=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
/**
|
2016-02-23 12:58:39 +01:00
|
|
|
* Copyright (C) 2009-2016 Lightbend Inc. <http://www.lightbend.com>
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
*/
|
2015-11-21 00:01:51 +08:00
|
|
|
package akka.util
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
|
2014-08-29 17:01:56 +02:00
|
|
|
import org.scalatest.FlatSpec
|
|
|
|
|
import org.scalatest.Matchers
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
|
|
|
|
|
class PrettyDurationSpec extends FlatSpec with Matchers {
|
|
|
|
|
|
|
|
|
|
behavior of "PrettyDuration"
|
|
|
|
|
|
2015-11-21 00:01:51 +08:00
|
|
|
import akka.util.PrettyDuration._
|
2014-08-29 17:01:56 +02:00
|
|
|
|
|
|
|
|
import scala.concurrent.duration._
|
|
|
|
|
|
|
|
|
|
val cases: Seq[(Duration, String)] =
|
2016-06-02 14:06:57 +02:00
|
|
|
9.nanos → "9.000 ns" ::
|
|
|
|
|
95.nanos → "95.00 ns" ::
|
|
|
|
|
999.nanos → "999.0 ns" ::
|
|
|
|
|
1000.nanos → "1.000 μs" ::
|
|
|
|
|
9500.nanos → "9.500 μs" ::
|
|
|
|
|
9500.micros → "9.500 ms" ::
|
|
|
|
|
9500.millis → "9.500 s" ::
|
|
|
|
|
95.seconds → "1.583 min" ::
|
|
|
|
|
95.minutes → "1.583 h" ::
|
|
|
|
|
95.hours → "3.958 d" ::
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
Nil
|
|
|
|
|
|
|
|
|
|
cases foreach {
|
2014-08-29 17:01:56 +02:00
|
|
|
case (d, expectedValue) ⇒
|
2014-09-05 11:34:04 +02:00
|
|
|
it should s"print $d nanos as $expectedValue" in {
|
2015-01-16 11:09:59 +01:00
|
|
|
d.pretty should ===(expectedValue)
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
}
|
|
|
|
|
}
|
2014-05-09 16:15:45 +02:00
|
|
|
|
|
|
|
|
it should "work with infinity" in {
|
|
|
|
|
Duration.Inf.pretty should include("infinity")
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
it should "work with -infinity" in {
|
|
|
|
|
Duration.MinusInf.pretty should include("minus infinity")
|
|
|
|
|
}
|
2014-09-05 11:34:04 +02:00
|
|
|
|
|
|
|
|
it should "work with undefined" in {
|
|
|
|
|
Duration.Undefined.pretty should include("undefined")
|
|
|
|
|
}
|
=act,tes Initial draft of internal MetricsKit
Note: This is NOT aimed to provide an micro-benchmarking solution.
The goal is to provide data for broad trend analysis. For techniques
that fight the inliner and other specialised techniques, refer to JMH.
+ custom console and graphite reporters
- had to be custom because it's not possible to add custom metric
types to the existing reporters
+ initial hdr.Histogram histogram() provider, see
http://latencyutils.github.io/LatencyUtils/
+ Not using timers provided by Metrics, instead use the above histogram
+ Added average Actor size measurement
+ Measuring the "blocking time" when an actor is created, before we fire
of the async part of this process; Measures in loop and will fluctuate
a lot. Times are in `us` -- System.nanoTime should provide good enough
resolution.
+ Measuring total actor creation time by using
`KnownOpsInTimespanTimer`, which given a known number of ops, in a
large amount of time, roughtly estimates time per one operation.
// Yes, we are aware of the possibility of GC pauses and other horrors
+ All classes are `private[akka]`, we should not encourage people to use
this yet
+ Counters use Java 8's `LongAdder`, which is metric's private;
The new trend in Java land will be copy paste-ing this class ;)
+ Metrics are logged to Graphite, so we can long-term analyse these
+ Reporters are configurable using typesafe-config
! I'm not very happy about how I work around Metrics not being too open
for adding additional custom metrics. Seems like a hack at places.
I will consider removing the Metrics dependency all together.
numbers
Example output:
```
-- KnownOpsInTimespanTimer-------------------------------------------
actor-creation.total.creating-100000-actors.Props|new-EmptyArgsActor|…||-same
ops = 100000
time = 1.969 s
ops/s = 50782.22
avg = 19.69 μs
-- AveragingGauge---------------------------------------------------
actor-creation.Props|new-EmptyArgsActor|…||-same.avg-mem-per-actor
avg = 439.67
```
2014-04-29 10:50:36 +02:00
|
|
|
}
|