def test_cli_create_force_success():
program = Data('cpu.utilization').publish()
chart = TimeSeriesChart().with_name('lol').with_program(program)
with global_recorder.use_cassette('cli_create_force_success',
serialize_with='prettyjson'):
dashboard = Dashboard(session=global_session)\
.with_name('testy mctesterson')\
.with_charts(chart)
cli = CliBuilder().with_resources(dashboard).build()
runner = CliRunner()
result = runner.invoke(cli, args=['--api-key', 'foo', 'create', '-f'])
assert result.exit_code == 0
def test_cli_create_interactive_failure(sfx_recorder, session):
program = Data('cpu.utilization').publish()
chart = TimeSeriesChart().with_name('lol').with_program(program)
with sfx_recorder.use_cassette('cli_create_interactive_failure',
serialize_with='prettyjson'):
dashboard = Dashboard(session=session)\
.with_name('testy mctesterson')\
.with_charts(chart)
cli = CliBuilder().with_resources(dashboard).build()
runner = CliRunner()
result = runner.invoke(cli, args=['--api-key', 'foo', 'create', '-i'], input='n')
click.echo(result.exception)
assert result.exception
app_filter = DashboardFilters() \
.with_time(time)
dashboard_with_time_as_integer_filter = Dashboard()\
.with_name('Dashboard With Time as Integer Filter')\
.with_charts(chart)\
.with_filters(app_filter)
"""
Example 6: Update an existing Dashboard by removing one of the filters
"""
app_filter = DashboardFilters().with_variables(app_var)
dashboard_remove_filter = Dashboard()\
.with_name('Dashboard With Multiple Filters')\
.with_charts(chart)\
.with_filters(app_filter)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(dashboard_with_single_filter_variable,
dashboard_with_multiple_filter_variables,
dashboard_with_source_filter,
dashboard_with_time_as_string_filter,
dashboard_with_time_as_integer_filter,
dashboard_remove_filter)\
.build()
cli()
Example 2: make a re-usable chart (or template)
This is useful when you want your chart to be broadly applicable/used by others
"""
class UserCpuUsedPercentChart(TimeSeriesChart):
def __init__(self, app, env='prod'):
super(UserCpuUsedPercentChart, self).__init__()
self.with_name('CPU Used % from template class')
self.with_description(
'% CPU used by type (user, system, i/o, stolen, etc)')
self.stack_chart(True)
self.with_default_plot_type(PlotType.area_chart)
self.with_program(self.__program__(app, env))
self.with_publish_label_options(
PublishLabelOptions('A', 0, PaletteColor.hot_pink,
PlotType.area_chart, 'User CPU %'))
def __program__(self, app, env):
app_filter = And(Filter('app', 'my-app'), Filter('env', 'prod'))
return Program(Data('cpu.user', filter=app_filter).mean().publish('A'))
chart_from_templ = UserCpuUsedPercentChart('my-app')
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(chart, chart_from_templ).build()
cli()
dashboard_with_multiple_charts = Dashboard()\
.with_name('Dashboard With Multiple Charts')\
.with_charts(chart, chart1, chart2)
"""
Example 3: Update existing dashboard by removing one of the charts
"""
dashboard_remove_chart = Dashboard()\
.with_name('Dashboard Name')\
.with_charts(chart, chart1)
"""
Example 3: Rename and existing chart
"""
chart1 = TimeSeriesChart()\
.with_name('Chart_Name_Renamed')\
.with_program(program)
dashboard_rename_chart = Dashboard()\
.with_name('Dashboard Name')\
.with_charts(chart, chart1)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(dashboard_with_single_chart,
dashboard_with_multiple_charts,
dashboard_remove_chart,
dashboard_rename_chart)\
.build()
cli()
.with_rules(info_rule)
"""
Example 2: from an existing chart
This is useful when you want to alert on behavior seen in a chart that was
created using the signal_analog library.
"""
test_program = Program(data)
chart = TimeSeriesChart()\
.with_name('TEST: example chart for detector')\
.with_program(test_program) # Defined above
def detector_helper(prog):
d = prog.find_label('A')
return Program(Detect(LT(d, 1)).publish(alert_label))
c_detector = Detector()\
.with_name('TEST: example detector from chart')\
.from_chart(chart, detector_helper)\
.with_rules(info_rule)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(detector, c_detector).build()
cli()
from signal_analog.charts import TimeSeriesChart
from signal_analog.dashboards import Dashboard, DashboardGroup
from signal_analog.combinators import And
"""
Creating a new Dashboard Group with Dashboards
"""
filters = And(Filter('host', 'learn-signalfx'))
program = Data('cpu.utilization', filter=filters).publish()
cpu_chart = TimeSeriesChart().with_name('CPU').with_program(program)
program = Data('postgres_query_time', rollup='rate', filter=filters).publish()
query_time = TimeSeriesChart().with_name('Query Time').with_program(program)
program = Data('memory.utilization', filter=filters).publish()
memory_chart = TimeSeriesChart().with_name('Memory').with_program(program)
dashboard1 = Dashboard().with_name('Dashboard 1').with_charts(
query_time, cpu_chart, memory_chart)
dashboard_group = DashboardGroup() \
.with_name('Learn Signal Analog') \
.with_dashboards(dashboard1)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(dashboard_group)\
.build()
cli()
.with_time(FilterTime().with_start("-7d").with_end("Now"))
)
if __name__ == '__main__':
dashboards = []
dashboards.append(
create_pipeline_dashboard("My Data Pipeline Dashboard", [
AwsResouce(AwsResourceType.SqsQueue, 'some-queue',
'This queue is triggered by a SNS topic'),
AwsResouce(AwsResourceType.Lambda, 'some-lambda',
'Lambda listens to Queue and writes to DynamoDB'),
AwsResouce(AwsResourceType.DynamoDbWithStream, 'my-table',
'Updated by lambda'),
AwsResouce(
AwsResourceType.Lambda, 'another-lambda',
'Lambda listens to DynamoDB stream and writes to Kinesis stream'
),
AwsResouce(AwsResourceType.KinesisStream, 'some-kinesis-stream',
'Some stream with consumers')
]))
# TODO: by simply calling the `create_pipeline_dashboard()` function we can add additional Dashboards in a config style
group = DashboardGroup() \
.with_name("My Data Pipeline Dashboard Group") \
.with_dashboards(*dashboards)
cli = CliBuilder().with_resources(group).build()
cli()
from signal_analog.eventoverlays import EventSignals, EventOverlays, SelectedEventOverlays
"""
Example 1: Creating a new Dashboard with event overlay markers set to show by default
This creates a new dashboard which will show event markers marked with "deploy" by default.
"""
program = Data('cpu.utilization').publish()
chart = TimeSeriesChart().with_name('TacoChart').with_program(program).show_event_lines(True)
events = EventSignals().with_event_search_text("deploy")\
.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)
dashboard_with_event_overlays = Dashboard().with_name('Dashboard Named Snek')\
.with_charts(chart)\
.with_event_overlay(eventoverlay)\
.with_selected_event_overlay(selectedeventoverlay)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(dashboard_with_event_overlays)\
.build()
cli()
dashboard_group_with_dashboards = DashboardGroup() \
.with_name('Dashboard Group Name') \
.with_dashboards(dashboard1, dashboard2, dashboard3)
"""
Example 3: Update existing dashboard group by removing one of the dashboards
"""
dashboard_group_remove_dashboard = DashboardGroup()\
.with_name('Dashboard Group Name')\
.with_dashboards(dashboard1, dashboard2)
"""
Example 4: Rename an existing Dashboard
"""
dashboard2 = Dashboard().with_name('Dashboard2_Renamed')\
.with_charts(chart, chart1)
dashboard_group_rename_dashboard = DashboardGroup()\
.with_name('Dashboard Group Name')\
.with_dashboards(dashboard1, dashboard2)
if __name__ == '__main__':
from signal_analog.cli import CliBuilder
cli = CliBuilder().with_resources(dashboard_group,
dashboard_group_with_dashboards,
dashboard_group_remove_dashboard,
dashboard_group_rename_dashboard)\
.build()
cli()