Monitoring#

Overview#

The monitoring scheme of OpenMLDB is outlined as follows：

Use prometheus to collect monitoring metrics, grafana to visualize metrics
OpenMLDB exporter exposes database-level and component-level monitoring metrics
Node uses node_exporter to expose machine and operating system related metrics

Install and Run OpenMLDB Exporter#

Introduction#

PyPI - Python Version

The OpenMLDB exporter is a prometheus exporter implemented in Python. The core connects the OpenMLDB instance through the database SDK and will query the exposed monitoring indicators through SQL statements. Exporter will follow the OpenMLDB version update and release to PyPI. For production use, you can install the latest openmldb-exporter directly through pip. For development and usage instructions, please refer to the code directory README.

Environmental Requirements#

Python >= 3.8
OpenMLDB >= 0.5.0

Preparation#

Get OpenMLDB

You can download precompiled packages from the OpenMLDB release page, or build from source.

Note that when compiling, make sure to enable the compile option: -DTCMALLOC_ENABLE=ON, the default is ON:
```
git clone https://github.com/4paradigm/OpenMLDB
cd OpenMLDB
# OpenMLDB exporter depends on compiled Python SDK
make SQL_PYSDK_ENABLE=ON
make install
```
See compile.md.
Start OpenMLDB

See install_deploy How to start OpenMLDB components

OpenMLDB exporter requires OpenMLDB to start the server status function, to do so, add the startup parameter --enable_status_service=true at startup, please make sure that --enable_status_service=true' in conf/(tablet|nameserver).flagsin the installation directory.

The default startup script bin/start.sh enables server status, no additional configuration is required.
Note: Make sure to select the binding IP addresses of OpenMLDB components OpenMLDB exporter as well as prometheus and grafana to ensure that grafana can access prometheus, and that prometheus, OpenMLDB exporter, and OpenMLDB components can access each other.

Deploy the OpenMLDB exporter#

Install openmldb-exporter from PyPi
```
pip install openmldb-exporter==0.5.0
```

Run

An executable openmldb-exporter will be installed by default, make sure pip install path is in your $PATH environment variable.

openmldb-exporter

Note that the appropriate parameters are passed in, openmldb-exporter -h to see help:

   usage: openmldb-exporter [-h] [--log.level LOG.LEVEL] [--web.listen-address WEB.LISTEN_ADDRESS]
                            [--web.telemetry-path WEB.TELEMETRY_PATH] [--config.zk_root CONFIG.ZK_ROOT]
                            [--config.zk_path CONFIG.ZK_PATH] [--config.interval CONFIG.INTERVAL]
   
   OpenMLDB exporter
   
   optional arguments:
     -h, --help            show this help message and exit
     --log.level LOG.LEVEL
                           config log level, default WARN
     --web.listen-address WEB.LISTEN_ADDRESS
                           process listen port, default 8000
     --web.telemetry-path WEB.TELEMETRY_PATH
                           Path under which to expose metrics, default metrics
     --config.zk_root CONFIG.ZK_ROOT
                           endpoint to zookeeper, default 127.0.0.1:6181
     --config.zk_path CONFIG.ZK_PATH
                           root path in zookeeper for OpenMLDB, default /
     --config.interval CONFIG.INTERVAL
                           interval in seconds to pull metrics periodically, default 30.0

View the list of metrics

   $ curl http://127.0.0.1:8000/metrics
   # HELP openmldb_connected_seconds_total duration for a component conncted time in seconds                              
   # TYPE openmldb_connected_seconds_total counter                                                                        
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9520",role="tablet"} 208834.70900011063                         
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9521",role="tablet"} 208834.70700001717                         
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9522",role="tablet"} 208834.71399998665                         
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9622",role="nameserver"} 208833.70000004768                     
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9623",role="nameserver"} 208831.70900011063                     
   openmldb_connected_seconds_total{endpoint="172.17.0.15:9624",role="nameserver"} 208829.7230000496                      
   # HELP openmldb_connected_seconds_created duration for a component conncted time in seconds                            
   # TYPE openmldb_connected_seconds_created gauge                                                                        
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9520",role="tablet"} 1.6501813860467942e+09                   
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9521",role="tablet"} 1.6501813860495396e+09                   
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9522",role="tablet"} 1.650181386050323e+09                    
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9622",role="nameserver"} 1.6501813860512116e+09               
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9623",role="nameserver"} 1.650181386051238e+09                
   openmldb_connected_seconds_created{endpoint="172.17.0.15:9624",role="nameserver"} 1.6501813860512598e+09               
   

Deploy Node Exporter#

For how to install and deploy prometheus, grafana, please refer to the official documents promtheus get started and [grafana get started](https://grafana.com/docs/ grafana/latest/getting-started/getting-started-prometheus/) .

node_exporter is an official implementation of prometheus that exposes system metrics.

Go to the release page, download and unzip the compressed package of the corresponding platform. For example, under linux amd64 platform:

curl -SLO https://github.com/prometheus/node_exporter/releases/download/v1.3.1/node_exporter-1.3.1.darwin-amd64.tar.gz
tar xzf node_exporter-1.3.1-*.tar.gz
cd node_exporter-1.3.1-*/

# Start node_exporter
./node_exporter

Deploy Prometheus and Grafana#

For installation and deployment of prometheus and grafana, please refer to the official documents promtheus get started and [grafana get started](https://grafana.com/docs/ grafana/latest/getting-started/getting-started-prometheus/). OpenMLDB provides prometheus and grafana configuration files for reference, see OpenMLDB mixin

prometheus_example.yml: Prometheus configuration example, intended to modify the target address in node, openmldb_components and openmldb_exporter jobs
openmldb_dashboard.json: grafana dashboard configuration for OpenMLDB metrics, divided into two steps:
1. Under the grafana data source page, add the started prometheus server address as the data source
2. Under the dashboard browsing page, click New to import a dashboard, and upload the json configuration file

Understand Existing Monitoring Metrics#

Taking the OpenMLDB cluster system as an example, the monitoring indicators are divided into two categories according to different prometheus pull jobs:

DB-Level metrics, exposed through the OpenMLDB exporter, correspond to the job_name=openmldb_exporter entry in the prometheus_example.yml configuration:
```
  - job_name: openmldb_exporter
    # pull OpenMLDB DB-Level specific metric
    # change the 'targets' value to your deployed OpenMLDB exporter endpoint
    static_configs:
      - targets:
        - 172.17.0.15:8000
```
The categories of indicators exposed are mainly:
- component status: cluster component status
- table status: database table related information, such as rows_cout, memory_bytes
- deploy query response time: the runtime of the deployment query inside the tablet
The full DB-Level metrics can be listed through the following command:
```
curl http://172.17.0.15:8000/metrics
```
Component-Level metrics. The related components of OpenMLDB (nameserver, tablet, etc), themselves as BRPC server, and expose [prometheus related metrics](https://github.com/apache/incubator-brpc/blob/master/docs/en/bvar .md#export-to-prometheus), you only need to configure the prometheus server to pull metrics from the corresponding address. It corresponds to the job_name=openmldb_components item in prometheus_example.yml:
```
  - job_name: openmldb_components
    # job to pull component metrics from OpenMLDB like tablet/nameserver
    # tweak the 'targets' list in 'static_configs' on your need
    # every nameserver/tablet component endpoint should be added into targets
    metrics_path: /brpc_metrics
    static_configs:
      - targets:
        - 172.17.0.15:9622
```
The metrics of exposure are mainly
- BRPC server process related information
- Corresponding to the RPC method related metrics defined by the BRPC server, such as the RPC request count, error_count, qps and response_time
Metrics and help information can be shown through the following command (Note that the metrics exposed by different components will vary):
```
curl http://${COMPONENT_IP}:${COMPONENT_PORT}/brpc_metrics
```

Monitoring

Contents