Monitoring Alarm

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.

Monitoring Indicators

The available metrics are:

be_broker_countpcsaverageNumber of brokers
be_brpc_endpoint_countpcsaverageNumber 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_failedpcs/saverageBase compaction failure of BE
be_clone_failedpcs/saverageBE clone failure
be_create_rollup_failedpcs/saverageMaterialized view creation failure of BE
be_create_tablet_failedpcs/saverageTablet creation failure of BE
be_cumulative_compaction_failedpcs/saverageCumulative compaction failure of BE
be_delete_failedpcs/saverageDelete failure of BE
be_finish_task_failedpcs/saverageTask failure of BE
be_publish_failedpcs/saverageVersion release failure of BE
be_report_tables_failedpcs/saverageTable report failure of BE
be_report_disk_failedpcs/saverageDisk report failure of BE
be_report_tablet_failedpcs/saverageTablet report failure of BE
be_report_task_failedpcs/saverageTask report failure of BE
be_schema_change_failedpcs/saverageSchema change failure of BE
be_base_compaction_requestspcs/saverageBase compaction request of BE
be_clone_total_requestspcs/saverageClone request of BE
be_create_rollup_requestspcs/saverageMaterialized view creation request of BE
be_create_tablet_requestspcs/saverageTablet creation request of BE
be_cumulative_compaction_requestspcs/saverageCumulative compaction request of BE
be_delete_requestspcs/saverageDelete request of BE
be_finish_task_requestspcs/saverageTask finish request of BE
be_publish_requestspcs/saverageVersion publish request of BE
be_report_tablets_requestspcs/saverageTablet report request of BE
be_report_disk_requestspcs/saverageDisk report request of BE
be_report_tablet_requestspcs/saverageTablet report request of BE
be_report_task_requestspcs/saverageTask report request of BE
be_schema_change_requestspcs/saverageSchema change report request of BE
be_storage_migrate_requestspcs/saverageMigration request of BE
be_fragment_endpoint_countpcsaverageNumber of BE DataStream
be_fragment_request_latency_avgmsaverageLatency of fragment requests
be_fragment_requests_per_secondpcs/saverageNumber of fragment requests
be_http_request_latency_avgmsaverageLatency of HTTP requests
be_http_requests_per_secondpcs/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_jobpcsaverageNumber of pending hadoop jobs
fe_committed_hadoop_load_jobpcsaverageNumber of committed hadoop jobs
fe_loading_hadoop_load_jobpcsaverageNumber of loading hadoop jobs
fe_finished_hadoop_load_jobpcsaverageNumber of completed hadoop jobs
fe_cancelled_hadoop_load_jobpcsaverageNumber of cancelled hadoop jobs
fe_pending_insert_load_jobpcsaverageNumber of pending insert jobs
fe_loading_insert_load_jobpcsaverageNumber of loading insert jobs
fe_committed_insert_load_jobpcsaverageNumber of committed insert jobs
fe_finished_insert_load_jobpcsaverageNumber of completed insert jobs
fe_cancelled_insert_load_jobpcsaverageNumber of cancelled insert jobs
fe_pending_broker_load_jobpcsaverageNumber of pending broker jobs
fe_loading_broker_load_jobpcsaverageNumber of loading broker jobs

|fe_committed_broker_load_job|pcs|average| Number of committed broker jobs| |fe_finished_broker_load_job|pcs|average| Number of finished broker jobs| |fe_cancelled_broker_load_job|pcs|average| Number of cancelled broker jobs | |fe_pending_delete_load_job|pcs|average| Number of pending delete jobs| |fe_loading_delete_load_job|pcs|average| Number of loading delete jobs| |fe_committed_delete_load_job|pcs|average| Number of committed delete jobs| |fe_finished_delete_load_job|pcs|average| Number of finished delete jobs| |fe_cancelled_delete_load_job|pcs|average| Number of cancelled delete jobs| |fe_rollup_running_alter_job|pcs|average| Number of jobs created in rollup | |fe_schema_change_running_job|pcs|average| Number of jobs in schema change | |cpu_util| percentage|average|CPU usage rate | |cpu_system | percentage|average|cpu_system usage rate | |cpu_user| percentage|average|cpu_user usage rate | |cpu_idle| percentage|average|cpu_idle usage rate | |cpu_guest| percentage|average|cpu_guest usage rate | |cpu_iowait| percentage|average|cpu_iowait usage rate | |cpu_irq| percentage|average|cpu_irq usage rate | |cpu_nice| percentage|average|cpu_nice usage rate | |cpu_softirq| percentage|average|cpu_softirq usage rate | |cpu_steal| percentage|average|cpu_steal usage rate | |disk_free|bytes|average| Free disk capacity | |disk_io_svctm|Ms|average| Disk IO service time | |disk_io_util|percentage|average| Disk usage | |disk_used|bytes|average| Used disk capacity |

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 pcs/sec. 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. Data source configuration

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 Dashboard

Dashboard Template Download Template after StarRocks-1.19.0 Download 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 Fileto import the downloaded json file. After importing, 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 master frontend 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