public function __construct(\alojaweb\inc\DBUtils $dbConnection)
{
$this->dbConnection = $dbConnection;
/* In this array there are the filter names with its default options
* that will be overwritten by the given custom defaults and options if given
* Array with filter => filter specific settings
*
* Specific settings is an array with
* types: inputText, inputNumber[{le,ge}], inputDate[{le,ge}], selectOne, selectMultiple, checkbox[Negated]
* default: null (any), array(values)
* table: associated DB table name
* parseFunction: function to parse special filter, for filters that need a lot of customization
*
* Very custom filters such as advanced filters not in this array
*
*/
$this->filters = array('bench' => array('table' => 'execs', 'default' => array('terasort', 'wordcount'), 'type' => 'selectMultiple', 'label' => 'Benchmarks:'), 'datasize' => array('database' => 'aloja2', 'table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Datasize: ', 'beautifier' => function ($value) {
return Utils::beautifyDatasize($value);
}, 'parseFunction' => 'parseDatasize'), 'scale_factor' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Scale factor: '), 'bench_type' => array('table' => 'execs', 'default' => array('HiBench'), 'type' => 'selectOne', 'label' => 'Bench suite:'), 'net' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Network:', 'beautifier' => function ($value) {
return Utils::getNetworkName($value);
}), 'disk' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Disk:'), 'blk_size' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Block size (b):', 'beautifier' => function ($value) {
return $value . ' MB';
}), 'comp' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Compression (c):', 'beautifier' => function ($value) {
return Utils::getCompressionName($value);
}), 'id_cluster' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Clusters (CL):', 'beautifier' => function ($value) {
return $this->filters['id_cluster']['namesClusters'][$value];
}, 'generateChoices' => function () {
$choices = $this->dbConnection->get_rows("select distinct id_cluster,CONCAT_WS('/',LPAD(id_cluster,2,0),c.vm_size,CONCAT(c.datanodes,'Dn')) as name from aloja2.execs e join aloja2.clusters c using (id_cluster) WHERE 1 " . DBUtils::getFilterExecs(' ') . " ORDER BY c.name ASC");
$returnChoices = array();
foreach ($choices as $choice) {
$returnChoices[] = $choice['id_cluster'];
//Not nice, but saves multiple queries to DB in the beautifier
$this->filters['id_cluster']['namesClusters'][$choice['id_cluster']] = $choice['name'];
}
return $returnChoices;
}), 'maps' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Maps:', 'beautifier' => function ($value) {
if ($value == 0) {
return 'N/A';
} else {
return $value;
}
}), 'replication' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Replication (r):'), 'iosf' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'I/O sort factor (I):'), 'iofilebuf' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'I/O file buffer:', 'beautifier' => function ($value) {
$suffix = ' KB';
if ($value >= 1024) {
$value /= 1024;
$suffix = ' MB';
}
return $value . $suffix;
}), 'provider' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Provider:'), 'vm_OS' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'VM OS:'), 'datanodes' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Cluster datanodes:'), 'vm_size' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'VM Size:'), 'vm_cores' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'VM cores:'), 'vm_RAM' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'VM RAM:', 'beautifier' => function ($value) {
if ($value * 10 % 10 != 0) {
return number_format($value, 1) . ' GB';
} else {
return number_format($value, 0) . ' GB';
}
}), 'type' => array('table' => 'clusters', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Cluster type:'), 'hadoop_version' => array('table' => 'execs', 'default' => null, 'type' => 'selectMultiple', 'label' => 'Hadoop version:'), 'minexetime' => array('table' => 'execs', 'field' => 'exe_time', 'default' => Utils::in_dev() ? 1 : 50, 'type' => 'inputNumberge', 'label' => 'Min exec time:'), 'maxexetime' => array('table' => 'execs', 'field' => 'exe_time', 'default' => null, 'type' => 'inputNumberle', 'label' => 'Max exec time:'), 'datefrom' => array('table' => 'execs', 'field' => 'start_time', 'default' => null, 'type' => 'inputDatege', 'label' => 'Date from:'), 'dateto' => array('table' => 'execs', 'field' => 'end_time', 'default' => null, 'type' => 'inputDatele', 'label' => 'Date to:'), 'money' => array('table' => 'mixed', 'field' => '(clustersAlias.cost_hour/3600)*execsAlias.exe_time', 'default' => null, 'type' => 'inputNumberle', 'label' => 'Max cost (US$):'), 'valid' => array('table' => 'execs', 'field' => 'valid', 'type' => 'checkbox', 'default' => 1, 'label' => 'Only valid execs'), 'filter' => array('table' => 'execs', 'field' => 'filter', 'type' => 'checkbox', 'default' => 1, 'label' => 'Filter', 'parseFunction' => function () {
$whereClause = "";
if (isset($_GET['filter'])) {
$values = 1;
} else {
if (!$this->formisSubmitted()) {
$values = $this->filters['filter']['default'];
} else {
$values = 0;
}
}
if ($values) {
$whereClause = " AND execsAlias.filter = 0 ";
}
return array('currentChoice' => $values, 'whereClause' => $whereClause);
}), 'prepares' => array('table' => 'execs', 'type' => 'checkbox', 'default' => Utils::in_dev() ? 1 : 0, 'label' => 'Include prepares', 'parseFunction' => function () {
$whereClause = "";
$values = 0;
if (isset($_GET['prepares'])) {
$values = 1;
} else {
$values = $this->filters['prepares']['default'];
if (!$values) {
$whereClause = " AND execsAlias.bench NOT LIKE 'prep_%' ";
}
}
return array('currentChoice' => $values, 'whereClause' => $whereClause);
}), 'perf_details' => array('table' => 'execs', 'type' => 'checkbox', 'default' => 0, 'label' => 'Only execs with perf details'), 'prediction_model' => array('type' => 'selectOne', 'default' => null, 'label' => 'Reference Model: ', 'generateChoices' => function () {
$query = "SELECT DISTINCT id_learner FROM aloja_ml.predictions";
$retval = $this->dbConnection->get_rows($query);
return array_column($retval, "id_learner");
}, 'parseFunction' => function () {
$choice = isset($_GET['prediction_model']) ? Utils::get_GET_stringArray('prediction_model') : array("");
if ($choice = array("")) {
$query = "SELECT DISTINCT id_learner FROM aloja_ml.predictions LIMIT 1";
$choice = $this->dbConnection->get_rows($query)[0]['id_learner'];
}
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'upred' => array('type' => 'checkbox', 'default' => 0, 'label' => 'Use predictions', 'parseFunction' => function () {
$choice = !isset($_GET['upred']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'uobsr' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Use observations', 'parseFunction' => function () {
$choice = !isset($_GET['uobsr']) && $this->formIssubmitted() ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'warning' => array('field' => 'outlier', 'table' => 'ml_predictions', 'type' => 'checkbox', 'default' => 0, 'label' => 'Show warnings', 'parseFunction' => function () {
$learner = $this->filters['prediction_model']['currentChoice'];
$whereClause = "";
$values = isset($_GET['warning']) ? 1 : 0;
if ($values && !empty($learner)) {
$whereClause = " AND (ml_predictionsAlias.outlier <= {$values} OR ml_predictionsAlias.outlier IS NULL) " . "AND (ml_predictionsAlias.id_learner = '{$learner[0]}' OR ml_predictionsAlias.id_learner IS NULL)";
}
return array('currentChoice' => $values, 'whereClause' => $whereClause);
}, 'filterGroup' => 'MLearning'), 'outlier' => array('table' => 'ml_predictions', 'type' => 'checkbox', 'default' => 0, 'label' => 'Show outliers', 'parseFunction' => function () {
$learner = $this->filters['prediction_model']['currentChoice'];
$whereClause = "";
$values = isset($_GET['outlier']) ? 2 : 0;
if ($values && !empty($learner)) {
$whereClause = " AND (ml_predictionsAlias.outlier <= 2 OR ml_predictionsAlias.outlier IS NULL) " . "AND (ml_predictionsAlias.id_learner = '{$learner}' OR ml_predictionsAlias.id_learner IS NULL)";
$values = 1;
} else {
if (!empty($learner) && !isset($_GET['warning'])) {
$whereClause = " AND (ml_predictionsAlias.outlier = 0 OR ml_predictionsAlias.outlier IS NULL) " . "AND (ml_predictionsAlias.id_learner = '{$learner}' OR ml_predictionsAlias.id_learner IS NULL)";
}
}
return array('currentChoice' => $values, 'whereClause' => $whereClause);
}, 'filterGroup' => 'MLearning'));
$this->aliasesTables = array('execs' => 'e', 'clusters' => 'c', 'ml_predictions' => 'p');
//To render groups on template. Rows are of 2 columns each. emptySpace puts an empty element on the rendered row
$this->filterGroups = array('basic' => array('label' => 'Basic filters', 'filters' => array('money', 'bench', 'bench_type', 'datasize', 'scale_factor', 'id_cluster', 'net', 'disk'), 'tabOpenDefault' => true), 'hardware' => array('label' => 'Hardware', 'filters' => array('datanodes', 'vm_size', 'vm_cores', 'vm_RAM', 'type', 'provider', 'vm_OS'), 'tabOpenDefault' => false), 'hadoop' => array('label' => 'Hadoop', 'filters' => array('maps', 'comp', 'replication', 'blk_size', 'iosf', 'iofilebuf', 'hadoop_version'), 'tabOpenDefault' => false), 'advanced' => array('label' => 'Advanced filters', 'filters' => array('valid', 'filter', 'prepares', 'perf_details', 'datefrom', 'dateto', 'minexetime', 'maxexetime'), 'tabOpenDefault' => false), 'MLearning' => array('label' => 'Machine Learning', 'filters' => array('prediction_model', 'upred', 'uobsr', 'warning', 'outlier'), 'tabOpenDefault' => true));
}
public function mlparamEvaluationAction()
{
$rows = $categories = $series = '';
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain_ml'], $this->container->get('config')['mysql_user'], $this->container->get('config')['mysql_pwd']);
$dbml->setAttribute(\PDO::ATTR_ERRMODE, \PDO::ERRMODE_EXCEPTION);
$dbml->setAttribute(\PDO::ATTR_EMULATE_PREPARES, false);
$db = $this->container->getDBUtils();
$where_configs = '';
$preset = null;
if (count($_GET) <= 1 || count($_GET) == 2 && array_key_exists('parameval', $_GET) || count($_GET) == 2 && array_key_exists('current_model', $_GET)) {
$preset = Utils::setDefaultPreset($db, 'mlparameval');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$params = array();
$param_names = array('benchs', 'nets', 'disks', 'mapss', 'iosfs', 'replications', 'iofilebufs', 'comps', 'blk_sizes', 'id_clusters', 'datanodess', 'bench_types', 'vm_sizes', 'vm_coress', 'vm_RAMs', 'types');
// Order is important
foreach ($param_names as $p) {
$params[$p] = Utils::read_params($p, $where_configs, FALSE);
sort($params[$p]);
}
$money = Utils::read_params('money', $where_configs);
$paramEval = isset($_GET['parameval']) && $_GET['parameval'] != '' ? $_GET['parameval'] : 'maps';
$minExecs = isset($_GET['minexecs']) ? $_GET['minexecs'] : -1;
$minExecsFilter = "";
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$where_configs = str_replace("`id_cluster`", "e.`id_cluster`", $where_configs);
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = Utils::getFilterOptions($db);
$paramOptions = array();
foreach ($options[$paramEval] as $option) {
if ($paramEval == 'id_cluster') {
$paramOptions[] = $option['name'];
} else {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option[$paramEval]);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option[$paramEval]);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option[$paramEval]);
} else {
$paramOptions[] = $option[$paramEval];
}
}
}
}
}
$benchOptions = $db->get_rows("SELECT DISTINCT bench FROM execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs} GROUP BY {$paramEval}, bench order by {$paramEval}");
// get the result rows
$query = "SELECT count(*) as count, {$paramEval}, e.id_exec, exec as conf, bench, " . "exe_time, avg(exe_time) avg_exe_time, min(exe_time) min_exe_time " . "from execs e LEFT JOIN clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs}" . "GROUP BY {$paramEval}, bench {$minExecsFilter} order by bench,{$paramEval}";
$rows = $db->get_rows($query);
if (!$rows) {
throw new \Exception("No results for query!");
}
$arrayBenchs = array();
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs[$bench['bench']][$param] = null;
$arrayBenchs[$bench['bench']][$param]['y'] = 0;
$arrayBenchs[$bench['bench']][$param]['count'] = 0;
}
}
$series = array();
$bench = '';
foreach ($rows as $row) {
if ($paramEval == 'comp') {
$row[$paramEval] = Utils::getCompressionName($row['comp']);
} else {
if ($paramEval == 'id_cluster') {
$row[$paramEval] = Utils::getClusterName($row[$paramEval], $db);
} else {
if ($paramEval == 'net') {
$row[$paramEval] = Utils::getNetworkName($row['net']);
} else {
if ($paramEval == 'disk') {
$row[$paramEval] = Utils::getDisksName($row['disk']);
} else {
if ($paramEval == 'iofilebuf') {
$row[$paramEval] /= 1024;
}
}
}
}
}
$arrayBenchs[$row['bench']][$row[$paramEval]]['y'] = round((int) $row['avg_exe_time'], 2);
$arrayBenchs[$row['bench']][$row[$paramEval]]['count'] = (int) $row['count'];
}
// ----------------------------------------------------
// Add predictions to the series
// ----------------------------------------------------
$jsonData = $jsonHeader = "[]";
$instance = "";
$arrayBenchs_pred = array();
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($param_names, $params, true, $db);
$model_info = MLUtils::generateModelInfo($param_names, $params, true, $db);
$instances = MLUtils::generateInstances($param_names, $params, true, $db);
// model for filling
$possible_models = $possible_models_id = array();
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = "";
if (array_key_exists('current_model', $_GET) && in_array($_GET['current_model'], $possible_models_id)) {
$current_model = $_GET['current_model'];
}
if (!empty($possible_models_id)) {
if ($current_model == "") {
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, p.id_learner FROM predictions p, learners l WHERE l.id_learner = p.id_learner AND p.id_learner IN ('" . implode("','", $possible_models_id) . "') AND predict_code > 0 ORDER BY MAE LIMIT 1";
$result = $dbml->query($query);
$row = $result->fetch();
$current_model = $row['id_learner'];
}
$config = $instance . '-' . $current_model . "-parameval";
$query_cache = "SELECT count(*) as total FROM trees WHERE id_learner = '" . $current_model . "' AND model = '" . $model_info . "'";
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$ret_data = null;
if (!$is_cached) {
// Call to MLFindAttributes, to fetch data
$_GET['pass'] = 2;
$_GET['unseen'] = 1;
$_GET['current_model'] = $current_model;
$mlfa1 = new MLFindAttributesController();
$mlfa1->container = $this->container;
$ret_data = $mlfa1->mlfindattributesAction();
if ($ret_data == 1) {
$must_wait = "YES";
$jsonData = $jsonHeader = '[]';
} else {
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
}
}
if ($is_cached) {
$must_wait = 'NO';
$query = "SELECT count(*) as count, {$paramEval}, bench, exe_time, avg(pred_time) avg_pred_time, min(pred_time) min_pred_time " . "FROM predictions e WHERE e.id_learner = '" . $current_model . "' {$filter_execs} {$where_configs}" . "GROUP BY {$paramEval}, bench {$minExecsFilter} order by bench, {$paramEval}";
$result = $dbml->query($query);
// Initialize array
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['count'] = 0;
}
}
foreach ($result as $row) {
$bench_n = $row['bench'] . '_pred';
$class = $row[$paramEval];
if ($paramEval == 'comp') {
$value = Utils::getCompressionName($class);
} else {
if ($paramEval == 'id_cluster') {
$value = Utils::getClusterName($class, $db);
} else {
if ($paramEval == 'net') {
$value = Utils::getNetworkName($class);
} else {
if ($paramEval == 'disk') {
$value = Utils::getDisksName($class);
} else {
if ($paramEval == 'iofilebuf') {
$value = $class / 1024;
} else {
$value = $class;
}
}
}
}
}
if (!in_array($value, $paramOptions)) {
$paramOptions[] = $value;
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['count'] = 0;
$arrayBenchs[$bench['bench']][$value] = null;
$arrayBenchs[$bench['bench']][$value]['y'] = 0;
$arrayBenchs[$bench['bench']][$value]['count'] = 0;
}
}
$arrayBenchs_pred[$bench_n][$value]['y'] = (int) $row['avg_pred_time'];
$arrayBenchs_pred[$bench_n][$value]['count'] = (int) $row['count'];
}
}
}
// ----------------------------------------------------
// END - Add predictions to the series
// ----------------------------------------------------
asort($paramOptions);
foreach ($arrayBenchs as $key => $arrayBench) {
$caregories = '';
$data_a = null;
$data_p = null;
foreach ($paramOptions as $param) {
if ($arrayBenchs[$key][$param]['count'] > 0 && empty($arrayBenchs_pred) || !empty($arrayBenchs_pred) && ($arrayBenchs_pred[$key . '_pred'][$param]['count'] > 0 || $arrayBenchs[$key][$param]['count'] > 0)) {
$data_a[] = $arrayBenchs[$key][$param];
if (!empty($arrayBenchs_pred)) {
$data_p[] = $arrayBenchs_pred[$key . '_pred'][$param];
}
$categories = $categories . "'{$param} " . Utils::getParamevalUnit($paramEval) . "',";
// FIXME - Redundant n times performed... don't care now
}
}
$series[] = array('name' => $key, 'data' => $data_a);
if (!empty($arrayBenchs_pred)) {
$series[] = array('name' => $key . '_pred', 'data' => $data_p);
}
}
$series = json_encode($series);
if (!empty($arrayBenchs_pred)) {
$colors = "['#7cb5ec','#9cd5fc','#434348','#636368','#90ed7d','#b0fd9d','#f7a35c','#f7c37c','#8085e9','#a0a5f9','#f15c80','#f17ca0','#e4d354','#f4f374','#8085e8','#a0a5f8','#8d4653','#ad6673','#91e8e1','#b1f8f1']";
} else {
$colors = "['#7cb5ec','#434348','#90ed7d','#f7a35c','#8085e9','#f15c80','#e4d354','#8085e8','#8d4653','#91e8e1']";
}
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$series = $jsonHeader = $colors = '[]';
$instance = $current_model = '';
$possible_models = $possible_models_id = array();
$must_wait = 'NO';
}
echo $this->container->getTwig()->render('mltemplate/mlconfigperf.html.twig', array('selected' => 'mlparameval', 'title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'categories' => $categories, 'series' => $series, 'benchs' => $params['benchs'], 'nets' => $params['nets'], 'disks' => $params['disks'], 'blk_sizes' => $params['blk_sizes'], 'comps' => $params['comps'], 'id_clusters' => $params['id_clusters'], 'mapss' => $params['mapss'], 'replications' => $params['replications'], 'iosfs' => $params['iosfs'], 'iofilebufs' => $params['iofilebufs'], 'datanodess' => $params['datanodess'], 'bench_types' => $params['bench_types'], 'vm_sizes' => $params['vm_sizes'], 'vm_coress' => $params['vm_coress'], 'vm_RAMs' => $params['vm_RAMs'], 'types' => $params['types'], 'money' => $money, 'paramEval' => $paramEval, 'instance' => $instance, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'current_model' => $current_model, 'gammacolors' => $colors, 'must_wait' => $must_wait, 'preset' => $preset, 'selPreset' => $selPreset, 'options' => Utils::getFilterOptions($db)));
}
public function mlfindattributesAction()
{
$instance = $instances = $message = $tree_descriptor = $model_html = $config = '';
$possible_models = $possible_models_id = $other_models = array();
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain'], $this->container->get('config')['mysql_user'], $this->container->get('config')['mysql_pwd']);
$dbml->setAttribute(\PDO::ATTR_ERRMODE, \PDO::ERRMODE_EXCEPTION);
$dbml->setAttribute(\PDO::ATTR_EMULATE_PREPARES, false);
$db = $this->container->getDBUtils();
if (array_key_exists('dump', $_GET)) {
$dump = $_GET["dump"];
unset($_GET["dump"]);
}
if (array_key_exists('pass', $_GET)) {
$pass = $_GET["pass"];
unset($_GET["pass"]);
}
$this->buildFilters(array('current_model' => array('type' => 'selectOne', 'default' => null, 'label' => 'Model to use: ', 'generateChoices' => function () {
return array();
}, 'parseFunction' => function () {
$choice = isset($_GET['current_model']) ? $_GET['current_model'] : array("");
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'unseen' => array('type' => 'checkbox', 'default' => 1, 'label' => 'Predict with unseen atributes ⚠', 'parseFunction' => function () {
$choice = isset($_GET['unseen']) && !isset($_GET['unseen']) ? 0 : 1;
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 1)));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('current_model', 'unseen'))));
$where_configs = $this->filters->getWhereClause();
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'vm_OS', 'vm_cores', 'vm_RAM', 'provider', 'vm_size', 'type', 'bench_type', 'hadoop_version');
// Order is important
$params = $this->filters->getFiltersSelectedChoices($param_names);
foreach ($param_names as $p) {
if (!is_null($params[$p]) && is_array($params[$p])) {
sort($params[$p]);
}
}
$learnParams = $this->filters->getFiltersSelectedChoices(array('current_model', 'unseen'));
$param_current_model = $learnParams['current_model'];
$unseen = $learnParams['unseen'] ? true : false;
// FIXME PATCH FOR PARAM LIBRARIES WITHOUT LEGACY
$where_configs = str_replace("AND .", "AND ", $where_configs);
$jsonData = $jsonHeader = "[]";
$mae = $rae = 0;
// compose instance
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, $unseen);
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, $unseen);
$instances = MLUtils::generateInstances($this->filters, $param_names, $params, $unseen, $db);
// Model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = '';
if (!is_null($possible_models_id) && in_array($param_current_model, $possible_models_id)) {
$current_model = $param_current_model;
}
// Other models for filling
$where_models = '';
if (!empty($possible_models_id)) {
$where_models = " WHERE id_learner NOT IN ('" . implode("','", $possible_models_id) . "')";
}
$result = $dbml->query("SELECT id_learner FROM aloja_ml.learners" . $where_models);
foreach ($result as $row) {
$other_models[] = $row['id_learner'];
}
if (!empty($possible_models_id) || $current_model != "") {
$result = $dbml->query("SELECT id_learner, model, algorithm, CASE WHEN `id_learner` IN ('" . implode("','", $possible_models_id) . "') THEN 'COMPATIBLE' ELSE 'NOT MATCHED' END AS compatible FROM aloja_ml.learners");
foreach ($result as $row) {
$model_html = $model_html . "<li>" . $row['id_learner'] . " => " . $row['algorithm'] . " : " . $row['compatible'] . " : " . $row['model'] . "</li>";
}
if ($current_model == "") {
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, p.id_learner FROM aloja_ml.predictions p, aloja_ml.learners l WHERE l.id_learner = p.id_learner AND p.id_learner IN ('" . implode("','", $possible_models_id) . "') AND predict_code > 0 ORDER BY MAE LIMIT 1";
$result = $dbml->query($query);
$row = $result->fetch();
$current_model = $row['id_learner'];
}
$config = $instance . '-' . $current_model . '-' . ($unseen ? 'U' : 'R');
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM aloja_ml.trees WHERE id_findattrs = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$tmp_file = md5($config) . '.tmp';
$in_process = file_exists(getcwd() . '/cache/query/' . md5($config) . '.lock');
$finished_process = $in_process && (int) shell_exec('ls ' . getcwd() . '/cache/query/' . md5($config) . '-*.lock | wc -w ') == count($instances);
if (!$in_process && !$finished_process && !$is_cached) {
// Retrieve file model from DB
$query = "SELECT file FROM aloja_ml.model_storage WHERE id_hash='" . $current_model . "' AND type='learner';";
$result = $dbml->query($query);
$row = $result->fetch();
$content = $row['file'];
$filemodel = getcwd() . '/cache/query/' . $current_model . '-object.rds';
$fp = fopen($filemodel, 'w');
fwrite($fp, $content);
fclose($fp);
// Run the predictor
exec('cd ' . getcwd() . '/cache/query ; touch ' . md5($config) . '.lock ; rm -f ' . $tmp_file);
$count = 1;
foreach ($instances as $inst) {
exec(getcwd() . '/resources/queue -d -c "cd ' . getcwd() . '/cache/query ; ../../resources/aloja_cli.r -m aloja_predict_instance -l ' . $current_model . ' -p inst_predict=\'' . $inst . '\' -v | grep -v \'Prediction\' >>' . $tmp_file . ' 2>/dev/null; touch ' . md5($config) . '-' . $count++ . '.lock" >/dev/null 2>&1 &');
}
}
$finished_process = (int) shell_exec('ls ' . getcwd() . '/cache/query/' . md5($config) . '-*.lock | wc -w ') == count($instances);
if ($finished_process && !$is_cached) {
// Read results and dump to DB
$i = 0;
$token = 0;
$token_i = 0;
$query = "INSERT IGNORE INTO aloja_ml.predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,datanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,pred_time,id_learner,instance,predict_code) VALUES ";
if (($handle = fopen(getcwd() . '/cache/query/' . $tmp_file, "r")) !== FALSE) {
while (($line = fgets($handle, 1000)) !== FALSE && $i < 1000) {
if ($line == '') {
break;
}
// Fetch Real Value
$inst_aux = preg_split("/\\s+/", $line);
$query_var = "SELECT AVG(exe_time) as AVG, id_exec, outlier FROM aloja_ml.predictions WHERE instance = '" . $inst_aux[1] . "' AND predict_code > 0";
$result = $dbml->query($query_var);
$row = $result->fetch();
$realexecval = is_null($row['AVG']) || $row['outlier'] == 2 ? 0 : $row['AVG'];
$realid_exec = is_null($row['id_exec']) || $row['outlier'] == 2 ? 0 : $row['id_exec'];
$query_var = "SELECT count(*) as num FROM aloja_ml.predictions WHERE instance = '" . $inst_aux[1] . "' AND id_learner = '" . $current_model . "'";
$result = $dbml->query($query_var);
$row = $result->fetch();
// Insert instance values
if ($row['num'] == 0) {
$token_i = 1;
$selected_instance = preg_replace('/,Cmp(\\d+),/', ',${1},', $inst_aux[1]);
$selected_instance = preg_replace('/,Cl(\\d+),/', ',${1},', $selected_instance);
if ($token > 0) {
$query = $query . ",";
}
$token = 1;
$query = $query . "('" . $realid_exec . "','" . $realexecval . "','" . str_replace(",", "','", $selected_instance) . "','" . $inst_aux[2] . "','" . $current_model . "','" . $inst_aux[1] . "','0') ";
}
$i++;
if ($i % 100 == 0 && $token_i > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
$query = "INSERT IGNORE INTO aloja_ml.predictions (id_exec,exe_time,bench,net,disk,maps,iosf,replication,iofilebuf,comp,blk_size,id_cluster,datanodes,vm_OS,vm_cores,vm_RAM,provider,vm_size,type,bench_type,hadoop_version,pred_time,id_learner,instance,predict_code) VALUES ";
$token = 0;
$token_i = 0;
}
}
if ($token_i > 0) {
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving into DB');
}
}
// Descriptive Tree
$tree_descriptor = shell_exec(getcwd() . '/resources/aloja_cli.r -m aloja_representative_tree -p method=ordered:dump_file="' . getcwd() . '/cache/query/' . $tmp_file . '":output="html" -v 2> /dev/null');
$tree_descriptor = substr($tree_descriptor, 5, -2);
$query = "INSERT INTO aloja_ml.trees (id_findattrs,id_learner,instance,model,tree_code) VALUES ('" . md5($config) . "','" . $current_model . "','" . $instance . "','" . $model_info . "','" . $tree_descriptor . "')";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving tree into DB');
}
// remove remaining locks
shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '*.lock');
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/query/' . md5($config) . '.tmp');
$is_cached = true;
}
fclose($handle);
}
if (!$is_cached) {
$jsonData = $jsonHeader = $jsonColumns = $jsonColor = '[]';
$must_wait = 'YES';
if (isset($dump)) {
$dbml = null;
echo "1";
exit(0);
}
if (isset($pass)) {
$dbml = null;
return "1";
}
} else {
if (isset($pass) && $pass == 2) {
$dbml = null;
return "2";
}
// Fetch results and compose JSON
$header = array('Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFS', 'Rep', 'IO.FBuf', 'Comp', 'Blk.Size', 'Cluster', 'Datanodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Version', 'Prediction', 'Observed');
$jsonHeader = '[{title:""}';
foreach ($header as $title) {
$jsonHeader = $jsonHeader . ',{title:"' . $title . '"}';
}
$jsonHeader = $jsonHeader . ']';
$query = "SELECT @i:=@i+1 as num, instance, AVG(pred_time) as pred_time, AVG(exe_time) as exe_time FROM aloja_ml.predictions, (SELECT @i:=0) d WHERE id_learner='" . $current_model . "' " . $where_configs . " GROUP BY instance";
$result = $dbml->query($query);
$jsonData = '[';
foreach ($result as $row) {
if ($jsonData != '[') {
$jsonData = $jsonData . ',';
}
$jsonData = $jsonData . "['" . $row['num'] . "','" . str_replace(",", "','", $row['instance']) . "','" . $row['pred_time'] . "','" . $row['exe_time'] . "']";
}
$jsonData = $jsonData . ']';
foreach (range(1, 33) as $value) {
$jsonData = str_replace('Cmp' . $value, Utils::getCompressionName($value), $jsonData);
}
// Fetch MAE & RAE values
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE FROM aloja_ml.predictions WHERE id_learner='" . md5($config) . "' AND predict_code > 0";
$result = $dbml->query($query);
$row = $result->fetch();
$mae = $row['MAE'];
$rae = $row['RAE'];
// Dump case
if (isset($dump)) {
echo "ID" . str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
echo str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
$dbml = null;
exit(0);
}
if (isset($pass) && $pass == 1) {
$retval = "ID" . str_replace(array("[", "]", "{title:\"", "\"}"), array('', '', ''), $jsonHeader) . "\n";
$retval .= str_replace(array('],[', '[[', ']]'), array("\n", '', ''), $jsonData);
$dbml = null;
return $retval;
}
// Display Descriptive Tree
$query = "SELECT tree_code FROM aloja_ml.trees WHERE id_findattrs = '" . md5($config) . "'";
$result = $dbml->query($query);
$row = $result->fetch();
$tree_descriptor = $row['tree_code'];
}
} else {
$message = "There are no prediction models trained for such parameters. Train at least one model in 'ML Prediction' section.";
$must_wait = 'NO';
if (isset($dump)) {
echo "-1";
exit(0);
}
if (isset($pass)) {
return "-1";
}
}
$dbml = null;
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$jsonData = $jsonHeader = "[]";
$must_wait = 'NO';
$mae = $rae = 0;
$dbml = null;
if (isset($pass)) {
return "-2";
}
}
$return_params = array('instance' => $instance, 'jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'models' => $model_html, 'models_id' => $possible_models_id, 'other_models_id' => $other_models, 'current_model' => $current_model, 'message' => $message, 'mae' => $mae, 'rae' => $rae, 'must_wait' => $must_wait, 'instance' => $instance, 'instances' => implode("<br/> ", $instances), 'model_info' => $model_info, 'id_findattr' => md5($config), 'unseen' => $unseen, 'tree_descriptor' => $tree_descriptor);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mlfindattributes.html.twig', $return_params);
}
public static function changeParamOptions(&$paramOptions, $paramEval)
{
if ($paramEval == 'comp') {
foreach ($paramOptions as &$option) {
$option['param'] = Utils::getCompressionName($option['param']);
}
}
}
public function mlparamEvaluationAction()
{
$rows = $categories = $series = $instance = $model_info = $config = $current_model = $slice_info = '';
$arrayBenchs_pred = $possible_models = $possible_models_id = $other_models = array();
$jsonData = $jsonHeader = "[]";
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain'], $this->container->get('config')['mysql_user'], $this->container->get('config')['mysql_pwd']);
$dbml->setAttribute(\PDO::ATTR_ERRMODE, \PDO::ERRMODE_EXCEPTION);
$dbml->setAttribute(\PDO::ATTR_EMULATE_PREPARES, false);
$db = $this->container->getDBUtils();
if (array_key_exists('parameval', $_GET)) {
$paramEval = isset($_GET['parameval']) && Utils::get_GET_string('parameval') != '' ? Utils::get_GET_string('parameval') : 'maps';
unset($_GET["parameval"]);
}
$this->buildFilters(array('current_model' => array('type' => 'selectOne', 'default' => null, 'label' => 'Model to use: ', 'generateChoices' => function () {
return array();
}, 'parseFunction' => function () {
$choice = isset($_GET['current_model']) ? $_GET['current_model'] : array("");
return array('whereClause' => '', 'currentChoice' => $choice);
}, 'filterGroup' => 'MLearning'), 'minExecs' => array('default' => 0, 'type' => 'inputNumber', 'label' => 'Minimum executions:', 'parseFunction' => function () {
return 0;
}, 'filterGroup' => 'basic'), 'minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 0)));
$this->buildFilterGroups(array('MLearning' => array('label' => 'Machine Learning', 'tabOpenDefault' => true, 'filters' => array('current_model'))));
$where_configs = $this->filters->getWhereClause();
$params = array();
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'vm_OS', 'vm_cores', 'vm_RAM', 'provider', 'vm_size', 'type', 'bench_type', 'hadoop_version');
// Order is important
$params = $this->filters->getFiltersSelectedChoices($param_names);
foreach ($param_names as $p) {
if (!is_null($params[$p]) && is_array($params[$p])) {
sort($params[$p]);
}
}
$params_additional = array();
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
$params_additional = $this->filters->getFiltersSelectedChoices($param_names_additional);
$param_variables = $this->filters->getFiltersSelectedChoices(array('current_model', 'minExecs'));
$param_current_model = $param_variables['current_model'];
$minExecs = $param_variables['minExecs'];
$where_configs = str_replace("AND .", "AND ", $where_configs);
$where_configs = str_replace("id_cluster", "e.id_cluster", $where_configs);
$minExecsFilter = "";
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = $this->filters->getFilterChoices();
$paramOptions = array();
foreach ($options[$paramEval] as $option) {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option);
} else {
$paramOptions[] = $option;
}
}
}
}
$param_eval_query = $paramEval == 'id_cluster' ? 'e.id_cluster' : $paramEval;
$benchOptions = $db->get_rows("SELECT DISTINCT bench FROM aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs} GROUP BY {$param_eval_query}, bench order by {$param_eval_query}");
// get the result rows
$query = "SELECT count(*) as count, {$param_eval_query}, e.id_exec, exec as conf, bench, " . "exe_time, avg(exe_time) avg_exe_time, min(exe_time) min_exe_time " . "from aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE 1 {$filter_execs} {$where_configs}" . "GROUP BY {$param_eval_query},bench {$minExecsFilter} order by bench,{$param_eval_query}";
$rows = $db->get_rows($query);
if (!$rows) {
throw new \Exception("No results for query!");
}
$arrayBenchs = array();
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs[$bench['bench']][$param] = null;
$arrayBenchs[$bench['bench']][$param]['y'] = 0;
$arrayBenchs[$bench['bench']][$param]['count'] = 0;
}
}
$series = array();
$bench = '';
foreach ($rows as $row) {
if ($paramEval == 'comp') {
$row[$paramEval] = Utils::getCompressionName($row['comp']);
} else {
if ($paramEval == 'net') {
$row[$paramEval] = Utils::getNetworkName($row['net']);
} else {
if ($paramEval == 'disk') {
$row[$paramEval] = Utils::getDisksName($row['disk']);
} else {
if ($paramEval == 'iofilebuf') {
$row[$paramEval] /= 1024;
}
}
}
}
$arrayBenchs[$row['bench']][$row[$paramEval]]['y'] = round((int) $row['avg_exe_time'], 2);
$arrayBenchs[$row['bench']][$row[$paramEval]]['count'] = (int) $row['count'];
}
// ----------------------------------------------------
// Add predictions to the series
// ----------------------------------------------------
$param_variables = $this->filters->getFiltersSelectedChoices(array('current_model'));
$param_current_model = $param_variables['current_model'];
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, true);
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, true);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
// model for filling
MLUtils::findMatchingModels($model_info, $possible_models, $possible_models_id, $dbml);
$current_model = '';
if (!is_null($possible_models_id) && in_array($param_current_model, $possible_models_id)) {
$current_model = $param_current_model;
}
// Other models for filling
$where_models = '';
if (!empty($possible_models_id)) {
$where_models = " WHERE id_learner NOT IN ('" . implode("','", $possible_models_id) . "')";
}
$result = $dbml->query("SELECT id_learner FROM aloja_ml.learners" . $where_models);
foreach ($result as $row) {
$other_models[] = $row['id_learner'];
}
if (!empty($possible_models_id)) {
if ($current_model == "") {
$query = "SELECT AVG(ABS(exe_time - pred_time)) AS MAE, AVG(ABS(exe_time - pred_time)/exe_time) AS RAE, p.id_learner FROM aloja_ml.predictions p, aloja_ml.learners l WHERE l.id_learner = p.id_learner AND p.id_learner IN ('" . implode("','", $possible_models_id) . "') AND predict_code > 0 ORDER BY MAE LIMIT 1";
$result = $dbml->query($query);
$row = $result->fetch();
$current_model = $row['id_learner'];
}
$config = $instance . '-' . $current_model . ' ' . $slice_info . "-parameval";
$query_cache = "SELECT count(*) as total FROM aloja_ml.trees WHERE id_learner = '" . $current_model . "' AND model = '" . $model_info . "'";
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$ret_data = null;
if (!$is_cached) {
// Call to MLFindAttributes, to fetch data
$_GET['pass'] = 2;
$_GET['unseen'] = 1;
$_GET['current_model'] = $current_model;
$mlfa1 = new MLFindAttributesController();
$mlfa1->container = $this->container;
$ret_data = $mlfa1->mlfindattributesAction();
if ($ret_data == 1) {
$must_wait = "YES";
$jsonData = $jsonHeader = '[]';
} else {
$is_cached_mysql = $dbml->query($query_cache);
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
}
}
if ($is_cached) {
$must_wait = 'NO';
$query = "SELECT count(*) as count, {$param_eval_query}, bench, exe_time, avg(pred_time) avg_pred_time, min(pred_time) min_pred_time " . "FROM aloja_ml.predictions e WHERE e.id_learner = '" . $current_model . "' {$filter_execs} {$where_configs}" . "GROUP BY {$param_eval_query}, bench {$minExecsFilter} order by bench,{$param_eval_query}";
$result = $dbml->query($query);
// Initialize array
foreach ($paramOptions as $param) {
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$param]['count'] = 0;
}
}
foreach ($result as $row) {
$bench_n = $row['bench'] . '_pred';
$class = $row[$paramEval];
if ($paramEval == 'comp') {
$value = Utils::getCompressionName($class);
} else {
if ($paramEval == 'id_cluster') {
$value = Utils::getClusterName($class, $db);
} else {
if ($paramEval == 'net') {
$value = Utils::getNetworkName($class);
} else {
if ($paramEval == 'disk') {
$value = Utils::getDisksName($class);
} else {
if ($paramEval == 'iofilebuf') {
$value = $class / 1024;
} else {
$value = $class;
}
}
}
}
}
if (!in_array($value, $paramOptions)) {
$paramOptions[] = $value;
foreach ($benchOptions as $bench) {
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value] = null;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['y'] = 0;
$arrayBenchs_pred[$bench['bench'] . '_pred'][$value]['count'] = 0;
$arrayBenchs[$bench['bench']][$value] = null;
$arrayBenchs[$bench['bench']][$value]['y'] = 0;
$arrayBenchs[$bench['bench']][$value]['count'] = 0;
}
}
$arrayBenchs_pred[$bench_n][$value]['y'] = (int) $row['avg_pred_time'];
$arrayBenchs_pred[$bench_n][$value]['count'] = (int) $row['count'];
}
}
}
// ----------------------------------------------------
// END - Add predictions to the series
// ----------------------------------------------------
asort($paramOptions);
foreach ($arrayBenchs as $key => $arrayBench) {
$caregories = '';
$data_a = null;
$data_p = null;
foreach ($paramOptions as $param) {
if ($arrayBenchs[$key][$param]['count'] > 0 && empty($arrayBenchs_pred) || !empty($arrayBenchs_pred) && ($arrayBenchs_pred[$key . '_pred'][$param]['count'] > 0 || $arrayBenchs[$key][$param]['count'] > 0)) {
$data_a[] = $arrayBenchs[$key][$param];
if (!empty($arrayBenchs_pred)) {
$data_p[] = $arrayBenchs_pred[$key . '_pred'][$param];
}
$categories = $categories . "'{$param} " . Utils::getParamevalUnit($paramEval) . "',";
// FIXME - Redundant n times performed... don't care now
}
}
$series[] = array('name' => $key, 'data' => $data_a);
if (!empty($arrayBenchs_pred)) {
$series[] = array('name' => $key . '_pred', 'data' => $data_p);
}
}
$series = json_encode($series);
if (!empty($arrayBenchs_pred)) {
$colors = "['#7cb5ec','#9cd5fc','#434348','#636368','#90ed7d','#b0fd9d','#f7a35c','#f7c37c','#8085e9','#a0a5f9','#f15c80','#f17ca0','#e4d354','#f4f374','#8085e8','#a0a5f8','#8d4653','#ad6673','#91e8e1','#b1f8f1']";
} else {
$colors = "['#7cb5ec','#434348','#90ed7d','#f7a35c','#8085e9','#f15c80','#e4d354','#8085e8','#8d4653','#91e8e1']";
}
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
$series = $jsonHeader = $colors = '[]';
$must_wait = 'NO';
}
$return_params = array('title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'categories' => $categories, 'series' => $series, 'paramEval' => $paramEval, 'instance' => $instance, 'models' => '<li>' . implode('</li><li>', $possible_models) . '</li>', 'models_id' => $possible_models_id, 'current_model' => $current_model, 'gammacolors' => $colors, 'model_info' => $model_info, 'slice_info' => $slice_info, 'must_wait' => $must_wait);
$this->filters->setCurrentChoices('current_model', array_merge($possible_models_id, array('---Other models---'), $other_models));
return $this->render('mltemplate/mlparameval.html.twig', $return_params);
}
public function mlobservedtreesAction()
{
$model_info = $instance = $slice_info = $message = $config = $tree_descriptor_ordered = $tree_descriptor_gini = '';
$jsonData = $jsonHeader = '[]';
$jsonObstrees = $jsonObstreesHeader = '[]';
$must_wait = 'NO';
try {
$dbml = new \PDO($this->container->get('config')['db_conn_chain'], $this->container->get('config')['mysql_user'], $this->container->get('config')['mysql_pwd']);
$dbml->setAttribute(\PDO::ATTR_ERRMODE, \PDO::ERRMODE_EXCEPTION);
$dbml->setAttribute(\PDO::ATTR_EMULATE_PREPARES, false);
$db = $this->container->getDBUtils();
// FIXME - This must be counted BEFORE building filters, as filters inject rubbish in GET when there are no parameters...
$instructions = count($_GET) <= 1;
$this->buildFilters(array('minexetime' => array('default' => 0), 'valid' => array('default' => 0), 'filter' => array('default' => 0), 'prepares' => array('default' => 1)));
if ($instructions) {
MLUtils::getIndexObsTrees($jsonObstrees, $jsonObstreesHeader, $dbml);
return $this->render('mltemplate/mlobstrees.html.twig', array('obstrees' => $jsonObstrees, 'header_obstrees' => $jsonObstreesHeader, 'jsonData' => '[]', 'jsonHeader' => '[]', 'instructions' => 'YES'));
}
$param_names = array('bench', 'net', 'disk', 'maps', 'iosf', 'replication', 'iofilebuf', 'comp', 'blk_size', 'id_cluster', 'datanodes', 'vm_OS', 'vm_cores', 'vm_RAM', 'provider', 'vm_size', 'type', 'bench_type', 'hadoop_version', 'datasize', 'scale_factor');
// Order is important
$params = $this->filters->getFiltersSelectedChoices($param_names);
foreach ($param_names as $p) {
if (!is_null($params[$p]) && is_array($params[$p])) {
sort($params[$p]);
}
}
$params_additional = array();
$param_names_additional = array('datefrom', 'dateto', 'minexetime', 'maxexetime', 'valid', 'filter');
// Order is important
$params_additional = $this->filters->getFiltersSelectedChoices($param_names_additional);
$where_configs = $this->filters->getWhereClause();
$where_configs = str_replace("AND .", "AND ", $where_configs);
// compose instance
$instance = MLUtils::generateSimpleInstance($this->filters, $param_names, $params, TRUE);
$model_info = MLUtils::generateModelInfo($this->filters, $param_names, $params, TRUE);
$slice_info = MLUtils::generateDatasliceInfo($this->filters, $param_names_additional, $params_additional);
$config = $instance . '-' . $slice_info . '-obstree';
$is_cached_mysql = $dbml->query("SELECT count(*) as total FROM aloja_ml.observed_trees WHERE id_obstrees = '" . md5($config) . "'");
$tmp_result = $is_cached_mysql->fetch();
$is_cached = $tmp_result['total'] > 0;
$in_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.lock');
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.fin');
$tmp_file = getcwd() . '/cache/ml/' . md5($config) . '.tmp';
// get headers for csv
$header_names = array('bench' => 'Benchmark', 'net' => 'Net', 'disk' => 'Disk', 'maps' => 'Maps', 'iosf' => 'IO.SFac', 'replication' => 'Rep', 'iofilebuf' => 'IO.FBuf', 'comp' => 'Comp', 'blk_size' => 'Blk.size', 'e.id_cluster' => 'Cluster', 'datanodes' => 'Datanodes', 'vm_OS' => 'VM.OS', 'vm_cores' => 'VM.Cores', 'vm_RAM' => 'VM.RAM', 'provider' => 'Provider', 'vm_size' => 'VM.Size', 'type' => 'Type', 'bench_type' => 'Bench.Type', 'hadoop_version' => 'Hadoop.Version', 'IFNULL(datasize,0)' => 'Datasize', 'scale_factor' => 'Scale.Factor');
$special_header_names = array('id_exec' => 'ID', 'exe_time' => 'Exe.Time');
$headers = array_keys($header_names);
$special_headers = array_keys($special_header_names);
if (!$in_process && !$finished_process && !$is_cached) {
// Dump the DB slice to csv
$query = "SELECT " . implode(",", $headers) . ", " . implode(",", $special_headers) . " FROM aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE hadoop_version IS NOT NULL" . $where_configs . ";";
$rows = $db->get_rows($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
if (($key = array_search('e.id_cluster', $headers)) !== false) {
$headers[$key] = 'id_cluster';
}
$fp = fopen($tmp_file, 'w');
foreach ($rows as $row) {
$row['id_cluster'] = "Cl" . $row['id_cluster'];
// Cluster is numerically codified...
$row['comp'] = "Cmp" . $row['comp'];
// Compression is numerically codified...
$line = '';
foreach ($headers as $hn) {
$line = $line . ($line != '' ? ',' : '') . $row[$hn];
}
$line = $row['id_exec'] . ' ' . $line . ' ' . $row['exe_time'] . "\n";
fputs($fp, $line);
}
fclose($fp);
if (($key = array_search('id_cluster', $headers)) !== false) {
$headers[$key] = 'e.id_cluster';
}
// Execute R Engine
$exe_query = 'cd ' . getcwd() . '/cache/ml;';
$exe_query = $exe_query . ' touch ' . md5($config) . '.lock;';
$exe_query = $exe_query . ' ../../resources/aloja_cli.r -m aloja_representative_tree -p method=ordered:dump_file=' . $tmp_file . ':output=nodejson -v >' . md5($config) . '-split.dat 2>/dev/null;';
$exe_query = $exe_query . ' ../../resources/aloja_cli.r -m aloja_representative_tree -p method=gini:dump_file=' . $tmp_file . ':output=nodejson -v >' . md5($config) . '-gini.dat 2>/dev/null;';
$exe_query = $exe_query . ' rm -f ' . md5($config) . '.lock; rm -f ' . $tmp_file . '; touch ' . md5($config) . '.fin';
exec(getcwd() . '/resources/queue -d -c "' . $exe_query . '" >/dev/null 2>&1 &');
}
if (!$is_cached) {
$finished_process = file_exists(getcwd() . '/cache/ml/' . md5($config) . '.fin');
if ($finished_process) {
// Read results and dump to DB
$tree_descriptor_ordered = '';
try {
$file = fopen(getcwd() . '/cache/ml/' . md5($config) . '-split.dat', "r");
$tree_descriptor_ordered = fgets($file);
$tree_descriptor_ordered = substr($tree_descriptor_ordered, 5, -2);
$tree_descriptor_ordered = str_replace("\\\"", "\"", $tree_descriptor_ordered);
$tree_descriptor_ordered = str_replace("desc:\"\"", "desc:\"---\"", $tree_descriptor_ordered);
fclose($file);
} catch (\Exception $e) {
throw new \Exception("Error on retrieving result file. Check that R is working properly.");
}
$tree_descriptor_gini = '';
/* try
{
$file = fopen(getcwd().'/cache/ml/'.md5($config).'-gini.dat', "r");
$tree_descriptor_gini = fgets($file);
$tree_descriptor_gini = substr($tree_descriptor_gini, 5, -2);
$tree_descriptor_gini = str_replace("\\\"","\"",$tree_descriptor_gini);
fclose($file);
} catch (\Exception $e) { throw new \Exception ("Error on retrieving result file. Check that R is working properly."); }
*/
$query = "INSERT INTO aloja_ml.observed_trees (id_obstrees,instance,model,dataslice,tree_code_split,tree_code_gain) VALUES ('" . md5($config) . "','" . $instance . "','" . $model_info . "','" . $slice_info . "','" . $tree_descriptor_ordered . "','" . $tree_descriptor_gini . "')";
if ($dbml->query($query) === FALSE) {
throw new \Exception('Error when saving tree into DB');
}
// Remove temporal files
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '-*.dat');
$output = shell_exec('rm -f ' . getcwd() . '/cache/ml/' . md5($config) . '.fin');
} else {
$must_wait = 'YES';
throw new \Exception('WAIT');
}
}
// Fetch results and compose JSON
$header = array('Benchmark', 'Net', 'Disk', 'Maps', 'IO.SFS', 'Rep', 'IO.FBuf', 'Comp', 'Blk.Size', 'Cluster', 'Datanodes', 'VM.OS', 'VM.Cores', 'VM.RAM', 'Provider', 'VM.Size', 'Type', 'Bench.Type', 'Version', 'Datasize', 'Scale.Factor', 'Observed');
$jsonHeader = '[{title:""}';
foreach ($header as $title) {
$jsonHeader = $jsonHeader . ',{title:"' . $title . '"}';
}
$jsonHeader = $jsonHeader . ']';
// Fetch observed values
$query = "SELECT " . implode(",", $headers) . ", " . implode(",", $special_headers) . " FROM aloja2.execs e LEFT JOIN aloja2.clusters c ON e.id_cluster = c.id_cluster WHERE hadoop_version IS NOT NULL" . $where_configs . ";";
$rows = $db->get_rows($query);
if (empty($rows)) {
throw new \Exception('No data matches with your critteria.');
}
if (($key = array_search('e.id_cluster', $headers)) !== false) {
$headers[$key] = 'id_cluster';
}
$jsonData = '[';
foreach ($rows as $row) {
$row['id_cluster'] = "Cl" . $row['id_cluster'];
// Cluster is numerically codified...
$row['comp'] = "Cmp" . $row['comp'];
// Compression is numerically codified...
$line = '';
foreach ($headers as $hn) {
$line = $line . ($line != '' ? ',' : '') . $row[$hn];
}
$line = $row['id_exec'] . ',' . $line . ',' . $row['exe_time'];
if ($jsonData != '[') {
$jsonData = $jsonData . ',';
}
$jsonData = $jsonData . "['" . str_replace(",", "','", $line) . "']";
}
$jsonData = $jsonData . ']';
foreach (range(1, 32) as $value) {
$jsonData = str_replace('Cmp' . $value, Utils::getCompressionName($value), $jsonData);
}
if ($tree_descriptor_ordered == '') {
// Display Descriptive Tree, if not processed yet
$query = "SELECT tree_code_split, tree_code_gain FROM aloja_ml.observed_trees WHERE id_obstrees = '" . md5($config) . "'";
$result = $dbml->query($query);
$row = $result->fetch();
$tree_descriptor_ordered = $row['tree_code_split'];
$tree_descriptor_gini = $row['tree_code_gain'];
}
} catch (\Exception $e) {
if ($e->getMessage() != "WAIT") {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
$jsonData = $jsonHeader = '[]';
}
$dbml = null;
$return_params = array('jsonData' => $jsonData, 'jsonHeader' => $jsonHeader, 'obstrees' => $jsonObstrees, 'header_obstrees' => $jsonObstreesHeader, 'message' => $message, 'must_wait' => $must_wait, 'instance' => $instance, 'model_info' => $model_info, 'slice_info' => $slice_info, 'id_obstrees' => md5($config), 'tree_descriptor_ordered' => $tree_descriptor_ordered, 'tree_descriptor_gini' => $tree_descriptor_gini);
return $this->render('mltemplate/mlobstrees.html.twig', $return_params);
}
public function paramEvaluationAction()
{
$db = $this->container->getDBUtils();
$this->buildFilters(array('minexecs' => array('default' => null, 'type' => 'inputNumber', 'label' => 'Minimum executions:', 'parseFunction' => function () {
return 0;
}, 'filterGroup' => 'basic')));
$whereClause = $this->filters->getWhereClause();
$model_html = '';
$model_info = $db->get_rows("SELECT id_learner, model, algorithm, dataslice FROM aloja_ml.learners");
foreach ($model_info as $row) {
$model_html = $model_html . "<li><b>" . $row['id_learner'] . "</b> => " . $row['algorithm'] . " : " . $row['model'] . " : " . $row['dataslice'] . "</li>";
}
$categories = '';
$series = '';
try {
$paramEval = isset($_GET['parameval']) && Utils::get_GET_string('parameval') != '' ? Utils::get_GET_string('parameval') : 'maps';
$minExecs = isset($_GET['minexecs']) ? Utils::get_GET_int('minexecs') : -1;
$this->filters->changeCurrentChoice('minexecs', $minExecs == -1 ? null : $minExecs);
$shortAliasParamEval = array('maps' => 'e', 'comp' => 'e', 'id_cluster' => 'c', 'net' => 'e', 'disk' => 'e', 'replication' => 'e', 'iofilebuf' => 'e', 'blk_size' => 'e', 'iosf' => 'e', 'vm_size' => 'c', 'vm_cores' => 'c', 'vm_ram' => 'c', 'datanodes' => 'c', 'hadoop_version' => 'e', 'type' => 'c');
$minExecsFilter = "";
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = $this->filters->getFiltersArray()[$paramEval]['choices'];
$benchOptions = "SELECT DISTINCT e.bench FROM aloja2.execs e JOIN aloja2.clusters c USING (id_cluster) LEFT JOIN aloja_ml.predictions p USING (id_exec) WHERE 1 {$filter_execs} {$whereClause} GROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench order by {$shortAliasParamEval[$paramEval]}.{$paramEval}";
$params = $this->filters->getFiltersSelectedChoices(array('prediction_model', 'upred', 'uobsr'));
$whereClauseML = str_replace("exe_time", "pred_time", $whereClause);
$whereClauseML = str_replace("start_time", "creation_time", $whereClauseML);
$query = "SELECT COUNT(*) AS count, {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench, avg(e.exe_time) avg_exe_time, min(e.exe_time) min_exe_time\n\t\t\t\t\t FROM aloja2.execs AS e JOIN aloja2.clusters AS c USING (id_cluster)\n\t\t\t\t\t LEFT JOIN aloja_ml.predictions AS p USING (id_exec)\n\t\t\t\t\t WHERE 1 {$filter_execs} {$whereClause}\n\t\t\t\t\t GROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench {$minExecsFilter} ORDER BY e.bench, {$shortAliasParamEval[$paramEval]}.{$paramEval}";
$queryPredictions = "\n\t\t\t\t\tSELECT COUNT(*) AS count, {$shortAliasParamEval[$paramEval]}.{$paramEval}, CONCAT('pred_',e.bench) as bench,\n\t\t\t\t\t\tavg(e.pred_time) as avg_exe_time, min(e.pred_time) as min_exe_time\n\t\t\t\t\t\tFROM aloja_ml.predictions AS e\n\t\t\t\t\t\tJOIN clusters c USING (id_cluster)\n\t\t\t\t\t\tWHERE 1 {$filter_execs} " . str_replace("p.", "e.", $whereClauseML) . " AND e.id_learner = '" . $params['prediction_model'] . "'\n\t\t\t\t\t\tGROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench {$minExecsFilter} ORDER BY e.bench, {$shortAliasParamEval[$paramEval]}.{$paramEval}";
// get the result rows
if ($params['uobsr'] == 1 && $params['upred'] == 1) {
$query = "({$query}) UNION ({$queryPredictions})";
$benchOptions = "SELECT DISTINCT e.bench FROM aloja2.execs e JOIN aloja2.clusters c USING (id_cluster) LEFT JOIN aloja_ml.predictions p USING (id_exec) WHERE 1 {$filter_execs} {$whereClause} GROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench\n\t\t\t\t\t\t\t\t UNION\n\t\t\t\t\t\t\t\t (SELECT DISTINCT CONCAT('pred_', e.bench) as bench FROM aloja_ml.predictions AS e\n\t\t\t\t\t\t\t\t JOIN clusters c USING (id_cluster)\n\t\t\t\t\t\t\t\t WHERE 1 {$filter_execs} " . str_replace("p.", "e.", $whereClauseML) . " AND e.id_learner = '" . $params['prediction_model'] . "'\n\t\t\t\t\t\t\t\t GROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench {$minExecsFilter})\n\t\t\t\t\t\t\t\t ORDER BY bench";
$optionsPredictions = "SELECT DISTINCT {$shortAliasParamEval[$paramEval]}.{$paramEval} FROM aloja_ml.predictions AS e JOIN clusters c USING (id_cluster) WHERE 1 {$filter_execs} " . str_replace("p.", "e.", $whereClauseML) . " AND e.id_learner = '" . $params['prediction_model'] . "' ORDER BY {$shortAliasParamEval[$paramEval]}.{$paramEval}";
$optionsPredictions = $db->get_rows($optionsPredictions);
foreach ($optionsPredictions as $predOption) {
$options[] = $predOption[$paramEval];
}
} else {
if ($params['uobsr'] == 0 && $params['upred'] == 1) {
$query = $queryPredictions;
$benchOptions = "SELECT DISTINCT CONCAT('pred_', e.bench) as bench FROM aloja_ml.predictions AS e\n \t\t\t\t\t\t\t\t JOIN clusters c USING (id_cluster)\n\t\t\t\t\t\t\t\t WHERE 1 {$filter_execs} " . str_replace("p.", "e.", $whereClauseML) . " AND e.id_learner = '" . $params['prediction_model'] . "'\n\t\t\t\t\t\t\t\t GROUP BY {$shortAliasParamEval[$paramEval]}.{$paramEval}, e.bench {$minExecsFilter} ORDER BY e.bench, {$shortAliasParamEval[$paramEval]}.{$paramEval}";
$options = array();
$optionsPredictions = "SELECT DISTINCT {$shortAliasParamEval[$paramEval]}.{$paramEval} FROM aloja_ml.predictions AS e JOIN clusters c USING (id_cluster) WHERE 1 {$filter_execs} " . str_replace("p.", "e.", $whereClauseML) . " AND e.id_learner = '" . $params['prediction_model'] . "' ORDER BY {$shortAliasParamEval[$paramEval]}.{$paramEval}";
$optionsPredictions = $db->get_rows($optionsPredictions);
foreach ($optionsPredictions as $predOption) {
$options[] = $predOption[$paramEval];
}
} else {
if ($params['uobsr'] == 0 && $params['upred'] == 0) {
$this->container->getTwig()->addGlobal('message', "Warning: No data selected (Predictions|Observations) from the ML Filters. Adding the Observed executions to the figure by default.\n");
}
}
}
$rows = $db->get_rows($query);
$benchOptions = $db->get_rows($benchOptions);
if (!$rows) {
throw new \Exception("No results for query!");
}
$paramOptions = array();
foreach ($options as $option) {
if ($paramEval == 'id_cluster') {
$paramOptions[] = Utils::getClusterName($option, $db);
} else {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option);
} else {
if ($paramEval == 'vm_ram') {
$paramOptions[] = Utils::getBeautyRam($option);
} else {
$paramOptions[] = $option;
}
}
}
}
}
}
$categories = '';
$arrayBenchs = array();
foreach ($paramOptions as $param) {
$categories .= "'{$param}" . Utils::getParamevalUnit($paramEval) . "',";
foreach ($benchOptions as $bench) {
$arrayBenchs[$bench['bench']][$param] = null;
}
}
$series = array();
foreach ($rows as $row) {
if ($paramEval == 'comp') {
$row[$paramEval] = Utils::getCompressionName($row['comp']);
} else {
if ($paramEval == 'id_cluster') {
$row[$paramEval] = Utils::getClusterName($row[$paramEval], $db);
} else {
if ($paramEval == 'net') {
$row[$paramEval] = Utils::getNetworkName($row['net']);
} else {
if ($paramEval == 'disk') {
$row[$paramEval] = Utils::getDisksName($row['disk']);
} else {
if ($paramEval == 'vm_ram') {
$row[$paramEval] = Utils::getBeautyRam($row['vm_ram']);
}
}
}
}
}
$arrayBenchs[strtolower($row['bench'])][$row[$paramEval]]['y'] = round((int) $row['avg_exe_time'], 2);
$arrayBenchs[strtolower($row['bench'])][$row[$paramEval]]['count'] = (int) $row['count'];
}
foreach ($arrayBenchs as $key => $arrayBench) {
$series[] = array('name' => $key, 'data' => array_values($arrayBench));
}
$series = json_encode($series);
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
return $this->render('configEvaluationViews/parameval.html.twig', array('title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'minexecs' => $minExecs, 'categories' => $categories, 'series' => $series, 'paramEval' => $paramEval, 'models' => $model_html));
}
public function paramEvaluationAction()
{
$db = $this->container->getDBUtils();
$preset = null;
if (sizeof($_GET) <= 1) {
$preset = Utils::setDefaultPreset($db, 'Parameter Evaluation');
}
$selPreset = isset($_GET['presets']) ? $_GET['presets'] : "none";
$rows = '';
$categories = '';
$series = '';
try {
$where_configs = '';
$concat_config = "";
if (!isset($_GET['benchs'])) {
$_GET['benchs'] = array('wordcount', 'terasort', 'sort');
}
$datefrom = Utils::read_params('datefrom', $where_configs);
$dateto = Utils::read_params('dateto', $where_configs);
$benchs = Utils::read_params('benchs', $where_configs);
$nets = Utils::read_params('nets', $where_configs);
$disks = Utils::read_params('disks', $where_configs);
$blk_sizes = Utils::read_params('blk_sizes', $where_configs);
$comps = Utils::read_params('comps', $where_configs);
$id_clusters = Utils::read_params('id_clusters', $where_configs);
$mapss = Utils::read_params('mapss', $where_configs);
$replications = Utils::read_params('replications', $where_configs);
$iosfs = Utils::read_params('iosfs', $where_configs);
$iofilebufs = Utils::read_params('iofilebufs', $where_configs);
$money = Utils::read_params('money', $where_configs);
$datanodes = Utils::read_params('datanodess', $where_configs, false);
$benchtype = Utils::read_params('bench_types', $where_configs);
$vm_sizes = Utils::read_params('vm_sizes', $where_configs, false);
$vm_coress = Utils::read_params('vm_coress', $where_configs, false);
$vm_RAMs = Utils::read_params('vm_RAMs', $where_configs, false);
$hadoop_versions = Utils::read_params('hadoop_versions', $where_configs, false);
$types = Utils::read_params('types', $where_configs, false);
$valid = Utils::read_params('valids', $where_configs);
$filters = Utils::read_params('filters', $where_configs, false);
$allunchecked = isset($_GET['allunchecked']) ? $_GET['allunchecked'] : '';
$minexetime = Utils::read_params('minexetime', $where_configs, false);
$maxexetime = Utils::read_params('maxexetime', $where_configs, false);
$provider = Utils::read_params('providers', $where_configs, false);
$vm_OS = Utils::read_params('vm_OSs', $where_configs, false);
$paramEval = isset($_GET['parameval']) && $_GET['parameval'] != '' ? $_GET['parameval'] : 'maps';
$minExecs = isset($_GET['minexecs']) ? $_GET['minexecs'] : -1;
$minExecsFilter = "";
if ($minExecs > 0) {
$minExecsFilter = "HAVING COUNT(*) > {$minExecs}";
}
$filter_execs = DBUtils::getFilterExecs();
$options = Utils::getFilterOptions($db);
$paramOptions = array();
foreach ($options[$paramEval] as $option) {
if ($paramEval == 'id_cluster') {
$paramOptions[] = $option['name'];
} else {
if ($paramEval == 'comp') {
$paramOptions[] = Utils::getCompressionName($option[$paramEval]);
} else {
if ($paramEval == 'net') {
$paramOptions[] = Utils::getNetworkName($option[$paramEval]);
} else {
if ($paramEval == 'disk') {
$paramOptions[] = Utils::getDisksName($option[$paramEval]);
} else {
if ($paramEval == 'vm_ram') {
$paramOptions[] = Utils::getBeautyRam($option['vm_RAM']);
} else {
$paramOptions[] = $option[$paramEval];
}
}
}
}
}
}
// if($paramEval == 'maps')
// $paramOptions = array(4,6,8,10,12,16,24,32);
// else if($paramEval == 'comp')
// $paramOptions = array('None','ZLIB','BZIP2','Snappy');
// else if($paramEval == 'id_cluster')
// $paramOptions = array('rl-06');
// else if($paramEval == 'net')
// $paramOptions = array('Ethernet','Infiniband');
// else if($paramEval == 'disk')
// $paramOptions = array('Hard-disk drive','1 HDFS remote(s)/tmp local','2 HDFS remote(s)/tmp local','3 HDFS remote(s)/tmp local','1 HDFS remote(s)', '2 HDFS remote(s)', '3 HDFS remote(s)', 'SSD');
// else if($paramEval == 'replication')
// $paramOptions = array(1,2,3);
// else if($paramEval == 'iofilebuf')
// $paramOptions = array(1,4,16,32,64,128,256);
// else if($paramEval == 'blk_size')
// $paramOptions = array(32,64,128,256);
// else if($paramEval == 'iosf')
// $paramOptions = array(5,10,20,50);
$benchOptions = $db->get_rows("SELECT DISTINCT bench FROM execs e JOIN clusters c USING (id_cluster) WHERE 1 {$filter_execs} {$where_configs} GROUP BY {$paramEval}, bench order by {$paramEval}");
// get the result rows
$query = "SELECT count(*) as count, {$paramEval}, e.id_exec, exec as conf, bench, " . "exe_time, avg(exe_time) avg_exe_time, min(exe_time) min_exe_time " . "from execs e JOIN clusters c USING (id_cluster) WHERE 1 {$filter_execs} {$where_configs}" . "GROUP BY {$paramEval}, bench {$minExecsFilter} order by bench,{$paramEval}";
$rows = $db->get_rows($query);
if (!$rows) {
throw new \Exception("No results for query!");
}
$categories = '';
$arrayBenchs = array();
foreach ($paramOptions as $param) {
$categories .= "'{$param}" . Utils::getParamevalUnit($paramEval) . "',";
foreach ($benchOptions as $bench) {
$arrayBenchs[$bench['bench']][$param] = null;
}
}
$series = array();
$bench = '';
foreach ($rows as $row) {
if ($paramEval == 'comp') {
$row[$paramEval] = Utils::getCompressionName($row['comp']);
} else {
if ($paramEval == 'id_cluster') {
$row[$paramEval] = Utils::getClusterName($row[$paramEval], $db);
} else {
if ($paramEval == 'net') {
$row[$paramEval] = Utils::getNetworkName($row['net']);
} else {
if ($paramEval == 'disk') {
$row[$paramEval] = Utils::getDisksName($row['disk']);
} else {
if ($paramEval == 'vm_ram') {
$row[$paramEval] = Utils::getBeautyRam($row['vm_ram']);
}
}
}
}
}
$arrayBenchs[strtolower($row['bench'])][$row[$paramEval]]['y'] = round((int) $row['avg_exe_time'], 2);
$arrayBenchs[strtolower($row['bench'])][$row[$paramEval]]['count'] = (int) $row['count'];
}
foreach ($arrayBenchs as $key => $arrayBench) {
$series[] = array('name' => $key, 'data' => array_values($arrayBench));
}
$series = json_encode($series);
} catch (\Exception $e) {
$this->container->getTwig()->addGlobal('message', $e->getMessage() . "\n");
}
echo $this->container->getTwig()->render('parameval/parameval.html.twig', array('selected' => 'Parameter Evaluation', 'title' => 'Improvement of Hadoop Execution by SW and HW Configurations', 'categories' => $categories, 'series' => $series, 'datefrom' => $datefrom, 'dateto' => $dateto, 'benchs' => $benchs, 'nets' => $nets, 'disks' => $disks, 'blk_sizes' => $blk_sizes, 'comps' => $comps, 'id_clusters' => $id_clusters, 'mapss' => $mapss, 'replications' => $replications, 'iosfs' => $iosfs, 'iofilebufs' => $iofilebufs, 'money' => $money, 'datanodess' => $datanodes, 'bench_types' => $benchtype, 'vm_sizes' => $vm_sizes, 'vm_coress' => $vm_coress, 'vm_RAMs' => $vm_RAMs, 'vm_OS' => $vm_OS, 'hadoop_versions' => $hadoop_versions, 'types' => $types, 'providers' => $provider, 'filters' => $filters, 'allunchecked' => $allunchecked, 'minexetime' => $minexetime, 'maxexetime' => $maxexetime, 'preset' => $preset, 'selPreset' => $selPreset, 'paramEval' => $paramEval, 'options' => $options));
}
