prometheus-export

python-prometheus-export

Prometheus provides client libraries based on Python, Go, Ruby and others that we can use to generate metrics with the necessary labels.

• Such an exporter can be included directly in the code of your application
• it can be run as a separate service that will poll one of your services and receive data from it, which will then be converted into the Prometheus format and sent to the Prometheus server.

When Prometheus scrapes your instance's HTTP endpoint, the client library sends the current state of all tracked metrics to the server.

The prometheus_client package supports exposing metrics from software written in Python, so that they can be scraped by a Prometheus service. Metrics can be exposed through a standalone web server, or through Twisted, WSGI and the node exporter textfile collector.

Python V3.9 Install

sudo yum install gcc openssl-devel bzip2-devel libffi-devel zlib-devel git 
wget https://www.python.org/ftp/python/3.9.0/Python-3.9.0.tgz 
tar –zxvf Python-3.9.0.tgz or tar -xvf Python-3.9.0.tgz 
cd Python-3.9.0 
./configure --libdir=/usr/lib64 
sudo make 
sudo make altinstall 

# python3 -m venv .venv --without-pip
sudo yum install python3-pip

sudo ln -s /usr/lib64/python3.9/lib-dynload/ /usr/local/lib/python3.9/lib-dynload

python3 -m venv .venv
source .venv/bin/activate

# pip install -r ./dev-requirement.txt
pip install prometheus-client
pip install requests
pip install JPype1
pip install psycopg2-binary
pip install jaydebeapi
pip install pytz
pip install httpx

# when error occur like this
# ImportError: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'OpenSSL 1.0.2k-fips  26 Jan 2017'. See: https://github.com/urllib3/urllib3/issues/2168
pip install urllib3==1.26.18
pip install pytz

Using Poetry: Create the virtual environment in the same directory as the project and install the dependencies:

python -m venv .venv
source .venv/bin/activate
pip install poetry

# --
poetry config virtualenvs.in-project true
poetry init
poetry add prometheus-client
poetry add psutil
poetry add pytz
poetry add JPype1
poetry add psycopg2-binary
poetry add jaydebeapi

or you can run this shell script ./create_virtual_env.sh to make an environment. then go to virtual enviroment using source .venv/bin/activate

Installation

pip install prometheus-client

Custom Promethues Exporter

• Expose my metrics for dev kafka cluster to http://localhost:9115
• Expose ES cluster/Kafka/Kibana/Logstash metrics by using this exporter based on ES API/socket library
• EXpose DB records as metrics from DB-Interface-Export API with HTTP POST Method
• Interface with DB Interface Export (https://github.com/euiyounghwang/DB-Interface-Export) by using FastAPI Framework to get the records as metrics from the specific databse

# HELP python_gc_objects_collected_total Objects collected during gc
# TYPE python_gc_objects_collected_total counter
python_gc_objects_collected_total{generation="0"} 302.0
python_gc_objects_collected_total{generation="1"} 342.0
python_gc_objects_collected_total{generation="2"} 0.0
...
kafka_health_metric{server_job="localhost"} 3.0
# HELP kafka_connect_nodes_metric Metrics scraped from localhost
# TYPE kafka_connect_nodes_metric gauge
kafka_connect_nodes_metric{server_job="localhost"} 3.0
# HELP kafka_connect_listeners_metric Metrics scraped from localhost
# TYPE kafka_connect_listeners_metric gauge
kafka_connect_listeners_metric{host="localhost",name="test_jdbc",running="RUNNING",server_job="localhost"} 1.0
# HELP zookeeper_health_metric Metrics scraped from localhost
# TYPE zookeeper_health_metric gauge
zookeeper_health_metric{server_job="localhost"} 3.0
# HELP kibana_health_metric Metrics scraped from localhost
# TYPE kibana_health_metric gauge
kibana_health_metric{server_job="localhost"} 1.0
# HELP logstash_health_metric Metrics scraped from localhost
# TYPE logstash_health_metric gauge
logstash_health_metric{server_job="localhost"} 1.0
...

Run Custom Promethues Exporter

• Run this command : $ python ./standalone-es-service-export.py or ./standalone-export-run.sh

#-- HTTP Server/Client Based
# HTTP Server
$  ./es-service-all-server-export-run.sh status/start/stop or ./server-export-run.sh
Server started.

# Client export
$  ./es-service-all-client-export-run.sh status/start/stop or ./client-export-run.sh

# Client only
./standalone-es-service-export.sh status/start/stop

...
[2024-05-20 20:44:06] [INFO] [prometheus_client_export] [work] http://localhost:9999/health?kafka_url=localhost:9092,localhost:9092,localhost:9092&es_url=localhost:9200,localhost:9200,localhost:9200,localhost:9200&kibana_url=localhost:5601&logstash_url=process
[2024-05-20 20:44:06] [INFO] [prometheus_client_export] [get_metrics_all_envs] 200
[2024-05-20 20:44:06] [INFO] [prometheus_client_export] [get_metrics_all_envs] <Response [200]>
[2024-05-20 20:44:06] [INFO] [prometheus_client_export] [get_metrics_all_envs] {'kafka_url': {'localhost:9092': 'OK', 'GREEN_CNT': 3, 'localhost:9092': 'OK', 'localhost:9092': 'OK'}, 'es_url': {'localhost:9200': 'OK', 'GREEN_CNT': 4, 'localhost:9200': 'OK', 'localhost:9200': 'OK', 'localhost:9200': 'OK'}, 'kibana_url': {'localhost:5601': 'OK', 'GREEN_CNT': 1}, 'logstash_url': 1}
...

Create script with arguments with python

• Run this command : euiyoung.hwang@US-5CD4021CL1-L MINGW64 ~/Git_Workspace/python-prometheus-export/create-script (master) $ python ./create-script-by-hosts.py

euiyoung.hwang@US-5CD4021CL1-L MINGW64 ~/Git_Workspace/python-prometheus-export/create-script (master)
$ python ./create-script-by-hosts.py
2024-05-31 19:58:16,573 : INFO : {
  "dev": {
    "kibana": "kibana:5601",
    "es_url": [
      "es1:9200",
      "es2:9200",
      "es3:9200",
      "es4:9200"
    ],
    "kafka_url": [
      "data1:9092",
      "data2:9092",
      "data3:9092"
    ],
    "kafka_connect_url": [
      "data1:8083",
      "data2:8083",
      "data3:8083"
    ],
    "zookeeper_url": [
      "data1:2181",
      "data2:2181",
      "data3:2181"
    ]
  },
  "localhost": {
    "kafka_url": [
      "data11:9092",
      "data21:9092",
      "data31:9092"
    ],
    "kafka_connect_url": [
      "data11:8083",
      "data21:8083",
      "data31:8083"
    ],
    "zookeeper_url": [
      "data11:2181",
      "data21:2181",
      "data31:2181"
    ],
    "kibana": "kibana1:5601",
    "es_url": [
      "es11:9200",
      "es21:9200",
      "es31:9200",
      "es41:9200",
      "es51:9200"
    ]
  }
}


# dev ENV
python ./standalone-es-service-export.py --interface http --db_http_host localhost:8002 --url jdbc:oracle:thin:bi"$"reporting/None --db_run false --kafka_url data1:9092,data2:9092,data3:9092 --kafka_connect_url data1:8083,data2:8083,data3:8083 --zookeeper_url  data1:2181,data2:2181,data3:2181 --es_url es1:9200,es2:9200,es3:9200,es4:9200 --kibana_url kibana:5601 --interval 30 --sql "SELECT * FROM TEST*"


# localhost ENV
python ./standalone-es-service-export.py --interface http --db_http_host localhost:8002 --url jdbc:oracle:thin:bi"$"reporting/None --db_run false --kafka_url data11:9092,data21:9092,data31:9092 --kafka_connect_url data11:8083,data21:8083,data31:8083 --zookeeper_url  data11:2181,data21:2181,data31:2181 --es_url es11:9200,es21:9200,es31:9200,es41:9200,es51:9200 --kibana_url kibana1:5601 --interval 30 --sql "SELECT * FROM TEST*"

Service Maintance

• Kafka Service

 /apps/kafka_2.11-0.11.0.0/bin/kafka-topics.sh --describe --zookeeper localhost.am.co.gxo.com:2181,localhost.am.co.gxo.com:2181,localhost.am.co.gxo.com:2181 --topic ELASTIC_PIPELINE_QUEUE
 curl -X POST http://localhost:8083/connectors/epq_wmxd_jdbc/tasks/0/restart

Services

• Reference : https://whiteklay.com/kafka/
• What Is Kafka Connect Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector. Kafka Connect helps use to perform Extract (E) and Transform(T) of ETL Process. Connect contains the set of connectors which allows to import and export the data.

Cconfiguration for common source and sink Connectors. Connectors comes in two flavors:

• Source Connector Source Connector imports data from other System to Kafka Topic. For eg; Source Connector can ingest entire databases and stream table updates to Kafka topics.

• Sink Connector Sink Connector exports data from Kafka topic to other Systems. For eg; Sink Connector can deliver data from Kafka topic to an HDFS File.