Github user simonellistonball commented on a diff in the pull request:
https://github.com/apache/metron/pull/1150#discussion_r210596217
--- Diff:
metron-analytics/metron-profiler-spark/src/main/java/org/apache/metron/profiler/spark/function/ProfileBuilderFunction.java
---
@@ -0,0 +1,107 @@
+/*
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF 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
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+ * 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.apache.metron.profiler.spark.function;
+
+import org.apache.metron.profiler.DefaultMessageDistributor;
+import org.apache.metron.profiler.MessageDistributor;
+import org.apache.metron.profiler.MessageRoute;
+import org.apache.metron.profiler.ProfileMeasurement;
+import org.apache.metron.profiler.spark.ProfileMeasurementAdapter;
+import org.apache.metron.stellar.dsl.Context;
+import org.apache.spark.api.java.function.MapGroupsFunction;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.lang.invoke.MethodHandles;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.Properties;
+import java.util.concurrent.TimeUnit;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import java.util.stream.StreamSupport;
+
+import static java.util.Comparator.comparing;
+import static
org.apache.metron.profiler.spark.BatchProfilerConfig.PERIOD_DURATION;
+import static
org.apache.metron.profiler.spark.BatchProfilerConfig.PERIOD_DURATION_UNITS;
+
+/**
+ * The function responsible for building profiles in Spark.
+ */
+public class ProfileBuilderFunction implements MapGroupsFunction<String,
MessageRoute, ProfileMeasurementAdapter> {
+
+ protected static final Logger LOG =
LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+ private long periodDurationMillis;
+ private Map<String, String> globals;
+
+ public ProfileBuilderFunction(Properties properties, Map<String, String>
globals) {
+ TimeUnit periodDurationUnits =
TimeUnit.valueOf(PERIOD_DURATION_UNITS.get(properties, String.class));
+ int periodDuration = PERIOD_DURATION.get(properties, Integer.class);
+ this.periodDurationMillis =
periodDurationUnits.toMillis(periodDuration);
+ this.globals = globals;
+ }
+
+ /**
+ * Build a profile from a set of message routes.
+ *
+ * <p>This assumes that all of the necessary routes have been provided
+ *
+ * @param group The group identifier.
+ * @param iterator The message routes.
+ * @return
+ */
+ @Override
+ public ProfileMeasurementAdapter call(String group,
Iterator<MessageRoute> iterator) throws Exception {
+ // create the distributor; some settings are unnecessary because it is
cleaned-up immediately after processing the batch
+ int maxRoutes = Integer.MAX_VALUE;
+ long profileTTLMillis = Long.MAX_VALUE;
+ MessageDistributor distributor = new
DefaultMessageDistributor(periodDurationMillis, profileTTLMillis, maxRoutes);
+ Context context = TaskUtils.getContext(globals);
+
+ // sort the messages/routes
+ List<MessageRoute> routes = toStream(iterator)
+ .sorted(comparing(rt -> rt.getTimestamp()))
+ .collect(Collectors.toList());
+ LOG.debug("Building a profile for group '{}' from {} message(s)",
group, routes.size());
+
+ // apply each message/route to build the profile
+ for(MessageRoute route: routes) {
+ distributor.distribute(route, context);
+ }
--- End diff --
I can see how people would want ordered timestamps. If you're using
profiles to find sequences of events that indicate state of an attack, you do
need order. This does make it hard to use reduceByKey thinking about it,
because that assumes distribution. Maybe we do have to use the shuffled method
after all. There may be an argument for group and sort in a 'sub-query' and
then a grouped aggregate over a deterministic ordering, which I believe you
could do with spark sql easily enough, and let catalyst sort out the shuffling
and sorting for you, but that's a top of the head theory at the moment.
---
