ltparse | Python tool for creating Terraform | Infrastructure Automation library

by datarobot Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | ltparse Summary

ltparse is a Python library typically used in Devops, Infrastructure Automation, Terraform applications. ltparse has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Python tool for creating Terraform out of simple layout files

Support

Quality

Security

License

Reuse

Support

ltparse has a low active ecosystem.

It has 11 star(s) with 1 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 1697 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ltparse is current.

Quality

ltparse has 0 bugs and 0 code smells.

Security

ltparse has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ltparse code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ltparse does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ltparse releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 717 lines of code, 34 functions and 13 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ltparse and discovered the below as its top functions. This is intended to give you an instant insight into ltparse implemented functionality, and help decide if they suit your requirements.

Run Terraform
Remove ebs_volumes
Load a layout
Dump data to YAML
Dump data to JSON
Write data to destination
Convert unicode to str
Add security groups
Format the data
Add AWS instances
Get a list of provisioners
Recursively update a dictionary
Get vpc security groups
Add ELB
Return a drive letter
Add route53 records
Get eBS block devices
Add a new instance
Read global options

Get all kandi verified functions for this library.

ltparse Key Features

No Key Features are available at this moment for ltparse.

ltparse Examples and Code Snippets

No Code Snippets are available at this moment for ltparse.

Community Discussions

Trending Discussions on Infrastructure Automation

Create CloudFormation Yaml from existing RDS DB instance (Aurora PostgreSQL)

Azure DevOps CI with Web Apps for Containers

QUESTION

Create CloudFormation Yaml from existing RDS DB instance (Aurora PostgreSQL)

Asked 2020-Jun-05 at 00:59

I have an RDS DB instance (Aurora PostgreSQL) setup in my AWS account. This was created manually using AWS Console. I now want to create CloudFormation template Yaml for that DB, which I can use to create the DB later if needed. That will also help me replicate the DB in another environment. I would also use that as part of my Infrastructure automation.

...

ANSWER

Answered 2020-Jun-05 at 00:59

Unfortunately, there is no such functionality provided by AWS.

However, you mean hear about two options that people could wrongfully recommend.

CloudFormer

CloudFormer is a template creation beta tool that creates an AWS CloudFormation template from existing AWS resources in your account. You select any supported AWS resources that are running in your account, and CloudFormer creates a template in an Amazon S3 bucket.

Although it sounds good, the tool is no longer maintained and its not reliable (for years in beta).

Importing Existing Resources Into a Stack

Often people mistakenly think that this "generates yaml" for you from existing resources. The truth is that it does not generate template files for you. You have to write your own template which matches your resource exactly, before you can import any resource under control to CloudFormation stack.

Your only options is to manually write the template for the RDS and import it, or look for an external tools that could reverse-engineer yaml templates from existing resources.

Source https://stackoverflow.com/questions/62206364

QUESTION

Azure DevOps CI with Web Apps for Containers

Asked 2020-Mar-16 at 08:59

I'm struggling to set up a CI process for a web application in Azure. I'm used to deploying built code directly into Web Apps in Azure but decided to use docker this time.

In the build pipeline, I build the docker images and push them to an Azure Container Registry, tagged with the latest build number. In the release pipeline (which has DEV, TEST and PROD), I need to deploy those images to the Web Apps of each environment. There are 2 relevant tasks available in Azure releases: "Azure App Service deploy" and "Azure Web App for Containers". Neither of these allow the image source for the Web App to be set to Azure Conntainer Registry. Instead they take custom registry/repository names and set the image source in the Web App to Private Registry, which then requires login and password. I'm also deploying all Azure resources using ARM templates so I don't like the idea of configuring credentials when the 2 resources (the Registry and the Web App) are integrated already. Ideally, I would be able to set the Web App to use the repository and tag in Azure Container Registry that I specify in the release. I even tried to manually configure the Web Apps first with specific repositories and tags, and then tried to change the tags used by the Web Apps with the release (with the tasks I mentioned) but it didn't work. The tags stay the same.

Another option I considered was to configure all Web Apps to specific and permanent repositories and tags (e.g. "dev-latest") from the start (which doesn't fit well with ARM deployments since the containers need to exist in the Registry before the Web Apps can be configured so my infrastructure automation is incomplete), enable "Continuous Deployment" in the Web Apps and then tag the latest pushed repositories accordingly in the release so they would be picked up by Web Apps. I could not find a reasoble way to add tags to existing repositories in the Registry.

What is Azure best practice for CI with containerised web apps? How do people actually build their containers and then deploy them to each environment?

...

ANSWER

Answered 2020-Mar-16 at 08:59

Just set up a CI pipeline for building an image and pushing it to a container registry.

You could then use both Azure App Service deploy and Azure Web App for Containers task to handle the deploy.

The Azure WebApp Container task similar to other built-in Azure tasks, requires an Azure service connection as an input. The Azure service connection stores the credentials to connect from Azure Pipelines or Azure DevOps Server to Azure.

I'm also deploying all Azure resources using ARM templates so I don't like the idea of configuring credentials when the 2 resources (the Registry and the Web App)

You could also be able to Deploy Azure Web App for Containers with ARM and Azure DevOps.

How do people actually build their containers and then deploy them to each environment?

Kindly take a look at below blogs and official doc which may be helpful:

Source https://stackoverflow.com/questions/60693622

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ltparse

You can download it from GitHub.
You can use ltparse like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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