public void testDefaultParsing() throws Exception {
MovAvgPipelineAggregationBuilder expected =
new MovAvgPipelineAggregationBuilder("commits_moving_avg", "commits");
String json =
"{"
+ " \"commits_moving_avg\": {"
+ " \"moving_avg\": {"
+ " \"buckets_path\": \"commits\""
+ " }"
+ " }"
+ "}";
XContentParser parser = XContentFactory.xContent(json).createParser(json);
QueryParseContext parseContext =
new QueryParseContext(queriesRegistry, parser, parseFieldMatcher);
assertSame(XContentParser.Token.START_OBJECT, parser.nextToken());
assertSame(XContentParser.Token.FIELD_NAME, parser.nextToken());
assertEquals(expected.getName(), parser.currentName());
assertSame(XContentParser.Token.START_OBJECT, parser.nextToken());
assertSame(XContentParser.Token.FIELD_NAME, parser.nextToken());
assertEquals(expected.type(), parser.currentName());
assertSame(XContentParser.Token.START_OBJECT, parser.nextToken());
PipelineAggregationBuilder newAgg =
aggParsers
.pipelineParser(expected.getWriteableName(), ParseFieldMatcher.STRICT)
.parse(expected.getName(), parseContext);
assertSame(XContentParser.Token.END_OBJECT, parser.currentToken());
assertSame(XContentParser.Token.END_OBJECT, parser.nextToken());
assertSame(XContentParser.Token.END_OBJECT, parser.nextToken());
assertNull(parser.nextToken());
assertNotNull(newAgg);
assertNotSame(newAgg, expected);
assertEquals(expected, newAgg);
assertEquals(expected.hashCode(), newAgg.hashCode());
}
@Override
protected MovAvgPipelineAggregationBuilder createTestAggregatorFactory() {
String name = randomAsciiOfLengthBetween(3, 20);
String bucketsPath = randomAsciiOfLengthBetween(3, 20);
MovAvgPipelineAggregationBuilder factory =
new MovAvgPipelineAggregationBuilder(name, bucketsPath);
if (randomBoolean()) {
factory.format(randomAsciiOfLengthBetween(1, 10));
}
if (randomBoolean()) {
factory.gapPolicy(randomFrom(GapPolicy.values()));
}
if (randomBoolean()) {
switch (randomInt(4)) {
case 0:
factory.modelBuilder(new SimpleModel.SimpleModelBuilder());
factory.window(randomIntBetween(1, 100));
break;
case 1:
factory.modelBuilder(new LinearModel.LinearModelBuilder());
factory.window(randomIntBetween(1, 100));
break;
case 2:
if (randomBoolean()) {
factory.modelBuilder(new EwmaModel.EWMAModelBuilder());
factory.window(randomIntBetween(1, 100));
} else {
factory.modelBuilder(new EwmaModel.EWMAModelBuilder().alpha(randomDouble()));
factory.window(randomIntBetween(1, 100));
}
break;
case 3:
if (randomBoolean()) {
factory.modelBuilder(new HoltLinearModel.HoltLinearModelBuilder());
factory.window(randomIntBetween(1, 100));
} else {
factory.modelBuilder(
new HoltLinearModel.HoltLinearModelBuilder()
.alpha(randomDouble())
.beta(randomDouble()));
factory.window(randomIntBetween(1, 100));
}
break;
case 4:
default:
if (randomBoolean()) {
factory.modelBuilder(new HoltWintersModel.HoltWintersModelBuilder());
factory.window(randomIntBetween(2, 100));
} else {
int period = randomIntBetween(1, 100);
factory.modelBuilder(
new HoltWintersModel.HoltWintersModelBuilder()
.alpha(randomDouble())
.beta(randomDouble())
.gamma(randomDouble())
.period(period)
.seasonalityType(randomFrom(SeasonalityType.values()))
.pad(randomBoolean()));
factory.window(randomIntBetween(2 * period, 200 * period));
}
break;
}
}
factory.predict(randomIntBetween(1, 50));
if (factory.model().canBeMinimized() && randomBoolean()) {
factory.minimize(randomBoolean());
}
return factory;
}