FluentMetrics is an easy-to-use Python module that makes logging CloudWatch custom metrics a breeze. The goal is to provide a framework for logging detailed metrics with a minimal footprint. When you look at your code logic, you want to see your actual code logic, not line after line of metrics logging. FluentMetrics lets you maximize your metrics footprint while minimizing your metrics code footprint.

You can install directly from PyPI:

pip install cloudwatch-fluent-metrics

Fluent describes an easy-to-read programming style. The goal of fluent development is to make code easier to read and reduce the amount of code required to build objects. It's easier to take a look a comparison between fluent and non-fluent style.

g = Game()
f = Frame(Name='Tom')
f.add_score(7)
f.add_score(3)
g.add_frame(f)
f = Frame(Name='Tom')
f.add_strike()
g.add_frame(f)

g = Game()
g.add_frame(Frame(Name='Tom', Score1=7, Score2=3)
g.add_frame(Frame(Name='Tom', Score1=10)

g = Game()
g.add_frame(Frame().with_name('Tom').score(3).spare())
g.add_frame(Frame().with_name('Tom').strike())

While the difference may seem to be nitpicking, a frame is really just a constructed object. In the first example, we're taking up three lines of code to create the object--there's nothing wrong with that. However, in the second example, we're using constructors. This is slightly more readable, but there's a great deal of logic bulked up in our constructor. In the third example, we're using fluent-style code as it starts at creating the frame and fluently continues until it's created the entire frame in a single line. And more importantly, it's readable. We're not just creating an object with a massive constructor or spending several lines of code just to create a single object.

Every metric needs to live in a namespace. Since you are logging your own custom metrics, you need to provide a custom namespace for your metric. Click here for a list of the standard AWS namespaces. Example: In this example, we're creating a simple FluentMetric in a namespace called Performance. This means that every time we log a metric with m, we will automatically log it to the Performance namespace.

from fluentmetrics import FluentMetric
m = FluentMetric().with_namespace('Performance')

The metric name is the thing you are actually logging. Each value that you log must be tied to a metric name. When you log a custom metric with a new metric name, the name will automatically be created if it doesn't already exist. Click here to see existing metrics that can help you define names for your custom metrics. Example: In this example, we're logging two metrics called StartupTime and StuffTime to the Performance namespace (we only needed to define the namespace once).

m = FluentMetric().with_namespace('Performance')
m.log(MetricName='StartupTime', Value=27, Unit='Seconds')
do_stuff()
m.log(MetricName='StuffTime', Value=12000, Unit='Milliseconds')

Obviously we need to log a value with each metric. This needs to be a number since we convert this value to a float before sending to CloudWatch. IMPORTANT: When logging multiple values for the same custom metric within a minute, CloudWatch aggregates an average over a minute. Click here for more details.

The PutMetricData function now accepts an optional StorageResolution parameter. Set this parameter to 1 to publish high-resolution metrics; omit it (or set it to 60) to publish at standard 1-minute resolution. Example: In this example, we're logging metric at one-second resolution:

m = FluentMetric().with_namespace('Application/MyApp')
                  .with_storage_resolution(1)
m.log(MetricName='Transactions/Sec', Value=trans_count, Unit='Count/Sec')
```sh
#### Dimensions
A dimension defines how you want to slice and dice the metric. These are simply name-value pairs and you can define up to 10 per metric. Click [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/publishingMetrics.html#usingDimensions) for more details on using dimensions.
**IMPORTANT:** When you define multiple dimensions, CloudMetrics attaches all of those dimensions to the metric as a single combined dimension set--think of them as an aggregate primary key. For example, if you log a metric with the dimensions `os = 'linux'` and `flavor='ubunutu'` you will only be able to aggregate by **both** `os` and `flavor`. You **cannot** aggregate only by just `os` or just `flavor`. `FluentMetrics` solves this problem by automatically logging three metrics--one for `os`, one for `flavor` and then one for the combied dimensions, giving you maximum flexibility.
*Example*:
In this example, we're logging boot/restart time metrics. When this code executes, we will end up with 6 metrics:
* `BootTime` and `RestartTime` for `os`
* `BootTime` and `RestartTime` for `instance-id`
* `BootTime` and `RestartTime` for 'os` and `instance-id`
```sh
m = FluentMetric().with_namespace('Performance/EC2') \
                  .with_dimension('os', 'linux'). \
                  .with_dimension('instance-id', 'i-123456')
boot_time = start_instance()
m.log(MetricName='BootTime', Value=boot_time, Unit='Milliseconds')
restart_time = restart_instance()
m.log(MetricName='RestartTime', Value=restart_time, Unit='Milliseconds')

CloudWatch has built-in logic to provide meaning to the metric values. We're not just logging a value--we're logging a value of some unit. By defining the unit type, CloudWatch will know how to properly present, aggregate and compare that value with other values. For example, if you submit a value with unit Milliseconds, then it can properly aggregate it up to seconds, minutes or hours. This is a list of the most current valid list of units. A more up-to-date list should be available here under the Unit section,.

"Seconds"|"Microseconds"|"Milliseconds"|"Bytes"|"Kilobytes"|"Megabytes"|
"Gigabytes"|"Terabytes"|"Bits"|"Kilobits"|"Megabits"|"Gigabits"|"Terabits"|
"Percent"|"Count"|"Bytes/Second"|"Kilobytes/Second"|"Megabytes/Second"|
"Gigabytes/Second"|"Terabytes/Second"|"Bits/Second"|"Kilobits/Second"|
"Megabits/Second"|"Gigabits/Second"|"Terabits/Second"|"Count/Second"|"None"

If you don't want to type out the individual unit name, there are shortcut methods for each unit.

m = FluentMetric().with_namespace('Performance/EC2') \
                  .with_dimension('os', 'linux'). \
                  .with_dimension('instance-id', 'i-123456')
m.seconds(MetricName='CompletionInSeconds', Value='1000')
m.microseconds(MetricName='CompletionInMicroseconds', Value='1000')
m.milliseconds(MetricName='CompletionInMilliseconds', Value='1000')
m.bytes(MetricName='SizeInBytes', Value='1000')
m.kb(MetricName='SizeInKb', Value='1000')
m.mb(MetricName='SizeInMb', Value='1000')
m.gb(MetricName='SizeInGb', Value='1000')
m.tb(MetricName='SizeInTb', Value='1000')
m.bits(MetricName='SizeInBits', Value='1000')
m.kbits(MetricName='SizeInKilobits', Value='1000')
m.mbits(MetricName='SizeInMegabits', Value='1000')
m.gbits(MetricName='SizeInGigabits', Value='1000')
m.tbits(MetricName='SizeInTerabits', Value='1000')
m.pct(MetricName='Percent', Value='20')
m.count(MetricName='ItemCount', Value='20')
m.bsec(MetricName='BandwidthBytesPerSecond', Value='1000')
m.kbsec(MetricName='BandwidthKilobytesPerSecond', Value='1000')
m.mbsec(MetricName='BandwidthMegabytesPerSecond', Value='1000')
m.gbsec(MetricName='BandwidthGigabytesPerSecond', Value='1000')
m.tbsec(MetricName='BandwidthTerabytesPerSecond', Value='1000')
m.bitsec(MetricName='BandwidthBitsPerSecond', Value='1000')
m.kbitsec(MetricName='BandwidthKilobitsPerSecond', Value='1000')
m.mbitsec(MetricName='BandwidthMegabitsPerSecond', Value='1000')
m.gbitsec(MetricName='BandwidthGigabitsPerSecond', Value='1000')
m.tbitsec(MetricName='BandwidthTerabitsPerSecond', Value='1000')
m.countsec(MetricName='ItemCountsPerSecond', Value='1000')

One of the most common uses of logging is measuring performance. FluentMetrics allows you to activate multiple built-in timers by name and log the elapsed time in a single line of code. NOTE: The elapsed time value is automatically stored as unit Milliseconds. Example: In this example, we're starting timers workflow and job1 at the same time. Timers start as soon as you create them and never stop running. When you call elapsed, FluentMetrics will log the number of elapsed milliseconds with the MetricName.

m = FluentMetric()
m.with_timer('workflow').with_timer('job1')
do_job1()
m.elapsed(MetricName='Job1CompletionTime', TimerName='job1')
m.with_timer('job2')
do_job2()
m.elapsed(MetricName='Job2CompletionTime', TimerName='job2')
finish_workflow()
m.elapsed(MetricName='WorkflowCompletionTime', TimerName='workflow')

A key feature of FluentMetrics is the metric stream ID. This ID will be added as a dimension and logged with every metric. The benefit of this dimension is to provide a distinct stream of metrics for an end-to-end operation. When you create a new instance of FluentMetric, you can either pass in your own value or FluentMetrics will generate a GUID. In CloudWatch, you can then see all of the metrics for a particular stream ID in chronological order. A metric stream can be a job, or a server or any way that you want to unique group a contiguous stream of metrics. Example: In this example, we'll have two metrics in the Performance namespace, each with metric stream ID of abc-123. We can then go to CloudWatch and filter by that stream ID to see the entire operation performance at a glance.

m = FluentMetric().with_namespace('Performance').with_stream_id('abc-123')
m.log(MetricName='StartupTime', Value=100, Unit='Seconds')
do_work()
m.log(MetricName='WorkCompleted', Value=1000, Unit='Milliseconds')

This is the minimal amount of work you need to log--create a FluentMetric with a namespace, then log a value. Result: This code will log a single value 100 for ActiveServerCount in the Stats namespace.

from fluentmetrics import FluentMetric
m = FluentMetric().with_namespace('Stats')
m.log(MetricName='ActiveServerCount', Value='100', Unit='Count')

If you are logging multiple metrics to the same namespace, this is a great use case for FluentMetrics. You only need to create one instance of FluentMetric and specify a different metric name when you call log. Result: This code will log a single value 100 for ActiveServerCount in the Stats namespace.

from fluentmetrics import FluentMetric   
m = FluentMetric().with_namespace('Stats')
m.log(MetricName='ActiveServerCount', Value='10', Unit='Count') \
 .log(MetricName='StoppedServerCount', Value='20', Unit='Count') \
 .log(MetricName='ActiveLinuxCount', Value='50', Unit='Count') \
 .log(MetricName='ActiveWindowsCount', Value='50', Unit='Count')

In the previous example, we logged a metric and identified the unit Count. Instead of specifying the unit, you can specify the type of object Result: This code will log a single value 100 for ActiveServerCount in the Stats namespace.

from fluentmetrics import FluentMetric
m = FluentMetric().with_namespace('Stats')
m.count(MetricName='ActiveServerCount', Value='10')

Normally, with FluentMetric, metrics are sent immediately when log is called (or count, milliseconds, etc). This can result in a lot of put_metric_data calls to CloudWatch that are not full. When you use BufferedFluentMetric instead of FluentMetric, it waits until it has the maximum (20) metrics before calling put_metric_data. This optimizes traffic to cloudwatch.

In general, BufferedFluentMetric behaves identically to FluentMetric, except that now it is possible to "forget" to send some metrics. The BufferedFluentMetric.flush() method pushes out all metrics immediately (clears the buffer). It is often best to do this at the end of a request (or some other obviously bounded interval).

Here is an example of how it works in Flask:

from flask import g
from fluentmetrics import BufferedFluentMetric

@app.before_request
def start_request():
	g.metrics = BufferedFluentMetric()
	g.metrics.with_namespace('MyApp')
	g.metrics.with_timer('RequestLatency')

@app.after_request
def end_request(response):
	def error_counter(hundred):
		if response.status_code / 100 == hundred:
			return 1
		else:
			return 0

	g.metrics.count(MetricName='4xxError', Value=error_counter(400))
	g.metrics.count(MetricName='5xxError', Value=error_counter(500))
	g.metrics.count(MetricName='Availability', Value=(1 - error_counter(500)))
	g.metrics.elapsed(MetricName='RequestLatency', TimerName='RequestLatency')

	# Finally, ensure that all metrics end up in CloudWatch before this request finally ends.
	g.metrics.flush()

This library is licensed under the Apache 2.0 License.