aws-python-sample | Sample project to demonstrate usage | SDK library

by aws-samples Python Version: Current License: Apache-2.0

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kandi X-RAY | aws-python-sample Summary

aws-python-sample is a Python library typically used in Utilities, SDK applications. aws-python-sample has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However aws-python-sample build file is not available. You can download it from GitHub.

Sample project to demonstrate usage of the AWS SDK for Python

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aws-python-sample has a low active ecosystem.

It has 166 star(s) with 149 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 1272 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of aws-python-sample is current.

Quality

aws-python-sample has 0 bugs and 0 code smells.

Security

aws-python-sample has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

aws-python-sample code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

aws-python-sample is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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aws-python-sample releases are not available. You will need to build from source code and install.

aws-python-sample has no build file. You will be need to create the build yourself to build the component from source.

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

aws-python-sample saves you 14 person hours of effort in developing the same functionality from scratch.

It has 40 lines of code, 0 functions and 1 files.

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

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Community Discussions

Trending Discussions on aws-python-sample

Airflow S3KeySensor - How to make it continue running

python aws s3 file distributing

QUESTION

Airflow S3KeySensor - How to make it continue running

Asked 2018-May-29 at 20:47

With the help of this Stackoverflow post I just made a program (the one shown in the post) where when a file is placed inside an S3 bucket a task in one of my running DAGs is triggered and then I perform some work using the BashOperator. Once it's done though the DAG is no longer in a running state but instead goes into a success state and if I want to have it pick up another file I need to clear all the 'Past', 'Future', 'Upstream', 'Downstream' activity. I would like to make this program so that it's always running and anytime a new file is placed inside the S3 bucket the program kicks off the tasks.

Can I continue using the S3KeySenor to do this or do I need to figure out a way of setting up an External Trigger to run my DAG? As of now my S3KeySensor is pretty pointless if it's only going to ever run once.

...

ANSWER

Answered 2018-May-29 at 20:38

Within Airflow, there isn't a concept that maps to an always running DAG. You could have a DAG run very frequently like every 1 to 5 minutes if that suits your use case.

The main thing here is that the S3KeySensor checks until it detects that the first file exists in the key's wildcard path (or timeout), then it runs. But when a second, or third, or fourth file lands, the S3 sensor will have already completed running for that DAG run. It won't get scheduled to run again until the next DAG run. (The looping idea you described is roughly equivalent to what the scheduler does when it creates DAG runs except not forever.)

An external trigger definitely sounds like the best approach for your use case, whether that trigger comes via the Airflow CLI's trigger_dag command ($ airflow trigger_dag ...):

https://github.com/apache/incubator-airflow/blob/972086aeba4616843005b25210ba3b2596963d57/airflow/bin/cli.py#L206-L222

Or via the REST API:

https://github.com/apache/incubator-airflow/blob/5de22d7fa0d8bc6b9267ea13579b5ac5f62c8bb5/airflow/www/api/experimental/endpoints.py#L41-L89

Both turn around and call the trigger_dag function in the common (experimental) API:

https://github.com/apache/incubator-airflow/blob/089c996fbd9ecb0014dbefedff232e8699ce6283/airflow/api/common/experimental/trigger_dag.py#L28-L67

You could, for instance, setup an AWS Lambda function, called when a file lands on S3, that runs the trigger DAG call.

Source https://stackoverflow.com/questions/50591886

QUESTION

python aws s3 file distributing

Asked 2017-Dec-21 at 15:16

I am looking to this tutorial. I would like to know is the anyway to distribute large amount of file over the different objects. As the example let's say I have video file with size 60 GB and I have S3 bucklets with size 4 x 15 GB. Now how can I split my file for keeping that at these size storages. I will be happy if you can share any tutorial.

...

ANSWER

Answered 2017-Dec-21 at 14:45

S3 buckets don't have restrictions on size so there is typically no reason to split a file across buckets.

If you really want to split the file across buckets (and I would not recommend doing this) you can write the first 25% of bytes to an object in bucket A, the next 25% of bytes to an object in bucket B, etc. But that's moderately complicated (you have to split the source file and upload just the relevant bytes) and then you have to deal with combining them later in order to retrieve the complete file.

Why do you want to split the file across buckets?

Source https://stackoverflow.com/questions/47924089

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

Vulnerabilities

No vulnerabilities reported

Install aws-python-sample

You can download it from GitHub.
You can use aws-python-sample 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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