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nexpie-grafana-theme
Commit 5896903b by Torkel Ödegaard
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Began work on alias support and alias patterns for InfluxDB 0.9, #1525

parent f41c3143
Showing with 369 additions and 214 deletions
public/app/plugins/datasource/influxdb/datasource.js
......@@ -69,8 +69,11 @@ function (angular, _, kbn, InfluxSeries, InfluxQueryBuilder) {
var query = annotation.query.replace('$timeFilter', timeFilter);
query = templateSrv.replace(query);
return this._seriesQuery(query).then(function(results) {
return new InfluxSeries({ seriesList: results, annotation: annotation }).getAnnotations();
return this._seriesQuery(query).then(function(data) {
if (!data || !data.results || !data.results[0]) {
throw { message: 'No results in response from InfluxDB' };
}
return new InfluxSeries({ series: data.results[0].series, annotation: annotation }).getAnnotations();
});
};
......@@ -168,9 +171,11 @@ function (angular, _, kbn, InfluxSeries, InfluxQueryBuilder) {
return deferred.promise;
};
function handleInfluxQueryResponse(alias, seriesList) {
var influxSeries = new InfluxSeries({ seriesList: seriesList, alias: alias });
return influxSeries.getTimeSeries();
function handleInfluxQueryResponse(alias, data) {
if (!data || !data.results || !data.results[0]) {
throw { message: 'No results in response from InfluxDB' };
}
return new InfluxSeries({ series: data.results[0].series, alias: alias }).getTimeSeries();
}
function getTimeFilter(options) {
......
public/app/plugins/datasource/influxdb/influxSeries.js
......@@ -5,8 +5,7 @@ function (_) {
'use strict';
function InfluxSeries(options) {
this.seriesList = options.seriesList && options.seriesList.results && options.seriesList.results.length > 0
? options.seriesList.results[0].series || [] : [];
this.series = options.series;
this.alias = options.alias;
this.annotation = options.annotation;
}
......@@ -17,23 +16,25 @@ function (_) {
var output = [];
var self = this;
console.log(self.seriesList);
if (self.seriesList.length === 0) {
if (self.series.length === 0) {
return output;
}
_.each(self.seriesList, function(series) {
_.each(self.series, function(series) {
var datapoints = [];
for (var i = 0; i < series.values.length; i++) {
datapoints[i] = [series.values[i][1], new Date(series.values[i][0]).getTime()];
}
var seriesName = series.name;
var tags = _.map(series.tags, function(value, key) {
return key + ': ' + value;
});
if (tags.length > 0) {
if (self.alias) {
seriesName = self.alias;
} else if (series.tags) {
var tags = _.map(series.tags, function(value, key) {
return key + ': ' + value;
});
seriesName = seriesName + ' {' + tags.join(', ') + '}';
}
......
public/app/plugins/datasource/influxdb/partials/query.editor.html
......@@ -141,14 +141,103 @@
</ul>
</li>
</ul>
<ul class="tight-form-list pull-right">
<li class="tight-form-item">
Alias pattern
</li>
<li>
<input type="text" class="input-medium tight-form-input" ng-model="target.alias" spellcheck='false' placeholder="alias" ng-blur="get_data()">
</li>
</ul>
<div class="clearfix"></div>
</div>
</div>
</div>
<section class="grafana-metric-options">
<div class="tight-form">
<ul class="tight-form-list">
<li class="tight-form-item tight-form-item-icon">
<i class="fa fa-wrench"></i>
</li>
<li class="tight-form-item">
Group by time interval
</li>
<li>
<input type="text" class="input-medium tight-form-input" ng-model="panel.interval" ng-blur="get_data();"
spellcheck='false' placeholder="example: >10s">
</li>
<li class="tight-form-item">
<i class="fa fa-question-circle" bs-tooltip="'Set a low limit by having a greater sign: example: >60s'" data-placement="right"></i>
</li>
</ul>
<div class="clearfix"></div>
</div>
<div class="tight-form">
<ul class="tight-form-list">
<li class="tight-form-item tight-form-item-icon">
<i class="fa fa-info-circle"></i>
</li>
<li class="tight-form-item">
<a ng-click="toggleEditorHelp(1);" bs-tooltip="'click to show helpful info'" data-placement="bottom">
alias patterns
</a>
</li>
<li class="tight-form-item">
<a ng-click="toggleEditorHelp(2)" bs-tooltip="'click to show helpful info'" data-placement="bottom">
stacking &amp; and fill
</a>
</li>
<li class="tight-form-item">
<a ng-click="toggleEditorHelp(3)" bs-tooltip="'click to show helpful info'" data-placement="bottom">
group by time
</a>
</li>
</ul>
<div class="clearfix"></div>
</div>
</section>
<div class="editor-row">
<div class="pull-left" style="margin-top: 30px;">
<div class="grafana-info-box span6" ng-if="editorHelpIndex === 1">
<h5>Alias patterns</h5>
<ul>
<li>$m = replaced with measurement name</li>
<li>$measurement = replaced with measurement name</li>
<li>$tag_hostname = replaced with the value of the hostname tag</li>
<li>You can also use [[tag_hostname]] pattern replacement syntax</li>
</ul>
</div>
<div class="grafana-info-box span6" ng-if="editorHelpIndex === 2">
<h5>Stacking and fill</h5>
<ul>
<li>When stacking is enabled it important that points align</li>
<li>If there are missing points for one series it can cause gaps or missing bars</li>
<li>You must use fill(0), and select a group by time low limit</li>
<li>Use the group by time option below your queries and specify for example &gt;10s if your metrics are written every 10 seconds</li>
<li>This will insert zeros for series that are missing measurements and will make stacking work properly</li>
</ul>
</div>
<div class="grafana-info-box span6" ng-if="editorHelpIndex === 3">
<h5>Group by time</h5>
<ul>
<li>Group by time is important, otherwise the query could return many thousands of datapoints that will slow down Grafana</li>
<li>Leave the group by time field empty for each query and it will be calculated based on time range and pixel width of the graph</li>
<li>If you use fill(0) or fill(null) set a low limit for the auto group by time interval</li>
<li>The low limit can only be set in the group by time option below your queries</li>
<li>You set a low limit by adding a greater sign before the interval</li>
<li>Example: &gt;60s if you write metrics to InfluxDB every 60 seconds</li>
</ul>
</div>
</div>
</div>
public/css/less/tightform.less
......@@ -23,7 +23,7 @@
.tight-form-container-no-item-borders {
border: 1px solid @grafanaTargetBorder;
.tight-form, .tight-form-item {
.tight-form, .tight-form-item, [type=text].tight-form-input {
border: none;
}
}
......
public/test/specs/influxSeries-specs.js
define([
'plugins/datasource/influxdb_08/influxSeries'
'plugins/datasource/influxdb/influxSeries'
], function(InfluxSeries) {
'use strict';
describe('when generating timeseries from influxdb response', function() {
describe('given two series', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
},
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server2.cpu',
points: [[1402596000, 15, 1], [1402596001, 16, 2]]
}
]
});
var result = series.getTimeSeries();
it('should generate two time series', function() {
expect(result.length).to.be(2);
expect(result[0].target).to.be('prod.server1.cpu.mean');
expect(result[0].datapoints[0][0]).to.be(10);
expect(result[0].datapoints[0][1]).to.be(1402596000);
expect(result[0].datapoints[1][0]).to.be(12);
expect(result[0].datapoints[1][1]).to.be(1402596001);
expect(result[1].target).to.be('prod.server2.cpu.mean');
expect(result[1].datapoints[0][0]).to.be(15);
expect(result[1].datapoints[0][1]).to.be(1402596000);
expect(result[1].datapoints[1][0]).to.be(16);
expect(result[1].datapoints[1][1]).to.be(1402596001);
});
});
describe('given an alias format', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$s.testing'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('prod.server1.cpu.testing');
});
});
describe('given an alias format with segment numbers', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$1.mean'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('server1.mean');
});
});
describe('given an alias format and many segments', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'a0.a1.a2.a3.a4.a5.a6.a7.a8.a9.a10.a11.a12',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$5.$11.mean'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('a5.a11.mean');
});
});
describe('given an alias format with group by field', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'host'],
name: 'prod.cpu',
points: [[1402596000, 10, 'A']]
}
],
groupByField: 'host',
alias: '$g.$1'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('A.cpu');
});
});
describe('given group by column', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'host'],
name: 'prod.cpu',
points: [
[1402596000, 10, 'A'],
[1402596001, 11, 'A'],
[1402596000, 5, 'B'],
[1402596001, 6, 'B'],
]
}
],
groupByField: 'host'
});
var result = series.getTimeSeries();
it('should generate two time series', function() {
expect(result.length).to.be(2);
expect(result[0].target).to.be('prod.cpu.A');
expect(result[0].datapoints[0][0]).to.be(10);
expect(result[0].datapoints[0][1]).to.be(1402596000);
expect(result[0].datapoints[1][0]).to.be(11);
expect(result[0].datapoints[1][1]).to.be(1402596001);
expect(result[1].target).to.be('prod.cpu.B');
expect(result[1].datapoints[0][0]).to.be(5);
expect(result[1].datapoints[0][1]).to.be(1402596000);
expect(result[1].datapoints[1][0]).to.be(6);
expect(result[1].datapoints[1][1]).to.be(1402596001);
});
});
});
describe("when creating annotations from influxdb response", function() {
describe('given column mapping for all columns', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'text', 'sequence_number', 'title', 'tags'],
name: 'events1',
points: [[1402596000000, 'some text', 1, 'Hello', 'B'], [1402596001000, 'asd', 2, 'Hello2', 'B']]
}
],
annotation: {
query: 'select',
titleColumn: 'title',
tagsColumn: 'tags',
textColumn: 'text',
var options = { series: [
{
name: 'cpu',
tags: {app: 'test'},
columns: ['time', 'mean'],
values: [["2015-05-18T10:57:05Z", 10], ["2015-05-18T10:57:06Z", 12]]
},
{
name: 'cpu',
tags: {app: 'test2'},
columns: ['time', 'mean'],
values: [["2015-05-18T10:57:05Z", 15], ["2015-05-18T10:57:06Z", 16]]
}
});
]};
var result = series.getAnnotations();
describe('and no alias', function() {
it(' should generate 2 annnotations ', function() {
expect(result.length).to.be(2);
expect(result[0].annotation.query).to.be('select');
expect(result[0].title).to.be('Hello');
expect(result[0].time).to.be(1402596000000);
expect(result[0].tags).to.be('B');
expect(result[0].text).to.be('some text');
});
it('should generate two time series', function() {
var series = new InfluxSeries(options);
var result = series.getTimeSeries();
});
expect(result.length).to.be(2);
expect(result[0].target).to.be('cpu {app: test}');
expect(result[0].datapoints[0][0]).to.be(10);
expect(result[0].datapoints[0][1]).to.be(1431946625000);
expect(result[0].datapoints[1][0]).to.be(12);
expect(result[0].datapoints[1][1]).to.be(1431946626000);
describe('given no column mapping', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'text', 'sequence_number'],
name: 'events1',
points: [[1402596000000, 'some text', 1]]
}
],
annotation: { query: 'select' }
expect(result[1].target).to.be('cpu {app: test2}');
expect(result[1].datapoints[0][0]).to.be(15);
expect(result[1].datapoints[0][1]).to.be(1431946625000);
expect(result[1].datapoints[1][0]).to.be(16);
expect(result[1].datapoints[1][1]).to.be(1431946626000);
});
});
var result = series.getAnnotations();
describe('and simple alias', function() {
it('should use alias', function() {
options.alias = 'new series';
var series = new InfluxSeries(options);
var result = series.getTimeSeries();
it('should generate 1 annnotation', function() {
expect(result.length).to.be(1);
expect(result[0].title).to.be('some text');
expect(result[0].time).to.be(1402596000000);
expect(result[0].tags).to.be(undefined);
expect(result[0].text).to.be(undefined);
});
expect(result[0].target).to.be('new series');
});
});
});
});
......
public/test/specs/influxSeries08-specs.js 0 → 100644
define([
'plugins/datasource/influxdb_08/influxSeries'
], function(InfluxSeries) {
'use strict';
describe('when generating timeseries from influxdb response', function() {
describe('given two series', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
},
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server2.cpu',
points: [[1402596000, 15, 1], [1402596001, 16, 2]]
}
]
});
var result = series.getTimeSeries();
it('should generate two time series', function() {
expect(result.length).to.be(2);
expect(result[0].target).to.be('prod.server1.cpu.mean');
expect(result[0].datapoints[0][0]).to.be(10);
expect(result[0].datapoints[0][1]).to.be(1402596000);
expect(result[0].datapoints[1][0]).to.be(12);
expect(result[0].datapoints[1][1]).to.be(1402596001);
expect(result[1].target).to.be('prod.server2.cpu.mean');
expect(result[1].datapoints[0][0]).to.be(15);
expect(result[1].datapoints[0][1]).to.be(1402596000);
expect(result[1].datapoints[1][0]).to.be(16);
expect(result[1].datapoints[1][1]).to.be(1402596001);
});
});
describe('given an alias format', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$s.testing'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('prod.server1.cpu.testing');
});
});
describe('given an alias format with segment numbers', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'prod.server1.cpu',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$1.mean'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('server1.mean');
});
});
describe('given an alias format and many segments', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'sequence_number'],
name: 'a0.a1.a2.a3.a4.a5.a6.a7.a8.a9.a10.a11.a12',
points: [[1402596000, 10, 1], [1402596001, 12, 2]]
}
],
alias: '$5.$11.mean'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('a5.a11.mean');
});
});
describe('given an alias format with group by field', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'host'],
name: 'prod.cpu',
points: [[1402596000, 10, 'A']]
}
],
groupByField: 'host',
alias: '$g.$1'
});
var result = series.getTimeSeries();
it('should generate correct series name', function() {
expect(result[0].target).to.be('A.cpu');
});
});
describe('given group by column', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'mean', 'host'],
name: 'prod.cpu',
points: [
[1402596000, 10, 'A'],
[1402596001, 11, 'A'],
[1402596000, 5, 'B'],
[1402596001, 6, 'B'],
]
}
],
groupByField: 'host'
});
var result = series.getTimeSeries();
it('should generate two time series', function() {
expect(result.length).to.be(2);
expect(result[0].target).to.be('prod.cpu.A');
expect(result[0].datapoints[0][0]).to.be(10);
expect(result[0].datapoints[0][1]).to.be(1402596000);
expect(result[0].datapoints[1][0]).to.be(11);
expect(result[0].datapoints[1][1]).to.be(1402596001);
expect(result[1].target).to.be('prod.cpu.B');
expect(result[1].datapoints[0][0]).to.be(5);
expect(result[1].datapoints[0][1]).to.be(1402596000);
expect(result[1].datapoints[1][0]).to.be(6);
expect(result[1].datapoints[1][1]).to.be(1402596001);
});
});
});
describe("when creating annotations from influxdb response", function() {
describe('given column mapping for all columns', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'text', 'sequence_number', 'title', 'tags'],
name: 'events1',
points: [[1402596000000, 'some text', 1, 'Hello', 'B'], [1402596001000, 'asd', 2, 'Hello2', 'B']]
}
],
annotation: {
query: 'select',
titleColumn: 'title',
tagsColumn: 'tags',
textColumn: 'text',
}
});
var result = series.getAnnotations();
it(' should generate 2 annnotations ', function() {
expect(result.length).to.be(2);
expect(result[0].annotation.query).to.be('select');
expect(result[0].title).to.be('Hello');
expect(result[0].time).to.be(1402596000000);
expect(result[0].tags).to.be('B');
expect(result[0].text).to.be('some text');
});
});
describe('given no column mapping', function() {
var series = new InfluxSeries({
seriesList: [
{
columns: ['time', 'text', 'sequence_number'],
name: 'events1',
points: [[1402596000000, 'some text', 1]]
}
],
annotation: { query: 'select' }
});
var result = series.getAnnotations();
it('should generate 1 annnotation', function() {
expect(result.length).to.be(1);
expect(result[0].title).to.be('some text');
expect(result[0].time).to.be(1402596000000);
expect(result[0].tags).to.be(undefined);
expect(result[0].text).to.be(undefined);
});
});
});
});
public/test/test-main.js
......@@ -125,6 +125,7 @@ require([
'specs/graphiteTargetCtrl-specs',
'specs/graphiteDatasource-specs',
'specs/influxSeries-specs',
'specs/influxSeries08-specs',
'specs/influxQueryBuilder-specs',
'specs/influx09-querybuilder-specs',
'specs/influxdb-datasource-specs',
......
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