MySQL GRANT 


grant all privileges on TABLE_NAME.* to 'username'@'xxx.xxx.xxx.xxx' identified by 'password';

flush privileges;

모든 아이피에서 접근 허용하기


grant all privileges on TABLE_NAME.* to 'username'@'%' identified by 'password';

flush privileges;

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Using the MinIO API via curl

Using the MinIO API via curl is straightforward, as MinIO is compatible with Amazon S3 API, so most commands follow a similar syntax. Here’s a guide on how to use curl with the MinIO API for some common operations like uploading, downloading, and managing objects. Prerequisites Access Key and Secret Key : Obtain your MinIO Access Key and Secret Key. MinIO Endpoint : Know your MinIO server endpoint, e.g., http://localhost:9000 . Bucket : You may need an existing bucket name, or create a new one using the commands below. Authentication Header For requests to work with MinIO, you need to include authentication in the headers. MinIO uses AWS Signature Version 4 for signing requests. Common Examples 1. List Buckets To list all buckets in your MinIO account, use: curl -X GET \ - -url "http://localhost:9000/" \ - H "Authorization: AWS <AccessKey>:<Signature>" 2. Create a Bucket To create a new bucket, use: curl -X PUT \ - -url "htt...
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Sparse encoder

A Sparse Encoder refers to a variant of neural network architectures where sparsity is introduced in the encoding process. This can mean either: Sparse Input Representations : The input features to the encoder are sparse (many values are zero). Sparse Output Representations : The encoder is designed to produce sparse outputs where most of the encoded feature values are zero. Sparse encoders are often used to improve model interpretability, efficiency, and generalization. They can be applied in various contexts, including traditional neural networks, autoencoders, and even transformer-based models. Key Characteristics of Sparse Encoders Sparsity in Representations : The model learns a feature representation where only a subset of neurons is active for a given input. This mimics how biological neurons operate, promoting interpretability and reducing noise in representations. Reduced Computational Cost : Sparse operations often result in lower computational overhead since...
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max_active_runs of Airflow

In Apache Airflow, if you want to ensure that a new run of a DAG doesn’t start before the previous one has completed, you can use the max_active_runs parameter in the DAG definition. Setting this parameter to 1 ensures that only one instance of the DAG is running at any given time. Setting max_active_runs for a DAG Here’s an example of how to set up a DAG with max_active_runs=1 : from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from datetime import datetime # Define the DAG with DAG( 'my_dag' , description= 'A sample DAG' , schedule_interval= '@daily' , # Set your schedule start_date=datetime( 2023 , 1 , 1 ), catchup= False , max_active_runs= 1 # This prevents new runs before the previous run completes ) as dag: # Define tasks start = DummyOperator(task_id= 'start' ) end = DummyOperator(task_id= 'end' ) # Define task dependencies ...
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how to delete all issues on project in sentry, 센트리 이슈 삭제하기

As of now, Sentry doesn't have a direct feature to delete all issues in bulk through the web interface. However, there are a few methods you can use to archive, resolve, or delete issues programmatically or by adjusting project settings: 1. Bulk Archive Issues from the Web Interface Though there is no mass delete option, you can bulk archive or resolve issues, which essentially hides them from active issue lists. Steps: Go to your Sentry project. In the issue list view, select the issues you want to archive or resolve. Use the "Select All" checkbox to select multiple issues. Choose "Resolve" or "Archive" from the bulk action dropdown. However, this doesn't delete the issues permanently; they will be archived or marked as resolved. 2. Adjust Retention Policy (Data Retention Settings) If you're looking to remove older issues, you can adjust the retention policy at the organization level. Sentry allows you to configure how long issue...
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Nginx Openssl 설정

인증서 만들기 #openssl req -new -newkey rsa:2048 -nodes -keyout open_ssl.key -out open_ssl.csr Generating a 2048 bit RSA private key ... ... Please enter the following 'extra' attributes to be sent with your certificate request A challenge password []: 테스트를 위한 SSL 인증서 생성 #openssl x509 -req -days 365 -in open_ssl.csr -signkey open_ssl.key -out open_ssl.crt #ls -al -rw-r--r-- 1 root root 1306 Jun 18 11:27 open_ssl.crt -rw-r--r-- 1 root root 1110 Jun 18 11:21 open_ssl.csr -rw-r--r-- 1 root root 1704 Jun 18 11:21 open_ssl.key Nginx 의 SSL 모듈 탑재 확인 #/usr/local/nginx/sbin/nginx -V nginx version: nginx/1.5.8 built by gcc 4.4.6 20120305 (Red Hat 4.4.6-4) (GCC) TLS SNI support enabled configure arguments: --prefix=/daum/program/nginx --with-http_ssl_module "--with-http_ssl_module" 부분 없다면 아래 방식으로 Nginx 재설치 # ./configure --prefix=/usr/local/nginx --with-http_ssl_module ... # make && make install Nginx 서버 config 설정 # HTTPS server # serve...
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difference between truncate and truncate_preserve in hbase

In HBase, both truncate and truncate_preserve commands are used to delete all the data in a table, but they behave differently in terms of how they handle the table’s schema and regions. 1. truncate Purpose : Deletes all data and regions in a table and then recreates it with the same schema. What It Does : When you run truncate , it: Disables the table. Deletes all data and metadata (including region splits) in the table. Drops all regions and recreates the table as a single region with the original schema. Re-enables the table. Effect on Regions : After truncate , all region splits are lost, so the table will start with only one region. This is useful for development or quick data purging but is inefficient for large tables that rely on pre-split regions for load balancing. Command Syntax : truncate 'table_name' 2. truncate_preserve Purpose : Deletes all data in a table but preserves the table’s regions and schema. What It Does : When you use truncate_...
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To monitor logs for a Kubernetes CronJob

To monitor logs for a Kubernetes CronJob , you can follow these steps: 1. List the CronJob and Its Jobs First, get the list of CronJobs to identify the specific one you're interested in: kubectl get cronjobs After identifying the CronJob, list the Jobs it created using the following command (replace my-cronjob with the name of your CronJob): kubectl get jobs --selector=job-name=my-cronjob 2. Get the Pod Created by the Job CronJobs create Kubernetes Jobs, and Jobs spawn Pods to execute the tasks. To view the Pods created by a specific Job: kubectl get pods --selector=job-name=my-cronjob-<timestamp> Replace my-cronjob-<timestamp> with the name of the Job you identified earlier. If you're unsure about the exact name, you can list all Pods and filter the results: kubectl get pods 3. View the Logs of the Pod Once you have the name of the Pod, you can check its logs. Use the following command (replace my-pod with the actual ...
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Sort the distinct values by their count in descending order using the DataFrame API

To sort the distinct values by their count in descending order using the DataFrame API in Spark, you can use the orderBy() function with the ascending=False parameter. Here’s how you can modify the DataFrame API example to include sorting in descending order: Example: Get Distinct Values and Their Counts with Descending Order Sorting # Read the table into a DataFrame df = spark. read . table ( "table_name" ) # Group by the field and count , then order by count in descending order distinct_counts = df.groupBy( "field_name" ). count ().orderBy( "count" , ascending=False) # Show the results distinct_counts.show() Explanation: groupBy("field_name") : Groups the data by the distinct values in the field_name column. count() : Counts the number of occurrences for each distinct value. orderBy("count", ascending=False) : Sorts the result by the count in descending order . Optional: Collecting the Sorted Results If you...
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Chromium 개발 환경 세팅, 크로미움 개발 준비하기

Chromium 개발 환경을 구축하는 것은 여러 단계로 이루어져 있으며, 이를 위해서는 적절한 도구와 의존성을 설치해야 합니다. 아래는 일반적인 Chromium 개발 환경을 설정하는 방법입니다. 1. 시스템 요구사항 확인 Chromium은 리눅스, 맥OS, 윈도우에서 빌드할 수 있습니다. 이 가이드에서는 Ubuntu를 기준으로 설명하지만, 다른 운영체제에서도 유사한 과정으로 설정할 수 있습니다. 메모리 : 최소 16GB RAM (권장 32GB 이상) 디스크 공간 : 최소 100GB 이상의 여유 공간 운영체제 : Ubuntu 20.04 이상 권장 2. 필수 패키지 설치 Chromium을 빌드하려면 여러 패키지가 필요합니다. 아래 명령어를 사용하여 설치합니다. sudo apt- get update sudo apt- get install build-essential git python3 python3-pip sudo apt- get install libx11- dev libxcomposite- dev libxcursor- dev libxdamage- dev libxrandr- dev libxtst- dev libxss- dev libglib2 .0 - dev sudo apt- get install libnss3- dev libasound2- dev libpulse- dev libjpeg- dev libpng- dev sudo apt- get install curl nodejs npm 3. Chromium 소스 코드 가져오기 Chromium 소스 코드를 가져오려면 depot_tools 라는 도구가 필요합니다. depot_tools 설치 : git clone https://chromium.googlesource.com/chromium/tools/depot_tools.git export PATH=` pwd `/depot_tools: " $PATH " 이 경로를 영구적으로 PATH에 추...
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Bash script samples

Bash scripting is a powerful tool for automating tasks and configuring environments in Unix-like systems. Here's a collection of bash style examples covering basic syntax, best practices, and various features of the Bash shell: 1. Basic Variables and Echo #!/bin/bash # Define variables name= "John" age=30 # Print variables echo "Hello, my name is $name , and I am $age years old." 2. Reading Input from User #!/bin/bash # Ask user for input echo "Please enter your name:" read name # Greet user echo "Hello, $name !" 3. Conditional Statements (If-Else) #!/bin/bash # Ask for the user's age echo "Enter your age:" read age # Check if the user is old enough to drive if [ $age -ge 18 ]; then echo "You are old enough to drive." else echo "Sorry, you are not old enough to drive." fi 4. Loops (For, While, Until) For Loop #!/bin/bash # Print numbers from 1 to 5 for i i...
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