timeseries [3/6] RUM-13949: Add CPU timeseries data-point reader and serializer
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| /* | ||
| * Unless explicitly stated otherwise all files in this repository are licensed under the Apache License Version 2.0. | ||
| * This product includes software developed at Datadog (https://www.datadoghq.com/). | ||
| * Copyright 2016-Present Datadog, Inc. | ||
| */ | ||
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| package com.datadog.android.rum.internal.timeseries.provider | ||
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| import com.datadog.android.api.InternalLogger | ||
| import com.datadog.android.core.internal.persistence.file.canReadSafe | ||
| import com.datadog.android.core.internal.persistence.file.existsSafe | ||
| import com.datadog.android.core.internal.persistence.file.readTextSafe | ||
| import com.datadog.android.internal.time.TimeProvider | ||
| import java.io.File | ||
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| /** | ||
| * Reads CPU usage as a percentage by computing successive deltas of the utime field | ||
| * from `/proc/self/stat`. | ||
| * | ||
| * CLK_TCK = 100 Hz on Android bionic: 100 ticks/s ≡ 100% single-core CPU. | ||
| * Multi-core spikes are clamped to 100.0. The first call always returns null. | ||
| */ | ||
| internal class CpuDatapointReader( | ||
| internal val statFile: File = STAT_FILE, | ||
| private val cpuTimeProvider: TimeProvider, | ||
| override val intervalMs: Long, | ||
| private val internalLogger: InternalLogger | ||
| ) : DataPointsReader<Double>(cpuTimeProvider) { | ||
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| private var lastUtime: Double? = null | ||
| private var lastTimestampMs: Long? = null | ||
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| @Suppress("ReturnCount") | ||
| override fun readValue(): Double? { | ||
| val nowMs = cpuTimeProvider.getDeviceTimestampMillis() | ||
| val cpuTicks = readCpuTicks() ?: return null | ||
| val prevTicks = lastUtime | ||
| val prevMs = lastTimestampMs | ||
| lastUtime = cpuTicks | ||
| lastTimestampMs = nowMs | ||
| if (prevTicks == null || prevMs == null) return null | ||
| val elapsedMs = (nowMs - prevMs).coerceAtLeast(1L) | ||
| // CLK_TCK = 100 Hz on Android bionic → 100 ticks/s = 100% CPU on one core | ||
| return ((cpuTicks - prevTicks) * MS_PER_SECOND / elapsedMs).coerceIn(0.0, MAX_CPU_PERCENT) | ||
| } | ||
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| @Suppress("ReturnCount") | ||
| private fun readCpuTicks(): Double? { | ||
| if (!statFile.existsSafe(internalLogger) || !statFile.canReadSafe(internalLogger)) return null | ||
| val stat = statFile.readTextSafe(internalLogger = internalLogger) ?: return null | ||
| val tokens = stat.split(' ') | ||
| return when { | ||
| tokens.size <= UTIME_IDX -> null | ||
| tokens.size <= STIME_IDX -> tokens[UTIME_IDX].toDoubleOrNull() | ||
| else -> { | ||
| val utime = tokens[UTIME_IDX].toDoubleOrNull() ?: return null | ||
| val stime = tokens[STIME_IDX].toDoubleOrNull() ?: return null | ||
| utime + stime | ||
| } | ||
| } | ||
| } | ||
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| companion object { | ||
| private const val STAT_PATH = "/proc/self/stat" | ||
| internal val STAT_FILE = File(STAT_PATH) | ||
| private const val UTIME_IDX = 13 | ||
| private const val STIME_IDX = 14 | ||
| private const val MS_PER_SECOND = 1000.0 | ||
| private const val MAX_CPU_PERCENT = 100.0 | ||
| } | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| /* | ||
| * Unless explicitly stated otherwise all files in this repository are licensed under the Apache License Version 2.0. | ||
| * This product includes software developed at Datadog (https://www.datadoghq.com/). | ||
| * Copyright 2016-Present Datadog, Inc. | ||
| */ | ||
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| package com.datadog.android.rum.internal.timeseries.serializer | ||
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| import com.datadog.android.internal.time.TimeProvider | ||
| import com.datadog.android.rum.RumSessionType | ||
| import com.datadog.android.rum.internal.timeseries.DataPoint | ||
| import com.datadog.android.rum.internal.timeseries.DeltaCompression | ||
| import com.datadog.android.rum.internal.timeseries.DeltaCompression.mapToDeltaCompressed | ||
| import com.datadog.android.rum.internal.timeseries.DeltaCompression.roundToLongSafely | ||
| import com.datadog.android.rum.internal.toTimeseriesCpuSessionType | ||
| import com.datadog.android.rum.model.TimeseriesCpuEvent | ||
| import com.google.gson.JsonObject | ||
| import java.util.UUID | ||
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| internal class CpuEventSerializer( | ||
| private val sessionId: String, | ||
| private val applicationId: String, | ||
| private val sessionType: RumSessionType, | ||
| private val timeProvider: TimeProvider, | ||
| private val useDeltaCompression: Boolean = false | ||
| ) : JsonSerializer<Double> { | ||
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| override fun serialize(dataPoints: List<DataPoint<Double>>): JsonObject? { | ||
| if (dataPoints.isEmpty()) return null | ||
| val data = dataPoints.map { sample -> | ||
| TimeseriesCpuEvent.Data( | ||
| timestamp = sample.timestampNs, | ||
| dataPoint = TimeseriesCpuEvent.DataPoint(sample.value) | ||
| ) | ||
| } | ||
| val start = data.firstOrNull()?.timestamp ?: 0L | ||
| val end = data.lastOrNull()?.timestamp ?: 0L | ||
| val deltaEncoded = if (useDeltaCompression) encodeDelta(data) else null | ||
| val schema = if (deltaEncoded != null) { | ||
| TimeseriesCpuEvent.Schema.DELTA_SCALAR | ||
| } else { | ||
| TimeseriesCpuEvent.Schema.OBJECT | ||
| } | ||
| val json = TimeseriesCpuEvent( | ||
| dd = TimeseriesCpuEvent.Dd(), | ||
| application = TimeseriesCpuEvent.Application(id = applicationId), | ||
| session = TimeseriesCpuEvent.Session( | ||
| id = sessionId, | ||
| type = sessionType.toTimeseriesCpuSessionType() | ||
| ), | ||
| source = TimeseriesCpuEvent.Source.ANDROID, | ||
| date = timeProvider.getDeviceTimestampMillis(), | ||
|
There was a problem hiding this comment.
When a CPU batch is flushed after buffering samples or at session stop, this sets the top-level Useful? React with 👍 / 👎. |
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| service = null, | ||
| version = null, | ||
| timeseries = TimeseriesCpuEvent.Timeseries( | ||
| id = UUID.randomUUID().toString(), | ||
| schema = schema, | ||
| start = start, | ||
| end = end, | ||
| data = data | ||
| ) | ||
| ).toJson() as JsonObject | ||
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| if (deltaEncoded != null) { | ||
| val timeseriesJson = json.getAsJsonObject("timeseries") | ||
| timeseriesJson.remove("data") | ||
| timeseriesJson.add("data", deltaEncoded) | ||
| } | ||
| return json | ||
| } | ||
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| private fun encodeDelta(data: List<TimeseriesCpuEvent.Data>): JsonObject? { | ||
| if (data.size <= 1) return null | ||
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| val ts = data.mapToDeltaCompressed { it.timestamp } | ||
| val cpuUsageArray = data.mapToDeltaCompressed { | ||
| roundToLongSafely(it.dataPoint.cpuUsage.toDouble(), replaceNaNWith = 0L) | ||
| } | ||
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| return JsonObject().apply { | ||
| addProperty("precision", DeltaCompression.PRECISION) | ||
| addProperty("resolution", RESOLUTION_NS) | ||
| add("ts", ts) | ||
| add("value", cpuUsageArray) | ||
| } | ||
| } | ||
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| companion object { | ||
| private const val RESOLUTION_NS = "ns" | ||
| } | ||
| } | ||
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When the device wall clock is adjusted while a RUM session is sampling, this timestamp can jump backward or forward because
getDeviceTimestampMillis()is backed bySystem.currentTimeMillis(). That makeselapsedMsnegative/huge, so a backward correction is coerced to 1 ms and reports a clamped 100% CPU spike, while a forward correction suppresses CPU usage. The CPU delta interval should be measured with the monotonic elapsed time provider and only use wall-clock time for the emitted sample timestamp.Useful? React with 👍 / 👎.