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128 changes: 62 additions & 66 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,7 @@
# signal_analog

A [`troposphere`](https://github.com/cloudtools/troposphere)-inspired library
for programmatic, declarative definition and management of SignalFx Charts,
Dashboards, and Detectors.
for programmatic, declarative definition and management of SignalFx Charts, Dashboards, and Detectors.

This library assumes a basic familiarity with resources in SignalFx. For a
good overview of the SignalFx API consult the [upstream documentation][sfxdocs].
Expand Down Expand Up @@ -59,7 +58,7 @@ pip install -r requirements.txt
## Usage

`signal_analog` provides two kinds of abstractions, one for building resources
in the SignalFx API and the other for describing metric timeseries through the
in the SignalFx API and the other for describing metric time series through the
[Signal Flow DSL][signalflow].

The following sections describe how to use `Resource` abstractions in
Expand All @@ -73,38 +72,36 @@ conjunction with the [Signal Flow DSL][signalflow].
Consult the [upstream documentation][charts] for more information Charts.

Let's consider an example where we would like to build a chart to monitor
memory utilization for a single applicaton in a single environment.
memory utilization for a single application in a single environment.

This assumes a service reports metrics for application name as `app` and
Our example assumes a service reports the metrics for application name as `app` and
environment as `env` with memory utilization reporting via the
`memory.utilization` metric name.

In a timeseries chart, all data displayed on the screen comes from at least one
In a time series chart, all data displayed on the screen comes from at least one
`data` definition in the SignalFlow language. Let's begin by defining our
timeseries:
time series:

```python
from signal_analog.flow import Data

ts = Data('memory.utilization')
```

In SignalFlow parlance a timeseries is only displayed on a chart if it has been
"published". All stream functions in SignalFlow have a `publish` method that
may be called at the _end_ of all timeseries transformations.
In SignalFlow terminology, a time series is only displayed on a chart if it has been
"published". All stream functions in SignalFlow have a `publish` method that may be called at the _end_ of a time series transformation.

```python
ts = Data('memory.utilization').publish()
```

As a convenience, all transformations on stream functions return the callee,
so in the above example `ts` remains bound to an instance of `Data`.
so in the above example, `ts` remains bound to an instance of `Data`.

Now, this timeseries isn't very useful by itself; if we attached this program
to a chart we would see _all_ timeseries for _all_ [Riposte] applications
Now, this time series isn't super useful by itself; if we attached this program to a chart, we would see _all_ time series for _all_ [Riposte] applications
reporting to SignalFx!

We can restrict our view of the data by adding a filter on application name:
We can restrict our view of the data by adding a filter on the application name:

```python
from signal_analog.flow import Data, Filter
Expand All @@ -114,9 +111,9 @@ app_filter = Filter('app', 'foo')
ts = Data('memory.utilization', filter=app_filter).publish()
```

Now if we created a chart with this program we would only be looking at metrics
that relate to the `foo` application. Much better, but we're still
looking at instance of `foo` _regardless_ of the environment it
If we created a chart with this program, we would only be looking at metrics
related to the `foo` application. Much better, but we're still
looking at an instance of `foo` _regardless_ of the environment it
lives in.

What we'll want to do is combine our `app_filter` with another filter for the
Expand Down Expand Up @@ -153,7 +150,7 @@ memory_chart = TimeSeriesChart().with_name('Memory Used %').with_program(ts)
```

Each Chart understands how to serialize our SignalFlow programs appropriately,
so it is sufficient to simply pass in our reference here.
so it is sufficient to pass in only our reference here.

Finally, let's change the plot type on our chart so that we see solid areas
instead of flimsy lines:
Expand All @@ -169,7 +166,7 @@ memory_chart = TimeSeriesChart()\

[Terrific]; there's only a few more details before we have a complete chart.

In the following sections we'll see how we can create dashboards from
In the following sections, we demonstrate creating a dashboard from a
collections of charts.

### Building Dashboards
Expand All @@ -192,14 +189,14 @@ dash = Dashboard()
```

Many of the same methods for charts are available on dashboards as well, so
let's give our dashboard a memorable name and configure it's API token:
let's give our dashboard a memorable name and configure its API token:

```python
dash.with_name('My Little Dashboard: Metrics are Magic')\
.with_api_token('my-api-token')
```

Our final task will be to add charts to our dashboard and create it in the API!
Our final task will be to add charts to the dashboard and create it in the API!

```python
response = dash\
Expand All @@ -208,7 +205,7 @@ response = dash\
.create()
```

At this point one of two things will happen:
At this point, one of two things will happen:

- We receive some sort of error from the SignalFx API and an exception
is thrown
Expand All @@ -225,7 +222,7 @@ response = dash\
.create(group_id="asdf;lkj")
```

Now, storing API keys in source isn't ideal, so if you'd like to see how you
Now, storing API keys in the source isn't ideal, so if you'd like to see how you
can pass in your API keys at runtime check the documentation below to see how
you can [dynamically build a CLI for your resources](#cli-builder).

Expand All @@ -246,7 +243,7 @@ dash.update(

### Providing Dashboard Filters

Dashboards can be configured to provide various filters that affect the behavior of all configured charts (overriding any conflicting filters at the chart level). You may wish to do this in order to quickly change the environment that you're observing for a given set of charts.
Dashboards can be configured to provide various filters that affect all configured charts (overriding any conflicting filters at the chart level). You may wish to do this to quickly change the environment that you're observing for a given set of charts.


```python
Expand Down Expand Up @@ -293,8 +290,7 @@ response = dash\

### Dashboard Event Overlays and Selected Event Overlays

To view events overlayed on your charts within a dashboard requires an event to be viewed, a chart with showEventLines
enabled, and a dashboard with the correct eventOverlays settings (and selectedEventOverlays to show events by default).
To view events overlayed on your charts within a dashboard requires a viewed event, a chart with `showEventLines` enabled, and a dashboard with the correct `eventOverlays` settings (and selectedEventOverlays to show events by default).

Assuming that the events you would like to see exist; you would make a chart with showEventLines like so:

Expand All @@ -305,11 +301,11 @@ program = Data('cpu.utilization').publish()
chart = TimeSeriesChart().with_name('Chart With Event Overlays')\
.with_program(program).show_event_lines(True)
```
With our chart defined, we are ready to prepare our event overlays and selected event overlays for the dashboard.
First we define the event signals we would like to match. In this case, we will look for an event named "test" (include
leading and/or trailing asterisks as wildcards if you need partial matching).
Next we use those event signals to create our eventOverlays, making sure to include a color index for our event's symbol,
and setting event line to True.
We are ready to prepare our event overlays and selected event overlays for the dashboard with our chart defined.
First, we define the event signals we would like to match. In this case, we will look for an event named "test" (include either or both
leading and trailing asterisks as wildcards if you need partial matching).
Next, we use those event signals to create our eventOverlays, making sure to include a color index for our event's symbol,
and setting the event line to `True`.
We also pass our event signals along to the selectedEventOverlays, which will tell the dashboard to display matching
events by default.

Expand Down Expand Up @@ -348,8 +344,8 @@ if __name__ == '__main__':

### Creating Detectors

`signal_analog` provides a means of managing the lifecycle of `Detectors` in
the `signal_analog.detectors` module. As of `v0.21.0` only a subset of
`signal_analog` provides a means of managing the `Detectors` lifecycle in
the `signal_analog.detectors` module. As of `v0.21.0`, only a subset of
the full Detector API is supported.

Consult the [upstream documentation][detectors] for more information about
Expand Down Expand Up @@ -388,7 +384,7 @@ With our name and program in hand, it's time to build up an alert rule that we
can use to notify our teammates:

```python
# We provide a number of notification strategies in the detectors module.
# We provide several notification strategies in the detectors module.
from signal_analog.detectors import EmailNotification, Rule, Severity

info_rule = Rule()\
Expand All @@ -405,16 +401,16 @@ detector.with_api_token('foo').create()
# section below.
```

To add multiple alerting rules we would need to use different `detect`
To add multiple alerting rules, we would need to use different `detect`
statements with distinct `label`s to differentiate them from one another.

#### Detectors that Combine Data Streams

More complex detectors, like those created as a function of two other data
streams, require a more complex setup including data stream assignments.
If we wanted to create a detector that watched for an average above a certain
threshold, we may want to use the quotient of the sum() of the data and the
count() of the datapoints over a given period of time.
streams require a more complex setup, including data stream assignments.
If we wanted to make a detector that watched for an average above a certain
threshold, we may wish to use the quotient of the sum() of the data and the
count() of the data points over a given interval.

```python
from signal_analog.flow import \
Expand Down Expand Up @@ -464,7 +460,7 @@ program = Program(Data('cpu.utilization').publish(label='A'))
cpu_chart = TimeSeriesChart().with_name('Disk Utilization').with_program(program)
```

In order to alert on this chart we'll use the `from_chart` builder for
To alert the chart, we'll use the `from_chart` builder for
detectors:

```python
Expand All @@ -482,20 +478,20 @@ detector = Detector()\
)
```

The above example won't actually alert on anything until we add a `Rule`, which
you can find examples for in the previous section.
The above example won't alert on anything until we add a `Rule`, which
you can find examples in the previous section.

### Linking Charts to Existing Detectors

To see a visualization of a Detector's status from within a chart, the `signal_analog.flow` module provides an Alert data stream that can create a signal flow statement. That statement can be appended to the charts Program object. In this example we assume a Detector was previously created. To create the link we will need the detector id. One place to obtain the detector id is to navigate to the detector in the web user interface. The url will have the id in it. The url has the form: https://app.signalfx.com/#/detector/v2/{detector_id}
To see a visualization of a Detector's status from within a chart, the `signal_analog.flow` module provides an Alert data stream that can create a signal flow statement. That statement can be appended to the chart's Program object. In this example, we assume a Detector was previously created. To create the link, we will need the detector id. One place to obtain the detector id is to navigate to the detector in the web user interface. The URL will have the id in it. The URL has the form: https://app.signalfx.com/#/detector/v2/{detector_id}

To refresh our memory, our data in the previous chart example was:

```python
ts = Data('memory.utilization', filter=all_filters).publish()
```

We can append an additional alert data stream. Import Program and Alerts form the `signal_analog.flow` module. First we need to wrap the Data object in a Program object:
We can append an additional alert data stream. Import Program and Alerts form the `signal_analog.flow` module. First, we need to wrap the Data object in a Program object:

```python
ts_program = Program(ts)
Expand All @@ -516,7 +512,7 @@ ts_program.statements.append(notifications)
.with_default_plot_type(PlotType.area_chart)
```

By default the alert will show as a green box around the chart when the Detector is not in Alarm. The Detector can also be accessed from the bell icon in the upper right corner of the chart.
By default, the alert will show as a green box around the chart when the Detector is not in Alarm. The Detector can also be accessed from the bell icon in the upper right corner of the chart.

### Using Flow and Combinator Functions In Formulas

Expand Down Expand Up @@ -555,7 +551,7 @@ the Dashboard Groups API.
Building on the examples described in the previous section, we'd now like to
build a dashboard group containing our dashboards.

First, lets build a couple of Dashboard objects similar to how we did it in
First, let us build a couple of Dashboard objects similar to how we did it in
the `Building Dashboards` example:

```python
Expand All @@ -567,7 +563,7 @@ dash1 = Dashboard().with_name('My Little Dashboard1: Metrics are Magic')\
dash2 = Dashboard().with_name('My Little Dashboard2: Metrics are Magic')\
.with_charts(memory_chart)
```
**Note: we do not create Dashboard objects ourselves, the DashboardGroup object
**Note: we do not create Dashboard objects ourselves; the DashboardGroup object
is responsible for creating all child resources.**

Many of the same methods for dashboards are available on dashboard groups as
Expand All @@ -580,7 +576,7 @@ dg.with_name('My Dashboard Group')\
.with_api_token('my-api-token')
```

Our final task will be to add dashboard to our dashboard group and create it
Our final task will be to add a dashboard to our dashboard group and create it
in the API!

```python
Expand All @@ -590,7 +586,7 @@ response = dg\
.create()
```

Now, storing API keys in source isn't ideal, so if you'd like to see how you
Now, storing API keys in the source isn't ideal, so if you'd like to see how you
can pass in your API keys at runtime check the documentation below to see how
you can [dynamically build a CLI for your resources](#cli-builder).

Expand All @@ -615,13 +611,13 @@ dg.with_api_token('my-api-token').with_dashboards(dash1, dash2).update()

### Talking to the SignalFlow API Directly

If you need to process SignalFx data outside the confince of the API it may be
If you need to process SignalFx data outside the API's confines, it may be
useful to call the SignalFlow API directly. Note that you may incur time
penalties when pulling data out depending on the source of the data
penalties when pulling data out depending on the data source
(e.g. AWS/CloudWatch).

SignalFlow constructs are contained in the `flow` module. The following is an
example SignalFlow program that monitors an API services (like [Riposte])
SignalFlow constructs reside in the `flow` module. The following is an
example SignalFlow program that monitors an API service (like [Riposte])
RPS metrics for the `foo` application in the `test` environment.

```python
Expand All @@ -634,7 +630,7 @@ program = Data('requests.count', filter=all_filters)).publish()
```

You now have an object representation of the SignalFlow program. To take it for
a test ride you can use the official SignalFx client like so:
a test ride, you can use the official SignalFx client like so:

```python
# Original example found here:
Expand Down Expand Up @@ -663,17 +659,17 @@ with signalfx.SignalFx().signalflow('MY_TOKEN') as flow:

#### Charts Always Belong to Dashboards

It is always assumed that a Chart belongs to an existing Dashboard. This makes
We assume that a Chart belongs to an existing Dashboard making
it easier for the library to manage the state of the world.

#### Resource Names are Unique per Account

In a `signal_analog` world it is assumed that all resource names are unique.
That is, if we have two dashboards 'Foo Dashboard', when we attempt to update
In a `signal_analog` world, it is assumed that all resource names are unique.
That is, if we have two dashboards, 'Foo Dashboard' when we attempt to update
_either_ dashboard via `signal_analog` we expect to see errors.

Resource names are assumed to be unique in order to simplify state management
by the library itself. In practice we have not found this to be a major
Resource names are assumed to be unique to simplify state management
by the library itself. In practice, we have not found this to be a significant
inconvenience.

#### Configuration is the Source of Truth
Expand All @@ -684,7 +680,7 @@ local configuration.

#### Only "CCRUD" Methods Interact with the SignalFx API

`Resource` objects contain a number of builder methods to enable a "fluent" API
`Resource` objects contain many builder methods to enable a "fluent" API
when describing your project's dashboards in SignalFx. It is assumed that these
methods do not perform state-affecting actions in the SignalFx API.

Expand All @@ -693,7 +689,7 @@ state of your resources in SignalFx.

### Creating a CLI for your Resources

`signal_analog` provides builders for fully featured command line clients that
`signal_analog` provides builders for fully-featured command-line clients that
can manage the lifecycle of sets of resources.

#### Simple CLI integration
Expand Down Expand Up @@ -726,11 +722,11 @@ Assuming we called this `dashboards.py` we could run it in one of two ways:
- Give the script execution rights and run it directly
(typically `chmod +x dashboards.py`)
- `./dashboards.py --api-key mykey update`
- Pass the script in to the Python executor
- Pass the script into the Python executor
- `python dashboards.py --api-key mykey update`

If you want to know about the available actions you can take with your new
CLI you can always the `--help` command.
If you want to know about the available actions, you can take with your new
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I think this comma should not be added.

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@vladdoster vladdoster Jan 26, 2021 •
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It is an introductory clause warranting a comma.

https://owl.purdue.edu/owl/general_writing/punctuation/commas/commas_after_introductions.html

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@jrduncans jrduncans Jan 26, 2021 •
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The problem is that "you can take with your new CLI" is not an independent clause. The "with" is meaningless without the first part, because the noun phrase is actually "the available actions you can take with your new CLI" with "actions" being the object of the preposition "about" in the conditional clause "If you want to know about...".

It's really the whole thing up to "CLI" that is the introductory clause; the independent clause is "you can always use the --help command".

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@jrduncans Ah okay, that makes sense. I appreciate the thorough explanation. I am reverting this change.

CLI, you can always use the `--help` command.

```shell
./dashboards.py --help
Expand All @@ -740,12 +736,12 @@ This gives you the following features:
- Consistent resource management
- All resources passed to the CLI builder can be updated with one
`update` invocation, rather than calling the `update()` method on each
resource indvidually
resource individually
- API key handling for all resources
- Rather than duplicating your API key for each resource, you can instead
invoke the CLI with an API key
- This also provides a way to supply keys for users who don't want to
store them in source control (that's you! don't store your keys in
store them in source control (that's you! don't store keys in
source control)

## Documentation
Expand Down