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Version: 3.0

Monitor and Alerting

You can build your own monitoring services, or use the Prometheus + Grafana solution. StarRocks provides a Prometheus-compatible interface that directly links to the HTTP port of the BE and FE to obtain monitoring information from the cluster.


The available metrics are:

be_broker_countcountaverageNumber of brokers
be_brpc_endpoint_countcountaverageNumber of StubCache in bRPC
be_bytes_read_per_secondbytes/saverageRead speed of BE
be_bytes_written_per_secondbytes/saverageWrite speed of BE
be_base_compaction_bytes_per_secondbytes/saverageBase compaction speed of BE
be_cumulative_compaction_bytes_per_secondbytes/saverageCumulative compaction speed of BE
be_base_compaction_rowsets_per_secondrowsets/saverageBase compaction speed of BE rowsets
be_cumulative_compaction_rowsets_per_secondrowsets/saverageCumulative compaction speed of BE rowsets
be_base_compaction_failedcount/saverageBase compaction failure of BE
be_clone_failedcount/saverageBE clone failure
be_create_rollup_failedcount/saverageMaterialized view creation failure of BE
be_create_tablet_failedcount/saverageTablet creation failure of BE
be_cumulative_compaction_failedcount/saverageCumulative compaction failure of BE
be_delete_failedcount/saverageDelete failure of BE
be_finish_task_failedcount/saverageTask failure of BE
be_publish_failedcount/saverageVersion release failure of BE
be_report_tables_failedcount/saverageTable report failure of BE
be_report_disk_failedcount/saverageDisk report failure of BE
be_report_tablet_failedcount/saverageTablet report failure of BE
be_report_task_failedcount/saverageTask report failure of BE
be_schema_change_failedcount/saverageSchema change failure of BE
be_base_compaction_requestscount/saverageBase compaction request of BE
be_clone_total_requestscount/saverageClone request of BE
be_create_rollup_requestscount/saverageMaterialized view creation request of BE
be_create_tablet_requestscount/saverageTablet creation request of BE
be_cumulative_compaction_requestscount/saverageCumulative compaction request of BE
be_delete_requestscount/saverageDelete request of BE
be_finish_task_requestscount/saverageTask finish request of BE
be_publish_requestscount/saverageVersion publish request of BE
be_report_tablets_requestscount/saverageTablet report request of BE
be_report_disk_requestscount/saverageDisk report request of BE
be_report_tablet_requestscount/saverageTablet report request of BE
be_report_task_requestscount/saverageTask report request of BE
be_schema_change_requestscount/saverageSchema change report request of BE
be_storage_migrate_requestscount/saverageMigration request of BE
be_fragment_endpoint_countcountaverageNumber of BE DataStream
be_fragment_request_latency_avgmsaverageLatency of fragment requests
be_fragment_requests_per_secondcount/saverageNumber of fragment requests
be_http_request_latency_avgmsaverageLatency of HTTP requests
be_http_requests_per_secondcount/saverageNumber of HTTP requests
be_http_request_send_bytes_per_secondbytes/saverageNumber of bytes sent for HTTP requests
fe_connections_per_secondconnections/saverageNew connection rate of FE
fe_connection_totalconnectionscumulativeTotal number of FE connections
fe_edit_log_readoperations/saverageRead speed of FE edit log
fe_edit_log_size_bytesbytes/saverageSize of FE edit log
fe_edit_log_writebytes/saverageWrite speed of FE edit log
fe_checkpoint_push_per_secondoperations/saverageNumber of FE checkpoints
fe_pending_hadoop_load_jobcountaverageNumber of pending hadoop jobs
fe_committed_hadoop_load_jobcountaverageNumber of committed hadoop jobs
fe_loading_hadoop_load_jobcountaverageNumber of loading hadoop jobs
fe_finished_hadoop_load_jobcountaverageNumber of completed hadoop jobs
fe_cancelled_hadoop_load_jobcountaverageNumber of cancelled hadoop jobs
fe_pending_insert_load_jobcountaverageNumber of pending insert jobs
fe_loading_insert_load_jobcountaverageNumber of loading insert jobs
fe_committed_insert_load_jobcountaverageNumber of committed insert jobs
fe_finished_insert_load_jobcountaverageNumber of completed insert jobs
fe_cancelled_insert_load_jobcountaverageNumber of cancelled insert jobs
fe_pending_broker_load_jobcountaverageNumber of pending broker jobs
fe_loading_broker_load_jobcountaverageNumber of loading broker jobs
fe_committed_broker_load_jobcountaverageNumber of committed broker jobs
fe_finished_broker_load_jobcountaverageNumber of finished broker jobs
fe_cancelled_broker_load_jobcountaverageNumber of cancelled broker jobs
fe_pending_delete_load_jobcountaverageNumber of pending delete jobs
fe_loading_delete_load_jobcountaverageNumber of loading delete jobs
fe_committed_delete_load_jobcountaverageNumber of committed delete jobs
fe_finished_delete_load_jobcountaverageNumber of finished delete jobs
fe_cancelled_delete_load_jobcountaverageNumber of cancelled delete jobs
fe_rollup_running_alter_jobcountaverageNumber of jobs created in rollup
fe_schema_change_running_jobcountaverageNumber of jobs in schema change
cpu_utilpercentageaverageCPU usage rate
cpu_systempercentageaveragecpu_system usage rate
cpu_userpercentageaveragecpu_user usage rate
cpu_idlepercentageaveragecpu_idle usage rate
cpu_guestpercentageaveragecpu_guest usage rate
cpu_iowaitpercentageaveragecpu_iowait usage rate
cpu_irqpercentageaveragecpu_irq usage rate
cpu_nicepercentageaveragecpu_nice usage rate
cpu_softirqpercentageaveragecpu_softirq usage rate
cpu_stealpercentageaveragecpu_steal usage rate
disk_freebytesaverageFree disk capacity
disk_io_svctmmsaverageDisk IO service time
disk_io_utilpercentageaverageDisk usage
disk_usedbytesaverageUsed disk capacity
starrocks_fe_meta_log_countcountInstantaneousThe number of Edit Logs without a checkpoint. A value within 100000 is considered reasonable.
starrocks_fe_query_resource_groupcountcumulativeThe number of queries for each resource group
starrocks_fe_query_resource_group_latencysecondaveragethe query latency percentile for each resource group
starrocks_fe_query_resource_group_errcountcumulativeThe number of incorrect queries for each resource group
starrocks_be_resource_group_cpu_limit_ratiopercentageInstantaneousInstantaneous value of resource group cpu quota ratio
starrocks_be_resource_group_cpu_use_ratiopercentageaverageThe ratio of CPU time used by the resource group to the CPU time of all resource groups
starrocks_be_resource_group_mem_limit_bytesbyteInstantaneousInstantaneous value of resource group memory quota
starrocks_be_resource_group_mem_allocated_bytesbyteInstantaneousInstantaneous value of resource group memory usage

Monitoring Alarm Best Practices

Background information on the monitoring system:

  1. The system collects information every 15 seconds.
  2. Some indicators are divided by 15 seconds and the unit is count/s. Some indicators are not divided, and the count is still 15 seconds.
  3. P90, P99 and other quantile values are currently counted within 15 seconds. When calculating at a greater granularity (1 minute, 5 minutes, etc.), use "how many alarms greater than a certain value" rather than "what is the average value".


  1. The purpose of monitoring is to only alert on abnormal conditions, not normal conditions.
  2. Different clusters have different resources (e.g., memory, disk), different usage, and need to be set to different values; however, "percentage" is universal as a measurement unit.
  3. For indicators such as number of failures, it is necessary to monitor the change of the total number, and calculate the alarm boundary value according to a certain proportion (for example, for the amount of P90, P99, P999).
  4. A value of 2x or more or a value higher than the peak can generally be used as a warning value for the growth of used/query.

Alarm settings

Low frequency alarms

Trigger the alarm if one or more failures occur. Set a more advanced alarm if there are multiple failures.

For operations (e.g.,schema change) that are not frequently performed, "alarm on failure" is sufficient.

No task started

Once the monitoring alarm is turned on, there may be a lot of successful and failed tasks. You can set failed > 1 to alert and modify it later.


Large fluctuations

Need to focus on data with different time granularity, as the peaks and valleys in data with large granularity may be averaged out. Generally, you need to look at 15 days, 3 days, 12 hours, 3 hours, and 1 hour (for different time ranges).

The monitoring interval may need to be slightly longer (e.g. 3 minutes, 5 minutes, or even longer) to shield the alarm caused by fluctuations.

Small fluctuations

Set shorter intervals to quickly get alarms when problems occur.

High spikes

It depends on whether the spikes need to be alarmed or not. If there are too many spikes, setting longer intervals may help smooth out the spikes.

Resource usage

High resource usage

You can set the alarm to reserve a little resource.For example, set the memory alert to mem_avaliable<=20%.

Low resource usage

You can set a stricter value than "high resource usage".For example, for a CPU with low usage (less than 20%), set the alarm to cpu_idle<60%.


Usually FE/BE are monitored together, but there are some values that only FE or BE has.

There may be some machines that need to be set up in batches for monitoring.

Additional information

P99 Batch calculation rules

The node collects data every 15 seconds and calculates a value, the 99th percentile is the 99th percentile in those 15 seconds. When the QPS is not high (e.g. QPS is below 10), these percentiles are not very accurate. Also, it is meaningless to aggregate four values generated in one minute (4 x 15 seconds) whether using sum or average function.

The same applies to P50, P90, and so on.

Cluster Monitoring for errors

Some undesired cluster errors need to be found and resolved in time to keep the cluster stable. If the errors are less critical (e.g. SQL syntax errors, etc.) but can't be stripped out from the important error items, it’s recommended to monitor first and distinguish those at a later stage.

Using Prometheus+Grafana

StarRocks can use Prometheus to monitor data storage and use Grafana to visualize results.


This document describes StarRocks’ visual monitoring solution based on Prometheus and Grafana implementations. StarRocks is not responsible for maintaining or developing these components. For more detailed information about Prometheus and Grafana, please refer to their official websites.


Prometheus is a temporal database with multi-dimensional data models and flexible query statements. It collects data by pulling or pushing them from monitored systems and stores these data in its temporal database. It meets different user needs through its rich multi-dimensional data query language.


Grafana is an open-source metric analysis and visualization system that supports a variety of data sources. Grafana retrieves data from data sources with corresponding query statements. It allows users to create charts and dashboards to visualize data.

Monitoring architecture


Prometheus pulls the metrics from the FE/BE interface and then stores the data into its temporal database.

In Grafana, users can configure Prometheus as a data source to customize the Dashboard.



1. Download the latest version of Prometheus from the Prometheus official website. Take the prometheus-2.29.1.linux-amd64 version for example.

tar -xf prometheus-2.29.1.linux-amd64.tar.gz

2. Add configuration in vi prometheus.yml

# my global config
scrape_interval: 15s # global acquisition interval, 1m by default, here set to 15s
evaluation_interval: 15s # global rule trigger interval, 1m by default, here set to 15s

# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
- job_name: 'StarRocks_Cluster01' # Each cluster is called a job, job name is customizable
metrics_path: '/metrics' # Specify the Restful API to get metrics

- targets: ['fe_host1:http_port','fe_host3:http_port','fe_host3:http_port']
group: fe # Here the group of FE is configured which contains 3 Frontends

- targets: ['be_host1:http_port', 'be_host2:http_port', 'be_host3:http_port']
group: be # The group of BE is configured here which contains three Backends
- job_name: 'StarRocks_Cluster02' # Multiple StarRocks clusters can be monitored in Prometheus
metrics_path: '/metrics'

- targets: ['fe_host1:http_port','fe_host3:http_port','fe_host3:http_port']
group: fe

- targets: ['be_host1:http_port', 'be_host2:http_port', 'be_host3:http_port']
group: be

3. Start Prometheus

nohup ./prometheus \
--config.file="./prometheus.yml" \
--web.listen-address=":9090" \
--log.level="info" &

This command runs Prometheus in the background and specifies its web port as 9090. Once set up, Prometheus starts collecting data and stores it in the . /data directory.

4. Accessing Prometheus

Prometheus can be accessed via BUI. You simply need to open port 9090 in your browser. Go toStatus -> Targets to see the monitored host nodes for all grouped jobs. Under normal circumstances, all nodes should be UP. If the node status is not UP, you can visit the StarRocks metrics (http://fe_host:fe_http_port/metrics or http://be_host:be_http_port/metrics) interface first to check if it is accessible, or check the Prometheus documentation for troubleshooting.


A simple Prometheus has been built and configured. For more advanced usage, please refer to the official documentation


1. Download the latest version of Grafana from Grafana official website. Take thegrafana-8.0.6.linux-amd64 version for example.

tar -zxf grafana-8.0.6.linux-amd64.tar.gz

2. Add configuration in vi . /conf/defaults.ini

data = ./data
logs = ./data/log
plugins = ./data/plugins
http_port = 8000
domain = localhost

3. Start Grafana

nohup ./bin/grafana-server \
--config="./conf/grafana.ini" &


DashBoard Configuration

Log in to Grafana through the address configured in the previous step http://grafana_host:8000 with the default username,password (i.e. admin,admin).

1. Add a data source.

Configuration path: Configuration-->Data sources-->Add data source-->Prometheus

Data Source Configuration Introduction


  • Name: Name of the data source. Can be customized, e.g. starrocks_monitor
  • URL: The web address of Prometheus, e.g. http://prometheus_host:9090
  • Access: Select the Server method, i.e., the server where Grafana is located for Prometheus to access. The rest of the options are default.

Click Save & Test at the bottom, if it shows Data source is working, it means the data source is available.

2. Add a dashboard.

Download a dashboard.


Metric names in StarRocks v1.19.0 and v2.4.0 are changed. You must download a dashboard template based on your StarRocks version:

Dashboard templates will be updated from time to time.

After confirming the data source is available, click on the + sign to add a new Dashboard, here we use the StarRocks Dashboard template downloaded above. Go to Import -> Upload Json File to load the downloaded json file.

After loading, you can name the Dashboard. The default name is StarRocks Overview. Then selectstarrocks_monitoras the data source. ClickImport to complete the import. Then you should see the Dashboard.

Dashboard Description

Add a description for your dashboard. Update the description for each version.

1. Top bar


The top left corner shows the Dashboard name. The top right corner shows the current time range. Use the drop down to select a different time range and specify an interval for page refresh. cluster_name: The job_name of each job in the Prometheus configuration file, representing a StarRocks cluster. You can select a cluster and view its monitoring information in the chart.

  • fe_master: The leader node of the cluster.
  • fe_instance: All frontend nodes of the corresponding cluster. Select to view the monitoring information in the chart.
  • be_instance: All backend nodes of the corresponding cluster. Select to view the monitoring information in the chart.
  • interval: Some charts show intervals related to monitoring items. Interval is customizable(Note: 15s interval may cause some charts not to display).

2. Row


In Grafana, the concept of a Row is a collection of diagrams. You can collapse a Row by clicking on it. The current Dashboard has the following Rows :

  • Overview: Display of all StarRocks clusters.
  • Cluster Overview: Display of selected clusters.
  • Query Statistic: Monitoring for Queries of selected clusters.
  • Jobs: Monitoring for Import jobs.
  • Transaction: Monitoring for Transactions.
  • FE JVM: Monitoring for JVM of selected Frontend.
  • BE: Display of Backends of selected clusters.
  • BE Task: Display of Backends tasks of selected clusters.

3. A typical chart is divided into the following parts.


  • Hover over the i icon in the upper left corner to see the chart description.
  • Click on the legend below to view a particular item. Click again to display all.
  • Drag and drop in the chart to select a time range.
  • The name of the selected cluster is displayed in [] of the title.
  • Values may correspond to the left Y-axis or the right Y-axis, which can be distinguished by the -right at the end of the legend.
  • Click on the chart name to edit the name.


If you need to access the monitoring data in your own Prometheus system, access it through the following interface.

  • FE: fe_host:fe_http_port/metrics
  • BE: be_host:be_web_server_port/metrics

If JSON format is required, access the following instead.

  • FE: fe_host:fe_http_port/metrics?type=json
  • BE: be_host:be_web_server_port/metrics?type=json