https://python-poetry.org/docs/?ref=dylancastillo.co#installing-with-the-official-installer 

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

• Gunicorn is a Python WSGI HTTP Server that usually lives between a reverse proxy (e.g., Nginx) or load balancer (e.g., AWS ELB) and a web application such as Django or Flask.
• Better performance by optimizing Gunicorn config (https://medium.com/building-the-system/gunicorn-3-means-of-concurrency-efbb547674b7)
• The suggested number of workers is (2*CPU)+1.
• gunicorn --workers=5 --threads=2 --worker-class=gthread main:app, the maximum concurrent requests areworkers * threads 10 in our case.

poetry config virtualenvs.in-project true poetry init poetry add fastapi poetry add gunicorn poetry add uvicorn poetry add pytz poetry add httpx poetry add pytest poetry add pytest-cov poetry add requests poetry add python-dotenv poetry add pyyaml ... # start with gunicorn config gunicorn.config.py import multiprocessing workers = multiprocessing.cpu_count() * 2 + 1 worker_class = "uvicorn.workers.UvicornWorker" wsgi_app = "app.main:app" timeout = 60 loglevel = "info" bind = "0.0.0.0:8000" max_requests = 1000 max_requests_jitter = 100 ... gunicorn -c app/gunicorn.config.py gunicorn -k uvicorn.workers.UvicornWorker main:app --bind 0.0.0.0:8004 --workers 4 .. uvicorn app.main:app --reload for dev

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

• sudo service k8s_es_api status/stop/start/restart

#-- /etc/systemd/system/k8s_es_api.service [Unit] Description=k8s ES Service [Service] User=devuser Group=devuser Type=simple ExecStart=/bin/bash /home/devuser/k8s_interface_api/service-start.sh ExecStop= /usr/bin/killall k8s_es_api [Install] WantedBy=default.target # Service command sudo systemctl daemon-reload sudo systemctl enable k8s_es_api.service sudo systemctl start k8s_es_api.service sudo systemctl status k8s_es_api.service sudo systemctl stop k8s_es_api.service sudo service k8s_es_api status/stop/start

• Run this command ./start-start.sh or python -m uvicorn main:app --reload --host=0.0.0.0 --port=8888 --workers 4
• Run Dockerfile: CMD ["gunicorn", "-w", "4", "--worker-class", "uvicorn.workers.UvicornWorker", "-b", "0.0.0.0:8888", "-t", "30", "--pythonpath", "/app/FN-Basic-Services", "main:app"]
• Service : http://VM_Node_#1:8888/docs

• Go to virtual enviroment using source .venv/bin/activate
• Run this command manually: poetry run py.test -v --junitxml=test-reports/junit/pytest.xml --cov-report html --cov tests/ or ./pytest.sh

$ ./pytest.sh tests\test_api.py::test_api [2025-10-23 15:26:04,750] [INFO] [main] [root] /hello [2025-10-23 15:26:04,759] [INFO] [_client] [_send_single_request] HTTP Request: GET http://testserver/ "HTTP/1.1 200 OK" PASSED ============================================================= tests coverage ============================================================== _____________________________________________ coverage: platform win32, python 3.11.7-final-0 _____________________________________________ Name Stmts Miss Cover Missing ---------------------------------------------------- config\log_config.py 32 1 97% 42 injector.py 6 0 100% main.py 13 0 100% tests\__init__.py 0 0 100% tests\conftest.py 8 0 100% tests\test_api.py 6 0 100% ---------------------------------------------------- TOTAL 65 1 98% ============================================================ 1 passed in 0.23s ============================================================ (.venv) 

• Run the command using ./docker-compose.yml or ./docker-build.sh and ./docker-run.sh

* Executing task in folder python-fastapi-openapi-k8s: 'C:\Program Files\Docker\Docker\resources\bin\docker.EXE' compose -f 'docker-compose.yml' up -d --build 'fn-basic-docker-api' 'fn-basic-docker-api-test' [+] Building 7.5s (19/19) FINISHED => [internal] load local bake definitions 0.0s => => reading from stdin 925B 0.0s => [fn-basic-docker-api-test internal] load build definition from Dockerfile 0.0s => => transferring dockerfile: 2.39kB 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 3) 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 34) 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 49) 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 3) 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 34) 0.0s => WARN: FromPlatformFlagConstDisallowed: FROM --platform flag should not use constant value "linux/amd64" (line 49) 0.0s => [fn-basic-docker-api internal] load metadata for docker.io/library/python:3.9-slim-buster 0.7s => [fn-basic-docker-api internal] load .dockerignore 0.0s => => transferring context: 74B 0.0s => [fn-basic-docker-api-test environment 1/5] FROM docker.io/library/python:3.9-slim-buster@sha256:320a7a4250aba4249f458872adecf92ee 0.0s => [fn-basic-docker-api-test internal] load build context 0.7s => => transferring context: 226.34kB 0.7s => CACHED [fn-basic-docker-api-test runtime 2/5] WORKDIR /app 0.0s => CACHED [fn-basic-docker-api-test environment 2/5] RUN python3 -m venv /app/poetry-venv && /app/poetry-venv/bin/pip install -U pi 0.0s => CACHED [fn-basic-docker-api-test environment 3/5] WORKDIR /app 0.0s => CACHED [fn-basic-docker-api-test environment 4/5] COPY poetry.lock pyproject.toml ./ 0.0s => CACHED [fn-basic-docker-api-test environment 5/5] RUN /bin/bash -c 'source $POETRY_VENV/bin/activate && poetry install --no-r 0.0s   => CACHED [fn-basic-docker-api-test runtime 3/5] COPY --from=environment /app . 0.0s   => [fn-basic-docker-api runtime 4/5] COPY . FN-Basic-Services 1.4s  => [fn-basic-docker-api-test test 5/5] RUN sed -i -e 's/\r$//' /app/FN-Basic-Services/*.sh 0.6s   => [fn-basic-docker-api runtime 5/5] RUN sed -i -e 's/\r$//' /app/FN-Basic-Services/*.sh 0.7s   => [fn-basic-docker-api-test] exporting to image 1.0s   => => exporting layers 1.0s   => => writing image sha256:9efc497a918564a8c7d0de13bbd04f07e82df3e852a8ad87c6fdbf7fde2a5db5 0.0s   => => naming to docker.io/library/ffn-basic-docker-api:test 0.0s   => [fn-basic-docker-api] exporting to image 1.0s   => => exporting layers 1.0s   => => writing image sha256:b4bfe71d730f3ace7f14eba1540487c482628029037648f59ffd31541df26df8 0.0s   => => naming to docker.io/library/fn-basic-docker-api:es 0.0s   => [fn-basic-docker-api] resolving provenance for metadata file 0.0s   => [fn-basic-docker-api-test] resolving provenance for metadata file 0.0s  [+] Running 4/4  ✔ fn-basic-docker-api Built 0.0s   ✔ fn-basic-docker-api-test Built 0.0s   ✔ Container fn-basic-docker-api Started 12.9s   ✔ Container fn-basic-docker-api-test Started 12.9s   * Terminal will be reused by tasks, press any key to close it. 

• An Apache Spark cluster is a distributed computing system designed for processing large datasets in parallel. It consists of a master node and multiple worker nodes.

  • Master Nodes: This node hosts the driver program, which is responsible for coordinating and managing the execution of Spark applications. It tracks the status of worker nodes and allocates tasks to them.
  • Worker Nodes: These nodes, also known as data nodes, are responsible for storing data and executing the actual data processing tasks. They run Spark executor processes that perform computations on data partitions assigned by the master

• Download : https://spark.apache.org/downloads.html, https://repo1.maven.org/maven2/org/elasticsearch/elasticsearch-spark-30_2.12/8.7.0/

• Reference : https://velog.io/@jskim/Spark-%EB%B0%B0%ED%8F%AC-%EB%B0%8F-%EC%8B%A4%ED%96%89-%EB%B0%A9%EB%B2%95%EC%97%90-%EB%8C%80%ED%95%9C-%EC%9D%B4%ED%95%B4

• Spark Run Mode : Cluster, Standalone(Sparck cluster with Master/Worker nodes in local env - spark-submit with master address, start-master.sh, start-worker.sh <master_url>), Local Mode(without cluster), Reference (https://wooono.tistory.com/140)

  • Spark Local Mode (https://bluehorn07.github.io/2024/08/18/run-spark-on-local-1/):
    • python3 -m venv venv
    • source venv/bin/activate
    • pip3 install pyspark==3.5.2
    • pyspark
    • spark-submit --master "local[2]" ~/ES/spark/utils/hello-spark.py

• Spark Cluster with slave nodes: Rererence(https://fwani.tistory.com/21)

• SSH into Local VM_#1, #2

• Installation Commands

  • lsb_release -a
  • sudo apt update
  • sudo apt install openjdk-17-jdk -y
  • wget https://dlcdn.apache.org/spark/spark-4.0.1/spark-4.0.1-bin-hadoop3.tgz
  • tar -xvf ./spark-4.0.1-bin-hadoop3.tgz
  • pwd
  • sudo groupadd spark
  • sudo useradd -g spark spark
  • sudo chown -R spark:spark ./spark
  • /home/devuser/ES/spark
  • ln -s ./spark-4.0.1-bin-hadoop3 latest
  • sudo dpkg --configure -a
  • sudo apt install python3.9
  • export SPARK_HOME=/home/devuser/ES/spark/latest
  • export PYSPARK_PYTHON=/usr/bin/python3.9
  • export PATH=$SPARK_HOME/bin:$PATH
  • echo 'export SPARK_HOME=/home/devuser/ES/spark/latest' >> ~/.bashrc
  • echo 'export PATH=$SPARK_HOME/bin:$PATH' >> ~/.bashrc
  • echo 'export PATH=$PYSPARK_PYTHON:$PATH' >> ~/.bashrc
  • source ~/.bashrc
  • spark-shell or pyspark

  WARNING: Using incubator modules: jdk.incubator.vector Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties 25/10/24 15:36:41 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/  /___/ .__/\_,_/_/ /_/\_\ version 4.0.1  /_/  Using Scala version 2.13.16 (OpenJDK 64-Bit Server VM, Java 17.0.9) Type in expressions to have them evaluated. Type :help for more information. 25/10/24 15:36:44 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Spark context Web UI available at http://VM_Node_#1:4040 Spark context available as 'sc' (master = local[*], app id = local-1761338204619). Spark session available as 'spark'.  >>> myRange = spark.range(1000).toDF("number") >>> divisBy2 = myRange.where("number % 2 = 0") >>> divisBy2.count() 500 scala> exit()

  • Setup Spark Cluster: Run the Master Node spark-class org.apache.spark.deploy.master.Master -h VM_Node_#1 on VM_Node_#1, http://VM_Node_#1:8080, Reference (https://bluehorn07.github.io/topic/development#apache-spark)

    • If there is no JAVA_HOME Path: Modify spark-env.sh
    • cp conf/spark-env.sh.template conf/spark-env.sh

    #!/usr/bin/env bash export SPARK_HOME=/apps/monitoring_script/spark/latest export PYSPARK_PYTHON=/usr/local/bin/python3.9 export PATH=$SPARK_HOME/bin:$PATH export MASTER_PORT=7077 export JAVA_HOME=/apps/monitoring_script/spark/java/latest # Options for beeline # - SPARK_BEELINE_OPTS, to set config properties only for the beeline cli (e.g. "-Dx=y") # - SPARK_BEELINE_MEMORY, Memory for beeline (e.g. 1000M, 2G) (Default: 1G) export JAVA_HOME=/apps/java/latest export SPARK_WORKER_CORES=1 export SPARK_WORKER_MEMORY="1g" export SPARK_WORKER_PORT=5000 #export SPARK_EXECUTOR_INSTANCES=5 export SPARK_WORKER_INSTANCES=4 export SPARK_CONF_DIR="/apps/spark/latest/conf" export SPARK_TMP_DIR="/apps/var/spark/tmp" export SPARK_PID_DIR="/apps/var/spark/pids" export SPARK_LOG_DIR="/apps/var/spark/logs" export SPARK_WORKER_DIR="/apps/var/spark/work" export SPARK_MASTER_HOST="localhost" #export SPARK_MASTER_IP="localhost" #master node's IP address here. export SPARK_MASTER_PORT=7077 export SPARK_LOCAL_IP="localhost" # local node IP export SPARK_WORKER_OPTS="-Dspark.worker.cleanup.enabled=true -Dspark.worker.cleanup.interval=10 -Dspark.worker.cleanup.appDataTtl=604800" export SPARK_WORKER_WEBUI_PORT="8085"

    • ./spark-env.sh

  WARNING: Using incubator modules: jdk.incubator.vector Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties 25/10/27 15:31:43 WARN Utils: Your hostname, ubuntu-node-1, resolves to a loopback address: 127.0.1.1; using VM_Node_#1 instead (on interface enp0s1) 25/10/27 15:31:43 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address 25/10/27 15:31:44 INFO Master: Started daemon with process name: 2849@ubuntu-node-1 25/10/27 15:31:44 INFO SignalUtils: Registering signal handler for TERM 25/10/27 15:31:44 INFO SignalUtils: Registering signal handler for HUP 25/10/27 15:31:44 INFO SignalUtils: Registering signal handler for INT 25/10/27 15:31:44 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties 25/10/27 15:31:44 INFO SecurityManager: Changing view acls to: devuser 25/10/27 15:31:44 INFO SecurityManager: Changing modify acls to: devuser 25/10/27 15:31:44 INFO SecurityManager: Changing view acls groups to: devuser 25/10/27 15:31:44 INFO SecurityManager: Changing modify acls groups to: devuser 25/10/27 15:31:44 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: devuser groups with view permissions: EMPTY; users with modify permissions: devuser; groups with modify permissions: EMPTY; RPC SSL disabled 25/10/27 15:31:44 INFO Utils: Successfully started service 'sparkMaster' on port 7077. 25/10/27 15:31:44 INFO Master: Starting Spark master at spark://VM_Node_#1:7077 25/10/27 15:31:44 INFO Master: Running Spark version 4.0.1 25/10/27 15:31:44 INFO JettyUtils: Start Jetty 0.0.0.0:8080 for MasterUI 25/10/27 15:31:44 INFO Utils: Successfully started service 'MasterUI' on port 8080. 25/10/27 15:31:44 INFO MasterWebUI: Bound MasterWebUI to 0.0.0.0, and started at http://VM_Node_#1:8080 25/10/27 15:31:44 INFO Master: I have been elected leader! New state: ALIVE

  • Run spark application on Master node : spark-submit --master spark://VM_Node_#1:7077 ~/ES/spark/utils/hello-spark.py (Need to join the Worker node into Spark Cluster), WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

  25/10/27 15:45:23 INFO DAGScheduler: Submitting 2 missing tasks from ResultStage 0 (PythonRDD[1] at count at /home/devuser/ES/spark/utils/hello-spark.py:10) (first 15 tasks are for partitions Vector(0, 1)) 25/10/27 15:45:23 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks resource profile 0 25/10/27 15:45:38 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

  • Setup Spark Cluster: Run the Worker Node spark-class org.apache.spark.deploy.worker.Worker spark://VM_Node_#1:7077 on VM_Node_#2
  • Run the spark application on Master node (VM_Node_#1): spark-submit --master spark://VM_Node_#1:7077 --total-executor-cores 1 --executor-memory 512m ~/ES/spark/utils/hello-spark.py

• Server_Practice Commands

  • Spark Master, Spark Worker

  [spark@localhost spark]$ ./spark-master.sh start 🦄 Starting es-spark-cluster-service starting org.apache.spark.deploy.master.Master, logging to /apps/monitoring_script/spark/latest/logs/spark-spark-org.apache.spark.deploy.master.Master-1-localhost.out [spark@localhost spark]$ ./spark-master.sh status 🦄 es-spark-cluster-service is Running as PID: 10574 [spark@localhost spark]$ ./spark-worker.sh start 🦄 Starting es-spark-workers-service starting org.apache.spark.deploy.worker.Worker, logging to /apps/monitoring_script/spark/latest/logs/spark-spark-org.apache.spark.deploy.worker.Worker-1-localhost.out [spark@localhost spark]$ ./spark-worker.sh status 🦄 es-spark-workers-service is Running as PID: 11019

  • Spark Submit Job (API for the spark custom job: http://localhost:8080/json/) : Args -> --deploy-mode client : Cluster 내부의 Node에서 Driver 실행, Cluster: Spark Application을 실행하는 Node에서 Driver 실행

  [spark@localhost spark]$ /apps/monitoring_script/spark/latest/bin/spark-submit --master spark://localhost:7077 /apps/monitoring_script/spark/utils/hello-spark.py [spark@localhost spark]$ ls java latest spark-4.0.1-bin-hadoop3 spark-4.0.1-bin-hadoop3.tgz spark-master.sh sparkSubmit.sh spark-worker.sh utils [spark@localhost spark]$ [spark@localhost spark]$ ./sparkSubmit.sh start ... 25/12/11 14:59:05 INFO DAGScheduler: Job 0 finished: count at /apps/monitoring_script/spark/utils/hello-spark.py:10, took 2867.462486 ms 10000 25/12/11 14:59:06 INFO SparkContext: Invoking stop() from shutdown hook ...

  • API JSON Result (http://localhost:8080/json/)

  { "url" : "spark://localhost:7077", "workers" : [ { "id" : "worker-20251211110141-127.0.0.1-14946", "host" : "127.0.0.1", "port" : 14946, "webuiaddress" : "http://127.0.0.1:8081", "cores" : 1, "coresused" : 0, "coresfree" : 1, "memory" : 1024, "memoryused" : 0, "memoryfree" : 1024, "resources" : { }, "resourcesused" : { }, "resourcesfree" : { }, "state" : "ALIVE", "lastheartbeat" : 1765483182554 } ], "aliveworkers" : 1, "cores" : 1, "coresused" : 0, "memory" : 1024, "memoryused" : 0, "resources" : [ { } ], "resourcesused" : [ { } ], "activeapps" : [ ], "completedapps" : [ { "id" : "app-20251211145804-0000", "starttime" : 1765483084690, "name" : "hello-spark.py", "cores" : 1, "user" : "spark", "memoryperexecutor" : 1024, "memoryperslave" : 1024, "resourcesperexecutor" : [ ], "resourcesperslave" : [ ], "submitdate" : "Thu Dec 11 14:58:04 EST 2025", "state" : "FINISHED", "duration" : 9093 }, { "id" : "app-20251211145856-0001", "starttime" : 1765483136792, "name" : "hello-spark.py", "cores" : 1, "user" : "spark", "memoryperexecutor" : 1024, "memoryperslave" : 1024, "resourcesperexecutor" : [ ], "resourcesperslave" : [ ], "submitdate" : "Thu Dec 11 14:58:56 EST 2025", "state" : "FINISHED", "duration" : 9393 } ], "activedrivers" : [ ], "completeddrivers" : [ ], "status" : "ALIVE" }

• Kubernetes is an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications. It works by providing an API to manage clusters of virtual machines, scheduling containers, and automatically handling tasks like service discovery, load balancing, and self-healing to ensure applications remain available.

• Installation Commands
  • lsb_release -a
  • pwd
  • /home/devuser/ES/k8s

• Meilisearch(https://github.com/meilisearch/meilisearch) : Meilisearch is an open-source, lightning-fast search engine designed to provide a highly relevant and intuitive search experience for applications and websites

curl \ -X POST 'http://localhost:7700/indexes/movies/documents' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer CMJuRd2KKqt1UmxntJZCelvlDuxrbiUzzQ9AW9B7lnQ' \ --data-binary @movies.json

• Zincsearch (https://github.com/zincsearch/zincsearch): ZincSearch is a search engine that does full text indexing. It is a lightweight alternative to Elasticsearch and runs using a fraction of the resources. While Elasticsearch is a very good product, it is complex and requires lots of resources. ZincSearch becomes easier for folks to use full text search indexing without doing a lot of work.
• Docs : https://zincsearch-docs.zinc.dev/quickstart/, Swagger : http://localhost:4080/swagger/
• Run : ./zincsearch ZINC_PROMETHEUS_ENABLE=true or ./Search_Engine/zincsearch-service.sh
• Bulk Sample : $ curl http://localhost:4080/api/_bulk -i -u test:test --data-binary "@./Search_Engine/olympics.json"

ZINC_FIRST_ADMIN_USER=test ZINC_FIRST_ADMIN_PASSWORD=test

• Search

curl \ -X POST 'http://localhost:4080/api/olympics/_search' \ -H 'Content-Type: application/json' \ -H 'Authorization: Basic test:test' \ -d'  {  "query": {  "match_all": {}  }  }'