Metrics.NET实践(1)
起因：对应用的监控和测量是WEB应用的一个重要话题，尤其在监控错误率，并发量，以及框架库中的动态值。于是，在性能优化的时候找到了metrics.net。
- 简介
- 开始使用
- 度量
- Gauges
- Counters
- Meters
- Histograms
- Timers
1. 簡介
- 項目主頁：https://github.com/etishor/Metrics.NET
- WIKI：https://github.com/etishor/Metrics.NET/wiki
- 可視化項目：https://github.com/etishor/Metrics.NET.FlotVisualization
- 可視化Demo：http://www.erata.net/Metrics.NET/demo/#/
- Java版地址：https://github.com/dropwizard/metrics
Metrics.NET - a .NET Port, with lots of additional functionality, of the awesome Java metrics library by Coda Hale.
2. Getting Started
Install-Package Metrics.NET
Install-Package Metrics.NET.ElasticSearch -Version 0.5.0 # with ES
Install-Package Metrics.NET.Graphite -Version 0.5.0 #with Graphite
other:
- Metrics.NET.RabbitMQ
- Metrics.NET.InfluxDB
- Metrics.NET.SignalFX
- Metrics.NET.InfluxDbReporting
- Metrics.NET.CloudWatch
Application_Start：
using Metrics;
Metric.Config
.WithHttpEndpoint("http://localhost:1234/")
.WithAllCounters();
访问：http://localhost:1234/ 即可看到效果。
3. 度量
- Meters record the rate at which an event occurs。某事件发生的比率
- Histograms measure the distribution of values in a stream of data。数据流的分布
- Timers keep a histogram of the duration of a type of event and a meter of the rate of its occurrence。Meters和Histograms的结合。
- Counters 64 bit integers that can be incremented or decremented。64位计数器
- Gauges instantaneous values。简单值
3.1 Gauges
最简单的度量方式。代表一个瞬时值。
代码片段：
// gauge from Func<double>
Metric.Gauge("MyValue", () => ComputeMagicValue(), Unit.Items);
// gauge that reads its value from a performance counter
Metric.PerformanceCounter("CPU Usage", "Processor", "% Processor Time",
"_Total", Unit.Custom("%"));
// gauge that transforms the value of another gauge
Metric.Advanced.Gauge("Kbytes gauge",
() => new DerivedGauge(gaugeReturningValueInBytes, v => v / 1024.0 ),
Unit.KiloBytes);
Metric.Context("[LogPool]").Gauge("dict.Count_Gauge",
() => { return dict.Count; }, Unit.Custom("個"), "log");
3.2 Counters
代表可以增減的64位整數。
代碼片段——緩存的數量
public class Cache
{
private static readonly Counter counter =
Metric.Counter("ItemsInCache", Unit.Items);
private void AddItems(object[] items)
{
counter.Increment(items.Length);
}
private void AddItem(object item)
{
counter.Increment();
}
private void RemoveItem(object item)
{
counter.Decrement();
}
}
Counter提供分组计数的能力，针对标记接口可以实现：
public class SetCounterSample
{
private readonly Counter commandCounter =
Metric.Counter("Command Counter", Unit.Custom("Commands"));
public interface Command { }
public class SendEmail : Command { }
public class ShipProduct : Command { }
public class BillCustomer : Command { }
public class MakeInvoice : Command { }
public class MarkAsPreffered : Command { }
public void Process(Command command)
{
this.commandCounter.Increment(command.GetType().Name);
// do actual command processing
}
}
输出：
Command Counter
Count = 2550 Commands
Total Items = 5
Item 0 = 20.90% 533 Commands [BillCustomer]
Item 1 = 19.22% 490 Commands [MakeInvoice]
Item 2 = 19.41% 495 Commands [MarkAsPreffered]
Item 3 = 20.98% 535 Commands [SendEmail]
Item 4 = 19.49% 497 Commands [ShipProduct]
应用场景：可以用在WEB API中统计各种请求的数量。
3.3 Meters
A meter measures the rate at which an event occurs.meter测量一个事件发生的比率。
示例代码：请求异常的比率
public class RequestProcessor
{
private readonly Meter meter =
Metric.Meter("Errors", Unit.Requests, TimeUnit.Seconds);
public void ProcessRequest()
{
try
{
// do actual processing
}
catch
{
meter.Mark(); // records an error
throw;
}
}
}
同样，也可以支持多态的分组：
public class SetMeterSample
{
private readonly Meter errorMeter = Metric.Meter("Errors", Unit.Errors);
public interface Command { }
public class SendEmail : Command { }
public class ShipProduct : Command { }
public class BillCustomer : Command { }
public class MakeInvoice : Command { }
public class MarkAsPreffered : Command { }
public void Process(Command command)
{
try
{
ActualCommandProcessing(command);
}
catch
{
errorMeter.Mark(command.GetType().Name);
}
}
}
输出：
Errors
Count = 450 Errors
Mean Value = 35.68 Errors/s
1 Minute Rate = 25.44 Errors/s
5 Minute Rate = 24.30 Errors/s
15 Minute Rate = 24.10 Errors/s
Total Items = 5
Item 0 = 19.56% 88 Errors [BillCustomer]
Count = 88 Errors
Mean Value = 6.98 Errors/s
1 Minute Rate = 6.05 Errors/s
5 Minute Rate = 6.01 Errors/s
15 Minute Rate = 6.00 Errors/s
Item 1 = 18.67% 84 Errors [MakeInvoice]
Count = 84 Errors
Mean Value = 6.66 Errors/s
1 Minute Rate = 4.23 Errors/s
5 Minute Rate = 3.89 Errors/s
15 Minute Rate = 3.83 Errors/s
Item 2 = 20.22% 91 Errors [MarkAsPreffered]
Count = 91 Errors
Mean Value = 7.22 Errors/s
1 Minute Rate = 5.38 Errors/s
5 Minute Rate = 5.24 Errors/s
15 Minute Rate = 5.21 Errors/s
Item 3 = 19.78% 89 Errors [SendEmail]
Count = 89 Errors
Mean Value = 7.06 Errors/s
1 Minute Rate = 4.92 Errors/s
5 Minute Rate = 4.67 Errors/s
15 Minute Rate = 4.62 Errors/s
Item 4 = 21.78% 98 Errors [ShipProduct]
Count = 98 Errors
Mean Value = 7.77 Errors/s
1 Minute Rate = 4.86 Errors/s
5 Minute Rate = 4.50 Errors/s
15 Minute Rate = 4.43 Errors/s
3.4 Histograms
代码片段：搜索结果的分布。
开箱即用的三种抽样方法：
- Exponentially Decaying Reservoir - 最近五分钟数据的分位数。
- Uniform Reservoir - 产生整个週期有效的分位数
- Sliding Window Reservoir - 产生代表过去N次测量的分位数
private readonly Histogram histogram = Metric.Histogram("Search Results", Unit.Items);
public void Search(string keyword)
{
var results = ActualSearch(keyword);
histogram.Update(results.Length);
}
// The histogram has the ability to track for which user value a Min, Max or Last Value has been recorded.
// The user value can be any string value (documentId, operationId, etc).
public class UserValueHistogramSample
{
private readonly Histogram histogram =
Metric.Histogram("Results", Unit.Items);
public void Process(string documentId)
{
var results = GetResultsForDocument(documentId);
this.histogram.Update(results.Length, documentId);
}
}
输出：
Results
Count = 90 Items
Last = 46.00 Items
Last User Value = document-3
Min = 2.00 Items
Min User Value = document-7
Max = 98.00 Items
Max User Value = document-4
Mean = 51.52 Items
StdDev = 30.55 Items
Median = 50.00 Items
75% <= 80.00 Items
95% <= 97.00 Items
98% <= 98.00 Items
99% <= 98.00 Items
99.9% <= 98.00 Items
3.5 Timers
示例代碼：
private readonly Timer timer =
Metric.Timer("HTTP Requests",Unit.Requests);
public void ProcessRequest()
{
using(timer.NewContext())
{
// Actual Processing of the request
}
}
private readonly Timer timer =
Metric.Timer("Requests", Unit.Requests);
public void Process(string documentId)
{
using (var context = timer.NewContext(documentId))
{
ActualProcessingOfTheRequest(documentId);
// if needed elapsed time is available in context.Elapsed
}
}
輸出：
Requests
Count = 14 Requests
Mean Value = 1.86 Requests/s
1 Minute Rate = 1.80 Requests/s
5 Minute Rate = 1.80 Requests/s
15 Minute Rate = 1.80 Requests/s
Count = 14 Requests
Last = 869.03 ms
Last User Value = document-1
Min = 59.90 ms
Min User Value = document-6
Max = 869.03 ms
Max User Value = document-1
Mean = 531.81 ms
StdDev = 212.98 ms
Median = 594.83 ms
75% <= 670.18 ms
95% <= 869.03 ms
98% <= 869.03 ms
99% <= 869.03 ms
99.9% <= 869.03 ms
