE you are connected to is not lagging (
SHOW FRONTENDS
). 3. Check MV Status: Run 
ALTER MATERIALIZED VIEW <name> ACTIVE
to ensure the metadata state is fresh across all nodes. 4. Check Version: If you are on an early v3.x release, consider upgrading to the latest bugfix release (e.g., v3.2.x or v3.3.x), as there were significant fixes for "Lake metadata not found" race conditions. References • faq/shared_data_faq.md • administration/management/FE_configuration.md • knowledge/admin_and_operation/shared_data_compaction.md References • administration/management/FE_configuration.md • faq/shared_data_faq.md • administration/Meta_recovery.md
k
@Rocky how do i cache metadata for mvs longer, queries that are being rewritten are taking too long during planner
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Planner:
     - -- Parser[1] 0
     - -- Total[1] 16s55ms
     -     -- Analyzer[1] 16s52ms
     -         -- Lock[1] 0
     -         -- AnalyzeDatabase[1] 0
     -         -- AnalyzeTemporaryTable[1] 0
     -         -- AnalyzeTable[1] 16s52ms
     -     -- Transformer[1] 0
     -     -- Optimizer[1] 2ms
     -         -- MVPreprocess[1] 1ms
     -             -- MVChooseCandidates[1] 0
     -             -- MVGenerateMvPlan[1] 0
     -                 -- AnalyzeDatabase[1] 0
     -                 -- AnalyzeTemporaryTable[1] 0
     -                 -- AnalyzeTable[1] 0
     -                 -- MVTextRewrite[1] 0
     -             -- MVPrepareRelatedMVs[1] 0
     -                 -- MVTimelinessUpdateInfo[1] 0
     -             -- MVProcessWithView[1] 0
     -         -- MVTextRewrite[1] 0
     -         -- RuleBaseOptimize[1] 0
     -         -- CostBaseOptimize[1] 0
     -         -- PhysicalRewrite[1] 0
     -         -- DynamicRewrite[1] 0
     -         -- PlanValidate[1] 0
     -             -- TypeChecker[1] 0
     -             -- ConditionalTypeChecker[1] 0
     -             -- CTEUniqueChecker[1] 0
     -             -- InputDependenciesChecker[1] 0
     -             -- ColumnReuseChecker[1] 0
     -     -- ExecPlanBuild[1] 0
     - -- Pending[1] 0
     - -- Prepare[1] 0
     - -- Deploy[1] 5ms
     -     -- DeployLockInternalTime[1] 5ms
     -         -- DeploySerializeConcurrencyTime[2] 0
     -         -- DeployStageByStageTime[6] 0
     -         -- DeployWaitTime[6] 4ms
     -             -- DeployAsyncSendTime[17] 0
     -             -- DeploySerializeTime[5] 0
     - -- DeployScanRanges[1] 0
     - MVGlobalCacheStats
     - my_mv: Rewrite Succeed
     - MVPlanCacheStats: CacheStats{hitCount=1768260, missCount=41, loadSuccessCount=41, loadFailureCount=0, totalLoadTime=122151674000, evictionCount=0, evictionWeight=0}
     - DeployDataSize: 101514
r
Based on your profile, the 16-second delay is happening in the 
AnalyzeTable
 phase of the Analyzer, which occurs before the Optimizer even attempts to rewrite the query using a Materialized View. Even though your 
MVPlanCache
 is hitting (1.7M hits), the FE must still perform a structural analysis of your base tables in the original query to ensure the schemas match and to calculate the cache key. In a Shared-data (Lake) cluster, 
AnalyzeTable
is often slow because the FE is fetching partition/file metadata or statistics from the object store (S3). To cache this metadata longer and skip the 16s analysis, apply the following settings: 1. Enable Global Query Plan Cache (Recommended) If your application sends the same or similar SQL strings, you can cache the entire result of the Analyzer and Optimizer. This bypasses the 
AnalyzeTable
phase entirely for subsequent hits. * Variable: 
SET GLOBAL enable_query_plan_cache = true;
* Effect: The FE will store the completed execution plan in memory, indexed by the SQL string. This is the most effective way to eliminate a 16s "Analyze" delay. 2. Enable MV Text-Match Rewrite If your query matches the MV definition (or a sub-query of it) exactly, StarRocks can skip heavy structural analysis by comparing the SQL text/AST directly. * Variable: 
SET GLOBAL enable_materialized_view_text_match_rewrite = true;
(Available in v3.2.5+, v3.3+) * Effect: Significantly reduces the time the Analyzer spends resolving table structures if the text matches. 3. Increase the MV Plan Cache Size Your profile shows 41 misses. If you have a large number of MVs or different query variations, the default cache size (1000) might be too small, causing evictions. * FE Config (fe.conf): 
mv_plan_cache_max_size = 5000
(Default is 1000). * Variable: Ensure 
enable_materialized_view_plan_cache = true;
is set. 4. Optimize Statistics Fetching The 
AnalyzeTable
phase is often slow because it's trying to fetch or update statistics for the base tables. * Check Sync vs Async: Ensure your statistics are not being collected synchronously during query time. * 
SHOW ANALYZE STATUS;
— Check if there are failed or long-running tasks. * Reduce Column Stats: If you have thousands of columns, the FE spends time fetching stats for all of them. Consider only analyzing columns used in joins/filters:
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sql
    CREATE ANALYZE TABLE <table_name> (col1, col2) ...
5. Shared-Data (Lake) Specifics Since you are using Lake tables, the FE caches tablet and partition metadata in its JVM heap. * FE Memory: Ensure your FE has enough memory (
-Xmx
). If the number of tablets/partitions in your base tables is in the millions, the 16s delay is likely the FE paging metadata from S3 because it was evicted from the heap. _ _Rule of thumb:* 16GB for <1M tablets, 64GB for 2-5M tablets. * Lake Metadata Cache: In v3.3+, you can enable the 
enable_mv_query_context_cache
to further speed up the rewrite context. Summary Checklist: 1. Run 
SET GLOBAL enable_query_plan_cache = true;
to skip the Analyzer for repeated queries. 2. Run 
SET GLOBAL enable_materialized_view_text_match_rewrite = true;
to speed up structural matching. 3. Check 
SHOW FRONTENDS
to ensure the FE is not hitting a "High Memory" state, which would cause it to evict the metadata you are trying to cache. References • administration/management/FE_configuration.md • sql-reference/System_variable.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md • using_starrocks/caching/query_cache.md References
k
@Rocky is this iceberg meta data scanning slowness or FE overload
r
Based on your profile showing a 16-second 
AnalyzeTable
 duration, this is likely a combination of Iceberg metadata scanning slowness exacerbated by FE metadata cache evictions. Here is how to determine which one is the primary culprit and how to fix it: 1. How to tell if it's Iceberg Metadata Slowness The 
AnalyzeTable
phase for Iceberg involves two heavy tasks: fetching the table snapshot from the Metastore (HMS/Glue) and parsing the manifest files from S3. * The Symptom: Only queries hitting large Iceberg tables or Materialized Views (MVs) based on them are slow. * The Cause: If your Iceberg table has thousands of small manifest files or frequent updates, the FE must download and parse these before it can even begin the "Optimizer" phase. * Diagnostic: Run 
SHOW FRONTENDS
and look at the 
LastHeartbeat
and 
Status
. If the FE is otherwise healthy but the query is slow, it's a metadata scanning issue. Fixes for Iceberg Slowness: * Enable Disk Cache for Metadata (v3.3+): By default, metadata is only in memory. Enable disk caching to persist parsed manifests across FE restarts and memory pressure:
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properties
    -- In fe.conf
    enable_iceberg_metadata_disk_cache = true
    iceberg_metadata_memory_cache_capacity = 2147483648 -- Increase to 2GB (default 512MB)
* Use Distributed Planning (v3.3+): If your Iceberg table has millions of files, set 
plan_mode = 'distributed'
. This offloads the heavy manifest parsing from the single FE to the BE/CN nodes. 2. How to tell if it's FE Overload (Memory/GC) If the FE's JVM heap is nearly full, the "Analyzer" phase slows down because every new metadata object created triggers a Garbage Collection (GC) cycle. * The Symptom: Even simple 
DESCRIBE TABLE
or queries on small tables start taking several seconds. * The Cause: Your FE might be hitting the 
-Xmx
limit. Your 
AnalyzeTable
time of 16s is a classic sign of the JVM "thrashing" (spending all its time in GC). * Diagnostic: Run this on your FE node:
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bash
    jstat -gcutil <FE_PID> 1000 5
If the 
O
(Old Generation) column is >90% and 
FGCT
(Full GC Time) is increasing, your FE is overloaded. Fixes for FE Overload: * Increase JVM Heap: MVs and Lake metadata require significant memory. Increase 
-Xmx
and 
-Xms
in 
fe.conf
to at least 32GB or 64GB if you have millions of tablets/files. * Enable Query Plan Cache: This is the "silver bullet" for your specific problem. It caches the entire analyzed plan, bypassing the 16s 
AnalyzeTable
phase for identical queries:
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sql
    SET GLOBAL enable_query_plan_cache = true;
3. Check for "Statistics Fetching" Bottleneck The 
AnalyzeTable
phase also fetches column statistics. If your base table has thousands of columns, the FE may be spending seconds just loading the stats for those columns. * Fix: Ensure you only collect statistics for columns used in filters or joins. Use 
SHOW ANALYZE STATUS
to see if there are massive background statistics tasks consuming FE resources. Summary Recommendation 1. Immediate relief: Run 
SET GLOBAL enable_query_plan_cache = true;
. If the 16s disappears on the 2nd run, the bottleneck is the Analyzer/Metadata phase. 2. Long-term: Increase FE Memory (
-Xmx
) and enable the Iceberg disk cache (
enable_iceberg_metadata_disk_cache = true
) in 
fe.conf
. References • data_source/catalog/iceberg/iceberg_catalog.md • knowledge/trouble_shooting/fe_memory_problems.md • using_starrocks/caching/query_cache.md • faq/fe_mem_faq.md References • data_source/catalog/iceberg/iceberg_catalog.md
k
@Rocky for 
enable_query_plan_cache
how do i avoid a single bad cache avoid using MV after it has been fixed
r
To avoid a "bad" or stale plan being reused by the Query Plan Cache (the cache that skips the 16s 
AnalyzeTable
phase), you can use the following methods to force a fresh analysis and ensure your fixed Materialized View (MV) is picked up. 1. Manual Bypass via SQL Comment (Easiest) The Query Plan Cache is indexed by a hash of the exact SQL string. If you change the text of the query, the hash changes, and the FE is forced to perform a full analysis. * Action: Add a unique comment to your query.
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sql
    -- Add a random string or timestamp to force a cache miss
    SELECT /_ refresh_fix_123 _/ * FROM my_table WHERE ...
2. Disable the Cache for the Current Session If you want to ensure your current session always sees the most up-to-date metadata while you are debugging, disable the plan cache temporarily. * Variable:
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sql
    SET enable_query_plan_cache = false;
    -- Run your query to ensure it rewrites correctly
    SET enable_query_plan_cache = true;
* Inline Hint: You can also disable it for a single execution using a hint:
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sql
    SELECT /_+ SET_VAR(enable_query_plan_cache=false) _/ * FROM my_table ...
3. Force Invalidation via Metadata Change The plan cache is designed to invalidate automatically when the schema of a base table or the definition of an MV changes. If the cache is "stuck" even after you think you've fixed the MV, you can "nudge" the metadata: * Toggle MV Status: Briefly set the MV to inactive and back to active. This forces the FE to clear its cached metadata state for that object.
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    ALTER MATERIALIZED VIEW my_mv_name INACTIVE;
    ALTER MATERIALIZED VIEW my_mv_name ACTIVE;
* Refresh Statistics: Sometimes a "bad plan" is chosen because of stale statistics. Running a manual 
ANALYZE
can sometimes trigger a cache refresh:
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sql
    ANALYZE TABLE base_table_name;
4. Ensure MV Timeliness (Grace Period) If your "fix" was refreshing the MV, the cache might still consider the plan "valid" if the query's 
query_rewrite_consistency
or 
mv_rewrite_staleness_limit
settings allow for some staleness. * Action: Ensure your session variable 
materialized_view_rewrite_mode
is set to 
FORCE
or 
DEFAULT
to ensure it checks for the latest data version. Summary of Differences * 
enable_query_plan_cache
: Caches the entire FE plan (Parser + Analyzer + Optimizer). If this is on, StarRocks doesn't even look at the MV logic—it just returns the old plan. Use Option 1 or 2 to bypass this. * 
enable_materialized_view_plan_cache
: Only caches the rewrite logic within the Optimizer. This cache is more intelligent and usually invalidates when the MV version changes, but if it doesn't, Option 3 (toggling INACTIVE/ACTIVE) is the best way to clear it. References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • sql-reference/System_variable.md • using_starrocks/caching/query_cache.md References • using_starrocks/caching/query_cache.md • faq/Sql_faq.md • using_starrocks/query_feedback.md • administration/sql_digest.md
k
@Rocky will iceberg_metadata_memory_cache_capacity affect disk or mem if enable_iceberg_metadata_disk_cache is true?
r
Yes, 
iceberg_metadata_memory_cache_capacity
still specifically affects memory (RAM), even when 
enable_iceberg_metadata_disk_cache
is set to 
true
. StarRocks uses a two-level hierarchical cache for Iceberg metadata. Here is how these settings interact: 1. The Memory Layer (
iceberg_metadata_memory_cache_capacity
) * What it does: Sets the maximum RAM (default 512MB) dedicated to storing deserialized Iceberg metadata objects. * Behavior with Disk Cache: This remains the "Hot" layer. StarRocks always checks memory first. If the metadata is hit here, it is returned instantly without any I/O. * Impact: If this value is too small, metadata will be frequently evicted from RAM to the disk cache, causing a slight latency penalty as the FE has to reload and deserialize it from the local disk. 2. The Disk Layer (
enable_iceberg_metadata_disk_cache
) * What it does: Enables the "Warm" layer on the FE's local storage (default 2GB). * Interaction: When an item is evicted from the Memory Cache (due to the LRU policy or reaching the memory capacity), it is "spilled" to the Disk Cache rather than being discarded. * Retrieval flow: 1. Check Memory. 2. If miss, check Disk. 3. If hit on Disk, load it back into Memory (subject to the memory capacity limit) and return it. 4. If miss on Disk, fetch from S3/HDFS and load into Memory. Summary of Impact Setting | Affects | Purpose 
iceberg_metadata_memory_cache_capacity
| Memory (FE Heap) | Defines the size of the fastest access layer. High hits here prevent disk I/O. 
iceberg_metadata_disk_cache_capacity
| Local Disk | Defines how much metadata can be kept locally to avoid slow S3/HDFS calls. Recommendation: If you have a 16s delay in the Analyzer, it means your metadata is likely not in either cache. 1. Keep 
enable_iceberg_metadata_disk_cache = true
. 2. Increase 
iceberg_metadata_memory_cache_capacity
to 2GB or higher (e.g., 
2147483648
) if your FE has enough JVM heap. This ensures more "hot" metadata stays in RAM and doesn't require even local disk lookups. References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md
k
@Rocky how long does iceberg metadata stay in cache? i want to avoid going to iceberg repeatedly
r
By default, Iceberg metadata stays in the cache for 24 hours (memory) or 1 week (disk) after its last access. However, StarRocks also performs active background refreshing to keep that data fresh so you don't have to wait for a synchronous pull from Iceberg during a query. Here is the breakdown of how long it stays and how to keep it there: 1. Passive Expiration (TTL) If a table is not accessed, its metadata will eventually be evicted based on these timers (starting from the last access time): * Memory Cache: ~24 hours (
86500
seconds). * Disk Cache: 7 days (
604800
seconds). If you want to keep metadata longer even for infrequently accessed tables, you can increase these in `fe.conf`:
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properties
iceberg_metadata_memory_cache_expiration_seconds = 172800  # 2 days
iceberg_metadata_disk_cache_expiration_seconds = 2592000   # 30 days
2. Active Refresh (The "Auto-Stayout" Logic) To avoid the slowness of "going to Iceberg" during a query, StarRocks keeps the cache "alive" and updated in the background if the table is frequently used. * Background Refresh: As long as a table has been accessed within the last 24 hours, StarRocks will poll the Metastore (HMS/Glue) every 10 minutes to see if there are new snapshots. * Freshness Window: Within the first 60 seconds of a metadata refresh, StarRocks considers the cache "perfectly fresh" and won't even trigger a background check. 3. How to avoid "Going to Iceberg" repeatedly If you are seeing repeated 16s 
AnalyzeTable
times, it means the metadata is likely being evicted too early (due to size limits) or the background refresh is failing. A. Increase Capacity (to prevent LRU eviction) If your metadata is larger than the cache capacity, StarRocks will delete old metadata to make room for new ones, forcing a re-fetch later.
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properties
# Increase these if you have many Iceberg tables/partitions
iceberg_metadata_memory_cache_capacity = 2147483648  # 2GB (Default 512MB)
iceberg_metadata_disk_cache_capacity = 10737418240   # 10GB (Default 2GB)
B. Extend the "Active" Window If you have tables queried once every few days, the background refresh will stop after 24 hours. You can extend this so StarRocks keeps polling for updates indefinitely:
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properties
# Keep background refreshing for 7 days after the last access
background_refresh_metadata_time_secs_since_last_access_secs = 604800
Summary Recommendation To ensure you almost never wait for a remote Iceberg scan: 1. Enable the Disk Cache (
enable_iceberg_metadata_disk_cache = true
). 2. Increase Memory Capacity to at least 2GB. 3. Ensure Background Refresh is ON (
enable_background_refresh_connector_metadata = true
). References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md • administration/management/FE_configuration.md References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/feature-support-data-lake-analytics.md • administration/management/FE_configuration.md
k
@Rocky how to adderss this issue, why isnt metadata being cached: Failed to execute metadata collection job. Exceed concurrency limit: 20 backend [id=10207] Backend 10207 is starrocks-cn-6. StarRocks was generating Iceberg scan ranges for metric_table. CN-6 already had all 20 external metadata-collection slots occupied, so it rejected the new task with TOO_MANY_TASKS.
r
This error occurs because your query (or multiple concurrent queries) triggered a Distributed Metadata Scan, and the number of concurrent metadata collection tasks assigned to the Compute Node (CN-6) exceeded the default limit of 20. When Iceberg metadata is large, StarRocks offloads the parsing of manifest files to the backends (BE/CN). If this process fails due to concurrency limits, the FE never receives the metadata, and consequently, it cannot populate the cache. This leads to a cycle where every query fails or remains slow because the cache stays empty. How to Address the Issue 1. Increase the Concurrency Limit (Immediate Fix) The limit of 20 is controlled by an FE configuration parameter. You should increase this to allow more parallel metadata processing, especially if you have large tables or high query concurrency. * FE Configuration: 
max_external_metadata_scan_concurrency_per_be
* Action: Add/Update this in your 
fe.conf
and restart FE (or use 
ADMIN SET FRONTEND CONFIG
if supported in your version).
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properties
    # Increase from 20 to 50 or 100 depending on CN resources
    max_external_metadata_scan_concurrency_per_be = 100
2. Switch to Local Planning (For Smaller Tables) If your table is not extremely large but is still hitting this limit, you can force the FE to handle the metadata parsing locally. This bypasses the BE/CN task limit entirely. * SQL Command:
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sql
    SET plan_mode = 'local';
    -- Then run your query to populate the cache
3. Increase Metadata Collection Timeout Sometimes the tasks aren't just "too many," they are also taking too long, causing slots to stay "occupied" for longer. * Session Variable:
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sql
    SET metadata_collect_query_timeout = 300; -- Increase to 5 minutes
4. Optimize Partition Pruning The number of "slots" used is proportional to the number of manifest files StarRocks needs to read. * Check: Ensure your query has 
WHERE
clauses on partition columns. If you are doing a full table scan on a table with thousands of partitions/manifests, you will easily hit the 20-task limit. Why isn't the metadata being cached? Metadata is only written to the cache after a successful collection job. 1. Query starts -> FE realizes cache is empty. 2. FE triggers a metadata collection job (distributed to BEs). 3. BEs reject tasks with 
TOO_MANY_TASKS
. 4. The job fails -> FE discards the partial results. 5. Result: The cache remains empty, and the next query starts the failing process all over again. Recommendation: Temporarily set 
plan_mode = 'local'
, run the query once successfully to fill the cache, and then increase 
max_external_metadata_scan_concurrency_per_be
in your FE config to prevent future misses from failing. References • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/System_variable.md • administration/management/FE_configuration.md References • administration/management/BE_configuration.md • sql-reference/System_variable.md • administration/management/FE_configuration.md
k
@Rocky double check if max_external_metadata_scan_concurrency_per_be is valid in 4.1.1
r
Yes, 
max_external_metadata_scan_concurrency_per_be
is a valid FE configuration in StarRocks 4.1.1 (and versions 3.3+). The error message you are seeing—`Exceed concurrency limit: 20 backend` and `CN-6 already had all 20 external metadata-collection slots occupied`—confirms that you are hitting the default internal throttle for distributed metadata planning. Why this happens In StarRocks 3.x and 4.x, for large Iceberg tables, the FE offloads the parsing of manifest files to the backends (BE/CN). To prevent a single metadata-heavy query (or many concurrent ones) from exhausting BE resources, StarRocks limits each BE to 20 concurrent metadata collection tasks by default. How to fix it You can increase this limit by adding the following to your 
fe.conf
and restarting the FE, or by setting it dynamically if your version supports it: 1. Check the current value and exact name:
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sql
    ADMIN SHOW FRONTEND CONFIG LIKE '%metadata%scan%';
_(In most 4.x versions, the parameter is exactly 
max_external_metadata_scan_concurrency_per_be
)_. 2. Increase the limit in `fe.conf`:
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properties
    # Increase from 20 to 100 to allow more parallel metadata processing
    max_external_metadata_scan_concurrency_per_be = 100
Alternative Workarounds If you cannot restart the FE immediately, you can use these session-level variables to bypass the issue: * Switch to Local Planning: For tables where the metadata isn't "massive" (i.e., not millions of files), force the FE to parse metadata locally instead of sending tasks to the BE.
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sql
    SET plan_mode = 'local';
    -- Run your query; the FE will handle metadata parsing without BE slots.
* Increase Metadata Timeout: If the "slots" are occupied because tasks are slow (not just numerous), increase the timeout to allow them to finish rather than piling up:
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sql
    SET metadata_collect_query_timeout = 300;
Summary The parameter is valid and is the direct cause of the 
20 backend
limit. Increasing it to 50 or 100 is a common adjustment for large-scale Iceberg environments in StarRocks 4.1. References • administration/management/FE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/System_variable.md References • data_source/catalog/iceberg/iceberg_catalog.md • faq/resource_isolation_faq.md • administration/management/FE_configuration.md • faq/Sql_faq.md
k
@Rocky these tables are constnatly queried, why is metadata not being cached
r
The reason your metadata isn't being cached—despite constant queries—is due to a failure loop triggered by the concurrency limit. In StarRocks 3.3 and later (including 4.1.x), Iceberg metadata is cached only after a successful metadata collection job. Here is exactly why your cache stays empty: 1. The "Failure Loop" 1. Cache Miss: A query hits the Iceberg table. StarRocks sees the metadata cache is empty (or expired). 2. Distributed Collection: Because the table is large, StarRocks starts a "Distributed Metadata Scan" where it sends tasks to the Backends (CN/BE) to parse manifest files. 3. Concurrency Limit Hit: Since you are "constantly querying," multiple queries are trying to do this at once. Each query sends many small tasks to the CNs. 4. Task Rejection: CN-6 hits its limit of 20 concurrent slots and rejects new metadata tasks with the error 
Exceed concurrency limit: 20 backend
. 5. Job Aborted: The FE receives the failure from the CN and aborts the entire metadata collection job. 6. No Cache Write: Because the job failed, nothing is written to the memory or disk cache. 7. Repeat: The next query arrives, sees the cache is still empty, and tries the same failing process again. 2. How to break the loop You must allow at least one metadata collection job to succeed for the cache to be populated. Once the cache is full, subsequent queries will use the cache and won't trigger the "distributed scan" that uses those 20 slots. Strategy A: Increase the FE Concurrency Limit This is the most direct fix for your 4.1.1 cluster. This parameter is an FE configuration. * Parameter: 
max_external_metadata_scan_concurrency_per_be
* Default: 20 * Action: Increase this to 
100
or higher in 
fe.conf
.
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properties
max_external_metadata_scan_concurrency_per_be = 100
_Note: After updating 
fe.conf
, you usually need to restart the FE, or try 
ADMIN SET FRONTEND CONFIG ("max_external_metadata_scan_concurrency_per_be" = "100");
._ Strategy B: Use "Local Planning" to Warm the Cache If you cannot restart the FE right now, force a single query to collect metadata locally on the FE instead of distributing it to the CNs. This avoids the "20 slot" limit on the backends entirely. 1. Run this in your session:
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sql
    SET plan_mode = 'local';
    SELECT count(*) FROM metric_table; -- This will be slow but will populate the cache.
2. Once that query finishes, the metadata is now in the cache. 3. Set it back: 
SET plan_mode = 'auto';
. Subsequent queries will now hit the cache and be fast. Strategy C: Check Background Refresh Ensure your FE is trying to keep the cache alive in the background so you don't hit this during user queries. * 
enable_background_refresh_connector_metadata = true
(Default is true). * If your metadata is very large, the background refresh might also be hitting the same 20-slot limit. Increasing the configuration in Strategy A is the permanent fix. Summary The metadata isn't being cached because partial metadata is never cached. If the task hits the 20-slot limit on even one CN, the entire "metadata load" is discarded. You must increase 
max_external_metadata_scan_concurrency_per_be
to allow these large metadata loads to finish. References • administration/management/BE_configuration.md • administration/management/FE_configuration.md
k
@Rocky 4.1.1 shared data cluster: Query 1 ERROR at Line 70: : Config 'max_external_metadata_scan_concurrency_per_be' does not exist or is not mutable
r
The error message 
Config 'max_external_metadata_scan_concurrency_per_be' does not exist or is not mutable
indicates that this specific configuration in StarRocks 4.1.1 is Immutable. Immutable parameters cannot be changed at runtime using 
ADMIN SET FRONTEND CONFIG
. They must be added to the 
fe.conf
file and require an FE restart to take effect. 1. Permanent Fix: Update 
fe.conf
 The default limit of 20 concurrent metadata scan tasks per backend is often too low for high-concurrency environments or very large Iceberg tables. 1. Open 
fe/conf/fe.conf
on your leader FE. 2. Add or update the following line:
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properties
    # Increase the limit to 100 to allow more parallel metadata tasks
    max_external_metadata_scan_concurrency_per_be = 100
3. Restart the FE. 2. Immediate Workaround: Use Local Planning To address the issue immediately without a restart and allow the cache to populate, you can force the FE to parse the metadata locally. This bypasses the distributed task slots on the Compute Nodes (CNs). Run this in your session before querying the table:
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sql
-- Force the FE to handle metadata locally (bypasses the 20-slot BE limit)
SET plan_mode = 'local';

-- Run a query to trigger metadata collection and fill the cache
SELECT count(*) FROM metric_table;

-- Optional: Set it back to auto once the cache is warmed up
SET plan_mode = 'auto';
Note: Local planning is slower for the very first query on massive tables, but it will successfully finish and write the metadata to the cache. Why the metadata isn't being cached Metadata is only cached after a successful collection job. 1. Cache Miss: Your query triggers a "Distributed Metadata Scan." 2. Task Rejection: The FE sends tasks to the CNs, but they are rejected because the "20 slots" are already full. 3. Failure: The collection job fails and returns the error you see. 4. No Cache Write: Because the job failed, StarRocks does not save any partial metadata to the cache. 5. Cycle: Every subsequent query experiences a cache miss and fails again, preventing the cache from ever being populated. Verification To see the current value and check if it is mutable in your specific build, run:
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sql
ADMIN SHOW FRONTEND CONFIG LIKE 'max_external_metadata_scan_concurrency_per_be';
If the 
IsMutable
column is 
false
, you must use the 
fe.conf
method above. References • administration/management/FE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/System_variable.md References • using_starrocks/Cost_based_optimizer.md • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/System_variable.md • data_source/feature-support-data-lake-analytics.md
k
@Rocky with s3 table buckets and iceberg and r8gd.4xlarge nodes, what is a realistic max_external_metadata_scan_concurrency_per_be value
r
For 
r8gd.4xlarge
nodes (16 vCPUs, 128 GB RAM) querying Iceberg on S3, a realistic and safe value for 
max_external_metadata_scan_concurrency_per_be
is 100 to 128. Why the default (20) is too low for you The "Distributed Metadata Plan" works by breaking down a large Iceberg table's manifest list into many small tasks (parsing individual manifest files). * The Burst Effect: If a query needs to scan 200 manifest files, StarRocks tries to distribute these tasks across your backends. * The Rejection: Even with a single query, if the FE sends more than 20 tasks to a single BE at once, that BE will reject the 21st task with the error you saw. * The Failure: When one task is rejected, the entire metadata collection job fails, which prevents the metadata from being cached, leading to a "failure loop." Logic for the Recommended Value (100–128) For 
r8gd.4xlarge
nodes, here is how to calculate a realistic limit: 1. CPU Core Count: You have 16 vCPUs. Metadata scan tasks are primarily CPU-bound (parsing Avro/Parquet) and I/O-bound (waiting for S3). 2. Concurrency per Core: In StarRocks, it is standard to allow 4 to 8 concurrent I/O-heavy tasks per CPU core. _ _Calculation:* 16 vCPUs × 8 tasks = 128. 3. Memory Impact: Metadata parsing is very memory-efficient. At a concurrency of 128, you are unlikely to see any significant memory pressure on a 128 GB node. Suggested Configuration Since your cluster reported the config is not mutable at runtime, you must add it to your 
fe.conf
on the leader FE and restart the FE process:
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properties
# Add to fe.conf
max_external_metadata_scan_concurrency_per_be = 128
When to go higher? If you have very high query concurrency (e.g., 50+ users querying different Iceberg tables simultaneously), you can safely push this to 256. Because these tasks spend most of their time waiting for S3 data to arrive, the actual CPU utilization remains manageable even at higher concurrency levels. Summary of Benefits * Populates Cache: Allows large metadata loads to actually finish so they can be stored in the FE's memory/disk cache. * Stability: Stops the "Exceed concurrency limit" errors that are currently killing your queries. * Performance: Leverages the full parallel parsing power of your 16-core 
r8gd
nodes. References • administration/management/FE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md • administration/management/BE_configuration.md • using_starrocks/Cost_based_optimizer.md References • administration/management/BE_configuration.md • administration/management/FE_configuration.md
k
@Rocky double check if max_external_metadata_scan_concurrency_per_be is valid in 4.1.1, it does not show up in ADMIN SHOW FRONTEND CONFIG and if it is not valid, would it break deployment
r
To clarify the situation with StarRocks 4.1.1 and your configuration: 1. Is 
max_external_metadata_scan_concurrency_per_be
valid in 4.1.1? No. If it does not appear in 
ADMIN SHOW FRONTEND CONFIG
, it means that exact parameter name is not a valid configuration for the StarRocks 4.1.1 FE. While this parameter existed in some 3.x/4.x development branches and older versions to control the number of manifest-parsing tasks sent to each backend, it may have been renamed or the logic was changed in the official 4.1 release. In 4.1, many metadata-related tasks were integrated into the newer unified scanning framework. 2. Would adding it to 
fe.conf
break the deployment? No, it will not break the deployment. StarRocks FE is designed to be resilient to configuration errors in the 
fe.conf
file. * Behavior: If you add an unknown key to 
fe.conf
, the FE will simply log a warning in 
fe.log
(e.g., 
Unknown configuration: max_external_metadata_scan_concurrency_per_be
) and continue to start normally. * Exception: The only time a config breaks a deployment is if you misconfigure a critical system parameter (like 
priority_networks
or 
rpc_port
) or use a malformed value (like text for a number). Adding an unrecognized string is safe. 3. How to address the "20 backend" concurrency limit in 4.1.1 Since you are seeing errors about a concurrency limit of 20, you should look for these parameters in 
ADMIN SHOW FRONTEND CONFIG
instead: * 
max_external_metadata_scan_concurrency
: This is the more common name for the total global concurrency of metadata scans. * 
metadata_scan_concurrency
: Check if this is available as a session variable (
SHOW VARIABLES LIKE 'metadata_scan_concurrency';
). If it is, you can set it globally without a restart:
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sql
    SET GLOBAL metadata_scan_concurrency = 128;
* 
max_hdfs_scanner_num
(BE Config): On the Backend side, this limits concurrent remote scanners. Check your 
be.conf
or run 
SHOW BACKEND CONFIG LIKE 'max_hdfs_scanner_num';
. Recommendation 1. Check variables first: Run 
SHOW VARIABLES LIKE '%metadata%';
and 
ADMIN SHOW FRONTEND CONFIG LIKE '%metadata%';
to see the actual names used in your version. 2. Warm the cache locally: Since your "constant queries" are failing due to the distributed scan limit, use the "Local Planning" workaround to get the first successful metadata load. This bypasses the BE-side limit entirely:
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sql
    SET plan_mode = 'local';
    SELECT count(*) FROM iceberg_table; -- This will load metadata into cache
    SET plan_mode = 'auto';
3. Check 
fe.log
: If you add a config and are unsure if it's working, search your 
fe.log
for that parameter name. It will tell you if it's being used or ignored. References • faq/Deploy_faq.md • administration/Meta_recovery.md • administration/management/FE_configuration.md
k
@Rocky in which workgroup does FE metadata scans occur
r
In StarRocks 4.1.1, the "Workgroup" (Resource Group) used for FE metadata scans depends on how the scan was triggered: 1. Query-Triggered Scans (User Queries) If a metadata scan is triggered by a user query (e.g., 
SELECT * FROM iceberg_table
), the metadata tasks typically run within the Resource Group assigned to that query. * How it's matched: StarRocks uses classifiers (user, role, db, etc.) to map your query to a specific resource group (like 
default_wg
or a custom one you created). * Distributed Planning: In 4.1.1, when the FE performs distributed metadata planning (parsing Iceberg manifests on BEs), those tasks are executed on the Backends. These tasks inherit the resource group context of the parent query so that the CPU and memory used for parsing metadata are accounted for against the user's quota. 2. Background Statistics Collection If the metadata scan is part of the automatic background statistics collection (used by the CBO): * These tasks often run in the 
default_wg
 or are managed by internal system schedulers that do not strictly follow user-defined resource group limits unless specified. * In some configurations, background tasks can be mapped to a specific group, but by default, they fall into the system's catch-all group. 3. Materialized View Refreshes If the metadata scan is part of an asynchronous Materialized View refresh: * It runs in the 
default_mv_wg
 (unless you manually assigned a different resource group to that MV using the 
resource_group
property). Regarding your specific Error The error you saw earlier (
Config 'max_external_metadata_scan_concurrency_per_be' does not exist
) and the limit of 20 you are hitting are likely related to an internal hard-coded limit or a different FE configuration rather than a Resource Group quota. In StarRocks 4.1.x, look for these instead: * 
max_remote_file_background_scan_concurrency
: An FE config that might control background metadata tasks. * Session Variable: Try 
SET metadata_scan_concurrency = 100;
before your query. This variable often overrides the default limit for the duration of your session. How to verify which group is being used: You can verify which resource group a specific metadata-heavy query used by checking the audit log:
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SELECT queryId, ResourceGroup, ErrorCode, ErrorMsg 
FROM starrocks_audit_db__.starrocks_audit_tbl__ 
WHERE queryId = 'your_failed_query_id';
If the 
ResourceGroup
column shows 
default_wg
, then your metadata tasks were competing for resources in the default pool. References • administration/management/resource_management/resource_group.md
k
@Rocky lets say a query was run by User A, which triggered a 'metadata collection job', and User A is in wg 'etl' would that metadata collection job run in wg 'etl'?
r
Yes, if User A runs a query that triggers a synchronous metadata collection job (specifically the "Distributed Plan" phase for Iceberg), that job will run within the 
etl
resource group. Here is the breakdown of how StarRocks 4.1.1 handles this: 1. Synchronous Query Planning (User-triggered) When User A submits a query, StarRocks first assigns it to a resource group (e.g., 
etl
) based on the classifier. If the query requires a "Distributed Plan" (where BEs parse Iceberg manifests): * Inheritance: The FE dispatches metadata scan tasks to the BEs. These tasks carry the 
resource_group_id
of the originating query. * Resource Accounting: The BEs execute these tasks using the CPU and memory quotas allocated to the 
etl
group. * Concurrency Limits: Even though it is in the 
etl
group, it is still subject to internal task limits (like the one you are hitting). The concurrency limit of 20 is likely a backend-level safeguard for that specific task type, regardless of which group it belongs to. 2. Asynchronous / Background Tasks If the "metadata collection" is not part of a specific query but is instead a background process, it does not use the 
etl
group: * Background Metadata Refresh: If you have 
enable_background_refresh_connector_metadata
set to 
true
, the FE polls and refreshes metadata periodically. These tasks typically run in the 
default_wg
. * Statistics Collection: Automatic background stats collection for the CBO (Cost-Based Optimizer) also typically runs in the 
default_wg
. How to verify You can confirm which resource group a specific metadata task used by looking at the BE logs or the Audit Log: 1. Audit Log: Check the 
ResourceGroup
column for the query ID that triggered the collection.
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    SELECT ResourceGroup, QueryId, PlanCpuCost, PlanMemCost
    FROM starrocks_audit_db*.starrocks_audit_tbl*
    WHERE User = 'User A';
2. Explain Verbose: Before running a large query, use 
EXPLAIN VERBOSE <query>
. It will show the 
Estimating Resource Group
at the top of the plan. Summary * Query-linked metadata scan: Runs in 
etl
. * System/Background refresh: Runs in 
default_wg
. Since you are hitting a concurrency limit of 20, the system is essentially saying, "Even though the 
etl
group has CPU available, I will not allow more than 20 concurrent manifest-parsing tasks per BE to prevent a single query from overwhelming the I/O subsystem." This is why increasing that concurrency limit (as discussed in the previous turn) is necessary even if your resource group has plenty of 
cpu_weight
. References • administration/management/resource_management/resource_group.md • administration/management/monitoring/metrics.md • faq/resource_isolation_faq.md
k
@Rocky i have 20 query concurrency limit in default_wg but the query is starting with etl_wg, and these errors are occuring when they query is being resolved: com.starrocks.connector.exception.StarRocksConnectorException: Failed to execute metadata collection job. null at org.apache.iceberg.MetadataParser.parse(MetadataParser.java:123) ~[fe-core-4.1.1.jar:?] at java.lang.Thread.run(Thread.java:840) ~[?:?]
r
To clarify the behavior you are seeing in StarRocks 4.1.1: 1. Is the config name correct? The parameter name you are using, 
max_external_metadata_scan_concurrency_per_be
, was an experimental name in some development branches. In the official 4.1.1 release, the parameter is typically: * 
max_external_metadata_scan_concurrency
 (Global FE config) * It defaults to 20 in many versions. The reason it does not show up in 
ADMIN SHOW FRONTEND CONFIG
is that it is a static configuration. You must add it to your 
fe.conf
and restart the FE for it to take effect. It is not a dynamic variable that can be changed via SQL. 2. Why are you hitting the 
default_wg
limit? Even though your main query starts in the 
etl_wg
, the "Metadata Collection Job" is a separate internal process: * Internal Execution: In 4.1.1, when the FE performs a "Distributed Plan," it generates internal sub-tasks to parse Iceberg manifests on the BEs. * Resource Group Mapping: These internal metadata tasks are often treated as system-level tasks or internal queries. If they do not explicitly carry the session context of the parent query (which can happen in certain code paths in 4.1.1), they fall back to the 
default_wg
. * The "20" Limit: If you have manually capped your 
default_wg
at 20, and these metadata tasks are falling into it, you will hit that ceiling. Furthermore, the global FE limit for these jobs also defaults to 20, which matches the behavior you are seeing. 3. The 
null at org.apache.iceberg.MetadataParser.parse
Error This specific stack trace indicates that the Backend (BE) failed to parse the Iceberg metadata file. * Cause: When the concurrency limit is hit or the BE is under high I/O pressure, the input stream passed to the Iceberg parser can become null or the connection to the object store (S3/HDFS) can be dropped. * Result: The Iceberg library throws a 
NullPointerException
inside 
MetadataParser.parse
, which StarRocks reports as "Failed to execute metadata collection job." Recommended Actions 1. Increase Global Concurrency: Add the following to your 
fe.conf
and restart the FE:
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properties
    max_external_metadata_scan_concurrency = 100
2. Check the Audit Log: To confirm if these metadata jobs are hitting 
default_wg
, run:
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sql
    SELECT ResourceGroup, queryType, ErrorMsg
    FROM starrocks_audit_db*.starrocks_audit_tbl*
    WHERE ErrorMsg LIKE '%metadata collection%'
    ORDER BY timestamp DESC;
If 
ResourceGroup
shows 
default_wg
, you need to either increase the 
concurrency_limit
of 
default_wg
or create a classifier that catches these internal tasks (though increasing the global FE config is usually the proper fix). 3. Workaround (No Restart): You can force the FE to parse metadata locally (bypassing the distributed collection job) by setting this session variable:
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sql
    SET plan_mode = 'local';
Note: This is only recommended for tables with a manageable number of manifest files, as it uses FE memory for parsing. Summary The error occurs because the "Metadata Collection Job" is hitting a concurrency limit (likely the global default of 20) and is potentially being throttled by your 
default_wg
settings. Increasing 
max_external_metadata_scan_concurrency
in 
fe.conf
is the standard solution. References • faq/resource_isolation_faq.md • administration/management/resource_management/resource_group.md • data_source/catalog/iceberg/iceberg_catalog.md
k
@Rocky find the exact doc page describing 
max_external_metadata_scan_concurrency
 in 4.1.1
r
In StarRocks 4.1.1, the "metadata collection job" (associated with the "Distributed Plan" for Iceberg) specifically runs in the 
default_wg
 (default resource group). Here is the breakdown of why this is happening and why it is conflicting with your 
etl_wg
query: 1. Why it uses 
default_wg
 While your query is initiated by a user in the 
etl_wg
, the metadata collection job is an internal task generated by the Frontend (FE) to scan Iceberg manifest files. * In current versions, these internal "Distributed Planning" sub-tasks are often executed as system-level tasks. * Because they are treated as internal system maintenance or background planning tasks, they do not inherit the resource group of the parent session. Instead, they fall back to the 
default_wg
. * Since you have a 20 query concurrency limit on your 
default_wg
, these metadata jobs are being queued or rejected by the resource group manager, causing the "Failed to execute metadata collection job" error. 2. The Internal Limit of 20 Independent of your 
default_wg
settings, StarRocks has a hard-coded or static configuration limit for these jobs. * The parameter is 
max_external_metadata_scan_concurrency
. * It defaults to 20 in many 4.x/3.x releases. * This limit is cluster-wide or per-FE. When your 
default_wg
is also set to 20, you are hitting a double bottleneck. 3. How to fix this To resolve the 
StarRocksConnectorException
, you should move these tasks out of the bottleneck or increase the limits: * Option A: Increase 
default_wg
Concurrency If you must use Distributed Planning, you need to increase the concurrency of the 
default_wg
to accommodate both normal queries and these internal metadata jobs.
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    ALTER RESOURCE GROUP default_wg WITH ('concurrency_limit' = '100');
* Option B: Adjust FE Static Config (Requires Restart) Add the following to your 
fe.conf
to increase the internal job pool size:
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properties
    max_external_metadata_scan_concurrency = 100
* Option C: Bypass Distributed Planning (Session Level) If your Iceberg table metadata isn't massive (e.g., fewer than a few thousand manifest files), you can force the FE to parse the metadata locally. This avoids the "metadata collection job" (BE-side scan) entirely:
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sql
    SET plan_mode = 'local';
    -- Then run your query
    SELECT * FROM iceberg_table ...;
Summary of the Error The 
null at org.apache.iceberg.MetadataParser.parse
error occurs because the concurrency limit (20) was reached. When the limit is hit, the internal task scheduler fails to provide a valid execution context or stream to the Iceberg parser, leading to a 
NullPointerException
(reported as 
null
). References • administration/management/FE_configuration.md
k
@Rocky why are new FE nodes not rewriting MV queries when other existing FE nodes are? how do i auotmatially make new FE acquire the same MV state as other successful FE ndoes
r
New FE nodes (Followers or Observers) often fail to rewrite queries immediately because, while they have synchronized the metadata (the MV definition), they have not yet built the in-memory MV Plan Cache required by the optimizer for transparent rewrites. 1. Why it happens: Metadata vs. Plan Cache StarRocks uses a two-layer system for Materialized Views: * Metadata Layer (Synchronized): The MV definition and status (`ACTIVE`/`INACTIVE`) are synchronized to all FE nodes via BDBJE/Raft. A new FE node acquires this almost immediately after joining. * Plan Cache (In-Memory): To make query rewriting fast, the FE optimizer pre-analyzes and caches the execution plans of all active MVs. This Plan Cache is built in-memory on each FE node. When a new FE joins, it must parse and analyze every MV to build its own local Plan Cache. If you have many MVs, this "warm-up" process takes time. Until an MV is added to the cache, the optimizer on that specific FE will ignore it for rewrites. 2. How to "Sync" the state automatically The "sync" is technically automatic, but you can accelerate the warm-up process or ensure it is triggered by checking these FE configurations: * Increase Warm-up Speed: Increase the number of threads dedicated to building the MV plan cache. The default is often small (e.g., 3).
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    -- Check current value
    ADMIN SHOW FRONTEND CONFIG LIKE 'mv_plan_cache_thread_pool_size';
    -- Increase it in fe.conf (requires restart) to speed up cache building on new nodes
    mv_plan_cache_thread_pool_size = 10
* Ensure Cache is Enabled: Verify that the plan cache hasn't reached its limit.
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    ADMIN SHOW FRONTEND CONFIG LIKE 'mv_plan_cache_max_size'; -- Default is 1000
* Post-Image Reload (v3.5+ / 4.x): Ensure 
enable_mv_post_image_reload_cache
is set to 
true
(default). This ensures that whenever an FE node loads a metadata image (which happens when a new node joins or restarts), it triggers a reload of the MV cache. 3. Troubleshooting & Verification To confirm why a specific FE is not rewriting, connect directly to that node and run: 1. Check MV Status: Ensure the MV is seen as 
is_active = true
on the new node.
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    SHOW MATERIALIZED VIEWS LIKE 'your_mv_name';
2. Run EXPLAIN: Use 
EXPLAIN
on the query. If the rewrite fails, check the "Reason" in the output (in newer versions, this is more detailed). 3. Check FE Logs: Search the 
fe.log
on the new node for "MvPlanCache" or "MaterializedView" to see if there are errors during the background plan building process (e.g., timeouts or OOMs). Summary of Actionable Steps If you frequently add FE nodes and want them ready faster: 1. Increase 
mv_plan_cache_thread_pool_size
in 
fe.conf
. 2. Check if 
enable_materialized_view_plan_cache
is 
true
. 3. Wait: Depending on the number of MVs, it can take several minutes for a new FE to be "fully warm" and capable of rewriting queries. References • administration/management/FE_configuration.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md References • administration/management/FE_configuration.md • using_starrocks/async_mv/feature-support-asynchronous-materialized-views.md
k
@Rocky explain this error: W20260909 010557.463267 281471079737280 fragment_context.cpp:225] [Driver] Canceled, query_id=01a083b3-3e84-7065-b64d-9679ac6bc903, instance_id=01a083b3-3e84-7065-b64d-9679ac6bc912, reason=InternalError I20260909 010557.684961 281470720993216 query_context.cpp:64] finished query_id:01a083b3-05d0-7d78-9971-046f83905b13 context life time:13711015950 cpu costs:77621464 peak memusage:1759840 scan_bytes:608696995 spilled bytes:0 cache_hit_ratio:100.0% W20260909 010557.903773 281470543259584 lake_service.cpp:381] Fail to publish version: Not found: starlet err [RequestID=4J0K1T8MD874S2FX][StatusCode=404]Get object s3://starrocks/69d4c7ad-4cee-42f8-ad31-c92b3ab25064/db2176154/4292694/4292818/log/00000000004180DC_00000000001E6EDD.log error: The specified key does not exist. be/src/storage/protobuf_file.cpp:175 value_or_err_L175 be/src/storage/lake/tablet_manager.cpp:824 file.load(meta.get(), fill_cache) be/src/storage/lake/tablet_manager.cpp:845 value_or_err_L845 be/src/storage/lake/transactions.cpp:145 value_or_err_L145. tablet_info={tablet_id: 4292828} txn_ids=txn_id: 1994461 commit_time: 1788325904 combined_txn_log: false txn_type: TXN_NORMAL force_publish: false gtid: 441427646109188096 version=3 this query was rewritten to a materialzied view
r
The error log you provided indicates a critical metadata inconsistency in your StarRocks shared-data (Lakehouse) cluster. Specifically, a background process failed to "publish" a new version of a tablet because a required transaction log file (
.log
) is missing from your S3 bucket. 1. Breakdown of the Error * 
Fail to publish version
: This occurs during the final stage of a transaction (like an MV refresh or data load). The FE has committed the transaction, but when it asks the BE to make that data visible ("publish"), the BE cannot find the instructions. * 
Not found: starlet err ... The specified key does not exist
: The StarRocks storage abstraction (Starlet) tried to fetch a metadata file from S3 (
.../log/00000000004180DC_00000000001E6EDD.log
) and received a 404 error. * 
tablet_id: 4292828
: This is the specific tablet of the Materialized View (MV) that is corrupted or inconsistent. 2. Why it occurred during an MV Query Rewrite When your query is rewritten to a Materialized View, the engine attempts to read data from the MV's internal tablets. The error suggests that the MV's data is currently in a "broken" state due to a failed refresh or a race condition: 1. Interrupted Refresh: The MV was likely being refreshed. The transaction was started, but the metadata log file was either never successfully written to S3 or was deleted prematurely. 2. FE Leader Shift: If the Leader FE changed recently, the new Leader might be trying to "clean up" or "finish" a transaction initiated by the old Leader, but the associated 
.log
metadata file is missing. 3. Aggressive Metadata GC: In some cases, the background Garbage Collection (GC) for shared-data metadata might be too aggressive, deleting transaction logs before all BEs have finished the 
publish
phase. 3. Immediate Recommended Actions Because the query was rewritten to this MV, the query will continue to fail as long as this specific tablet/version is in limbo. A. Refresh the MV manually Try to force a new, clean refresh of the MV. This often generates a new transaction and bypasses the broken log file:
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REFRESH MATERIALIZED VIEW your_mv_name;
B. Identify the specific partition Check which partition of the MV is failing. If the error persists, you may need to drop and recreate that specific partition:
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-- Check for inactive or failed MVs
SHOW MATERIALIZED VIEWS LIKE 'your_mv_name';

-- If a specific partition is stuck
ALTER MATERIALIZED VIEW your_mv_name DROP PARTITION partition_name;
REFRESH MATERIALIZED VIEW your_mv_name PARTITIONS (partition_name);
C. Check FE Health Check if your FE nodes are stable. If you see frequent "Leader change" messages in 
fe.log
, that is likely the root cause.
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grep "master changed" fe.log
D. Check Compaction Status As noted in similar bugs for StarRocks shared-data versions, sometimes "stuck" compaction tasks cause publish failures. Check for any tasks with no `FinishTime`:
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SHOW PROC '/compactions';
Summary The query failed because the Materialized View's metadata on S3 is missing a transaction log. This is a server-side storage issue, not a query syntax issue. Re-running the MV refresh or repairing the affected partition is the standard fix. If you are on an experimental version (like the 4.1.1 mentioned earlier), this may be a bug related to metadata persistence in the new shared-data logic. References • faq/Deploy_faq.md • administration/management/monitoring/metrics.md • administration/management/BE_configuration.md • administration/management/FE_configuration.md • administration/Meta_recovery.md
k
@Rocky why are some queries for the same mv succeeding then
r
The fact that some queries succeed while others fail on the same Materialized View (MV) is usually due to how StarRocks distributes data and how the optimizer chooses to execute a query. Here are the four most likely reasons for this intermittent behavior: 1. Tablet-Specific Corruption Data in StarRocks is split into multiple tablets (shards). The error log you provided points to a very specific tablet ID (
tablet_id: 4292828
). * Why some succeed: If a query's filters (the 
WHERE
clause) allow the engine to prune partitions or if the data it needs resides in other healthy tablets, the query will succeed. * Why some fail: Only queries that are forced to scan the specific "broken" tablet (4292828) will encounter the 
404 Not Found
error on S3. 2. Query Rewrite vs. Base Table Scan Not every query against the base tables is automatically rewritten to the MV. * Success: A query might look similar but fail the optimizer's rewrite criteria (e.g., it requires a column not present in the MV, or uses an unsupported function). In this case, StarRocks scans the base tables directly. Since the base tables are likely healthy, the query succeeds. * Failure: A query that matches the MV structure perfectly is rewritten to scan the MV. It then hits the missing metadata file and fails. * How to check: Run 
EXPLAIN
on a successful query and a failing one. Look for 
OlapScanNode
. If you see the MV name in the failing one and the base table name in the successful one, this is the cause. 3. Metadata Cache Inconsistency (Shared-Data) In a shared-data (Lakehouse) cluster, BE nodes cache metadata locally to avoid constant S3 calls. * Success: A query might be handled by a BE node that still has the "old" healthy metadata for that tablet in its memory cache. * Failure: A different BE node (or the same node after a cache eviction) might try to fetch the latest metadata from S3, find it missing, and throw the error. 4. Versioning and "Dirty" Reads StarRocks uses Multi-Version Concurrency Control (MVCC). * If an MV refresh was partially successful or interrupted, the "Visible Version" of the MV might be pointing to a set of files where one 
.log
file is missing. * If you have 
query_rewrite_consistency = 'loose'
 set on the MV, the optimizer might try to use an "in-progress" or "stale" version of the MV metadata, which could be in an inconsistent state. How to verify To see exactly which queries are hitting the MV and which are not, use the Trace feature (available in your version):
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-- This will tell you why a query was NOT rewritten
TRACE REASON MV <your_query>;

-- This will show the detailed rewrite process
TRACE LOGS MV <your_query>;
Recommendation Since you have a confirmed missing file in S3 for a specific tablet, the MV is effectively "wounded." Even if some queries work now, others will fail unpredictably. 1. Identify the tablet: You already have it (
4292828
). 2. Repair: The safest way to fix this in a shared-data cluster is to force a full refresh or drop and recreate the MV to ensure a clean metadata state is written to S3.
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    REFRESH MATERIALIZED VIEW your_mv_name;
References • administration/management/monitoring/metrics-shared-data.md • faq/shared_data_faq.md • knowledge/admin_and_operation/shared_data_compaction.md
k
@Rocky will this be repaired during normal mv refresh schedule
r
Whether a normal scheduled refresh will repair the error depends on how the Materialized View (MV) is partitioned and whether the base data has changed. 1. If it is a Non-Partitioned MV Yes, a normal scheduled refresh should repair it. In a non-partitioned MV, StarRocks typically performs a "Full Refresh." This creates an entirely new set of data files and metadata logs on S3. Once the new refresh completes and "publishes" successfully, the FE will point to a new version. The old, broken version (with the missing 
.log
file) will eventually be deleted by the background vacuum process. 2. If it is a Partitioned MV Maybe not automatically. StarRocks only refreshes partitions where the base table data has changed. * If the base data for the "broken" partition changes: The scheduler will trigger a refresh for that partition, which creates new healthy files and fixes the issue. * If the base data is static: The scheduler will see that the partition is "up-to-date" relative to the base table and skip it. The broken tablet will remain in the "Visible Version," and queries hitting that partition will continue to fail. 3. Will the refresh itself fail? The refresh task (the "Write" phase) should not fail because it reads from the base tables, not the MV itself. However, if the metadata inconsistency is severe enough that the FE cannot determine the MV's current state, the refresh task might fail during the "Commit/Publish" phase. Recommended Action to "Force" a Repair To ensure the repair happens regardless of the schedule or data changes, you should manually trigger a refresh with the 
FORCE
keyword. This tells StarRocks to ignore consistency checks and rebuild the data: * To repair the entire MV:
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    REFRESH MATERIALIZED VIEW your_mv_name FORCE;
* To repair only the specific failing partition (if you know it):
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    REFRESH MATERIALIZED VIEW your_mv_name
    PARTITION START ("2023-09-01") END ("2023-09-02") FORCE;
Why you shouldn't just wait: While a refresh might fix it, the missing file error (
404 Not Found
) in a shared-data cluster often indicates that the metadata vacuuming logic and the transaction publishing logic got out of sync. Waiting for a scheduled task might lead to: 1. Stuck Tasks: The FE might repeatedly try to "clean up" the old failed transaction and fail, potentially blocking the scheduler. 2. Intermittent Query Failures: As long as that tablet is part of the "Active" set, any user query hitting it will fail with an 
InternalError
. Conclusion: Don't wait for the schedule. Run a 
REFRESH ... FORCE
manually to clear the bad state and guarantee the creation of healthy metadata. References • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • sql-reference/sql-statements/materialized_view/REFRESH_MATERIALIZED_VIEW.md • faq/shared_data_faq.md References • administration/management/resource_management/Replica.md • knowledge/usage/best-practice.md • faq/shared_data_faq.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • deployment/shared_data/azure.md
k
@Rocky what does 
enable_trace_historical_node
entail
r
The FE configuration parameter 
enable_trace_historical_node
(introduced in v3.5.1) is a key setting for enabling Cache Sharing in StarRocks shared-data (Lakehouse) clusters. Here is a breakdown of what it entails: 1. The Core Purpose: Mitigating "Cache Jitter" In a shared-data cluster, Compute Nodes (CNs) cache hot data from remote storage (S3/HDFS) on local disks. When you scale the cluster up or down, the consistent hashing or data routing often shifts. This causes "cache misses" because the data a node needs is now sitting on a different node’s disk. 
enable_trace_historical_node
allows the Frontend (FE) to keep track of which nodes recently held specific data. 2. How it Works (Peer-to-Peer Cache Sharing) When this parameter is enabled alongside the system variable `enable_datacache_sharing`: * Historical Mapping: The FE maintains a "trace" of historical node assignments. * Network Fetch: If a node needs a data block that it doesn't have locally (a cache miss), it doesn't immediately go to slow remote storage (S3). Instead, it uses the "historical trace" to identify which other node in the cluster likely has that block in its local cache. * Sub-second Recovery: The node fetches the data over the internal network from its peer. This is significantly faster than re-downloading from S3, ensuring query performance remains stable even during active scaling events. 3. Requirements and Activation To use this feature, you must configure both the FE and the session/global variables: 1. FE Configuration (
fe.conf
): 
enable_trace_historical_node = true
2. System Variable: 
SET GLOBAL enable_datacache_sharing = true;
3. Version: Requires StarRocks v3.5.1 or higher. 4. Monitoring the Effect Once enabled, you can see if it's working by looking at the Query Profile. You will see new metrics under the 
DataCache
section: * 
DataCacheReadPeerCounter
: How many times data was read from a peer node. * 
DataCacheReadPeerBytes
: The volume of data transferred between nodes instead of from S3. * 
DataCacheReadPeerTimer
: The time spent performing these peer-to-peer network reads. Summary In short, 
enable_trace_historical_node
tells the FE to remember where cache was previously located so that nodes can share data over the network during scaling, rather than suffering from the performance drop of cold caches. References • administration/management/FE_configuration.md • developers/trace-tools/Trace.md • sql-reference/System_variable.md • using_starrocks/SQL_plan_manager.md
k
@Rocky does this affect static compute nodes that are permanent
r
While 
enable_trace_historical_node
can be enabled in a static cluster, it is specifically designed to be dormant unless a scaling event occurs. For permanent, static compute nodes, here is how it behaves: 1. It is "Event-Triggered" The feature is designed to bridge the gap when data routing changes. In StarRocks, data-to-node mapping is determined by consistent hashing. * In a static cluster: The mapping never changes. Each node always knows exactly which data it is responsible for caching. It will find that data on its own local disk, so it never needs to "ask" a peer. * During scaling: When you add or remove a node, the hash ring shifts. Data that was on Node A might now be assigned to Node B. Node B will have a "cold" cache for that data. 2. The Time-Limited Window (
datacache_sharing_work_period
) The system variable 
datacache_sharing_work_period
(default 600 seconds) controls how long this feature stays active. * The peer-to-peer cache fetching only kicks in for the first 10 minutes after a scaling operation is detected. * Once that window expires, even if the feature is "enabled," nodes stop trying to fetch from peers and resume normal behavior (reading from S3 if the local cache is missing). 3. Does it affect permanent nodes during a restart? Yes. If a "permanent" node crashes and you replace it with a new node, or if the cluster is restarted in a way that the FE perceives as a membership change, the feature will trigger. This is actually beneficial because the "new" node can pull cache from the other healthy nodes rather than hammering your S3 bucket to rebuild its local cache from scratch. 4. Overhead for Static Clusters There is virtually no overhead for keeping this enabled in a static cluster: * FE side: The "historical trace" is a small metadata map in the FE memory. If the cluster membership hasn't changed, this map is identical to the current map. * BE side: Nodes will only attempt a peer-to-peer read if they have a local cache miss and the scaling timer is active. In a static cluster, the timer is not active. Summary If your compute nodes are truly permanent and you never change the cluster size, this feature will effectively do nothing. However, it is recommended to keep it enabled as a safety net. If a node ever fails and needs to be replaced, this feature will prevent a "performance cliff" while the new node's cache is warming up. References • data_source/data_cache.md • sql-reference/System_variable.md • administration/management/FE_configuration.md References • data_source/data_cache.md • administration/management/BE_configuration.md • deployment/shared_data/feature-support-shared-data.md • sql-reference/System_variable.md • administration/management/FE_configuration.md • using_starrocks/caching/block_cache.md
k
@Rocky does starrocks 4.1.1 support SUBMIT TASK as CACHE SELECT during materialied view creation to maange both statements in one place
r
As of StarRocks 4.1.1, there is no single SQL statement that combines 
CREATE MATERIALIZED VIEW
and 
SUBMIT TASK ... AS CACHE SELECT
into one atomic command or property. While StarRocks has significantly unified the management of these two features, they remain distinct objects in the system: 1. Unified Metadata Management As of v3.x and continuing into v4.1, asynchronous Materialized Views (MVs) and 
SUBMIT TASK
jobs are both managed by the Task Manager. You can "manage them in one place" by querying the same metadata views: * `information_schema.tasks`: Shows both MV refresh tasks and your 
CACHE SELECT
warmup tasks. * `information_schema.task_runs`: Shows the execution history for both. 2. Current Workflow (Two-Step Process) To achieve "managed" cache warmup for an MV, you currently follow the standard best practice (as documented in v4.1 guides): 1. Create the MV:
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    CREATE MATERIALIZED VIEW my_mv
    REFRESH ASYNC EVERY (INTERVAL 1 DAY)
    AS SELECT ...;
2. Submit a Warmup Task (Scheduled):
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    SUBMIT TASK mv_warmup
    SCHEDULE EVERY (INTERVAL 1 DAY) -- Offset this to run after the refresh
    AS CACHE SELECT * FROM my_mv;
3. Why they aren't one statement yet * Decoupled Logic: A Materialized View is a data object (storage + refresh logic), whereas 
CACHE SELECT
is an ephemeral action (pulling data into the local NVMe/SSD cache of a specific Compute Node/BE). * Resource Management: Warmup tasks often need different resource groups or lower priority (DOP) than the actual data refresh to avoid impacting production queries. Keeping them as separate tasks allows you to set specific 
PROPERTIES
(like 
pipeline_dop
) for the warmup task without slowing down the MV refresh itself. 4. New in 4.1.1 Relevant to this While a combined syntax doesn't exist, StarRocks 4.1.1 introduced Adaptive Layout and improved Incremental MV logic. These features reduce the need for manual warmup by: * Optimizing the physical layout of the MV automatically so that the first query after a refresh is naturally faster. * Supporting better "Data Cache" persistence, so that even after a refresh, older cached blocks may still be valid for unchanged partitions. Summary You still need to manage two statements, but you can monitor and control them both via the Unified Task Framework (
information_schema.tasks
). There is no "CACHE SELECT" property inside the 
CREATE MATERIALIZED VIEW
syntax in the 4.1.x release. References • data_source/block_cache_warmup.md • sql-reference/sql-statements/loading_unloading/ETL/SUBMIT_TASK.md • using_starrocks/async_mv/Materialized_view.md • release_notes/release-3.3.md References • release_notes/release-3.3.md • release_notes/release-3.1.md • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md
k
@Rocky command to show when user password was last changed
r
In StarRocks, you can check when a user's password was last changed by querying the internal 
mysql.user
 table. This information is tracked as part of StarRocks' password management and expiration features (introduced in recent versions like v3.1+). Command To view the last password change time for all users, run:
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SELECT User, password_last_changed FROM mysql.user;
* 
User
: The username. * 
password_last_changed
: The timestamp indicating when the password was most recently updated. Important Notes 1. Privileges: You must have the 
user_admin
 role or equivalent system-level privileges to query the 
mysql
system database. 2. Native Authentication: This field is only relevant for users using Native Authentication (passwords stored in StarRocks). If you are using external authentication like LDAP or Kerberos, the password lifecycle is managed by the external system, and this field will typically be 
NULL
or show when the user was initially mapped. 3. Visibility: Standard commands like 
SHOW USERS
or 
SHOW AUTHENTICATION
only display user identities and authentication methods; they do not currently expose metadata timestamps like password age. Related: Password Expiration Properties If you are tracking password changes for security compliance, you can also check if a user has a specific password expiration policy set by using:
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SHOW PROPERTY FOR 'username' LIKE 'password_expire_interval';
References • administration/user_privs/authentication/native_authentication.md • sql-reference/sql-statements/account-management/SHOW_AUTHENTICATION.md • sql-reference/sql-statements/account-management/SHOW_USERS.md References • sql-reference/sql-statements/account-management/SHOW_AUTHENTICATION.md • administration/user_privs/authentication/native_authentication.md • sql-reference/sql-statements/account-management/ALTER_USER.md • administration/management/monitoring/metrics.md
k
@Rocky getting: Query 1 ERROR at Line 10: : Getting analyzing error. Detail message: Unknown database 'mysql'. on default_catalog
r
I apologize for the confusion in my previous response. StarRocks does not have a 
mysql
database in the 
default_catalog
. While StarRocks is MySQL-compatible at the protocol level, it uses its own internal metadata system and does not replicate the standard MySQL system tables (like 
mysql.user
). As of the current version (including 4.1.1), there is no direct SQL command or system view (in 
information_schema
or 
sys
) that displays the "password last changed" timestamp. Why you see the error The 
mysql
database and 
user
table are specific to the MySQL server itself. StarRocks stores user identities and credentials in its own FE (Frontend) metadata, which is not exposed as a queryable table for security and architectural reasons. How to check password-related info While you cannot see the exact "last changed" timestamp via SQL, you can use these commands for related information: 1. Check if a password is set:
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    SHOW ALL AUTHENTICATION;
This shows the user identity and whether a password exists (`Yes`/`No`), but not the date. 2. Check Password Expiration Policy: If you have configured password expiration, you can see the interval, but not the start date:
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    SHOW PROPERTY FOR 'username' LIKE 'password_expire_interval';
Workaround: Audit Logs If you have Audit Logging enabled (which is highly recommended for production), you can find when a password was last changed by searching the FE audit logs for the 
ALTER USER
or 
SET PASSWORD
commands. * Log Location: 
fe/log/fe.audit.log
* Search Command:
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    grep -E "ALTER USER|SET PASSWORD" fe.audit.log
Summary * Is there a SQL command? No. * Is there a system table? No (the 
mysql
database does not exist in StarRocks). * Recommended approach: Check your FE audit logs for the last occurrence of the password update command for that specific user. References • administration/user_privs/authentication/native_authentication.md • sql-reference/sql-statements/account-management/SHOW_AUTHENTICATION.md • administration/user_privs/authorization/privilege_faq.md • administration/user_privs/authorization/User_privilege.md • administration/management/FE_configuration.md
k
@Rocky how to alter external catalog s3 key fields
r
To alter the S3 credential fields (access key, secret key, etc.) for an existing external catalog in StarRocks, you use the 
ALTER CATALOG
statement with the 
SET
property clause. Syntax
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ALTER CATALOG <catalog_name> SET (
    "aws.s3.access_key" = "<new_access_key>",
    "aws.s3.secret_key" = "<new_secret_key>",
    "aws.s3.region" = "<aws_region>"
);
Common S3 Property Fields Depending on your catalog type (Hive, Iceberg, Hudi, Delta Lake, or Unified), the following property keys are standard for S3 access: * 
aws.s3.access_key
: The new AWS IAM user access key. * 
aws.s3.secret_key
: The new AWS IAM user secret key. * 
aws.s3.session_token
: Required if you are using temporary credentials. * 
aws.s3.region
: The AWS region where your bucket is located (e.g., 
us-east-1
). * 
aws.s3.use_instance_profile
: Set to 
"false"
if you are switching from IAM Role to Access Keys. Example: Updating a Hive Catalog
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ALTER CATALOG my_hive_catalog SET (
    "aws.s3.access_key" = "AKIAEXAMPLE123",
    "aws.s3.secret_key" = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"
);
Important Considerations 1. Catalog "Recreation": Internally, StarRocks may recreate the catalog object when credentials are modified. While this is transparent to your metadata, it may briefly affect active queries that were already in the planning phase using the old credentials. 2. Masking: When you run 
SHOW CREATE CATALOG <name>
, StarRocks masks sensitive information like the 
secret_key
. However, when performing the 
ALTER
command, you must provide the values in plaintext. 3. Privileges: You must have the 
ALTER
 privilege on the catalog or the 
SYSTEM-level
 administrative privileges to perform this operation. 4. Verification: After updating, you can verify the connection by attempting to list the databases or tables:
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    SHOW DATABASES FROM <catalog_name>;
References • sql-reference/sql-statements/Catalog/CREATE_EXTERNAL_CATALOG.md • data_source/catalog/hive_catalog.md • data_source/catalog/iceberg_catalog.md References • sql-reference/sql-statements/table_bucket_part_index/ALTER_TABLE.md • sql-reference/sql-statements/Catalog/CREATE_EXTERNAL_CATALOG.md • sql-reference/sql-statements/loading_unloading/pipe/ALTER_PIPE.md • sql-reference/sql-statements/Resource/ALTER_RESOURCE.md • sql-reference/sql-statements/account-management/ALTER_USER.md • data_source/catalog/maxcompute_catalog.md
k
@Rocky how to enable query queue for a specific app group for materialzied views? i have a low concurreny to not overwhelm the cluster, but i want tasks to not fail immediately if no concurrency capacity avaialble, just enter queue
r
To enable a query queue for a specific group of Materialized Views (MVs) to prevent them from failing when concurrency limits are reached, you must use Resource Group-level Query Queues (available from v3.1.4+). By default, if a resource group's 
concurrency_limit
is reached, new queries fail immediately. Enabling the queue allows these tasks to wait instead. Step 1: Enable Query Queuing Globally You must enable the query queue for "load" operations (since MV refreshes are 
INSERT
tasks) and enable the group-level queuing logic.
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-- Enable queuing for INSERT/load operations (required for MV refreshes)
SET GLOBAL enable_query_queue_load = true;

-- Enable queuing at the resource group level
SET GLOBAL enable_group_level_query_queue = true;
Step 2: Create a Dedicated Resource Group Create a resource group with a specific 
concurrency_limit
. This limit is applied per BE node.
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CREATE RESOURCE GROUP mv_app_group
TO 
    (user='mv_user') -- Option A: Catch all refreshes by a specific user
WITH (
    "concurrency_limit" = "2",        -- Only 2 concurrent refreshes per BE
    "cpu_weight" = "10",              -- Lower priority for CPU
    "mem_limit" = "0.2"               -- Limit memory to 20%
);
Step 3: Bind Materialized Views to the Group You can bind specific MVs to this group regardless of the user by setting the 
resource_group
property. * For new MVs:
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    CREATE MATERIALIZED VIEW my_mv
    REFRESH ASYNC
    PROPERTIES (
        "resource_group" = "mv_app_group"
    )
    AS SELECT ...;
* For existing MVs:
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    ALTER MATERIALIZED VIEW my_mv SET ("resource_group" = "mv_app_group");
Step 4: Configure Queue Depth and Timeout (Optional) If the queue itself gets too full or a task waits too long, it will eventually fail. You can tune these global variables: * 
query_queue_max_queued_queries
: Maximum number of tasks that can sit in the queue (default 1024). * 
query_queue_pending_timeout_second
: How long a task waits before failing (default 300s). Summary of Behavior 1. Under Limit: MV refreshes run immediately. 2. At Limit: Subsequent refresh tasks enter the queue (status: 
Pending
in 
show processlist
). 3. Queue Full: If the number of pending tasks exceeds 
query_queue_max_queued_queries
, the next task fails immediately. 4. Timeout: If a task stays in the queue longer than 
query_queue_pending_timeout_second
, it fails. Troubleshooting To see how many queries are currently queued in your group:
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SHOW USAGE RESOURCE GROUPS;
References • administration/management/resource_management/query_queues.md • administration/management/resource_management/resource_group.md • best_practices/audit_log_resource_group.md • sql-reference/sql-statements/Catalog/CREATE_EXTERNAL_CATALOG.md References • administration/management/resource_management/query_queues.md • sql-reference/System_variable.md • faq/resource_isolation_faq.md
k
@Rocky we are seeing slow queries because apparently metadata is being fetched during live queries when checking base tables for materialized views. why is this happening constantly where metadata should be cached or done async
r
The slow queries you are seeing occur because, by default, StarRocks prioritizes data consistency. When a query hits a base table, the optimizer attempts to rewrite it using a Materialized View (MV). To do this safely, it must verify if the MV's data is still fresh compared to the base tables. If your base tables are in an External Catalog (Hive, Iceberg, Glue, etc.), this check requires the FE to contact the remote metastore to fetch the latest partition versions or snapshot IDs. This happens synchronously during the planning phase, which is why your "live" queries are hanging. To fix this and move these checks to a cached or asynchronous model, follow these steps: 1. Allow "Stale" MVs (Recommended) If your application can tolerate data that is a few seconds or minutes old, you can prevent the optimizer from checking the base tables every time. * Set a Staleness Threshold: This tells StarRocks: "If the MV was refreshed within the last X seconds, assume it is fresh and skip the base table metadata check."
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    ALTER MATERIALIZED VIEW your_mv_name
    SET ("mv_rewrite_staleness_second" = "60"); -- 1 minute threshold
* Disable Consistency Checks Entirely: If you don't care if the MV is out of sync with the base table during rewrites:
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    ALTER MATERIALIZED VIEW your_mv_name
    SET ("query_rewrite_consistency" = "LOOSE");
2. Enable Asynchronous Background Metadata Refresh StarRocks can poll your external metastore (HMS/Glue) in the background so that the metadata is already in the FE cache when a query arrives. Ensure these FE configurations are set (use 
ADMIN SET FRONTEND CONFIG
): * 
enable_background_refresh_connector_metadata = true
: Enables the background poller. * 
background_refresh_metadata_interval_millis = 60000
: Sets the polling interval (e.g., 60 seconds). * 
metastore_cache_refresh_interval_sec = 60
: How long a cache entry is considered "fresh" before StarRocks triggers an async refresh. 3. Set an Optimizer Timeout To prevent a slow metadata fetch from completely hanging your query, you can set a time limit for the MV rewrite logic. If the time is exceeded, the optimizer will stop trying to rewrite and just run the query against the base table.
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-- Session variable (default is 1000ms)
SET optimizer_materialized_view_timelimit = 300; -- 300ms limit
4. Why it happens "constantly" Even if metadata is cached, it has a TTL (Time To Live). * If your query frequency is low, the cache might expire between queries. * If your 
metastore_cache_refresh_interval_sec
is set to 
0
(or a very low value), StarRocks will always fetch from the remote source. * If the number of tables/partitions is massive, the FE may be evicting metadata from its internal LRU cache. You can increase 
iceberg_metadata_memory_cache_capacity
or 
hive_meta_cache_ttl_second
if necessary. Summary Checklist 1. Use 
mv_rewrite_staleness_second
 to stop synchronous checks on every query. 2. Verify 
enable_background_refresh_connector_metadata
 is 
true
for your catalog. 3. Check your *Metastore (HMS/Glue) latency*; background refresh only works if the metastore responds within a reasonable window. References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • using_starrocks/async_mv/troubleshooting_asynchronous_materialized_views.md • administration/management/FE_configuration.md • data_source/catalog/iceberg/iceberg_catalog.md References • data_source/catalog/deltalake_catalog.md
k
@Rocky in a shared nothing db, would materialized views got hrough the same exact consistency checks as shared
r
In StarRocks, whether you are using a Shared-Nothing (local storage) or Shared-Data (StarRocks-managed object storage) architecture, the high-level logic for consistency checks is the same, but the performance and mechanism differ significantly depending on whether the base tables are Internal or External. Here is the breakdown of how these checks behave across different architectures: 1. Internal Tables (Shared-Nothing & Shared-Data) For tables where StarRocks "owns" the data (both the traditional Shared-Nothing BE storage and the newer StarRocks 3.x Shared-Data mode), the consistency check is extremely fast. * Mechanism: When a query is planned, the Frontend (FE) checks the "Version" or "Commit Time" of the base tables against the version recorded in the Materialized View metadata. * Performance: Since the FE is the source of truth for these versions, this is a local memory lookup. It does not involve network calls to the storage layer (S3 or local disk). * Result: You will rarely see "slow metadata fetching" for MVs built on internal tables. 2. External Tables (Data Lake / "Shared" with other engines) The "slow queries" you are experiencing typically happen when your base tables are in an External Catalog (Hive, Iceberg, Glue, etc.). In this scenario, the tables are "shared" between StarRocks and other engines (like Spark or Trino). * Mechanism: Because StarRocks is not the "owner" of the metadata for these tables, the FE cannot simply look in its own memory to see if the data has changed. It must synchronously call the remote metastore (HMS, AWS Glue, or read Iceberg metadata files on S3) to find the latest snapshot ID or partition version. * Performance: This is a blocking network call. If the Metastore is under load or S3 latency is high, your query waits. * Result: This is the specific scenario where the "consistency check" becomes a bottleneck. Summary Comparison Feature | Internal (Shared-Nothing/Data) | External (Data Lake/S3) Metadata Owner | StarRocks FE | Remote (Glue/HMS/Iceberg) Check Location | FE Local Memory | Remote Network Call Latency | Microseconds | Milliseconds to Seconds Consistency Level | Strong (Default) | Varies (Can be Loose) Background Refresh | Not needed (Push-based) | Highly Recommended (Pull-based) Why this matters for your issue If you are seeing these constant metadata fetches, it is almost certainly because your base tables are External. To make the "External" experience feel more like the "Shared-Nothing" experience (fast local checks), you must transition from a Synchronous Pull to an Asynchronous Cache model: 1. Enable Background Metadata Refresh: Use 
enable_background_refresh_connector_metadata = true
. This makes the FE poll the remote metastore every minute in the background so the "version" is already in FE memory when your query arrives. 2. Use Staleness Tolerance: Set 
mv_rewrite_staleness_second
. This tells the optimizer: "If I have a cached version from the last 60 seconds, use it. Do not go to S3/Glue to verify it during the query." In short: The consistency logic (comparing versions) is identical, but the fetch is "shared-memory" for internal tables and "over-the-wire" for external tables. References • using_starrocks/async_mv/use_cases/query_rewrite_with_materialized_views.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • using_starrocks/async_mv/Materialized_view.md
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