Metadata-Version: 2.1
Name: aws-xray-sdk
Version: 2.4.3
Summary: The AWS X-Ray SDK for Python (the SDK) enables Python developers to record and emit information from within their applications to the AWS X-Ray service.
Home-page: https://github.com/aws/aws-xray-sdk-python
Author: Amazon Web Services
License: Apache License 2.0
Description: [![Build Status](https://travis-ci.org/aws/aws-xray-sdk-python.svg?branch=master)](https://travis-ci.org/aws/aws-xray-sdk-python)
        [![codecov](https://codecov.io/gh/aws/aws-xray-sdk-python/branch/master/graph/badge.svg)](https://codecov.io/gh/aws/aws-xray-sdk-python)
        
        # AWS X-Ray SDK for Python
        
        ![Screenshot of the AWS X-Ray console](/images/example_servicemap.png?raw=true)
        
        ## Installing
        
        The AWS X-Ray SDK for Python is compatible with Python 2.7, 3.4, 3.5, 3.6, and 3.7.
        
        Install the SDK using the following command (the SDK's non-testing dependencies will be installed).
        
        ```
        pip install aws-xray-sdk
        ```
        
        To install the SDK's testing dependencies, use the following command.
        
        ```
        pip install tox
        ```
        
        ## Getting Help
        
        Use the following community resources for getting help with the SDK. We use the GitHub
        issues for tracking bugs and feature requests.
        
        * Ask a question in the [AWS X-Ray Forum](https://forums.aws.amazon.com/forum.jspa?forumID=241&start=0).
        * Open a support ticket with [AWS Support](http://docs.aws.amazon.com/awssupport/latest/user/getting-started.html).
        * If you think you may have found a bug, open an [issue](https://github.com/aws/aws-xray-sdk-python/issues/new).
        
        ## Opening Issues
        
        If you encounter a bug with the AWS X-Ray SDK for Python, we want to hear about
        it. Before opening a new issue, search the [existing issues](https://github.com/aws/aws-xray-sdk-python/issues)
        to see if others are also experiencing the issue. Include the version of the AWS X-Ray
        SDK for Python, Python language, and botocore/boto3 if applicable. In addition, 
        include the repro case when appropriate.
        
        The GitHub issues are intended for bug reports and feature requests. For help and
        questions about using the AWS SDK for Python, use the resources listed
        in the [Getting Help](https://github.com/aws/aws-xray-sdk-python#getting-help) section. Keeping the list of open issues lean helps us respond in a timely manner.
        
        ## Documentation
        
        The [developer guide](https://docs.aws.amazon.com/xray/latest/devguide) provides in-depth
        guidance about using the AWS X-Ray service.
        The [API Reference](http://docs.aws.amazon.com/xray-sdk-for-python/latest/reference/)
        provides guidance for using the SDK and module-level documentation.
        
        ## Quick Start
        
        ### Configuration
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        xray_recorder.configure(
            sampling=False,
            context_missing='LOG_ERROR',
            plugins=('EC2Plugin', 'ECSPlugin', 'ElasticBeanstalkPlugin'),
            daemon_address='127.0.0.1:3000',
            dynamic_naming='*mysite.com*'
        )
        ```
        
        ### Start a custom segment/subsegment
        
        Using context managers for implicit exceptions recording:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        with xray_recorder.in_segment('segment_name') as segment:
            # Add metadata or annotation here if necessary
            segment.put_metadata('key', dict, 'namespace')
            with xray_recorder.in_subsegment('subsegment_name') as subsegment:
                subsegment.put_annotation('key', 'value')
                # Do something here
            with xray_recorder.in_subsegment('subsegment2') as subsegment:
                subsegment.put_annotation('key2', 'value2')
                # Do something else 
        ```
        
        async versions of context managers:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        async with xray_recorder.in_segment_async('segment_name') as segment:
            # Add metadata or annotation here if necessary
            segment.put_metadata('key', dict, 'namespace')
            async with xray_recorder.in_subsegment_async('subsegment_name') as subsegment:
                subsegment.put_annotation('key', 'value')
                # Do something here
            async with xray_recorder.in_subsegment_async('subsegment2') as subsegment:
                subsegment.put_annotation('key2', 'value2')
                # Do something else 
        ```
        
        Default begin/end functions:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        # Start a segment
        segment = xray_recorder.begin_segment('segment_name')
        # Start a subsegment
        subsegment = xray_recorder.begin_subsegment('subsegment_name')
        
        # Add metadata or annotation here if necessary
        segment.put_metadata('key', dict, 'namespace')
        subsegment.put_annotation('key', 'value')
        xray_recorder.end_subsegment()
        
        # Close the segment
        xray_recorder.end_segment()
        ```
        
        ### Capture
        
        As a decorator:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        @xray_recorder.capture('subsegment_name')
        def myfunc():
            # Do something here
        
        myfunc()
        ```
        
        or as a context manager:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        with xray_recorder.capture('subsegment_name') as subsegment:
            # Do something here
            subsegment.put_annotation('mykey', val)
            # Do something more
        ```
        
        Async capture as decorator:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        @xray_recorder.capture_async('subsegment_name')
        async def myfunc():
            # Do something here
        
        async def main():
            await myfunc()
        ```
        
        or as context manager:
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        async with xray_recorder.capture_async('subsegment_name') as subsegment:
            # Do something here
            subsegment.put_annotation('mykey', val)
            # Do something more
        ```
        
        ### Adding annotations/metadata using recorder
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        # Start a segment if no segment exist
        segment1 = xray_recorder.begin_segment('segment_name')
        
        # This will add the key value pair to segment1 as it is active
        xray_recorder.put_annotation('key', 'value')
        
        # Start a subsegment so it becomes the active trace entity
        subsegment1 = xray_recorder.begin_subsegment('subsegment_name')
        
        # This will add the key value pair to subsegment1 as it is active
        xray_recorder.put_metadata('key', 'value')
        
        if xray_recorder.is_sampled():
            # some expensitve annotations/metadata generation code here
            val = compute_annotation_val()
            metadata = compute_metadata_body()
            xray_recorder.put_annotation('mykey', val)
            xray_recorder.put_metadata('mykey', metadata)
        ```
        
        ### Disabling X-Ray
        Often times, it may be useful to be able to disable X-Ray for specific use cases, whether to stop X-Ray from sending traces at any moment, or to test code functionality that originally depended on X-Ray instrumented packages to begin segments prior to the code call. For example, if your application relied on an XRayMiddleware to instrument incoming web requests, and you have a method which begins subsegments based on the segment generated by that middleware, it would be useful to be able to disable X-Ray for your unit tests so that `SegmentNotFound` exceptions are not thrown when you need to test your method.
        
        There are two ways to disable X-Ray, one is through environment variables, and the other is through the SDKConfig module.
        
        **Disabling through the environment variable:**
        
        Prior to running your application, make sure to have the environment variable `AWS_XRAY_SDK_ENABLED` set to `false`. 
        
        **Disabling through the SDKConfig module:**
        ```
        from aws_xray_sdk import global_sdk_config
        
        global_sdk_config.set_sdk_enabled(False)
        ```
        
        **Important Notes:**
        * Environment Variables always take precedence over the SDKConfig module when disabling/enabling. If your environment variable is set to `false` while your code calls `global_sdk_config.set_sdk_enabled(True)`, X-Ray will still be disabled.
        
        * If you need to re-enable X-Ray again during runtime and acknowledge disabling/enabling through the SDKConfig module, you may run the following in your application:
        ```
        import os
        from aws_xray_sdk import global_sdk_config
        
        del os.environ['AWS_XRAY_SDK_ENABLED']
        global_sdk_config.set_sdk_enabled(True)
        ```
        
        ### Trace AWS Lambda functions
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        def lambda_handler(event, context):
            # ... some code
        
            subsegment = xray_recorder.begin_subsegment('subsegment_name')
            # Code to record
            # Add metadata or annotation here, if necessary
            subsegment.put_metadata('key', dict, 'namespace')
            subsegment.put_annotation('key', 'value')
        
            xray_recorder.end_subsegment()
        
            # ... some other code
        ```
        
        ### Trace ThreadPoolExecutor
        
        ```python
        import concurrent.futures
        
        import requests
        
        from aws_xray_sdk.core import xray_recorder
        from aws_xray_sdk.core import patch
        
        patch(('requests',))
        
        URLS = ['http://www.amazon.com/',
                'http://aws.amazon.com/',
                'http://example.com/',
                'http://www.bilibili.com/',
                'http://invalid-domain.com/']
        
        def load_url(url, trace_entity):
            # Set the parent X-Ray entity for the worker thread.
            xray_recorder.set_trace_entity(trace_entity)
            # Subsegment captured from the following HTTP GET will be
            # a child of parent entity passed from the main thread.
            resp = requests.get(url)
            # prevent thread pollution
            xray_recorder.clear_trace_entities()
            return resp
        
        # Get the current active segment or subsegment from the main thread.
        current_entity = xray_recorder.get_trace_entity()
        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
            # Pass the active entity from main thread to worker threads.
            future_to_url = {executor.submit(load_url, url, current_entity): url for url in URLS}
            for future in concurrent.futures.as_completed(future_to_url):
                url = future_to_url[future]
                try:
                    data = future.result()
                except Exception:
                    pass
        ```
        
        ### Trace SQL queries
        By default, if no other value is provided to `.configure()`, SQL trace streaming is enabled
        for all the supported DB engines. Those currently are:
        - Any engine attached to the Django ORM.
        - Any engine attached to SQLAlchemy.
        
        The behaviour can be toggled by sending the appropriate `stream_sql` value, for example:
        ```python
        from aws_xray_sdk.core import xray_recorder
        
        xray_recorder.configure(service='fallback_name', stream_sql=True)
        ```
        
        ### Patch third-party libraries
        
        ```python
        from aws_xray_sdk.core import patch
        
        libs_to_patch = ('boto3', 'mysql', 'requests')
        patch(libs_to_patch)
        ```
        
        #### Automatic module patching
        
        Full modules in the local codebase can be recursively patched by providing the module references
        to the patch function.
        ```python
        from aws_xray_sdk.core import patch
        
        libs_to_patch = ('boto3', 'requests', 'local.module.ref', 'other_module')
        patch(libs_to_patch)
        ```
        An `xray_recorder.capture()` decorator will be applied to all functions and class methods in the
        given module and all the modules inside them recursively. Some files/modules can be excluded by
        providing to the `patch` function a regex that matches them.
        ```python
        from aws_xray_sdk.core import patch
        
        libs_to_patch = ('boto3', 'requests', 'local.module.ref', 'other_module')
        ignore = ('local.module.ref.some_file', 'other_module.some_module\.*')
        patch(libs_to_patch, ignore_module_patterns=ignore)
        ```
        
        ### Django
        #### Add Django middleware
        
        In django settings.py, use the following.
        
        ```python
        INSTALLED_APPS = [
            # ... other apps
            'aws_xray_sdk.ext.django',
        ]
        
        MIDDLEWARE = [
            'aws_xray_sdk.ext.django.middleware.XRayMiddleware',
            # ... other middlewares
        ]
        ```
        
        You can configure the X-Ray recorder in a Django app under the ‘XRAY_RECORDER’ namespace. For a minimal configuration, the 'AWS_XRAY_TRACING_NAME' is required unless it is specified in an environment variable.
        ```
        XRAY_RECORDER = {
            'AWS_XRAY_TRACING_NAME': 'My application', # Required - the segment name for segments generated from incoming requests
        }
        ```
        For more information about configuring Django with X-Ray read more about it in the [API reference](https://docs.aws.amazon.com/xray-sdk-for-python/latest/reference/frameworks.html)
        
        #### SQL tracing
        If Django's ORM is patched - either using the `AUTO_INSTRUMENT = True` in your settings file
        or explicitly calling `patch_db()` - the SQL query trace streaming can then be enabled or 
        disabled updating the `STREAM_SQL` variable in your settings file. It is enabled by default.
        
        #### Automatic patching
        The automatic module patching can also be configured through Django settings.
        ```python
        XRAY_RECORDER = {
            'PATCH_MODULES': [
                'boto3',
                'requests',
                'local.module.ref',
                'other_module',
            ],
            'IGNORE_MODULE_PATTERNS': [
                'local.module.ref.some_file',
                'other_module.some_module\.*',
            ],
            ...
        }
        ```
        If `AUTO_PATCH_PARENT_SEGMENT_NAME` is also specified, then a segment parent will be created 
        with the supplied name, wrapping the automatic patching so that it captures any dangling
        subsegments created on the import patching.
        
        ### Add Flask middleware
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        from aws_xray_sdk.ext.flask.middleware import XRayMiddleware
        
        app = Flask(__name__)
        
        xray_recorder.configure(service='fallback_name', dynamic_naming='*mysite.com*')
        XRayMiddleware(app, xray_recorder)
        ```
        
        ### Serverless Support for Flask & Django Using X-Ray
        Serverless is an application model that enables you to shift more of your operational responsibilities to AWS. As a result, you can focus only on your applications and services, instead of the infrastructure management tasks such as server provisioning, patching, operating system maintenance, and capacity provisioning. With serverless, you can deploy your web application to [AWS Lambda](https://aws.amazon.com/lambda/) and have customers interact with it through a Lambda-invoking endpoint, such as [Amazon API Gateway](https://aws.amazon.com/api-gateway/). 
        
        X-Ray supports the Serverless model out of the box and requires no extra configuration. The middlewares in Lambda generate `Subsegments` instead of `Segments` when an endpoint is reached. This is because `Segments` cannot be generated inside the Lambda function, but it is generated automatically by the Lambda container. Therefore, when using the middlewares with this model, it is important to make sure that your methods only generate `Subsegments`.
        
        The following guide shows an example of setting up a Serverless application that utilizes API Gateway and Lambda:
        
        [Instrumenting Web Frameworks in a Serverless Environment](https://docs.aws.amazon.com/xray/latest/devguide/xray-sdk-python-serverless.html)
        
        ### Working with aiohttp
        
        Adding aiohttp middleware. Support aiohttp >= 2.3.
        
        ```python
        from aiohttp import web
        
        from aws_xray_sdk.ext.aiohttp.middleware import middleware
        from aws_xray_sdk.core import xray_recorder
        from aws_xray_sdk.core.async_context import AsyncContext
        
        xray_recorder.configure(service='fallback_name', context=AsyncContext())
        
        app = web.Application(middlewares=[middleware])
        app.router.add_get("/", handler)
        
        web.run_app(app)
        ```
        
        Tracing aiohttp client. Support aiohttp >=3.
        
        ```python
        from aws_xray_sdk.ext.aiohttp.client import aws_xray_trace_config
        
        async def foo():
            trace_config = aws_xray_trace_config()
            async with ClientSession(loop=loop, trace_configs=[trace_config]) as session:
                async with session.get(url) as resp
                    await resp.read()
        ```
        
        ### Use SQLAlchemy ORM
        The SQLAlchemy integration requires you to override the Session and Query Classes for SQL Alchemy
        
        SQLAlchemy integration uses subsegments so you need to have a segment started before you make a query.
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        from aws_xray_sdk.ext.sqlalchemy.query import XRaySessionMaker
        
        xray_recorder.begin_segment('SQLAlchemyTest')
        
        Session = XRaySessionMaker(bind=engine)
        session = Session()
        
        xray_recorder.end_segment()
        app = Flask(__name__)
        
        xray_recorder.configure(service='fallback_name', dynamic_naming='*mysite.com*')
        XRayMiddleware(app, xray_recorder)
        ```
        
        ### Add Flask-SQLAlchemy
        
        ```python
        from aws_xray_sdk.core import xray_recorder
        from aws_xray_sdk.ext.flask.middleware import XRayMiddleware
        from aws_xray_sdk.ext.flask_sqlalchemy.query import XRayFlaskSqlAlchemy
        
        app = Flask(__name__)
        app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///:memory:"
        
        XRayMiddleware(app, xray_recorder)
        db = XRayFlaskSqlAlchemy(app)
        
        ```
        ## License
        
        The AWS X-Ray SDK for Python is licensed under the Apache 2.0 License. See LICENSE and NOTICE.txt for more information.
        
Keywords: aws xray sdk
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
