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lwc-events: improve data point conversion
Updates to support common statistics for timer and dist summary. Aggregates the values locally to reduce number of messages.
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atlas-lwc-events/src/main/scala/com/netflix/atlas/lwc/events/DatapointConverter.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,243 @@ | ||
| /* | ||
| * Copyright 2014-2024 Netflix, Inc. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package com.netflix.atlas.lwc.events | ||
|
|
||
| import com.netflix.atlas.core.model.DataExpr | ||
| import com.netflix.atlas.core.model.Query | ||
| import com.netflix.spectator.api.Clock | ||
| import com.netflix.spectator.impl.AtomicDouble | ||
| import com.netflix.spectator.impl.StepDouble | ||
|
|
||
| import java.util.concurrent.ConcurrentHashMap | ||
|
|
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| /** | ||
| * Helper to convert a sequence of events into a data point. | ||
| */ | ||
| private[events] trait DatapointConverter { | ||
|
|
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| def update(event: LwcEvent): Unit | ||
|
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| def flush(timestamp: Long): Unit | ||
| } | ||
|
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| private[events] object DatapointConverter { | ||
|
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| def apply( | ||
| id: String, | ||
| expr: DataExpr, | ||
| clock: Clock, | ||
| step: Long, | ||
| consumer: (String, LwcEvent) => Unit | ||
| ): DatapointConverter = { | ||
| val tags = Query.tags(expr.query) | ||
| val mapper = createValueMapper(tags, expr.finalGrouping) | ||
| val params = Params(id, Query.tags(expr.query), clock, step, mapper, consumer) | ||
| toConverter(expr, params) | ||
| } | ||
|
|
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| private def toConverter(expr: DataExpr, params: Params): DatapointConverter = { | ||
| expr match { | ||
| case _: DataExpr.Sum => Sum(params) | ||
| case _: DataExpr.Count => Count(params) | ||
| case _: DataExpr.Max => Max(params) | ||
| case _: DataExpr.Min => Min(params) | ||
| case by: DataExpr.GroupBy => GroupBy(by, params) | ||
| case _ => Sum(params) | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Extract value and map as needed based on the type. Uses statistic and grouping to | ||
| * coerce events so they structurally work like spectator composite types. | ||
| */ | ||
| def createValueMapper(tags: Map[String, String], grouping: List[String]): LwcEvent => Double = { | ||
| tags.get("value") match { | ||
| case Some(k) => | ||
| tags.get("statistic") match { | ||
| case Some("count") => _ => 1.0 | ||
| case Some("totalOfSquares") => event => squared(event.extractValue(k)) | ||
| case _ => event => toDouble(event.extractValue(k)) | ||
| } | ||
| case None => | ||
| _ => 1.0 | ||
| } | ||
| } | ||
|
|
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| private def squared(value: Any): Double = { | ||
| val v = toDouble(value) | ||
| v * v | ||
| } | ||
|
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| def toDouble(value: Any): Double = { | ||
| value match { | ||
| case v: Boolean => if (v) 1.0 else 0.0 | ||
| case v: Byte => v.toDouble | ||
| case v: Short => v.toDouble | ||
| case v: Int => v.toDouble | ||
| case v: Long => v.toDouble | ||
| case v: Float => v.toDouble | ||
| case v: Double => v | ||
| case v: Number => v.doubleValue() | ||
| case v: String => parseDouble(v) | ||
| case _ => 1.0 | ||
| } | ||
| } | ||
|
|
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| private def parseDouble(v: String): Double = { | ||
| try { | ||
| java.lang.Double.parseDouble(v) | ||
| } catch { | ||
| case _: Exception => Double.NaN | ||
| } | ||
| } | ||
|
|
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| case class Params( | ||
| id: String, | ||
| tags: Map[String, String], | ||
| clock: Clock, | ||
| step: Long, | ||
| valueMapper: LwcEvent => Double, | ||
| consumer: (String, LwcEvent) => Unit | ||
| ) { | ||
|
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| val buffer = new StepDouble(0.0, clock, step) | ||
| } | ||
|
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| /** Compute sum for a counter as a rate per second. */ | ||
| case class Sum(params: Params) extends DatapointConverter { | ||
|
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| override def update(event: LwcEvent): Unit = { | ||
| val value = params.valueMapper(event) | ||
| if (value.isFinite && value >= 0.0) { | ||
| params.buffer.getCurrent.addAndGet(value) | ||
| } | ||
| } | ||
|
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| override def flush(timestamp: Long): Unit = { | ||
| val value = params.buffer.pollAsRate(timestamp) | ||
| if (value.isFinite) { | ||
| val ts = timestamp / params.step * params.step | ||
| val event = DatapointEvent(params.id, params.tags, ts, value) | ||
| params.consumer(params.id, event) | ||
| } | ||
| } | ||
| } | ||
|
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| /** Compute count of contributing events. */ | ||
| case class Count(params: Params) extends DatapointConverter { | ||
|
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| override def update(event: LwcEvent): Unit = { | ||
| params.buffer.getCurrent.addAndGet(1.0) | ||
| } | ||
|
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| override def flush(timestamp: Long): Unit = { | ||
| val value = params.buffer.poll(timestamp) | ||
| if (value.isFinite) { | ||
| val ts = timestamp / params.step * params.step | ||
| val event = DatapointEvent(params.id, params.tags, ts, value) | ||
| params.consumer(params.id, event) | ||
| } | ||
| } | ||
| } | ||
|
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| /** Compute max value from contributing events. */ | ||
| case class Max(params: Params) extends DatapointConverter { | ||
|
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| override def update(event: LwcEvent): Unit = { | ||
| val value = params.valueMapper(event) | ||
| if (value.isFinite && value >= 0.0) { | ||
| params.buffer.getCurrent.max(value) | ||
| } | ||
| } | ||
|
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| override def flush(timestamp: Long): Unit = { | ||
| val value = params.buffer.poll(timestamp) | ||
| if (value.isFinite) { | ||
| val ts = timestamp / params.step * params.step | ||
| val event = DatapointEvent(params.id, params.tags, ts, value) | ||
| params.consumer(params.id, event) | ||
| } | ||
| } | ||
| } | ||
|
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| /** Compute min value from contributing events. */ | ||
| case class Min(params: Params) extends DatapointConverter { | ||
|
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| override def update(event: LwcEvent): Unit = { | ||
| val value = params.valueMapper(event) | ||
| if (value.isFinite && value >= 0.0) { | ||
| min(params.buffer.getCurrent, value) | ||
| } | ||
| } | ||
|
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| private def min(current: AtomicDouble, value: Double): Unit = { | ||
| if (value.isFinite) { | ||
| var min = current.get() | ||
| while (isLessThan(value, min) && !current.compareAndSet(min, value)) { | ||
| min = current.get() | ||
| } | ||
| } | ||
| } | ||
|
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| private def isLessThan(v1: Double, v2: Double): Boolean = { | ||
| v1 < v2 || v2.isNaN | ||
| } | ||
|
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| override def flush(timestamp: Long): Unit = { | ||
| val value = params.buffer.poll(timestamp) | ||
| if (value.isFinite) { | ||
| val ts = timestamp / params.step * params.step | ||
| val event = DatapointEvent(params.id, params.tags, ts, value) | ||
| params.consumer(params.id, event) | ||
| } | ||
| } | ||
| } | ||
|
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| /** Compute set of data points, one for each distinct group. */ | ||
| case class GroupBy(by: DataExpr.GroupBy, params: Params) extends DatapointConverter { | ||
|
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| private val groups = new ConcurrentHashMap[Map[String, String], DatapointConverter]() | ||
|
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| override def update(event: LwcEvent): Unit = { | ||
| // Ignore events that are missing dimensions used in the grouping | ||
| val values = by.keys.map(event.tagValue).filterNot(_ == null) | ||
| if (by.keys.size == values.size) { | ||
| val value = params.valueMapper(event) | ||
| if (value.isFinite) { | ||
| val tags = by.keys.zip(values).toMap | ||
| val converter = groups.computeIfAbsent(tags, groupConverter) | ||
| converter.update(event) | ||
| } | ||
| } | ||
| } | ||
|
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| private def groupConverter(tags: Map[String, String]): DatapointConverter = { | ||
| val ps = params.copy(consumer = (id, event) => groupConsumer(tags, id, event)) | ||
| toConverter(by.af, ps) | ||
| } | ||
|
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| private def groupConsumer(tags: Map[String, String], id: String, event: LwcEvent): Unit = { | ||
| event match { | ||
| case dp: DatapointEvent => params.consumer(id, dp.copy(tags = dp.tags ++ tags)) | ||
| case ev => params.consumer(id, ev) | ||
| } | ||
| } | ||
|
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| override def flush(timestamp: Long): Unit = { | ||
| groups.values().forEach(_.flush(timestamp)) | ||
| } | ||
| } | ||
| } |
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