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* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch licenses this file to you 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 org.elasticsearch.search.aggregations.pipeline.movavg;
import org.elasticsearch.common.ParseField;
import org.elasticsearch.common.ParsingException;
import org.elasticsearch.common.io.stream.StreamInput;
import org.elasticsearch.common.io.stream.StreamOutput;
import org.elasticsearch.common.xcontent.ParseFieldRegistry;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentParser;
import org.elasticsearch.index.query.QueryParseContext;
import org.elasticsearch.search.DocValueFormat;
import org.elasticsearch.search.aggregations.AggregatorFactory;
import org.elasticsearch.search.aggregations.PipelineAggregationBuilder;
import org.elasticsearch.search.aggregations.bucket.histogram.DateHistogramAggregatorFactory;
import org.elasticsearch.search.aggregations.bucket.histogram.HistogramAggregatorFactory;
import org.elasticsearch.search.aggregations.pipeline.AbstractPipelineAggregationBuilder;
import org.elasticsearch.search.aggregations.pipeline.BucketHelpers.GapPolicy;
import org.elasticsearch.search.aggregations.pipeline.PipelineAggregator;
import org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModel;
import org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModelBuilder;
import org.elasticsearch.search.aggregations.pipeline.movavg.models.SimpleModel;
import java.io.IOException;
import java.text.ParseException;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import static org.elasticsearch.search.aggregations.pipeline.PipelineAggregator.Parser.BUCKETS_PATH;
import static org.elasticsearch.search.aggregations.pipeline.PipelineAggregator.Parser.FORMAT;
import static org.elasticsearch.search.aggregations.pipeline.PipelineAggregator.Parser.GAP_POLICY;
public class MovAvgPipelineAggregationBuilder extends AbstractPipelineAggregationBuilder<MovAvgPipelineAggregationBuilder> {
public static final String NAME = "moving_avg";
public static final ParseField MODEL = new ParseField("model");
private static final ParseField WINDOW = new ParseField("window");
public static final ParseField SETTINGS = new ParseField("settings");
private static final ParseField PREDICT = new ParseField("predict");
private static final ParseField MINIMIZE = new ParseField("minimize");
private String format;
private GapPolicy gapPolicy = GapPolicy.SKIP;
private int window = 5;
private MovAvgModel model = new SimpleModel();
private int predict = 0;
private Boolean minimize;
public MovAvgPipelineAggregationBuilder(String name, String bucketsPath) {
super(name, NAME, new String[] { bucketsPath });
}
/**
* Read from a stream.
*/
public MovAvgPipelineAggregationBuilder(StreamInput in) throws IOException {
super(in, NAME);
format = in.readOptionalString();
gapPolicy = GapPolicy.readFrom(in);
window = in.readVInt();
model = in.readNamedWriteable(MovAvgModel.class);
predict = in.readVInt();
minimize = in.readOptionalBoolean();
}
@Override
protected void doWriteTo(StreamOutput out) throws IOException {
out.writeOptionalString(format);
gapPolicy.writeTo(out);
out.writeVInt(window);
out.writeNamedWriteable(model);
out.writeVInt(predict);
out.writeOptionalBoolean(minimize);
}
/**
* Sets the format to use on the output of this aggregation.
*/
public MovAvgPipelineAggregationBuilder format(String format) {
if (format == null) {
throw new IllegalArgumentException("[format] must not be null: [" + name + "]");
}
this.format = format;
return this;
}
/**
* Gets the format to use on the output of this aggregation.
*/
public String format() {
return format;
}
/**
* Sets the GapPolicy to use on the output of this aggregation.
*/
public MovAvgPipelineAggregationBuilder gapPolicy(GapPolicy gapPolicy) {
if (gapPolicy == null) {
throw new IllegalArgumentException("[gapPolicy] must not be null: [" + name + "]");
}
this.gapPolicy = gapPolicy;
return this;
}
/**
* Gets the GapPolicy to use on the output of this aggregation.
*/
public GapPolicy gapPolicy() {
return gapPolicy;
}
protected DocValueFormat formatter() {
if (format != null) {
return new DocValueFormat.Decimal(format);
} else {
return DocValueFormat.RAW;
}
}
/**
* Sets the window size for the moving average. This window will "slide"
* across the series, and the values inside that window will be used to
* calculate the moving avg value
*
* @param window
* Size of window
*/
public MovAvgPipelineAggregationBuilder window(int window) {
if (window <= 0) {
throw new IllegalArgumentException("[window] must be a positive integer: [" + name + "]");
}
this.window = window;
return this;
}
/**
* Gets the window size for the moving average. This window will "slide"
* across the series, and the values inside that window will be used to
* calculate the moving avg value
*/
public int window() {
return window;
}
/**
* Sets a MovAvgModel for the Moving Average. The model is used to
* define what type of moving average you want to use on the series
*
* @param model
* A MovAvgModel which has been prepopulated with settings
*/
public MovAvgPipelineAggregationBuilder modelBuilder(MovAvgModelBuilder model) {
if (model == null) {
throw new IllegalArgumentException("[model] must not be null: [" + name + "]");
}
this.model = model.build();
return this;
}
/**
* Sets a MovAvgModel for the Moving Average. The model is used to
* define what type of moving average you want to use on the series
*
* @param model
* A MovAvgModel which has been prepopulated with settings
*/
public MovAvgPipelineAggregationBuilder model(MovAvgModel model) {
if (model == null) {
throw new IllegalArgumentException("[model] must not be null: [" + name + "]");
}
this.model = model;
return this;
}
/**
* Gets a MovAvgModel for the Moving Average. The model is used to
* define what type of moving average you want to use on the series
*/
public MovAvgModel model() {
return model;
}
/**
* Sets the number of predictions that should be returned. Each
* prediction will be spaced at the intervals specified in the
* histogram. E.g "predict: 2" will return two new buckets at the end of
* the histogram with the predicted values.
*
* @param predict
* Number of predictions to make
*/
public MovAvgPipelineAggregationBuilder predict(int predict) {
if (predict <= 0) {
throw new IllegalArgumentException("predict must be greater than 0. Found [" + predict + "] in [" + name + "]");
}
this.predict = predict;
return this;
}
/**
* Gets the number of predictions that should be returned. Each
* prediction will be spaced at the intervals specified in the
* histogram. E.g "predict: 2" will return two new buckets at the end of
* the histogram with the predicted values.
*/
public int predict() {
return predict;
}
/**
* Sets whether the model should be fit to the data using a cost
* minimizing algorithm.
*
* @param minimize
* If the model should be fit to the underlying data
*/
public MovAvgPipelineAggregationBuilder minimize(boolean minimize) {
this.minimize = minimize;
return this;
}
/**
* Gets whether the model should be fit to the data using a cost
* minimizing algorithm.
*/
public Boolean minimize() {
return minimize;
}
@Override
protected PipelineAggregator createInternal(Map<String, Object> metaData) throws IOException {
// If the user doesn't set a preference for cost minimization, ask
// what the model prefers
boolean minimize = this.minimize == null ? model.minimizeByDefault() : this.minimize;
return new MovAvgPipelineAggregator(name, bucketsPaths, formatter(), gapPolicy, window, predict, model, minimize, metaData);
}
@Override
public void doValidate(AggregatorFactory<?> parent, AggregatorFactory<?>[] aggFactories,
List<PipelineAggregationBuilder> pipelineAggregatoractories) {
if (minimize != null && minimize && !model.canBeMinimized()) {
// If the user asks to minimize, but this model doesn't support
// it, throw exception
throw new IllegalStateException("The [" + model + "] model cannot be minimized for aggregation [" + name + "]");
}
if (bucketsPaths.length != 1) {
throw new IllegalStateException(PipelineAggregator.Parser.BUCKETS_PATH.getPreferredName()
+ " must contain a single entry for aggregation [" + name + "]");
}
if (parent instanceof HistogramAggregatorFactory) {
HistogramAggregatorFactory histoParent = (HistogramAggregatorFactory) parent;
if (histoParent.minDocCount() != 0) {
throw new IllegalStateException("parent histogram of moving average aggregation [" + name
+ "] must have min_doc_count of 0");
}
} else if (parent instanceof DateHistogramAggregatorFactory) {
DateHistogramAggregatorFactory histoParent = (DateHistogramAggregatorFactory) parent;
if (histoParent.minDocCount() != 0) {
throw new IllegalStateException("parent histogram of moving average aggregation [" + name
+ "] must have min_doc_count of 0");
}
} else {
throw new IllegalStateException("moving average aggregation [" + name
+ "] must have a histogram or date_histogram as parent");
}
}
@Override
protected XContentBuilder internalXContent(XContentBuilder builder, Params params) throws IOException {
if (format != null) {
builder.field(FORMAT.getPreferredName(), format);
}
builder.field(GAP_POLICY.getPreferredName(), gapPolicy.getName());
model.toXContent(builder, params);
builder.field(WINDOW.getPreferredName(), window);
if (predict > 0) {
builder.field(PREDICT.getPreferredName(), predict);
}
if (minimize != null) {
builder.field(MINIMIZE.getPreferredName(), minimize);
}
return builder;
}
public static MovAvgPipelineAggregationBuilder parse(
ParseFieldRegistry<MovAvgModel.AbstractModelParser> movingAverageMdelParserRegistry,
String pipelineAggregatorName, QueryParseContext context) throws IOException {
XContentParser parser = context.parser();
XContentParser.Token token;
String currentFieldName = null;
String[] bucketsPaths = null;
String format = null;
GapPolicy gapPolicy = null;
Integer window = null;
Map<String, Object> settings = null;
String model = null;
Integer predict = null;
Boolean minimize = null;
while ((token = parser.nextToken()) != XContentParser.Token.END_OBJECT) {
if (token == XContentParser.Token.FIELD_NAME) {
currentFieldName = parser.currentName();
} else if (token == XContentParser.Token.VALUE_NUMBER) {
if (WINDOW.match(currentFieldName)) {
window = parser.intValue();
if (window <= 0) {
throw new ParsingException(parser.getTokenLocation(), "[" + currentFieldName + "] value must be a positive, "
+ "non-zero integer. Value supplied was [" + predict + "] in [" + pipelineAggregatorName + "].");
}
} else if (PREDICT.match(currentFieldName)) {
predict = parser.intValue();
if (predict <= 0) {
throw new ParsingException(parser.getTokenLocation(), "[" + currentFieldName + "] value must be a positive integer."
+ " Value supplied was [" + predict + "] in [" + pipelineAggregatorName + "].");
}
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: [" + currentFieldName + "].");
}
} else if (token == XContentParser.Token.VALUE_STRING) {
if (FORMAT.match(currentFieldName)) {
format = parser.text();
} else if (BUCKETS_PATH.match(currentFieldName)) {
bucketsPaths = new String[] { parser.text() };
} else if (GAP_POLICY.match(currentFieldName)) {
gapPolicy = GapPolicy.parse(context, parser.text(), parser.getTokenLocation());
} else if (MODEL.match(currentFieldName)) {
model = parser.text();
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: [" + currentFieldName + "].");
}
} else if (token == XContentParser.Token.START_ARRAY) {
if (BUCKETS_PATH.match(currentFieldName)) {
List<String> paths = new ArrayList<>();
while ((token = parser.nextToken()) != XContentParser.Token.END_ARRAY) {
String path = parser.text();
paths.add(path);
}
bucketsPaths = paths.toArray(new String[paths.size()]);
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: [" + currentFieldName + "].");
}
} else if (token == XContentParser.Token.START_OBJECT) {
if (SETTINGS.match(currentFieldName)) {
settings = parser.map();
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: [" + currentFieldName + "].");
}
} else if (token == XContentParser.Token.VALUE_BOOLEAN) {
if (MINIMIZE.match(currentFieldName)) {
minimize = parser.booleanValue();
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: [" + currentFieldName + "].");
}
} else {
throw new ParsingException(parser.getTokenLocation(),
"Unexpected token " + token + " in [" + pipelineAggregatorName + "].");
}
}
if (bucketsPaths == null) {
throw new ParsingException(parser.getTokenLocation(), "Missing required field [" + BUCKETS_PATH.getPreferredName()
+ "] for movingAvg aggregation [" + pipelineAggregatorName + "]");
}
MovAvgPipelineAggregationBuilder factory =
new MovAvgPipelineAggregationBuilder(pipelineAggregatorName, bucketsPaths[0]);
if (format != null) {
factory.format(format);
}
if (gapPolicy != null) {
factory.gapPolicy(gapPolicy);
}
if (window != null) {
factory.window(window);
}
if (predict != null) {
factory.predict(predict);
}
if (model != null) {
MovAvgModel.AbstractModelParser modelParser = movingAverageMdelParserRegistry.lookup(model, parser.getTokenLocation());
MovAvgModel movAvgModel;
try {
movAvgModel = modelParser.parse(settings, pipelineAggregatorName, factory.window());
} catch (ParseException exception) {
throw new ParsingException(parser.getTokenLocation(), "Could not parse settings for model [" + model + "].", exception);
}
factory.model(movAvgModel);
}
if (minimize != null) {
factory.minimize(minimize);
}
return factory;
}
@Override
protected int doHashCode() {
return Objects.hash(format, gapPolicy, window, model, predict, minimize);
}
@Override
protected boolean doEquals(Object obj) {
MovAvgPipelineAggregationBuilder other = (MovAvgPipelineAggregationBuilder) obj;
return Objects.equals(format, other.format)
&& Objects.equals(gapPolicy, other.gapPolicy)
&& Objects.equals(window, other.window)
&& Objects.equals(model, other.model)
&& Objects.equals(predict, other.predict)
&& Objects.equals(minimize, other.minimize);
}
@Override
public String getWriteableName() {
return NAME;
}
}