Java tutorial
/* * Copyright 2014 LinkedIn Corp. All rights reserved * * 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.linkedin.databus.client.pub; import static org.testng.AssertJUnit.assertEquals; import static org.testng.AssertJUnit.assertFalse; import static org.testng.AssertJUnit.assertTrue; import static org.testng.AssertJUnit.fail; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.nio.charset.Charset; import org.apache.avro.Schema; //import org.apache.commons.math3.stat.StatUtils; import org.testng.annotations.Test; import com.codahale.metrics.MergeableExponentiallyDecayingReservoir; import com.linkedin.databus.client.pub.mbean.UnifiedClientStats; import com.linkedin.databus.core.DbusConstants; import com.linkedin.databus.core.DbusEvent; import com.linkedin.databus.core.DbusEventFactory; import com.linkedin.databus.core.DbusEventInfo; import com.linkedin.databus.core.DbusEventKey; import com.linkedin.databus.core.DbusEventV2Factory; import com.linkedin.databus.core.DbusOpcode; import com.linkedin.databus.core.KeyTypeNotImplementedException; import com.linkedin.databus2.schemas.utils.SchemaHelper; /** * Mainly tests the histogram/percentile metrics of UnifiedClientStats, including multi-connection * aggregation (merging). * * See also TestGenericDispatcher (unit test; tests numConsumerErrors) and TestRelayBootstrapSwitch * (integration test; tests all remaining UnifiedClientStats metrics: curBootstrappingPartitions, * curDeadConnections, numDataEvents, timeLagLastReceivedToNowMs). */ public class TestUnifiedClientStats { private static final Schema SOURCE1_SCHEMA = Schema .parse("{\"name\":\"source1\",\"type\":\"record\",\"fields\":[{\"name\":\"s\",\"type\":\"string\"}]}"); private static final String SOURCE1_SCHEMA_STR = SOURCE1_SCHEMA.toString(); private static final byte[] SOURCE1_SCHEMAID = SchemaHelper.getSchemaId(SOURCE1_SCHEMA_STR); private static final byte[] SOURCE1_PAYLOAD = new byte[] { 0x67, 0x72, 0x6f, 0x6e, 0x6b }; // alternatively: byte[] SOURCE1_PAYLOAD = javax.xml.bind.DatatypeConverter.parseHexBinary("67726f6e6b"); private DbusEventFactory _eventFactory = new DbusEventV2Factory(); private DbusEvent createEvent(long timestampNs) { DbusEventInfo eventInfo = new DbusEventInfo(DbusOpcode.UPSERT, 6004L, // SCN (short) 1, // physical partition ID (short) 1, // logical partition ID timestampNs, (short) 1, // srcId SOURCE1_SCHEMAID, // payloadSchemaMd5 SOURCE1_PAYLOAD, // payload false, // enableTracing true); // autocommit DbusEventKey key = new DbusEventKey("myKey".getBytes(Charset.forName("UTF-8"))); ByteBuffer buf = ByteBuffer.allocate(1000).order(ByteOrder.BIG_ENDIAN); try { DbusEventFactory.serializeEvent(key, buf, eventInfo); } catch (KeyTypeNotImplementedException ex) { fail("string key type not supported by DbusEventV2Factory?!? " + ex.getLocalizedMessage()); } return _eventFactory.createReadOnlyDbusEventFromBuffer(buf, 0); } /** * Tests the basic (non-aggregated) functionality of the histogram/percentile metrics * (timeLagSourceToReceiptMs and timeLagConsumerCallbacksMs). */ @Test public void testBasicHistogramMetrics() { // (1) create stats object UnifiedClientStats unifiedClientStats = new UnifiedClientStats(3 /* ownerId */, "stats_name", "stats_dim"); for (int i = 0; i < 200; ++i) { // Without the ability to override System.currentTimeMillis() (or hacking UnifiedClientStats to use an // overridable method to provide the time, and then overriding it here), there's a small chance that // our System.currentTimeMillis() call and that in registerDataEventReceived() will return values that // differ by a non-constant amount (i.e., jitter). But we can manage that with inequalities in our // assertions. // Expected histogram values for timeLagSourceToReceiptMs range from 0 to 1990 ms (approximately). long sourceTimestampNs = (System.currentTimeMillis() - 10 * i) * DbusConstants.NUM_NSECS_IN_MSEC; // We have perfect control over the values for timeLagConsumerCallbacksMs. Make calculations trivial: // histogram values will be 0 through 199 ms (exactly). long callbackTimeElapsedNs = (long) i * DbusConstants.NUM_NSECS_IN_MSEC; // (2) create 200 fake DbusEvents DbusEvent dbusEvent = createEvent(sourceTimestampNs); // (3) call registerDataEventReceived() and registerCallbacksProcessed() for each event // (normally there are more of the latter since there are more callback types than just onDataEvent(), // but it doesn't really matter, and it simplifies things if we keep a fixed ratio--here just 1:1) unifiedClientStats.registerDataEventReceived(dbusEvent); unifiedClientStats.registerCallbacksProcessed(callbackTimeElapsedNs); } // (4) verify histogram values are as expected // Both metrics-core and Apache Commons Math use the "R-6" quantile-estimation method, as described // at http://en.wikipedia.org/wiki/Quantile . // // N = 200 // p = 0.5, 0.9, 0.95, 0.99 // h = (N+1)*p = 100.5, 180.9, 190.95, 198.99 // // Q[50th] = x[100-1] + (100.5 - 100)*(x[100-1+1] - x[100-1]) = 99.0 + 0.5 *(100.0 - 99.0) = 99.5 // Q[90th] = x[180-1] + (180.9 - 180)*(x[180-1+1] - x[180-1]) = 179.0 + 0.9 *(180.0 - 179.0) = 179.9 // Q[95th] = x[190-1] + (190.95 - 190)*(x[190-1+1] - x[190-1]) = 189.0 + 0.95*(190.0 - 189.0) = 189.95 // Q[99th] = x[198-1] + (198.99 - 198)*(x[198-1+1] - x[198-1]) = 197.0 + 0.99*(198.0 - 197.0) = 197.99 assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile", 99.5, unifiedClientStats.getTimeLagConsumerCallbacksMs_HistPct_50()); assertEquals("unexpected timeLagConsumerCallbacksMs 90th percentile", 179.9, unifiedClientStats.getTimeLagConsumerCallbacksMs_HistPct_90()); assertEquals("unexpected timeLagConsumerCallbacksMs 95th percentile", 189.95, unifiedClientStats.getTimeLagConsumerCallbacksMs_HistPct_95()); assertEquals("unexpected timeLagConsumerCallbacksMs 99th percentile", 197.99, unifiedClientStats.getTimeLagConsumerCallbacksMs_HistPct_99()); assertEquals("unexpected timeLagConsumerCallbacksMs max value", 199.0, unifiedClientStats.getTimeLagConsumerCallbacksMs_Max()); // See sourceTimestampNs comment above. Approximately: // Q[50th] = x[100-1] + (100.5 - 100)*(x[100-1+1] - x[100-1]) = 990.0 + 0.5 *(1000.0 - 990.0) = 995.0 // Q[90th] = x[180-1] + (180.9 - 180)*(x[180-1+1] - x[180-1]) = 1790.0 + 0.9 *(1800.0 - 1790.0) = 1799.0 // Q[95th] = x[190-1] + (190.95 - 190)*(x[190-1+1] - x[190-1]) = 1890.0 + 0.95*(1900.0 - 1890.0) = 1899.5 // Q[99th] = x[198-1] + (198.99 - 198)*(x[198-1+1] - x[198-1]) = 1970.0 + 0.99*(1980.0 - 1970.0) = 1979.9 // ...but allow +/-1 for jitter double percentile = unifiedClientStats.getTimeLagSourceToReceiptMs_HistPct_50(); assertTrue("unexpected timeLagSourceToReceiptMs 50th percentile: " + percentile, 994.0 <= percentile && percentile <= 996.0); // nominal value is 995.0 percentile = unifiedClientStats.getTimeLagSourceToReceiptMs_HistPct_90(); assertTrue("unexpected timeLagSourceToReceiptMs 90th percentile: " + percentile, 1798.0 <= percentile && percentile <= 1800.0); // nominal value is 1799.0 percentile = unifiedClientStats.getTimeLagSourceToReceiptMs_HistPct_95(); assertTrue("unexpected timeLagSourceToReceiptMs 95th percentile: " + percentile, 1898.5 <= percentile && percentile <= 1900.5); // nominal value is 1899.5, but saw 1900.45 once percentile = unifiedClientStats.getTimeLagSourceToReceiptMs_HistPct_99(); assertTrue("unexpected timeLagSourceToReceiptMs 99th percentile: " + percentile, 1978.9 <= percentile && percentile <= 1980.9); // nominal value is 1979.9 } /** * Tests aggregation (merging) of the timeLagSourceToReceiptMs histogram/percentile metric in the case * that one of the connections is bootstrapping. * * Blast out 1000 data values for stats #1 and #2, but with the latter in bootstrap mode: * timestampLastDataEventWasReceivedMs will be zero for stats #2 (and its reservoir empty), so * merging it won't affect the aggregate value for timeLagSourceToReceiptMs; all such aggregate * stats should be identical to those for stats #1. Also, all values for stats #2 should be -1.0, * per our design spec. (This is similar to testHistogramMetricsAggregationDeadSourcesConnection().) */ @Test public void testHistogramMetricsAggregationBootstrapMode() { // create stats objects: two low-level (per-connection) ones and one aggregator UnifiedClientStats unifiedClientStats1 = new UnifiedClientStats(1 /* ownerId */, "test1", "dim1"); UnifiedClientStats unifiedClientStats2 = new UnifiedClientStats(2 /* ownerId */, "test2", "dim2"); UnifiedClientStats unifiedClientStatsAgg = new UnifiedClientStats(99 /* ownerId */, "testAgg", "dimAgg"); unifiedClientStats2.setBootstrappingState(true); for (int i = 0; i < MergeableExponentiallyDecayingReservoir.DEFAULT_SIZE; ++i) // 1028 { long now = System.currentTimeMillis(); long sourceTimestampNs1 = (now - 1000L - i) * DbusConstants.NUM_NSECS_IN_MSEC; long sourceTimestampNs2 = (now - 5000L - i) * DbusConstants.NUM_NSECS_IN_MSEC; unifiedClientStats1.registerDataEventReceived(createEvent(sourceTimestampNs1)); unifiedClientStats2.registerDataEventReceived(createEvent(sourceTimestampNs2)); } unifiedClientStatsAgg.merge(unifiedClientStats1); unifiedClientStatsAgg.merge(unifiedClientStats2); assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_50(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_90(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_95(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_99(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_99()); // bootstrap mode => should return -1.0 for all percentiles assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_99()); } /** * Tests aggregation (merging) of the timeLagSourceToReceiptMs histogram/percentile metric in the case * that one of the connections is dead (i.e., no data events received). * * Blast out 1000 data values for stats #1 but none for stats #2 (in particular, no registerDataEventReceived() * calls): timestampLastDataEventWasReceivedMs will be zero for stats #2 (and its reservoir empty), so * merging it won't affect the aggregate value for timeLagSourceToReceiptMs; all such aggregate stats should * be identical to those for stats #1. Also, all values for stats #2 should be -1.0, per our design spec. * (This is similar to testHistogramMetricsAggregationBootstrapMode().) */ @Test public void testHistogramMetricsAggregationDeadSourcesConnection() { // create stats objects: two low-level (per-connection) ones and one aggregator UnifiedClientStats unifiedClientStats1 = new UnifiedClientStats(1 /* ownerId */, "test1", "dim1"); UnifiedClientStats unifiedClientStats2 = new UnifiedClientStats(2 /* ownerId */, "test2", "dim2"); UnifiedClientStats unifiedClientStatsAgg = new UnifiedClientStats(99 /* ownerId */, "testAgg", "dimAgg"); // could break this into two (or more) parts and do multiple merges, but not clear there's any point... for (int i = 0; i < 2 * MergeableExponentiallyDecayingReservoir.DEFAULT_SIZE; ++i) // 2*1028 { long sourceTimestampNs1 = (System.currentTimeMillis() - 1000L - i) * DbusConstants.NUM_NSECS_IN_MSEC; // no data events for connection #2 => no need for sourceTimestampNs2 unifiedClientStats1.registerDataEventReceived(createEvent(sourceTimestampNs1)); } unifiedClientStatsAgg.merge(unifiedClientStats1); unifiedClientStatsAgg.merge(unifiedClientStats2); assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_50(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_90(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_95(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for aggregated stats", unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_99(), unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_99()); // no data values => should return -1.0 for all percentiles assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_99()); } /** * Tests aggregation (merging) of the histogram/percentile metrics (timeLagSourceToReceiptMs and * timeLagConsumerCallbacksMs) in the case that there have been no data events or callbacks, i.e., * there are no data points in the histogram reservoirs. All timeLagSourceToReceiptMs metrics * and timeLagConsumerCallbacksMs metrics should be -1.0, per the design spec. */ @Test public void testHistogramMetricsAggregationNoData() { UnifiedClientStats unifiedClientStats1 = new UnifiedClientStats(1 /* ownerId */, "test1", "dim1"); UnifiedClientStats unifiedClientStats2 = new UnifiedClientStats(2 /* ownerId */, "test2", "dim2"); UnifiedClientStats unifiedClientStatsAgg = new UnifiedClientStats(99 /* ownerId */, "testAgg", "dimAgg"); assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_99()); assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_99()); assertEquals("unexpected timeLagSourceToReceiptMs 50th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_50()); assertEquals("unexpected timeLagSourceToReceiptMs 90th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_90()); assertEquals("unexpected timeLagSourceToReceiptMs 95th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_95()); assertEquals("unexpected timeLagSourceToReceiptMs 99th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_99()); assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_HistPct_50()); assertEquals("unexpected timeLagConsumerCallbacksMs 90th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_HistPct_90()); assertEquals("unexpected timeLagConsumerCallbacksMs 95th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_HistPct_95()); assertEquals("unexpected timeLagConsumerCallbacksMs 99th percentile for connection #1", -1.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_HistPct_99()); assertEquals("unexpected timeLagConsumerCallbacksMs max for connection #1", -1.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_Max()); assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_HistPct_50()); assertEquals("unexpected timeLagConsumerCallbacksMs 90th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_HistPct_90()); assertEquals("unexpected timeLagConsumerCallbacksMs 95th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_HistPct_95()); assertEquals("unexpected timeLagConsumerCallbacksMs 99th percentile for connection #2", -1.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_HistPct_99()); assertEquals("unexpected timeLagConsumerCallbacksMs max for connection #2", -1.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_Max()); assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_HistPct_50()); assertEquals("unexpected timeLagConsumerCallbacksMs 90th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_HistPct_90()); assertEquals("unexpected timeLagConsumerCallbacksMs 95th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_HistPct_95()); assertEquals("unexpected timeLagConsumerCallbacksMs 99th percentile for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_HistPct_99()); assertEquals("unexpected timeLagConsumerCallbacksMs max for aggregated stats", -1.0, unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_Max()); } /** * Tests aggregation (merging) of the histogram/percentile metrics (timeLagSourceToReceiptMs and * timeLagConsumerCallbacksMs). This is basically the "happy path" case. * * Blast out 1000 low data values for stats #1 and 1000 high data values for stats #2 (interleaved so * timestamps [and therefore priorities] are comparable), then merge and verify that max is within #2's * range and that median falls between the two ranges. (There's no guarantee that #1's minimum or #2's * maximum will survive, but roughly half of each one's values should, so the min and max are guaranteed * to fall within #1's and #2's range, respectively.) */ @Test public void testHistogramMetricsAggregationNonOverlappingRanges() { // create stats objects: two low-level (per-connection) ones and one aggregator UnifiedClientStats unifiedClientStats1 = new UnifiedClientStats(1 /* ownerId */, "test1", "dim1"); UnifiedClientStats unifiedClientStats2 = new UnifiedClientStats(2 /* ownerId */, "test2", "dim2"); UnifiedClientStats unifiedClientStatsAgg = new UnifiedClientStats(99 /* ownerId */, "testAgg", "dimAgg"); for (int i = 0; i < MergeableExponentiallyDecayingReservoir.DEFAULT_SIZE; ++i) // 1028 { // As noted in testBasicHistogramMetrics(), our dependence on System.currentTimeMillis() may lead // to some jitter in the data values for timeLagSourceToReceiptMs. long now = System.currentTimeMillis(); long sourceTimestampNs1 = (now - 1000L - i) * DbusConstants.NUM_NSECS_IN_MSEC; long sourceTimestampNs2 = (now - 5000L - i) * DbusConstants.NUM_NSECS_IN_MSEC; long callbackTimeElapsedNs1 = (long) i * DbusConstants.NUM_NSECS_IN_MSEC; long callbackTimeElapsedNs2 = ((long) i + 2000L) * DbusConstants.NUM_NSECS_IN_MSEC; DbusEvent dbusEvent1 = createEvent(sourceTimestampNs1); DbusEvent dbusEvent2 = createEvent(sourceTimestampNs2); unifiedClientStats1.registerDataEventReceived(dbusEvent1); unifiedClientStats2.registerDataEventReceived(dbusEvent2); unifiedClientStats1.registerCallbacksProcessed(callbackTimeElapsedNs1); unifiedClientStats2.registerCallbacksProcessed(callbackTimeElapsedNs2); } unifiedClientStatsAgg.merge(unifiedClientStats1); unifiedClientStatsAgg.merge(unifiedClientStats2); // Expected timeLagConsumerCallbacksMs histogram values (exact): // unifiedClientStats1: 0 to 1027 ms // unifiedClientStats2: 2000 to 3027 ms assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile for connection #1", 513.5, unifiedClientStats1.getTimeLagConsumerCallbacksMs_HistPct_50()); assertEquals("unexpected timeLagConsumerCallbacksMs 50th percentile for connection #2", 2513.5, unifiedClientStats2.getTimeLagConsumerCallbacksMs_HistPct_50()); // The exact value depends on the relative fraction of '1' and '2' values that are retained in the // aggregate. If equal, the value should be near 1513.5, but even then the exact value depends on // whether the 1027 and 2000 values get bumped out of the aggregate. In the more common case that // the fractions retained are unequal, the median will fall between two values near the top end of // unifiedClientStats1 or near the bottom end of unifiedClientStats2. An allowance of 100 either // way should be safe. double percentile = unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_HistPct_50(); assertTrue("unexpected timeLagConsumerCallbacksMs 50th percentile for aggregated stats: " + percentile, 927.0 <= percentile && percentile <= 2100.0); assertEquals("unexpected timeLagConsumerCallbacksMs max value for connection #1", 1027.0, unifiedClientStats1.getTimeLagConsumerCallbacksMs_Max()); assertEquals("unexpected timeLagConsumerCallbacksMs max value for connection #2", 3027.0, unifiedClientStats2.getTimeLagConsumerCallbacksMs_Max()); double max = unifiedClientStatsAgg.getTimeLagConsumerCallbacksMs_Max(); assertTrue("unexpected timeLagConsumerCallbacksMs max value for aggregated stats: " + max, 2000.0 <= max && max <= 3027.0); // nominal value is 3027.0 // Expected timeLagSourceToReceiptMs histogram values (approximate): // unifiedClientStats1: 1000 to 2027 ms // unifiedClientStats2: 5000 to 6027 ms percentile = unifiedClientStats1.getTimeLagSourceToReceiptMs_HistPct_50(); assertTrue("unexpected timeLagSourceToReceiptMs 50th percentile for connection #1: " + percentile, 1512.5 <= percentile && percentile <= 1514.5); // nominal value is 1513.5 percentile = unifiedClientStats2.getTimeLagSourceToReceiptMs_HistPct_50(); assertTrue("unexpected timeLagSourceToReceiptMs 50th percentile for connection #2: " + percentile, 5512.5 <= percentile && percentile <= 5514.5); // nominal value is 5513.5 // same caveat as above: the median depends strongly on the relative proportion of unifiedClientStats1 // and unifiedClientStats2 data points retained in the aggregate, so the inequality is quite loose percentile = unifiedClientStatsAgg.getTimeLagSourceToReceiptMs_HistPct_50(); assertTrue("unexpected timeLagSourceToReceiptMs 50th percentile for aggregated stats: " + percentile, 1927.0 <= percentile && percentile <= 5100.0); } }