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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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Creating an synchronous HTTP request using Spring RestTemplate

While RestTemplate is now in maintenance mode and WebClient is recommended for new applications, it’s still widely used in existing projects. Here’s how you can configure and use RestTemplate in a Spring Boot application: 1. Add Dependencies Ensure the following dependency is present in your pom.xml : < dependency > < groupId > org.springframework.boot </ groupId > < artifactId > spring-boot-starter-web </ artifactId > </ dependency > 2. Configure a RestTemplate Bean Define a RestTemplate bean in a configuration class to enable dependency injection: import org.springframework.boot.web.client.RestTemplateBuilder; import org.springframework.context. annotation .Bean; import org.springframework.context. annotation .Configuration; import org.springframework.web.client.RestTemplate; @Configuration public class RestTemplateConfig { @Bean public RestTemplate restTemplate(RestTemplateBuilder builder) { ret...
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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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Sorting a list in python

To sort a list in Python , you can use either the sort() method or the sorted() function. Below are examples for both approaches: 1. Using the sort() Method This method sorts the list in-place (i.e., modifies the original list). It does not return a new list. Example: my_list = [3, 1, 4, 1, 5, 9, 2, 6] # Sort the list in -place (ascending order by default) my_list. sort () print (my_list) # Output: [1, 1, 2, 3, 4, 5, 6, 9] Descending Order: Use reverse=True . ```python my_list = [3, 1, 4, 1, 5, 9, 2, 6] Sort in descending order my_list.sort(reverse=True) print(my_list) # Output: [9, 6, 5, 4, 3, 2, 1, 1] --- ### 2. **Using the `sorted()` Function** - This function returns a **new sorted list** and leaves the original list unchanged. **Example:** ```python my_list = [ 3 , 1 , 4 , 1 , 5 , 9 , 2 , 6 ] # Create a new sorted list (ascending order) sorted_list = sorted(my_list) print(sorted_list) # Output: [1, 1, 2, 3, 4, 5, 6, 9] print(my_list) # Ori...
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형태소 분석 태그

때 형태소는 한국어 형태소 분석을 통해 문장에서 각 단어의 품사와 문법적 역할을 표시하는 것을 말합니다. 일반적으로 형태소 태그는 주로 형태소 분석기를 사용하여 추가되며, 다양한 형태소 태그 체계가 있을 수 있습니다. 가장 널리 사용되는 형태소 태그 체계 중 하나는 세종 형태소 품사 태그 체계입니다. 세종 형태소 품사 태그 체계의 일부 중요한 태그는 다음과 같습니다: 명사(NNG): 일반 명사 고유 명사(NNP): 고유한 개별 명사 대명사(NP): 대명사 동사(VV): 동사 형용사(VA): 형용사 부사(MAG): 일반 부사 관형사(MM): 관형사 감탄사(IC): 감탄사 조사(JKS, JKC, JKG 등): 조사 어미(EP, EC, ETN 등): 어미 접두사(XPN): 접두사 접미사(XSN): 접미사 어근(XR): 어근 한글 이외의 문자(SL): 외국어 및 숫자 등 이 외에도 다양한 형태소 태그가 있을 수 있으며, 분석 도구나 분석기에 따라 세부적인 태그 체계가 조금씩 다를 수 있습니다.
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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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Joining an additional control plane node to an existing Kubernetes cluster

Joining an additional control plane node to an existing Kubernetes cluster involves specific steps to ensure proper synchronization between the control plane components. Below is the detailed process: 1. Pre-Requisites Ensure the following on the new control plane node: Same Kubernetes Version : The new node must have the same Kubernetes version installed as the existing control plane. kubeadm version Kubeadm, Kubelet, and Kubectl Installed : Ensure these tools are installed and configured. Network Configuration : The new control plane must have network connectivity to the existing control plane nodes. 2. Retrieve the Join Command On an existing control plane node, generate the kubeadm join command with the --control-plane flag: kubeadm token create -- print -join-command You will get output similar to: kubeadm join <control-plane-endpoint> : 6443 --token <token> \ --discovery-token- ca -cert-hash sha256 : <hash> \ --control-plane 3. ...
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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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Read a file in Java, 자바 파일 읽기

To convert a File object into an InputStream in Java, you can use the FileInputStream class. Here's an example: import java.io.File; import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; public class FileToInputStreamExample { public static void main(String[] args) { File file = new File( "path/to/file.txt" ); try { InputStream inputStream = new FileInputStream(file); // Use the inputStream as needed... // Close the inputStream when you're done inputStream.close(); } catch (IOException e) { e.printStackTrace(); } } } In the code above, replace "path/to/file.txt" with the actual path to your file. The FileInputStream class is used to create an input stream from the file. You can then use the inputStream object to read the contents of the file or perform any other operations you need. Finally, rem...
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dagrun_timeout of Airflow

In Apache Airflow, you can set a timeout for an entire DAG run using the dagrun_timeout parameter in the DAG definition. This parameter allows you to specify the maximum amount of time a DAG run is allowed to execute. If the DAG run exceeds this time limit, it will be marked as failed, and all running tasks in that run will be terminated. Setting dagrun_timeout Here's how to set a timeout for a DAG run: from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from datetime import datetime, timedelta # Define the DAG with DAG( 'my_dag_with_timeout', description='A DAG with a run timeout', schedule_interval='@daily', start_date=datetime(2023, 1 , 1 ), catchup=False, dagrun_timeout=timedelta(hours=2) # Set a 2-hour timeout for the entire DAG run ) as dag: # Define tasks start = DummyOperator( task_id='start') end = DummyOperator( task_id='end') # Define ta...
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