![Introducing Enhanced Alert Actions and Triage Functionality](https://cdn.sanity.io/images/cgdhsj6q/production/fe71306d515f85de6139b46745ea7180362324f0-2530x946.png?w=800&fit=max&auto=format)
Product
Introducing Enhanced Alert Actions and Triage Functionality
Socket now supports four distinct alert actions instead of the previous two, and alert triaging allows users to override the actions taken for all individual alerts.
@opencensus/core
Advanced tools
Package description
The @opencensus/core package is a set of libraries for collecting, processing, and exporting telemetry data (metrics and traces) for analysis to improve the performance and reliability of applications. It is part of the OpenCensus project, which aims to provide a single, high-quality telemetry collection framework across multiple languages.
Tracing
This feature allows the collection and export of trace data, which helps in understanding the flow of requests through various services and in identifying bottlenecks and latency issues.
const { Tracing } = require('@opencensus/core');
const tracing = Tracing.instance;
// Configure tracing
tracing.start({samplingRate: 1});
// Create a custom span
const rootSpan = tracing.tracer.startRootSpan({name: 'main'}, rootSpan => {
// Do work within the span
rootSpan.end(); // End the span
});
Metrics
This feature enables the collection and aggregation of metrics data, such as counts or latencies, which can be used for monitoring application performance and health.
const { Metrics, MeasureUnit } = require('@opencensus/core');
const metrics = Metrics.instance;
// Create a measure
const requestCountMeasure = metrics.createMeasureInt64('request_count', MeasureUnit.UNIT, 'Count of requests received');
// Create and register a view to aggregate the data
metrics.createView('request_count_view', requestCountMeasure, 'count', [], 'The count of requests', []);
// Record data
metrics.record([{measure: requestCountMeasure, value: 1}]);
OpenTelemetry is the successor to OpenCensus and provides a single set of APIs, libraries, agents, and instrumentation to capture distributed traces and metrics from your application. It aims to make telemetry data (metrics, logs, and traces) a built-in feature of cloud-native software applications. Compared to @opencensus/core, OpenTelemetry offers broader community support and more active development, with a focus on compatibility and integration with a wide range of observability tools.
The prom-client package is specifically designed for creating metrics in the Prometheus format. It focuses solely on metrics collection and exposition, unlike @opencensus/core, which supports both tracing and metrics. This makes prom-client a good choice if you are specifically looking for Prometheus support, but it lacks the tracing capabilities of @opencensus/core.
Zipkin-js is a library for sending traces to Zipkin, a distributed tracing system. It focuses on tracing support and integrates with various JavaScript frameworks and libraries. Compared to @opencensus/core, zipkin-js is more specialized in tracing and does not include metrics collection functionality. It's a good option if your primary need is distributed tracing with Zipkin.
Changelog
0.0.9 - 2019-02-12
DoubleGauge
, LongGauge
, DerivedDoubleGauge
, DerivedLongGauge
) APIs.opencensus-resource-util
to auto detect AWS, GCE and Kubernetes(K8S) monitored resource, based on the environment where the application is running.uncompressedSize
and compressedSize
fields to MessageEvent
interface.setStatus
method in the Span.This release has multiple breaking changes. Please test your code accordingly after upgrading.
Logger
interface: level
made optional, silly
removed.new Stats()
has been deprecated on Stats class. The global singleton globalStats
object should be used instead. Also, registerView()
is separated out from createView()
.TagKey
, TagValue
and TagMap
to create the tag keys, tag values.status
field on Span
is no longer a number, use CanonicalCode
instead.MessageEvent
, Link
and SpanKind
, instead of string.const { Stats } = require("@opencensus/core");
const stats = new Stats();
// Counts/groups the lengths of lines read in.
const mLineLengths = stats.createMeasureInt64(
"demo/line_lengths",
MeasureUnit.BYTE,
"The distribution of line lengths"
);
// Create tag keys
const tagKeys = ["method", "status"];
// Create and register the view
stats.createView(
"demo/lines_in",
mLineLengths,
AggregationType.COUNT,
tagKeys,
"The number of lines from standard input"
);
// Records measurements
stats.record({
measure: mLineLengths,
tags,
value: 2
});
// Gets the global stats instance
const { globalStats } = require("@opencensus/core");
// Counts/groups the lengths of lines read in.
const mLineLengths = globalStats.createMeasureInt64(
"demo/line_lengths",
MeasureUnit.BYTE,
"The distribution of line lengths"
);
// Creates the method and status key
const methodKey = {name: "method"};
const statusKey = {name: "status"};
// Creates the view
const view = globalStats.createView(
"demo/lines_in",
mLineLengths,
AggregationType.COUNT,
[methodKey, statusKey],
"The number of lines from standard input"
);
// Registers the view
globalStats.registerView(view);
// Creates tags map -> key/value pair
const tagMap = new TagMap();
tagMap.set(methodKey, {value: 'REPL'});
tagMap.set(statusKey, {value: 'OK'});
// Creates measurements (measure + value)
const measurements = [{
measure: mLineLengths,
value: 2
}];
// Records measurement with tagMap
globalStats.record(measurements, tagMap);
Readme
OpenCensus for Node.js is an implementation of OpenCensus, a toolkit for collecting application performance and behavior monitoring data. It currently includes 3 apis: stats, tracing and tags.
The library is in alpha stage and the API is subject to change.
Install the opencensus-core package with NPM:
npm install @opencensus/core
To enable metrics, we’ll import a few items from OpenCensus Core package.
const { globalStats, MeasureUnit, AggregationType, TagMap } = require('@opencensus/core');
// The latency in milliseconds
const mLatencyMs = globalStats.createMeasureDouble(
"repl/latency",
MeasureUnit.MS,
"The latency in milliseconds"
);
We now determine how our metrics will be organized by creating Views
. We will also create the variable needed to add extra text meta-data to our metrics – methodTagKey
, statusTagKey
, and errorTagKey
.
const methodTagKey = { name: "method" };
const statusTagKey = { name: "status" };
const errorTagKey = { name: "error" };
// Create & Register the view.
const latencyView = globalStats.createView(
"demo/latency",
mLatencyMs,
AggregationType.DISTRIBUTION,
[methodTagKey, statusTagKey, errorTagKey],
"The distribution of the latencies",
// Bucket Boundaries:
// [>=0ms, >=25ms, >=50ms, >=75ms, >=100ms, >=200ms, >=400ms, >=600ms, >=800ms, >=1s, >=2s, >=4s, >=6s]
[0, 25, 50, 75, 100, 200, 400, 600, 800, 1000, 2000, 4000, 6000]
);
globalStats.registerView(latencyView);
Now we will record the desired metrics. To do so, we will use globalStats.record()
and pass in measurements.
const [_, startNanoseconds] = process.hrtime();
const tags = new TagMap();
tags.set(methodTagKey, { value: "REPL" });
tags.set(statusTagKey, { value: "OK" });
globalStats.record([{
measure: mLatencyMs,
value: sinceInMilliseconds(startNanoseconds)
}], tags);
function sinceInMilliseconds(startNanoseconds) {
const [_, endNanoseconds] = process.hrtime();
return (endNanoseconds - startNanoseconds) / 1e6;
}
Measures can be of type Int64
or DOUBLE
, created by calling createMeasureInt64
and createMeasureDouble
respectively. Its units can be:
MeasureUnit | Usage |
---|---|
UNIT | for general counts |
BYTE | bytes |
KBYTE | Kbytes |
SEC | seconds |
MS | millisecond |
NS | nanosecond |
Views can have agregations of type SUM
, LAST_VALUE
, COUNT
and DISTRIBUTION
. To know more about Stats core concepts, please visit: https://opencensus.io/core-concepts/metrics/
See Quickstart/Metrics for a full example of registering and collecting metrics.
Apache License 2.0
FAQs
OpenCensus is a toolkit for collecting application performance and behavior data.
The npm package @opencensus/core receives a total of 1,042,551 weekly downloads. As such, @opencensus/core popularity was classified as popular.
We found that @opencensus/core demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 6 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Product
Socket now supports four distinct alert actions instead of the previous two, and alert triaging allows users to override the actions taken for all individual alerts.
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