51Degrees Device Detection Java  4.4

Device detection services for 51Degrees Pipeline


The example illustrates the various metrics that can be obtained about the device detection process, for example, the degree of certainty about the result. Running the example outputs those properties and values..

The example also illustrates controlling properties that are returned from the detection process - reducing the number of components required to return the properties requested reduces the overall time taken.

There is a (discussion)[https://51degrees.com/documentation/_device_detection__hash.html#DeviceDetection_Hash_DataSetProduction_Performance] of metrics and controlling performance on our web site. See also the (performance options) [//51degrees.com/documentation/_device_detection__features__performance_options.html] page.


This example is available in full on (GitHub)[https://github.com/51Degrees/device-detection-java/blob/master/device-detection.examples/console/src/main/java/fiftyone/devicedetection/examples/console/MatchMetrics.java].

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package fiftyone.devicedetection.examples.console;
import fiftyone.devicedetection.DeviceDetectionPipelineBuilder;
import fiftyone.devicedetection.examples.shared.DataFileHelper;
import fiftyone.devicedetection.examples.shared.EvidenceHelper;
import fiftyone.devicedetection.hash.engine.onpremise.data.DeviceDataHash;
import fiftyone.devicedetection.hash.engine.onpremise.flowelements.DeviceDetectionHashEngine;
import fiftyone.devicedetection.shared.testhelpers.FileUtils;
import fiftyone.pipeline.core.data.ElementPropertyMetaData;
import fiftyone.pipeline.core.data.FlowData;
import fiftyone.pipeline.core.flowelements.Pipeline;
import fiftyone.pipeline.engines.Constants;
import fiftyone.pipeline.engines.data.AspectPropertyValue;
import fiftyone.pipeline.engines.fiftyone.data.ComponentMetaData;
import fiftyone.pipeline.engines.fiftyone.data.FiftyOneAspectPropertyMetaData;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.OutputStream;
import java.io.PrintWriter;
import java.util.Comparator;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import static fiftyone.common.testhelpers.LogbackHelper.configureLogback;
import static fiftyone.pipeline.util.FileFinder.getFilePath;
import static java.util.stream.Collectors.groupingBy;
public class MatchMetrics {
private static final Logger logger = LoggerFactory.getLogger(MatchMetrics.class);
public static void main(String[] args) throws Exception {
String dataFilename = args.length > 0 ? args[0] : null;
run(dataFilename, EvidenceHelper.setUpEvidence(), false, System.out);
static void run(String dataFile, List<Map<String, String>> evidenceList,
boolean showDescs, OutputStream out) throws Exception {
PrintWriter writer = new PrintWriter(out, true);
if (Objects.isNull(dataFile)) {
dataFile = FileUtils.getHashFileName();
logger.info("Constructing pipeline from file " + dataFile);
// Build a new Pipeline to use an on-premise Hash engine with the
// low memory performance profile.
try (Pipeline pipeline = new DeviceDetectionPipelineBuilder()
.useOnPremise(dataFile, true)
// Prefer low memory profile where all data streamed from disk
// on-demand. Experiment with other profiles.
// Disable share usage for this example.
// You can improve matching performance by specifying only those
// properties you wish to use. If you don't specify any properties
// you will get all those available in the data file tier that
// you have used. The free "Lite" tier contains fewer than 20.
// Since we are specifying properties here, we will only see
// those properties, along with the match metric properties
// in the output.
// Uncomment BrowserName to include Browser component profile ID
// in the device ID value.
// If using the full on-premise data file this property will be
// present in the data file. See https://51degrees.com/pricing
// Only use the predictive graph to better handle variances
// between the training data and the target User-Agent string.
// For a more detailed description of the differences between
// performance and predictive, see
// https://51degrees.com/documentation/_device_detection__hash.html#DeviceDetection_Hash_DataSetProduction_Performance
.build()) {
// A try-with-resource block MUST be used for the FlowData instance.
// This ensures that native resources created by the device
// detection engine are freed.
try (FlowData data = pipeline.createFlowData()) {
// Process a single evidence to retrieve the values
// associated with the user-agent and other evidence such as sec-ch-* for the
// selected properties.
DeviceDataHash device = data.get(DeviceDataHash.class);
writer.println("--- Compare evidence with what was matched ---\n");
// output the evidence in reverse value length order
.sorted(Comparator.comparingInt(e -> e.getValue().length()))
.forEach(e -> writer.format(" %-34s: %s%n", e.getKey(), e.getValue()));
// Obtain the matched User-Agents: the matched substrings in the
// User-Agents are separated with underscores - output in forward length order.
.forEach(v -> writer.format(" %-34s: %s%n", "Matched User-Agent", v));
writer.println("--- Listing all available properties, by component, by property " +
"name ---");
writer.println("For a discussion of what the match properties mean, see: " +
"https://51degrees.com/documentation/_device_detection__hash" +
// get the properties available from the DeviceDetection engine
// which has the key "device". For the sake of illustration we will
// retrieve it indirectly.
String hashEngineElementKey =
// retrieve the available properties from the hash engine. The properties
// available depends on
// a) the use of setProperty() in the builder (see above)
// which controls which properties will be extracted, and also affects
// the performance of extraction
// b) the tier of data file being used. The Lite data file contains fewer
// than 20 of the >200 available properties
Map<String, ElementPropertyMetaData> availableProperties =
// create a Map keyed on the component name of the properties available
// components being hardware, browser, OS and Crawler.
// Match metric properties are not allocated to a component, so we will
// add a key "MatchMetric"
Map<String, List<Map.Entry<String, ElementPropertyMetaData>>> categoryMap =
.collect(groupingBy(e -> {
ComponentMetaData component =
((FiftyOneAspectPropertyMetaData) e.getValue()).getComponent();
return Objects.nonNull(component) ?
component.getName() : "MatchMetric";
// iterate the map created above
categoryMap.forEach((component, propertyMapEntry) -> {
writer.format("%s%n", component);
propertyMapEntry.forEach(e -> {
FiftyOneAspectPropertyMetaData propertyMetaData =
(FiftyOneAspectPropertyMetaData) e.getValue();
String propertyName = propertyMetaData.getName();
String propertyDescription = propertyMetaData.getDescription();
// while we get the available properties and their metadata from the
// pipeline we get the values for the last detection from flowData
AspectPropertyValue<?> propertyValue =
(AspectPropertyValue<?>) device.get(propertyName);
// output property names, values and descriptions
// some property values are lists. the following check is to avoid compiler
// warning about unsafe casting. propertyMetaData.getList() will be true
if (propertyValue.hasValue() && propertyValue.getValue() instanceof List) {
List<?> values = ((List<?>) propertyValue.getValue());
writer.format(" %-24s: %s Values%n", propertyName, values.size());
values.forEach(a -> writer.format(" %-20s: %s%n", "", a));
} else {
writer.format(" %-24s: %s%n", propertyName, propertyValue);
if (showDescs) {
writer.format(" %s%n", propertyDescription);
logger.info("Finished Match Metrics Example");