com.datatorrent.lib.testbench.ThroughputCounter Maven / Gradle / Ivy
/**
* 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 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 com.datatorrent.lib.testbench;
import java.util.HashMap;
import java.util.Map;
import javax.validation.constraints.Min;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.datatorrent.api.Context.OperatorContext;
import com.datatorrent.api.DefaultInputPort;
import com.datatorrent.api.DefaultOutputPort;
import com.datatorrent.common.util.BaseOperator;
/**
* This operator expects incoming tuples to be of type HashMap<String, Integer>.
* These values are throughput per window from upstream operators.
* At the end of the application window, the total and average throughput are emitted.
*
* Benchmarks: This node has been benchmarked at over 5 million tuples/second in local/inline mode
* Tuple Schema
* Each input tuple is HashMap
* Output tuple is a HashMap, where strings are throughputs, averages etc
* Port Interface
* count: Output port for emitting the results
* data: Input port for receiving the incoming tuple
*
* Properties:
* rolling_window_count: Number of windows to average over
*
* Compile time checks are:
* none
*
* Benchmarks: Blast as many tuples as possible in inline mode
* Benchmarked at over 17 million tuples/second in local/in-line mode
*
* @displayName Throughput Counter
* @category Test Bench
* @tags count
* @since 0.3.2
*/
public class ThroughputCounter extends BaseOperator
{
private static Logger log = LoggerFactory.getLogger(ThroughputCounter.class);
/**
* The input port which receives throughput information from upstream operators.
*/
public final transient DefaultInputPort> data = new DefaultInputPort>()
{
@Override
public void process(HashMap tuple)
{
for (Map.Entry e: tuple.entrySet()) {
tuple_count += e.getValue().longValue();
}
}
};
/**
* The output port which emits throughput statistics.
*/
public final transient DefaultOutputPort> count = new DefaultOutputPort>();
public static final String OPORT_COUNT_TUPLE_AVERAGE = "avg";
public static final String OPORT_COUNT_TUPLE_COUNT = "count";
public static final String OPORT_COUNT_TUPLE_TIME = "window_time";
public static final String OPORT_COUNT_TUPLE_TUPLES_PERSEC = "tuples_per_sec";
public static final String OPORT_COUNT_TUPLE_WINDOWID = "window_id";
private long windowStartTime = 0;
@Min(1)
private int rolling_window_count = 1;
long[] tuple_numbers = null;
long[] time_numbers = null;
int tuple_index = 0;
int count_denominator = 1;
long count_windowid = 0;
long tuple_count = 1; // so that the first begin window starts the count down
boolean didemit = false;
@Min(1)
public int getRollingWindowCount()
{
return rolling_window_count;
}
public void setRollingWindowCount(int i)
{
rolling_window_count = i;
}
@Override
public void setup(OperatorContext context)
{
windowStartTime = System.currentTimeMillis();
log.debug(String.format("\nTupleCounter: set window to %d", rolling_window_count));
if (rolling_window_count != 1) { // Initialized the tuple_numbers
tuple_numbers = new long[rolling_window_count];
time_numbers = new long[rolling_window_count];
for (int i = tuple_numbers.length; i > 0; i--) {
tuple_numbers[i - 1] = 0;
time_numbers[i - 1] = 0;
}
tuple_index = 0;
}
}
@Override
public void beginWindow(long windowId)
{
if (tuple_count != 0) { // Do not restart time if no tuples were sent
windowStartTime = System.currentTimeMillis();
if (didemit) {
tuple_count = 0;
}
}
}
/**
* convenient method for not sending more than configured number of windows.
*/
@Override
public void endWindow()
{
if (tuple_count == 0) {
return;
}
long elapsedTime = System.currentTimeMillis() - windowStartTime;
if (elapsedTime == 0) {
didemit = false;
return;
}
long average;
long tuples_per_sec = (tuple_count * 1000) / elapsedTime; // * 1000 as elapsedTime is in millis
if (rolling_window_count == 1) {
average = tuples_per_sec;
} else { // use tuple_numbers
long slots;
if (count_denominator == rolling_window_count) {
tuple_numbers[tuple_index] = tuple_count;
time_numbers[tuple_index] = elapsedTime;
slots = rolling_window_count;
tuple_index++;
if (tuple_index == rolling_window_count) {
tuple_index = 0;
}
} else {
tuple_numbers[count_denominator - 1] = tuple_count;
time_numbers[count_denominator - 1] = elapsedTime;
slots = count_denominator;
count_denominator++;
}
long time_slot = 0;
long numtuples = 0;
for (int i = 0; i < slots; i++) {
numtuples += tuple_numbers[i];
time_slot += time_numbers[i];
}
average = (numtuples * 1000) / time_slot;
}
HashMap tuples = new HashMap();
tuples.put(OPORT_COUNT_TUPLE_AVERAGE, new Long(average));
tuples.put(OPORT_COUNT_TUPLE_COUNT, new Long(tuple_count));
tuples.put(OPORT_COUNT_TUPLE_TIME, new Long(elapsedTime));
tuples.put(OPORT_COUNT_TUPLE_TUPLES_PERSEC, new Long(tuples_per_sec));
tuples.put(OPORT_COUNT_TUPLE_WINDOWID, new Long(count_windowid++));
count.emit(tuples);
didemit = true;
}
}
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