PerSystSqlOperator.cpp 11 KB
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//================================================================================
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// Name        : PerSystSqlOperator.cpp
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// Author      : Carla Guillen
// Contact     : info@dcdb.it
// Copyright   : Leibniz Supercomputing Centre
// Description : Template implementing features to use Units in Operators.
//================================================================================

//================================================================================
// This file is part of DCDB (DataCenter DataBase)
// Copyright (C) 2018-2019 Leibniz Supercomputing Centre
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
//================================================================================

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#include "PerSystSqlOperator.h"

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#include <boost/log/sources/record_ostream.hpp>
#include <boost/log/trivial.hpp>
#include <boost/log/utility/formatting_ostream.hpp>
#include <boost/parameter/keyword.hpp>
#include <stddef.h>
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#include <cmath>
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#include <cstdint>
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#include <memory>
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#include <string>
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#include <numeric>
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#include <sstream>
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#include "../../../common/include/logging.h"
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#include "../../../common/include/sensorbase.h"
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#include "../../../common/include/timestamp.h"
#include "../../includes/CommonStatistics.h"
#include "../../includes/QueryEngine.h"
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#include "../../includes/UnitTemplate.h"
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int PerSystSqlOperator::_number_of_calls = 0;

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PerSystSqlOperator::PerSystSqlOperator(const std::string& name) :
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		OperatorTemplate(name), JobOperatorTemplate(name), _number_of_even_quantiles(
				0), _severity_formula(NOFORMULA), _severity_threshold(0), _severity_exponent(
				0), _severity_max_memory(0), _go_back_ns(0), _backend(DEFAULT), _scaling_factor(
				1) {
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}

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PerSystSqlOperator::~PerSystSqlOperator() {
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}

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void PerSystSqlOperator::compute(U_Ptr unit, qeJobData& jobData) {
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	// Clearing the buffer, if already allocated
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	_buffer.clear();
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	size_t elCtr = 0;
	uint64_t my_timestamp = getTimestamp() - _go_back_ns;
	// Making sure that the aggregation boundaries do not go past the job start/end time
	uint64_t jobEnd =
			jobData.endTime != 0 && my_timestamp > jobData.endTime ?
					jobData.endTime : my_timestamp;
	uint64_t jobStart =
			jobEnd - my_timestamp < jobData.startTime ?
					jobData.startTime : jobEnd - my_timestamp;
	// Job units are hierarchical, and thus we iterate over all sub-units associated to each single node
	for (const auto& subUnit : unit->getSubUnits()) {
		// Getting the most recent values as specified in _window
		// Since we do not clear the internal buffer, all sensor readings will be accumulated in the same vector
		for (const auto& in : subUnit->getInputs()) {
			if (!_queryEngine.querySensor(in->getName(), my_timestamp,
					my_timestamp, _buffer, false)) {
				LOG(debug)<< "PerSystSql Operator " << _name << " cannot read from sensor " << in->getName() << "!";
				return;
			}
		}
	}
	static bool persystdb_initialized = false;
  if(_backend == MARIADB){
	if (!persystdb_initialized) {
		LOG(debug)<< "PerSystSQL Operator Connection information:";
		LOG(debug) << "\tHost=" << _conn.host;
		LOG(debug) << "\tUser=" << _conn.user;
		LOG(debug) << "\tDatabase=" << _conn.database_name;
		LOG(debug) << "\tPort=" << _conn.port;
		LOG(debug) << "\tRotation=" << _conn.rotation;
		LOG(debug) << "\tEvery_X_days=" << _conn.every_x_days;
		bool persystdb_initialized = persystdb.initializeConnection(_conn.host, _conn.user, _conn.password, _conn.database_name, _conn.rotation, _conn.port, _conn.every_x_days);
		if(!persystdb_initialized) {
			LOG(error) << "Unable to establish connection to database";
			return;
		}
	}
  }
	Aggregate_info_t agg_info;
	std::string table_suffix;
  if(_backend == MARIADB){
	std::stringstream jobidBuilder;
	jobidBuilder << jobData.jobId;
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	std::vector<std::string> job_ids;
	job_ids.push_back(jobidBuilder.str());
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	std::map<std::string, std::string> job_map;
    	if(!persystdb.getTableSuffix(table_suffix)){
        	LOG(error) << "failed to create table!";
        	return;
    	}
    	if(!persystdb.getDBJobIDs(job_ids, job_map)){
       		return;
    	}

    	// handle jobs which are not present
   	 for(auto &job_id_string : job_ids ){
       	 	auto search = job_map.find(job_id_string);
        	if(search == job_map.end()){ //Not found
                	int job_id_db;
                	if(persystdb.insertIntoJob(job_id_string, jobData.userId, job_id_db, table_suffix)){
				agg_info.job_id_db = std::to_string(job_id_db); 
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                	} else {
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                        	continue;
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                	}
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        	}
   	}
	agg_info.timestamp = (my_timestamp/1e9);
  }
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	compute_internal(unit, _buffer, agg_info);

  if(_backend == MARIADB){
	persystdb.insertInAggregateTable(table_suffix, agg_info);
	if(_number_of_calls % 10 == 0  && persystdb_initialized){
		persystdb.finalizeConnection();
		persystdb_initialized = false;
	}
	_number_of_calls++;
  }
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}

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void PerSystSqlOperator::compute_internal(U_Ptr& unit,
		vector<reading_t>& buffer, Aggregate_info_t & agg_info) {
	_quantileSensors.clear();

	reading_t reading;
	AggregatorSensorBase::aggregationOps_t op;
	reading.timestamp = getTimestamp() - _go_back_ns;

	std::vector<double> douBuffer;
	punToDoubles(buffer, douBuffer);
	// Performing the actual aggregation operation
	for (const auto& out : unit->getOutputs()) {
		op = out->getOperation();
		if (op != AggregatorSensorBase::QTL) {
			switch (op) {
			case AggregatorSensorBase::AVG:
				if (_backend == CASSANDRA) {
					reading.value = std::accumulate(douBuffer.begin(),
							douBuffer.end(), 0.0) / douBuffer.size()
							* _scaling_factor;
				} else {
					agg_info.average = std::accumulate(douBuffer.begin(), douBuffer.end(), 0.0) / douBuffer.size();
				}
				break;
			case AggregatorSensorBase::OBS:
				reading.value = computeObs(buffer);
				agg_info.num_of_observations = computeObs(buffer);
				break;
			case AggregatorSensorBase::AVG_SEV:
				if (_backend == CASSANDRA) {
					reading.value = computeSeverityAverage(douBuffer) * _scaling_factor;
				} else {
					agg_info.severity_average = computeSeverityAverage(douBuffer);
				}
				break;
			default:
				LOG(warning)<< _name << ": Operation " << op << " not supported!";
				reading.value = 0;
				break;
			}
			if(_backend == CASSANDRA) {
				out->storeReading(reading);
			}
		} else {
			_quantileSensors.push_back(out);
		}
	}

	if (!_quantileSensors.empty()) {
		vector<double> quantiles;
		computeEvenQuantiles(douBuffer, _number_of_even_quantiles, quantiles);
		if (_backend == CASSANDRA) {
			for (unsigned idx = 0; idx < quantiles.size(); idx++) {
				reading.value = quantiles[idx]*_scaling_factor;
				_quantileSensors[idx]->storeReading(reading);
			}
		} else {
			for(auto q: quantiles){
				agg_info.quantiles.push_back(static_cast<float>(q));
			}
		}
	}
	agg_info.property_type_id = _property_id;

}

void PerSystSqlOperator::compute(U_Ptr unit) {
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//nothing here!
}
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double severity_formula1(double metric, double threshold, double exponent) {
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	double val = metric - threshold;
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	if (val > 0) {
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		double ret = (pow(val, exponent));
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		if (ret > 1) {
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			return 1;
		}
		return ret;
	}
	return 0;
}

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double severity_formula2(double metric, double threshold, double exponent) {
	if (!threshold) {
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		return -1;
	}
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	double val = metric / threshold - 1;
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	if (val > 0) {
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		double ret = (pow(val, exponent));
		if (ret > 1) {
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			return 1;
		}
		return ret;
	}
	return 0;
}

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double severity_formula3(double metric, double threshold, double exponent) {
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	if (!threshold) {
		return -1;
	}
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	double val = metric / threshold;
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	if (val > 0) {
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		double ret = (1 - pow(val, exponent));
		if (ret > 1) {
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			return 1;
		}
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		if (ret < 0) {
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			return 0;
		}
		return ret;
	}
	return 0;
}

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double severity_memory(double metric, double threshold, double max_memory) {
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	double denominator = max_memory - threshold;
	double severity = -1;
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	if (denominator) {
		severity = metric - threshold / (max_memory - threshold);
		if (severity > 1) {
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			severity = 1;
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		} else if (severity < 0) {
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			severity = 0;
		}
	}
	return severity;
}
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double PerSystSqlOperator::computeSeverityAverage(
		std::vector<double> & buffer) {
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	std::vector<double> severities;
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	switch (_severity_formula) {
	case (FORMULA1):
		for (auto val : buffer) {
			auto severity = severity_formula1(val, _severity_threshold,
					_severity_exponent);
			severities.push_back(severity);
		}
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		break;
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	case (FORMULA2):
		for (auto val : buffer) {
			auto severity = severity_formula2(val, _severity_threshold,
					_severity_exponent);
			severities.push_back(severity);
		}
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		break;
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	case (FORMULA3):
		for (auto val : buffer) {
			auto severity = severity_formula3(val, _severity_threshold,
					_severity_exponent);
			severities.push_back(severity);
		}
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		break;
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	case (MEMORY_FORMULA):
		for (auto val : buffer) {
			auto severity = severity_memory(val, _severity_threshold,
					_severity_max_memory);
			severities.push_back(severity);
		}
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		break;
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	case (NOFORMULA):
		for (auto val : buffer) {
			severities.push_back(severity_noformula());
		}
		break;
	default:
		return 0.0;
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		break;
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	}
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	if (severities.size()) {
		return (std::accumulate(severities.begin(), severities.end(), 0.0)
				/ severities.size());
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	}
	return 0.0;
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}
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void punToDoubles(std::vector<reading_t> & buffer,
		std::vector<double> & outDoubleVec) {
	for (auto & reading : buffer) {
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		outDoubleVec.push_back(punLLToDouble(reading.value));
	}
}

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double punLLToDouble(long long value) {
	double * returnval;
	returnval = (double *) (&value);
	return *returnval;
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}

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long long punDoubleToLL(double value) {
	long long * returnval;
	returnval = (long long *) (&value);
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	return *returnval;
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}

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void computeEvenQuantiles(std::vector<double> &data,
		const unsigned int NUMBER_QUANTILES, std::vector<double> &quantiles) {
	if (data.empty() || NUMBER_QUANTILES == 0) {
		return;
	}
	std::sort(data.begin(), data.end());
	int elementNumber = data.size();
	quantiles.resize(NUMBER_QUANTILES + 1); //+min
	double factor = elementNumber / static_cast<double>(NUMBER_QUANTILES);
	quantiles[0] = data[0]; //minimum
	quantiles[NUMBER_QUANTILES] = data[data.size() - 1]; //maximum
	for (unsigned int i = 1; i < NUMBER_QUANTILES; i++) {
		if (elementNumber > 1) {
			int idx = static_cast<int>(std::floor(i * factor));
			if (idx == 0) {
				quantiles[i] = data[0];
			} else {
				double rest = (i * factor) - idx;
				quantiles[i] = data[idx - 1]
						+ rest * (data[idx] - data[idx - 1]); //ToDo scaling factor??
			}
		} else { //optimization, we don't need to calculate all the quantiles
			quantiles[i] = data[0];
		}
	}
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}