signal-analog 2.10.1

A troposphere-like library for managing SignalFxCharts, Dashboards, and Detectors.

signal_analog

A troposphere-inspired library 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.

TOC

• Features
• Installation
• Usage
  • Building Charts
  • Building Dashboards
  • Updating Dashboards
  • Dashboard Filters
  • Dashboard Event Overlays
  • Creating Detectors
    • Detectors That Combine Data Streams
    • Building Detectors from Existing Charts
  • Using Flow and Combinator Functions In Formulas
  • Building Dashboard Groups
  • Updating Dashboard Group
  • Talking to the SignalFlow API Directly
  • General Resource Guidelines
  • Creating a CLI for your resources
• Documentation
• Example Code
• Contributing

Features

• Provides bindings for the SignalFlow DSL
• Provides abstractions for:
  • Charts
  • Dashboards, DashboardGroups
  • Detectors
• A CLI builder to wrap resource definitions (useful for automation)

Installation

Add signal_analog to the requirements file in your project:

# requirements.txt
# ... your other dependencies
signal_analog

Then run the following command to update your environment:

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 Signal Flow DSL.

The following sections describe how to use Resource abstractions in conjunction with the Signal Flow DSL.

Building Charts

signal_analog provides constructs for building charts in the signal_analog.charts module.

Consult the upstream documentation 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.

This assumes a service reports 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 data definition in the SignalFlow language. Let's begin by defining our timeseries:

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.

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.

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 reporting to SignalFx!

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

from signal_analog.flow import Data, Filter

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 lives in.

What we'll want to do is combine our app_filter with another filter for the environment. The signal_analog.combinators module provides some helpful constructs for achieving this goal:

from signal_analog.combinators import And

env_filter = Filter('env', 'prod')

all_filters = And(app_filter, env_filter)

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

Excellent! We're now ready to create our chart.

First, let's give our chart a name:

from signal_analog.charts import TimeSeriesChart

memory_chart = TimeSeriesChart().with_name('Memory Used %')

Like it's flow counterparts, charts adhere to the builder pattern for constructing objects that interact with the SignalFx API.

With our name in place, let's go ahead and add our program:

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.

Finally, let's change the plot type on our chart so that we see solid areas instead of flimsy lines:

from signal_analog.charts import PlotType

memory_chart = TimeSeriesChart()\
                 .with_name('Memory Used %')\
                 .with_program(ts)
                 .with_default_plot_type(PlotType.area_chart)

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 collections of charts.

Building Dashboards

signal_analog provides constructs for building charts in the signal_analog.dashboards module.

Consult the upstream documentation for more information on the Dashboard API.

Building on the examples described in the previous section, we'd now like to build a dashboard containing our memory chart.

We start with the humble Dashboard object:

from signal_analog.dashboards import Dashboard

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:

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!

response = dash\
  .with_charts(memory_chart)\
  .with_api_token('my-api-token')\
  .create()

At this point one of two things will happen:

• We receive some sort of error from the SignalFx API and an exception is thrown
• We successfully created the dashboard, in which case the JSON response is returned as a dictionary.

Also, if you have an existing Dashboard Group and you want this new dashboard to be part of that dashboard group, you can pass that group id of the dashboard group when creating the dashboard. Something like this:

response = dash\
  .with_charts(memory_chart)\
  .with_api_token('my-api-token')\
  .create(group_id="asdf;lkj")

Now, storing API keys in 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.

Updating Dashboards

Once you have created a dashboard you can update properties like name and description:

dash.update(
    name='updated_dashboard_name',
    description='updated_dashboard_description'
)

Dashboard updates will also update any Chart configurations it owns.

Note: If the given dashboard does not already exist, `update` will create a new dashboard for you

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.

from signal_analog.filters import DashboardFilters, FilterVariable, FilterSource, FilterTime
app_var = FilterVariable().with_alias('app')\
.with_property('app')\
.with_is_required(True)\
.with_value('foo')

env_var = FilterVariable().with_alias('env')\
.with_property('env')\
.with_is_required(True)\
.with_value('prod')

aws_src = FilterSource().with_property("aws_region").with_value('us-west-2')

time = FilterTime().with_start("-1h").with_end("Now")

app_filter = DashboardFilters() \
.with_variables(app_var, env_var) \ 
.with_sources(aws_src) \
.with_time(time)

So, here we are creating a few filters "app=foo" and "env=prod", a source filter "aws_region=us-west-2" and a time filter "-1h till Now" Now we can pass this config to a dashboard object:

response = dash\
.with_charts(memory_chart)\
.with_api_token('my-api-token')\
.with_filters(app_filter)\
.create()

If you are updating an existing dashboard:

response = dash\
.with_filters(app_filter)\
.update()

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).

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

from signal_analog.flow import Data
from signal_analog.charts import TimeSeriesChart
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 also pass our event signals along to the selectedEventOverlays, which will tell the dashboard to display matching events by default.

from signal_analog.eventoverlays import EventSignals, EventOverlays, SelectedEventOverlays
events = EventSignals().with_event_search_text("*test*")\
    .with_event_type("eventTimeSeries")

eventoverlay = EventOverlays().with_event_signals(events)\
    .with_event_color_index(1)\
    .with_event_line(True)

selectedeventoverlay = SelectedEventOverlays()\
    .with_event_signals(events)

Next we combine our chart, our event overlay, and our selected event overlay into a dashboard object:

from signal_analog.dashboards import Dashboard
dashboard_with_event_overlays = Dashboard().with_name('Dashboard With Overlays')\
    .with_charts(chart)\
    .with_event_overlay(eventoverlay)\
    .with_selected_event_overlay(selectedeventoverlay)

Finally we build our resources in SignalFX with the cli builder:

if __name__ == '__main__':
    from signal_analog.cli import CliBuilder
    cli = CliBuilder().with_resources(dashboard_with_event_overlays)\
        .build()
    cli()

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 the full Detector API is supported.

Consult the upstream documentation for more information about Detectors.

Detectors are comprised of a few key elements:

• A name
• A SignalFlow Program
• A set of rules for alerting

We start by building a Detector object and giving it a name:

from signal_analog.detectors import Detector

detector = Detector().with_name('My Super Serious Detector')

We'll now need to give it a program to alert on:

from signal_analog.flow import Program, Detect, Filter, Data
from signal_analog.combinators import GT

# This program fires an alert if memory utilization is above 90% for the
# 'bar' application.
data = Data('memory.utilization', filter=Filter('app', 'bar')).publish(label='A')
alert_label = 'Memory Utilization Above 90'
detect = Detect(GT(data, 90)).publish(label=alert_label)

detector.with_program(Program(detect))

With our name and program in hand, it's time to build up an alert rule that we can use to notify our teammates:

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

info_rule = Rule()\
  # From our detector defined above.
  .for_label(alert_label)\
  .with_severity(Severity.Info)\
  .with_notifications(EmailNotification('[e-mail]'))

detector.with_rules(info_rule)

# We can now create this resource in SignalFx:
detector.with_api_token('foo').create()
# For a more robust solution consult the "Creating a CLI for your Resources"
# section below.

To add multiple alerting rules we would need to use different detect statements with distinct labels 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.

from signal_analog.flow import \
    Assign, \
    Data, \
    Detect, \
    Ref, \
    When

from signal_analog.combinators import \
    Div, \
    GT

program = Program( \
    Assign('my_var', Data('cpu.utilization')) \
    Assign('my_other_var', Data('cpu.utilization').count()) \
    Assign('mean', Div(Ref('my_var'), Ref('my_other_var'))) \
    Detect(When(GT(Ref('mean'), 2000))) \
)

print(program)

The above code generates the following program:

my_var = data('cpu.utilization')
my_other_var = data('cpu.utilization').count()
mean = (my_var / my_other_var)

when(detect(mean > 2000))

Building Detectors from Existing Charts

We can also build up Detectors from an existing chart, which allows us to reuse our SignalFlow program and ensure consistency between what we're monitoring and what we're alerting on.

Let's assume that we already have a chart defined for our use:

from signal_analog.flow import Program, Data
from signal_analog.charts import TimeSeriesChart

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 detectors:

from signal_analog.combinators import GT
from signal_analog.detectors import Detector
from signal_analog.flow import Detect

# Alert when CPU utilization rises above 95%
detector = Detector()\
    .with_name('CPU Detector')\
    .from_chart(
        cpu_chart,
        # `p` is the Program object from the cpu_chart we passed in.
        lambda p: Detect(GT(p.find_label('A'), 95).publish(label='Info Alert'))
    )

The above example won't actually alert on anything until we add a Rule, which you can find examples for 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 refresh our memory, our data in the previous chart example was:

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:

ts_program = Program(ts)

Then we can create a new statement using an Alert object with the detector id, publish the stream, and append the new statement to our program:

notifications = Alerts(detector_id).publish()
ts_program.statements.append(notifications)

The program can be included in a chart as usual:

memory_chart = TimeSeriesChart()\
                .with_program(ts_program)
                .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.

Using Flow and Combinator Functions In Formulas

signal_analog also provides functions for combining SignalFlow statements into more complex SignalFlow Formulas. These sorts of Formulas can be useful when creating more complex detectors and charts. For instance, if you would like to multiply one data stream by another and receive the sum of that Formula, it can be accomplished using Op and Mul like so:

from signal_analog.flow import Op, Program, Data
from signal_analog.combinators import Mul

# Multiply stream A by stream B and sum the result
    A = Data('request.mean')

    B = Data('request.count')

    C = Op(Mul(A,B)).sum()

Print(C) in the above example would produce the following output:

(data("request.mean") * data("request.count")).sum()

Building Dashboard Groups

signal_analog provides abstractions for building dashboard groups in the signal_analog.dashboards module.

Consult the upstream documentation for more information on 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 the Building Dashboards example:

from signal_analog.dashboards import Dashboard, DashboardGroup

dg = DashboardGroup()
dash1 = Dashboard().with_name('My Little Dashboard1: Metrics are Magic')\
    .with_charts(memory_chart)
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 is responsible for creating all child resources.

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

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 in the API!

response = dg\
    .with_dashboards(dash1)\
    .with_api_token('my-api-token')\
    .create()

Now, storing API keys in 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.

Updating Dashboard Groups

Once you have created a dashboard group, you can update properties like name and description of a dashboard group or add/remove dashboards in a group.

Example 1:

dg.with_api_token('my-api-token')\
    .update(name='updated_dashboard_group_name',
            description='updated_dashboard_group_description')

Example 2:

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 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 (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) RPS metrics for the foo application in the test environment.

from signal_analog.flow import Data, Filter
from signal_analog.combinators import And

all_filters = And(Filter('env', 'prod'), Filter('app', 'foo'))

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:

# Original example found here:
# https://github.com/signalfx/signalfx-python#executing-signalflow-computations

import signalfx
from signal_analog.flow import Data, Filter
from signal_analog.combinators import And

app_filter = Filter('app', 'foo')
env_filter = Filter('env', 'prod')
program = Data('requests.count', filter=And(app_filter, env_filter)).publish()

with signalfx.SignalFx().signalflow('MY_TOKEN') as flow:
    print('Executing {0} ...'.format(program))
    computation = flow.execute(str(program))

    for msg in computation.stream():
        if isinstance(msg, signalfx.signalflow.messages.DataMessage):
            print('{0}: {1}'.format(msg.logical_timestamp_ms, msg.data))
        if isinstance(msg, signalfx.signalflow.messages.EventMessage):
            print('{0}: {1}'.format(msg.timestamp_ms, msg.properties))

General Resource Guidelines

Charts Always Belong to Dashboards

It is always assumed that a Chart belongs to an existing Dashboard. This makes 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 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 inconvenience.

Configuration is the Source of Truth

When conflicts arise between the state of a resource in your configuration and what SignalFx thinks that state should be, this library always prefers the local configuration.

Only "CCRUD" Methods Interact with the SignalFx API

Resource objects contain a number of 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.

Only "CCRUD" (Create, Clone, Read, Update, and Delete) methods will affect the state of your resources in SignalFx.

Creating a CLI for your Resources

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

Simple CLI integration

Integrating with the CLI is as simple as importing the builder and passing it your resources. Let's consider an example where we want to update two existing dashboards:

#!/usr/bin/env python

# ^ It's always good to include a "hashbang" so that your terminal knows
# how to run your script.

from signal_analog.dashboards import Dashboard
from signal_analog.cli import CliBuilder

ingest_dashboard = Dashboard().with_name('my-ingest-service')
service_dashboard = Dashboard().with_name('my-service')

if __name__ == '__main__':
  cli = CliBuilder()\
      .with_resources(ingest_dashboard, service_dashboard)\
      .build()
  cli()

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
  • 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.

./dashboards.py --help

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
• 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 source control)

Documentation

• Signal Analog Documentation
• Introductory Article on Medium

Example Code

• See examples included in this project.

Contributing

Please read our docs here for more info about contributing.

2.10.1 (2023-7-24)

• Added auto_resolve_after parameter in Detect class.

2.10.0 (2023-7-19)

• Added AmazonEventBridgeNotification for Amazon EventBridge integration within detectors.
• Added combinators EQ (Equal) and NE (Not Equal).

2.9.3 (2020-3-18)

• Added Alerts in signal_analog.flow module to allow linking Detectors to Charts.

2.9.2 (2019-11-11)

• StrArg will only reject None instead of all falsey values, allowing 0 to be given as a value.

2.9.1 (2019-10-30)

• Resources have learned that deleting nothing results in nothing, and will stop complaining about this scenario (it will still register it's displeasure in a debug log message)

2.9.0 (2019-09-26)

• Added BigPandaNotification for BigPanda integration within detectors.

2.8.0 (2019-09-06)

• Added groupBy support to the chart options. Allows grouping for example of HeatMap charts into groups on multiple levels.
• Added support for colorScale2 option on HeatMap charts. This allows to set custom chart colors for a defined range of values.

2.7.2 (2019-05-14)

Fixed

• signal_analog has learned to use the groupId field when updating Dashboard resources after the recent Sfx API changes

Updated

• As many documentation links as possible since the last doc update from Sfx. Notable missing updates are those for 3rd party integration Notifications in the signal_analog.detectors module.

2.7.0 (2019-04-03)

Updated

• Removed dashboard numbering for two reasons:
  1. There was a bug in the logic that caused dashboards to be deleted and recreated on update.
  2. The functionality is no longer needed as SignalFx automatically maintains the order that dashboards were provided and allows easy reordering in the UI.
• AxisOptions are now optional where used

Fixed

• Fixing applyIfExists option for Dashboard variables.

2.6.0 (2019-03-21)

• AxisOption parameters should be optional.
• Additional documentation.

2.5.0 (2019-03-20)

• Added Plot class, a helper class that gives an interface more like that found in the SignalFx UI.
• Added RollupType enum for specifying the roll-up used in Charts.
• Added additional documentation links to README.
• Fix: TextCharts weren't working
• Fix: YAML load deprecation warning in logging config

2.4.0

• Add numbering to dashboards in a dashboard group for better organization of dashboards

2.3.2 (2018-11-12)

• The percentile function on signal_analog.flow.Data objects has been fixed to use the correct constructor

2.3.1 (2018-11-06)

• signal-analog now prefers simplejson if it is available on the path, falling back to the json module otherwise.

2.3.0 (2018-10-30)

• DashboardGroup has learned how to accept SignalFX Team ids so that they can be associated with pre-existing teams via the with_teams builder method.

2.2.2 (2018-10-03)

Fixed

• Add deprecation to setup.py.

2.2.1 (2018-10-02)

Changed

• Added with_secondary_visualization function to enable display of various meters (Sparkline, Linear, Radial) in Single Value charts. This replaces the now defunct with_sparkline_hidden function. This will not be a 'breaking change' until version 3.0.0 when with_sparkline_hidden will be removed from signal_analog.

• Added the deprecation Python library to this project to note when with_sparkline_hidden should be removed. Upon version matching 3.0.0 or higher the tests for that function will begin to fail notifying whoever is releasing that version to remove the defunct with_sparkline_hidden function and tests.

2.2.0 (2018-09-27)

Changed

• Dashboard Create method to accept group id of an existing dashboard group in which case the new dashboard will be part of the dashboard group provided

Example:

response = dashboard\
  .with_charts(memory_chart)\
  .with_api_token('my-api-token')\
  .create(group_id="asdf;lkj")

• Dashboard Group create method to pass group id of the newly created dashboard group to the dashboard create method so that we can avoid a few redundant calls like cloning and deleting the dashboards

2.1.0 (2018-08-21)

Added

• ListCharts learned how to filter legend options via the with_legend_options builder
• Future chart types that can filter legend options may now take advantage of the signal_analog.charts.LegendOptionsMixin class
• The FieldOption class has learned to accept SignalFxFieldOptions which provide mappings between field options seen in the UI and those used in the API
  • e.g. Plot Name in the UI and sf_originatingMetric in the API
• A new TextChart object has been added to signal_analog.charts that enables text descriptions to be added to dashboards.
• PublishLabelOptions has learned to accept prefix, suffix, and unit arguments when labelling data on charts.

Changed

• PublishLabelOptions has learned to accept all arguments as optional with the exception of the label argument.

Fixed

• A fix has been added for Python 2 users that prevented successful dashboard updates.

2.0.0 (2018-07-24)

For assistance migrating from 1.x to 2.x please consult the migration guide.

Added

• Add support for the dimensions, fill, integrate, kpss, rateofchange methods

Removed

• map method support has been removed
  • It didn't work properly to begin with, and will require some finagling to get right given our approach to building SignalFlow statements

Fixes

• top and bottom method signatures have been fixed to use count, by, and percentage arguments
• The following functions have been updated to raise an error if both by and over are defined in the same method call:
  • count, max, mean, mean_plus_stddev, median, min, percentile, random, size, stddev, sum, variance
• delta has been updated to no longer accept any method arguments
• ewma has been updated to support the over key

1.6.0 (2018-07-18)

• Add combinators for less-than-or-equal-to (LTE) and greater-than-or-equal-to (GTE)

1.5.1 (2018-06-21)

• Fix detector update logic to include all fields instead of just name/description

1.5.0(2018-05-16)

• Added include_zero method to TimeSeriesChart to allow setting the includeZero option.

1.4.0(2018-05-08)

• Implements functionality to add event overlays and selected (default) event overlays to dashboards at dashboard creation or update. Includes wildcard matching using the asterisk (*) symbol.

1.3.0(2018-04-17)

• Implementing the rest of the Dashboard Filters: source and time

1.2.0 (2018-04-11)

• Added an Assign function that will enable more complex detectors which are constructed by combining multiple data streams
• Added a Ref flow operator that will enable referencing assignments in a way that can be validated at later steps by checking for an Assign object with a match between the reference string and the assignee

1.1.0 (2018-04-04)

• Introducing Dashboard Filters(only variables as of now) which 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.

1.0.0 (2018-04-02)

• Symbolic release for signal_analog. Future version bumps should conform to the semver policy outlined here.

0.25.1 (2018-03-22)

• The timeshift method's arguments changed. Now accepts a single argument for offset.

0.24.0 (2018-03-09)

• Fix string parsing to not exclude boolean False, which is required for certain functions like .publish()

0.23.0 (2018-03-06)

• Added Op class in flow.py to allow multiplying and dividing datastreams to create SignalFlow Functions

0.22.0 (2018-03-01)

• Added Mul and Div combinators for multiplying and dividing streams
• Added "enable" option for publishing a stream. Setting enable=False will hide that particular stream in a chart/detector.

0.21.0 (2018-02-28)

• Dashboard Group support has been added giving you the ability group sets of dashboards together in a convenient construct
• Detector support has been added giving you the ability to create detectors from scratch or re-use the SignalFlow program of an existing Chart
• Dashboards and Charts now update via their id instead of by name to mitigate name conflicts when creating multiple resources with the same name
• Dry-run results are now more consistent between all resources and expose the API call (sans-headers) that would have been made to use for the given resource

0.20.0 (2018-01-31)

• Dashboards have learned how to update their child resources (e.g. if you add a chart in your config, the change will be reflected when you next run your configuration against SignalFx)
• The CLI builder has learned how to pass dry-run options to its configured resources
• Minor bugfixes for the signal_analog.flow module

0.19.1 (2018-01-26)

• Added click to setup.py

0.19.0 (2018-01-19)

• Added CLI builder to create and update dashboard resources

0.18.0 (2018-01-11)

• Dashboard resources have learned to interactively prompt the user if the user wants to create a new dashboard if there is a pre-existing match (this behavior is disabled by default).
• Added "Update Dashboard" functionality where a user can update the properties of a dashboard(only name and description for now)

0.17.0 (2018-01-11)

• Added Heatmap Chart style
  • Added by Jeremy Hicks

0.16.0 (2018-01-10)

• Added the ability to sort a list chart by value ascending/descending
  • Added by Jeremy Hicks

0.15.0 (2018-01-08)

• Added "Scale" to ColorBy class for coloring thresholds in SingleValueChart
  • Added by Jeremy Hicks

0.14.0 (2018-01-04)

• Added List Chart style
  • Added by Jeremy Hicks

0.13.0 (2018-01-04)

• Dashboard resources have learned how to force create themselves in the SignalFx API regardless of a pre-existing match (this behavior is disabled by default).

0.12.0 (2017-12-21)

• Dashboard resources have learned how to check for themselves in the SignalFx API, and will no longer create themselves if an exact match is found

0.3.0 (2017-09-25)

• Adds support for base Resource object. Will be used for Chart/Dashboard abstractions in future versions.
• Adds support for base Chart and TimeSeriesChart objects. Note that some TimeSeriesChart builder options have not yet been implemented (and marked clearly with NotImplementedErrors)

0.2.0 (2017-09-18)

• Adds support for function combinators like and, or, and not

0.1.1 (2017-09-14)

• Add README documentation

0.1.0 (2017-09-14)

• Initial release