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        "author": "Nikita Makeev",
        "author_email": "whale2.box@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7",
            "Topic :: Internet",
            "Topic :: Software Development :: Libraries"
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        "description": "# cloudwatch-metrics-client\nPython CloudWatch metrics Client library utilising asyncio (along with sync variant)\n\nThis is yet another Python CloudWatch client. While there are alternatives, they are\neither not supporting asyncio or not actively maintained.\n\nhttps://github.com/awslabs/cloudwatch-fluent-metrics\n\nhttps://github.com/peterdemin/awsme\n\nThe client collects whatever metrics you feed it with and reports to CloudWatch regularly, though\nyou can use it for ad-hoc reporting as well\n\nNOTE: aioboto3 is not in the requirements, in case you only need sync version. If running under Python 3.6, you'll\nneed async_generator>=1.10 as well\n\nUsage:\n\n```python\nimport asyncio\nfrom cloudwatch_metrics_client.aiocloudwatch import CloudWatchAsyncMetrics, CloudWatchAsyncMetricReporter\n\nasync def setup():\n\n  reporter = CloudWatchAsyncMetricReporter(report_interval=REPORT INTERVAL)        # seconds\n  CloudWatchAsyncMetrics.with_namespace('<YOUR NAMESPACE').with_reporter(reporter)\n  # .with_client(aioboto3_cloudwatch_client) is you're not happy with default client\n  await reporter.run()\n\n\n@CloudWatchAsyncMetrics.monitored_task\nasync def process_request(request):\n\n  # processing logic...\n  #\n  # request_type = ... \n  # request_specific_data = \n\n  # Set details on monitored call\n  CloudWatchAsyncMetrics\\\n    .with_monitored_dimension('TypeOfRequest', request_type)\\\n    .with_monitored_dimension('SpecificAttribute', request_specific_data)\n\n  # Put particular metric\n  await CloudWatchAsyncMetrics.put_metric(\n        MetricName='particular-metric-name',\n        Unit='Count',\n        Dimensions={'Kind': 'metric-kind', 'Sort': 'metric-sort'},\n        Value=value\n  )\n\n  # Record statistical value\n  await CloudWatchAsyncMetrics.put_statistic(\n        MetricName='particular-metric-name',\n        Unit='Count',\n        Dimensions={'Kind': 'metric-kind', 'Sort': 'metric-sort'},\n        Value=value\n\n  # Elapsed time will be recorded  \n```\n\nElapsed time of each call of `process_request` coroutine will be recorded in internal data structure and aggregated \nstatistics will be regularly sent to CloudWatch.\nIndividual metrics could be collected with `put_metric` call - they too would be stored for a while and then sent\nto CloudWatch. `put_metric` keeps each unique value and sends list of them along with number of occurrencies of each\nvalue, `put_statistics` only sends aggregated data over a bunch of metrics - Sum, SampleCount, Min and Max\n(See https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/cloudwatch.html#CloudWatch.Client.put_metric_data for details)\n\nReporter has async `flush` method for flushing metric data still not sent to CloudWatch. If regular reporting is not \nneeded, just don't call `run` coro and instead call `flush` when it is time to report metrics.\n\nSync version works in the very same way, it utilises separate thread for reporting.\n\n```python\n\nfrom cloudwatch_metrics_client.cloudwatch import CloudWatchSyncMetrics, CloudWatchSyncMetricReporter\n\ndef setup():\n\n  reporter = CloudWatchSyncMetricReporter(report_interval=REPORT INTERVAL)        # seconds\n  CloudWatchSyncMetrics.with_namespace('<YOUR NAMESPACE').with_reporter(reporter)\n  reporter.run()\n\n@CloudWatchSyncMetrics.monitored_task  \ndef process_request(request):\n\n# Same here, but with CloudWatchSync*\n\n\n```  \n\n",
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