A random-random test for time-series data #132556
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License | ||
| * 2.0; you may not use this file except in compliance with the Elastic License | ||
| * 2.0. | ||
| */ | ||
|
|
||
| package org.elasticsearch.xpack.esql.action; | ||
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|
||
| import org.elasticsearch.Build; | ||
| import org.elasticsearch.action.DocWriteRequest; | ||
| import org.elasticsearch.action.admin.indices.template.put.TransportPutComposableIndexTemplateAction; | ||
| import org.elasticsearch.cluster.metadata.ComposableIndexTemplate; | ||
| import org.elasticsearch.common.Strings; | ||
| import org.elasticsearch.common.bytes.BytesReference; | ||
| import org.elasticsearch.common.compress.CompressedXContent; | ||
| import org.elasticsearch.common.settings.Settings; | ||
| import org.elasticsearch.common.xcontent.XContentHelper; | ||
| import org.elasticsearch.core.Nullable; | ||
| import org.elasticsearch.core.Tuple; | ||
| import org.elasticsearch.datastreams.DataStreamsPlugin; | ||
| import org.elasticsearch.index.IndexMode; | ||
| import org.elasticsearch.index.IndexSettings; | ||
| import org.elasticsearch.plugins.Plugin; | ||
| import org.elasticsearch.test.ESTestCase; | ||
| import org.elasticsearch.xcontent.XContentBuilder; | ||
| import org.elasticsearch.xcontent.XContentFactory; | ||
| import org.elasticsearch.xcontent.XContentType; | ||
| import org.elasticsearch.xpack.aggregatemetric.AggregateMetricMapperPlugin; | ||
| import org.elasticsearch.xpack.core.LocalStateCompositeXPackPlugin; | ||
| import org.elasticsearch.xpack.esql.plugin.EsqlPlugin; | ||
| import org.junit.Before; | ||
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|
||
| import java.io.IOException; | ||
| import java.time.Instant; | ||
| import java.util.ArrayList; | ||
| import java.util.Collection; | ||
| import java.util.HashMap; | ||
| import java.util.List; | ||
| import java.util.Locale; | ||
| import java.util.Map; | ||
| import java.util.Objects; | ||
| import java.util.stream.Collectors; | ||
|
|
||
| import static org.elasticsearch.test.hamcrest.ElasticsearchAssertions.assertAcked; | ||
| import static org.hamcrest.Matchers.closeTo; | ||
| import static org.hamcrest.Matchers.equalTo; | ||
|
|
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| public class RandomizedTimeSeriesIT extends AbstractEsqlIntegTestCase { | ||
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| private static final Long NUM_DOCS = 2500L; | ||
| private static final String DATASTREAM_NAME = "tsit_ds"; | ||
| private List<XContentBuilder> documents = null; | ||
| private TSDataGenerationHelper dataGenerationHelper; | ||
|
|
||
| List<List<Object>> consumeRows(EsqlQueryResponse resp) { | ||
| List<List<Object>> rows = new ArrayList<>(); | ||
| resp.rows().forEach(rowIter -> { | ||
| List<Object> row = new ArrayList<>(); | ||
| rowIter.forEach(row::add); | ||
| rows.add(row); | ||
| }); | ||
| return rows; | ||
| } | ||
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| Map<List<String>, List<Map<String, Object>>> groupedRows( | ||
| List<XContentBuilder> docs, | ||
| List<String> groupingAttributes, | ||
| int secondsInWindow | ||
| ) { | ||
| Map<List<String>, List<Map<String, Object>>> groupedMap = new HashMap<>(); | ||
| for (XContentBuilder doc : docs) { | ||
| Map<String, Object> docMap = XContentHelper.convertToMap(BytesReference.bytes(doc), false, XContentType.JSON).v2(); | ||
| @SuppressWarnings("unchecked") | ||
| List<String> groupingPairs = groupingAttributes.stream() | ||
| .map( | ||
| attr -> Tuple.tuple( | ||
| attr, | ||
| ((Map<String, Object>) docMap.getOrDefault("attributes", Map.of())).getOrDefault(attr, "").toString() | ||
| ) | ||
| ) | ||
| .filter(val -> val.v2().isEmpty() == false) // Filter out empty values | ||
| .map(tup -> tup.v1() + ":" + tup.v2()) | ||
| .toList(); | ||
| long timeBucketStart = windowStart(docMap.get("@timestamp"), secondsInWindow); | ||
| var keyList = new ArrayList<>(groupingPairs); | ||
| keyList.add(Long.toString(timeBucketStart)); | ||
| groupedMap.computeIfAbsent(keyList, k -> new ArrayList<>()).add(docMap); | ||
| } | ||
| return groupedMap; | ||
| } | ||
|
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||
| static Long windowStart(Object timestampCell, int secondsInWindow) { | ||
| // This calculation looks a little weird, but it simply performs an integer division that | ||
| // throws away the remainder of the division by secondsInWindow. It rounds down | ||
| // the timestamp to the nearest multiple of secondsInWindow. | ||
| var timestampSeconds = Instant.parse((String) timestampCell).toEpochMilli() / 1000; | ||
| return (timestampSeconds / secondsInWindow) * secondsInWindow; | ||
| } | ||
|
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||
| enum Agg { | ||
| MAX, | ||
| MIN, | ||
| AVG | ||
| } | ||
|
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| static List<Integer> valuesInWindow(List<Map<String, Object>> pointsInGroup, String metricName) { | ||
| @SuppressWarnings("unchecked") | ||
| var values = pointsInGroup.stream() | ||
| .map(doc -> ((Map<String, Integer>) doc.get("metrics")).get(metricName)) | ||
| .filter(Objects::nonNull) | ||
| .collect(Collectors.toList()); | ||
| return values; | ||
| } | ||
|
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| static Double aggregateValuesInWindow(List<Integer> values, Agg agg) { | ||
| if (values.isEmpty()) { | ||
| throw new IllegalArgumentException("No values to aggregate for " + agg + " operation"); | ||
| } | ||
| return switch (agg) { | ||
| case MAX -> Double.valueOf(values.stream().max(Integer::compareTo).orElseThrow()); | ||
| case MIN -> Double.valueOf(values.stream().min(Integer::compareTo).orElseThrow()); | ||
| case AVG -> values.stream().mapToDouble(Integer::doubleValue).average().orElseThrow(); | ||
| }; | ||
| } | ||
|
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| static List<String> getRowKey(List<Object> row, List<String> groupingAttributes) { | ||
| List<String> rowKey = new ArrayList<>(); | ||
| for (int i = 0; i < groupingAttributes.size(); i++) { | ||
| Object value = row.get(i + 4); // Skip the first four columns | ||
| if (value != null) { | ||
| rowKey.add(groupingAttributes.get(i) + ":" + value); | ||
| } | ||
| } | ||
| rowKey.add(Long.toString(Instant.parse((String) row.get(3)).toEpochMilli() / 1000)); | ||
| return rowKey; | ||
| } | ||
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| @Override | ||
| public EsqlQueryResponse run(EsqlQueryRequest request) { | ||
| assumeTrue("time series available in snapshot builds only", Build.current().isSnapshot()); | ||
| return super.run(request); | ||
| } | ||
|
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||
| @Override | ||
| protected Collection<Class<? extends Plugin>> nodePlugins() { | ||
| return List.of( | ||
| DataStreamsPlugin.class, | ||
| LocalStateCompositeXPackPlugin.class, | ||
| // Downsample.class, // TODO(pabloem): What are these | ||
|
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||
| AggregateMetricMapperPlugin.class, | ||
| EsqlPlugin.class | ||
| ); | ||
| } | ||
|
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| void putTSDBIndexTemplate(List<String> patterns, @Nullable String mappingString) throws IOException { | ||
| Settings.Builder settingsBuilder = Settings.builder(); | ||
| // Ensure it will be a TSDB data stream | ||
| settingsBuilder.put(IndexSettings.MODE.getKey(), IndexMode.TIME_SERIES); | ||
| CompressedXContent mappings = mappingString == null ? null : CompressedXContent.fromJSON(mappingString); | ||
| TransportPutComposableIndexTemplateAction.Request request = new TransportPutComposableIndexTemplateAction.Request( | ||
| RandomizedTimeSeriesIT.DATASTREAM_NAME | ||
| ); | ||
| request.indexTemplate( | ||
| ComposableIndexTemplate.builder() | ||
| .indexPatterns(patterns) | ||
| .template(org.elasticsearch.cluster.metadata.Template.builder().settings(settingsBuilder).mappings(mappings)) | ||
| .metadata(null) | ||
| .dataStreamTemplate(new ComposableIndexTemplate.DataStreamTemplate()) | ||
| .build() | ||
| ); | ||
| assertAcked(client().execute(TransportPutComposableIndexTemplateAction.TYPE, request)); | ||
| } | ||
|
|
||
| @Before | ||
| public void populateIndex() throws IOException { | ||
| dataGenerationHelper = new TSDataGenerationHelper(NUM_DOCS); | ||
| final XContentBuilder builder = XContentFactory.jsonBuilder(); | ||
| builder.map(dataGenerationHelper.mapping.raw()); | ||
| final String jsonMappings = Strings.toString(builder); | ||
|
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| putTSDBIndexTemplate(List.of(DATASTREAM_NAME + "*"), jsonMappings); | ||
| // Now we can push data into the data stream. | ||
| for (int i = 0; i < NUM_DOCS; i++) { | ||
| var document = dataGenerationHelper.generateDocument(Map.of()); | ||
| if (documents == null) { | ||
| documents = new ArrayList<>(); | ||
| } | ||
| documents.add(document); | ||
| var indexRequest = client().prepareIndex(DATASTREAM_NAME).setOpType(DocWriteRequest.OpType.CREATE).setSource(document); | ||
| indexRequest.setRefreshPolicy(org.elasticsearch.action.support.WriteRequest.RefreshPolicy.IMMEDIATE); | ||
| indexRequest.get(); | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * This test validates Gauge metrics aggregation with grouping by time bucket and a subset of dimensions. | ||
| * The subset of dimensions is a random subset of the dimensions present in the data. | ||
| * The test checks that the max, min, and avg values of the gauge metric - and calculates | ||
| * the same values from the documents in the group. | ||
| */ | ||
| public void testGroupBySubset() { | ||
| var dimensions = ESTestCase.randomNonEmptySubsetOf(dataGenerationHelper.attributesForMetrics); | ||
| var dimensionsStr = dimensions.stream().map(d -> "attributes." + d).collect(Collectors.joining(", ")); | ||
| try (EsqlQueryResponse resp = run(String.format(Locale.ROOT, """ | ||
| TS %s | ||
| | STATS max(max_over_time(metrics.gauge_hdd.bytes.used)), | ||
| min(min_over_time(metrics.gauge_hdd.bytes.used)), | ||
| avg(avg_over_time(metrics.gauge_hdd.bytes.used)) | ||
| BY tbucket=bucket(@timestamp, 1 minute), %s | ||
| | SORT tbucket | ||
| | LIMIT 1000""", DATASTREAM_NAME, dimensionsStr))) { | ||
| var groups = groupedRows(documents, dimensions, 60); | ||
| List<List<Object>> rows = consumeRows(resp); | ||
| for (List<Object> row : rows) { | ||
| var rowKey = getRowKey(row, dimensions); | ||
| var docValues = valuesInWindow(groups.get(rowKey), "gauge_hdd.bytes.used"); | ||
| // Max of int is always int, so we can safely round the result. | ||
| assertThat(row.getFirst(), equalTo(Math.round(aggregateValuesInWindow(docValues, Agg.MAX)))); | ||
| assertThat(row.get(1), equalTo(Math.round(aggregateValuesInWindow(docValues, Agg.MIN)))); | ||
| // We check the expected vs ES-calculated average. We divide them to normalize the error | ||
| // and allow for a 20% error margin. | ||
| Double esAvg = (Double) row.get(2); | ||
| Double expectedAvg = aggregateValuesInWindow(docValues, Agg.AVG); | ||
| var ratio = esAvg / expectedAvg; | ||
| assertThat(ratio, closeTo(1, 0.2)); | ||
|
|
||
| } | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * This test validates Gauge metrics aggregation with grouping by time bucket only. | ||
| * The test checks that the max, min, and avg values of the gauge metric - and calculates | ||
| * the same values from the documents in the group. Because there is no grouping by dimensions, | ||
| * there is only one metric group per time bucket. | ||
| */ | ||
| public void testGroupByNothing() { | ||
| try (EsqlQueryResponse resp = run(String.format(Locale.ROOT, """ | ||
| TS %s | ||
| | STATS | ||
| max(max_over_time(metrics.gauge_hdd.bytes.used)), | ||
| min(min_over_time(metrics.gauge_hdd.bytes.used)), | ||
| avg(avg_over_time(metrics.gauge_hdd.bytes.used)) BY tbucket=bucket(@timestamp, 1 minute) | ||
| | SORT tbucket | ||
| | LIMIT 1000""", DATASTREAM_NAME))) { | ||
| List<List<Object>> rows = consumeRows(resp); | ||
| var groups = groupedRows(documents, List.of(), 60); | ||
| for (List<Object> row : rows) { | ||
| var windowStart = windowStart(row.get(3), 60); | ||
| var docValues = valuesInWindow(groups.get(List.of(Long.toString(windowStart))), "gauge_hdd.bytes.used"); | ||
| // Min and Max of int are always int, so we can safely round the result. | ||
| assertThat(row.getFirst(), equalTo(Math.round(aggregateValuesInWindow(docValues, Agg.MAX)))); | ||
| assertThat(row.get(1), equalTo(Math.round(aggregateValuesInWindow(docValues, Agg.MIN)))); | ||
| // We check the expected vs ES-calculated average. We divide them to normalize the error | ||
| // and allow for a 20% error margin. | ||
| Double esAvg = (Double) row.get(2); | ||
| Double expectedAvg = aggregateValuesInWindow(docValues, Agg.AVG); | ||
| var ratio = esAvg / expectedAvg; | ||
| assertThat(ratio, closeTo(1, 0.2)); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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