dbinit.py revision 1758:74acd5b23964
1 2# Copyright (c) 2003-2004 The Regents of The University of Michigan 3# All rights reserved. 4# 5# Redistribution and use in source and binary forms, with or without 6# modification, are permitted provided that the following conditions are 7# met: redistributions of source code must retain the above copyright 8# notice, this list of conditions and the following disclaimer; 9# redistributions in binary form must reproduce the above copyright 10# notice, this list of conditions and the following disclaimer in the 11# documentation and/or other materials provided with the distribution; 12# neither the name of the copyright holders nor the names of its 13# contributors may be used to endorse or promote products derived from 14# this software without specific prior written permission. 15# 16# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 20# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 21# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 22# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 23# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 24# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 25# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 26# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 28#Permission is granted to use, copy, create derivative works and 29#redistribute this software and such derivative works for any purpose, 30#so long as the copyright notice above, this grant of permission, and 31#the disclaimer below appear in all copies made; and so long as the 32#name of The University of Michigan is not used in any advertising or 33#publicity pertaining to the use or distribution of this software 34#without specific, written prior authorization. 35# 36#THIS SOFTWARE IS PROVIDED AS IS, WITHOUT REPRESENTATION FROM THE 37#UNIVERSITY OF MICHIGAN AS TO ITS FITNESS FOR ANY PURPOSE, AND WITHOUT 38#WARRANTY BY THE UNIVERSITY OF MICHIGAN OF ANY KIND, EITHER EXPRESS OR 39#IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF 40#MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE REGENTS OF 41#THE UNIVERSITY OF MICHIGAN SHALL NOT BE LIABLE FOR ANY DAMAGES, 42#INCLUDING DIRECT, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL 43#DAMAGES, WITH RESPECT TO ANY CLAIM ARISING OUT OF OR IN CONNECTION 44#WITH THE USE OF THE SOFTWARE, EVEN IF IT HAS BEEN OR IS HEREAFTER 45#ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 46 47import MySQLdb 48 49class MyDB(object): 50 def __init__(self, options): 51 self.name = options.db 52 self.host = options.host 53 self.user = options.user 54 self.passwd = options.passwd 55 self.mydb = None 56 self.cursor = None 57 58 def admin(self): 59 self.close() 60 self.mydb = MySQLdb.connect(db='mysql', host=self.host, user=self.user, 61 passwd=self.passwd) 62 self.cursor = self.mydb.cursor() 63 64 def connect(self): 65 self.close() 66 self.mydb = MySQLdb.connect(db=self.name, host=self.host, 67 user=self.user, passwd=self.passwd) 68 self.cursor = self.mydb.cursor() 69 70 def close(self): 71 if self.mydb is not None: 72 self.mydb.close() 73 self.cursor = None 74 75 def query(self, sql): 76 self.cursor.execute(sql) 77 78 def drop(self): 79 self.query('DROP DATABASE IF EXISTS %s' % self.name) 80 81 def create(self): 82 self.query('CREATE DATABASE %s' % self.name) 83 84 def populate(self): 85 # 86 # Each run (or simulation) gets its own entry in the runs table to 87 # group stats by where they were generated 88 # 89 # COLUMNS: 90 # 'id' is a unique identifier for each run to be used in other 91 # tables. 92 # 'name' is the user designated name for the data generated. It is 93 # configured in the simulator. 94 # 'user' identifies the user that generated the data for the given 95 # run. 96 # 'project' another name to identify runs for a specific goal 97 # 'date' is a timestamp for when the data was generated. It can be 98 # used to easily expire data that was generated in the past. 99 # 'expire' is a timestamp for when the data should be removed from 100 # the database so we don't have years worth of junk. 101 # 102 # INDEXES: 103 # 'run' is indexed so you can find out details of a run if the run 104 # was retreived from the data table. 105 # 'name' is indexed so that two all run names are forced to be unique 106 # 107 self.query(''' 108 CREATE TABLE runs( 109 rn_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT, 110 rn_name VARCHAR(200) NOT NULL, 111 rn_sample VARCHAR(32) NOT NULL, 112 rn_user VARCHAR(32) NOT NULL, 113 rn_project VARCHAR(100) NOT NULL, 114 rn_date TIMESTAMP NOT NULL, 115 rn_expire TIMESTAMP NOT NULL, 116 PRIMARY KEY (rn_id), 117 UNIQUE (rn_name,rn_sample) 118 ) TYPE=InnoDB''') 119 120 # 121 # We keep the bin names separate so that the data table doesn't get 122 # huge since bin names are frequently repeated. 123 # 124 # COLUMNS: 125 # 'id' is the unique bin identifer. 126 # 'name' is the string name for the bin. 127 # 128 # INDEXES: 129 # 'bin' is indexed to get the name of a bin when data is retrieved 130 # via the data table. 131 # 'name' is indexed to get the bin id for a named bin when you want 132 # to search the data table based on a specific bin. 133 # 134 self.query(''' 135 CREATE TABLE bins( 136 bn_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT, 137 bn_name VARCHAR(255) NOT NULL, 138 PRIMARY KEY(bn_id), 139 UNIQUE (bn_name) 140 ) TYPE=InnoDB''') 141 142 # 143 # The stat table gives us all of the data for a particular stat. 144 # 145 # COLUMNS: 146 # 'stat' is a unique identifier for each stat to be used in other 147 # tables for references. 148 # 'name' is simply the simulator derived name for a given 149 # statistic. 150 # 'descr' is the description of the statistic and what it tells 151 # you. 152 # 'type' defines what the stat tells you. Types are: 153 # SCALAR: A simple scalar statistic that holds one value 154 # VECTOR: An array of statistic values. Such a something that 155 # is generated per-thread. Vectors exist to give averages, 156 # pdfs, cdfs, means, standard deviations, etc across the 157 # stat values. 158 # DIST: Is a distribution of data. When the statistic value is 159 # sampled, its value is counted in a particular bucket. 160 # Useful for keeping track of utilization of a resource. 161 # (e.g. fraction of time it is 25% used vs. 50% vs. 100%) 162 # VECTORDIST: Can be used when the distribution needs to be 163 # factored out into a per-thread distribution of data for 164 # example. It can still be summed across threads to find 165 # the total distribution. 166 # VECTOR2D: Can be used when you have a stat that is not only 167 # per-thread, but it is per-something else. Like 168 # per-message type. 169 # FORMULA: This statistic is a formula, and its data must be 170 # looked up in the formula table, for indicating how to 171 # present its values. 172 # 'subdata' is potentially used by any of the vector types to 173 # give a specific name to all of the data elements within a 174 # stat. 175 # 'print' indicates whether this stat should be printed ever. 176 # (Unnamed stats don't usually get printed) 177 # 'prereq' only print the stat if the prereq is not zero. 178 # 'prec' number of decimal places to print 179 # 'nozero' don't print zero values 180 # 'nonan' don't print NaN values 181 # 'total' for vector type stats, print the total. 182 # 'pdf' for vector type stats, print the pdf. 183 # 'cdf' for vector type stats, print the cdf. 184 # 185 # The Following are for dist type stats: 186 # 'min' is the minimum bucket value. Anything less is an underflow. 187 # 'max' is the maximum bucket value. Anything more is an overflow. 188 # 'bktsize' is the approximate number of entries in each bucket. 189 # 'size' is the number of buckets. equal to (min/max)/bktsize. 190 # 191 # INDEXES: 192 # 'stat' is indexed so that you can find out details about a stat 193 # if the stat id was retrieved from the data table. 194 # 'name' is indexed so that you can simply look up data about a 195 # named stat. 196 # 197 self.query(''' 198 CREATE TABLE stats( 199 st_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT, 200 st_name VARCHAR(255) NOT NULL, 201 st_descr TEXT NOT NULL, 202 st_type ENUM("SCALAR", "VECTOR", "DIST", "VECTORDIST", 203 "VECTOR2D", "FORMULA") NOT NULL, 204 st_print BOOL NOT NULL, 205 st_prereq SMALLINT UNSIGNED NOT NULL, 206 st_prec TINYINT NOT NULL, 207 st_nozero BOOL NOT NULL, 208 st_nonan BOOL NOT NULL, 209 st_total BOOL NOT NULL, 210 st_pdf BOOL NOT NULL, 211 st_cdf BOOL NOT NULL, 212 st_min DOUBLE NOT NULL, 213 st_max DOUBLE NOT NULL, 214 st_bktsize DOUBLE NOT NULL, 215 st_size SMALLINT UNSIGNED NOT NULL, 216 PRIMARY KEY (st_id), 217 UNIQUE (st_name) 218 ) TYPE=InnoDB''') 219 220 # 221 # This is the main table of data for stats. 222 # 223 # COLUMNS: 224 # 'stat' refers to the stat field given in the stat table. 225 # 226 # 'x' referrs to the first dimension of a multi-dimensional stat. For 227 # a vector, x will start at 0 and increase for each vector 228 # element. 229 # For a distribution: 230 # -1: sum (for calculating standard deviation) 231 # -2: sum of squares (for calculating standard deviation) 232 # -3: total number of samples taken (for calculating 233 # standard deviation) 234 # -4: minimum value 235 # -5: maximum value 236 # -6: underflow 237 # -7: overflow 238 # 'y' is used by a VECTORDIST and the VECTOR2D to describe the second 239 # dimension. 240 # 'run' is the run that the data was generated from. Details up in 241 # the run table 242 # 'tick' is a timestamp generated by the simulator. 243 # 'bin' is the name of the bin that the data was generated in, if 244 # any. 245 # 'data' is the actual stat value. 246 # 247 # INDEXES: 248 # 'stat' is indexed so that a user can find all of the data for a 249 # particular stat. It is not unique, because that specific stat 250 # can be found in many runs, bins, and samples, in addition to 251 # having entries for the mulidimensional cases. 252 # 'run' is indexed to allow a user to remove all of the data for a 253 # particular execution run. It can also be used to allow the 254 # user to print out all of the data for a given run. 255 # 256 self.query(''' 257 CREATE TABLE data( 258 dt_stat SMALLINT UNSIGNED NOT NULL, 259 dt_x SMALLINT NOT NULL, 260 dt_y SMALLINT NOT NULL, 261 dt_run SMALLINT UNSIGNED NOT NULL, 262 dt_tick BIGINT UNSIGNED NOT NULL, 263 dt_bin SMALLINT UNSIGNED NOT NULL, 264 dt_data DOUBLE NOT NULL, 265 INDEX (dt_stat), 266 INDEX (dt_run), 267 UNIQUE (dt_stat,dt_x,dt_y,dt_run,dt_tick,dt_bin) 268 ) TYPE=InnoDB;''') 269 270 # 271 # Names and descriptions for multi-dimensional stats (vectors, etc.) 272 # are stored here instead of having their own entry in the statistics 273 # table. This allows all parts of a single stat to easily share a 274 # single id. 275 # 276 # COLUMNS: 277 # 'stat' is the unique stat identifier from the stat table. 278 # 'x' is the first dimension for multi-dimensional stats 279 # corresponding to the data table above. 280 # 'y' is the second dimension for multi-dimensional stats 281 # corresponding to the data table above. 282 # 'name' is the specific subname for the unique stat,x,y combination. 283 # 'descr' is the specific description for the uniqe stat,x,y 284 # combination. 285 # 286 # INDEXES: 287 # 'stat' is indexed so you can get the subdata for a specific stat. 288 # 289 self.query(''' 290 CREATE TABLE subdata( 291 sd_stat SMALLINT UNSIGNED NOT NULL, 292 sd_x SMALLINT NOT NULL, 293 sd_y SMALLINT NOT NULL, 294 sd_name VARCHAR(255) NOT NULL, 295 sd_descr TEXT, 296 UNIQUE (sd_stat,sd_x,sd_y) 297 ) TYPE=InnoDB''') 298 299 300 # 301 # The formula table is maintained separately from the data table 302 # because formula data, unlike other stat data cannot be represented 303 # there. 304 # 305 # COLUMNS: 306 # 'stat' refers to the stat field generated in the stat table. 307 # 'formula' is the actual string representation of the formula 308 # itself. 309 # 310 # INDEXES: 311 # 'stat' is indexed so that you can just look up a formula. 312 # 313 self.query(''' 314 CREATE TABLE formulas( 315 fm_stat SMALLINT UNSIGNED NOT NULL, 316 fm_formula BLOB NOT NULL, 317 PRIMARY KEY(fm_stat) 318 ) TYPE=InnoDB''') 319 320 # 321 # Each stat used in each formula is kept in this table. This way, if 322 # you want to print out a particular formula, you can simply find out 323 # which stats you need by looking in this table. Additionally, when 324 # you remove a stat from the stats table and data table, you remove 325 # any references to the formula in this table. When a formula is no 326 # longer referred to, you remove its entry. 327 # 328 # COLUMNS: 329 # 'stat' is the stat id from the stat table above. 330 # 'child' is the stat id of a stat that is used for this formula. 331 # There may be many children for any given 'stat' (formula) 332 # 333 # INDEXES: 334 # 'stat' is indexed so you can look up all of the children for a 335 # particular stat. 336 # 'child' is indexed so that you can remove an entry when a stat is 337 # removed. 338 # 339 self.query(''' 340 CREATE TABLE formula_ref( 341 fr_stat SMALLINT UNSIGNED NOT NULL, 342 fr_run SMALLINT UNSIGNED NOT NULL, 343 UNIQUE (fr_stat,fr_run), 344 INDEX (fr_stat), 345 INDEX (fr_run) 346 ) TYPE=InnoDB''') 347 348 # COLUMNS: 349 # 'event' is the unique event id from the event_desc table 350 # 'run' is simulation run id that this event took place in 351 # 'tick' is the tick when the event happened 352 # 353 # INDEXES: 354 # 'event' is indexed so you can look up all occurences of a 355 # specific event 356 # 'run' is indexed so you can find all events in a run 357 # 'tick' is indexed because we want the unique thing anyway 358 # 'event,run,tick' is unique combination 359 self.query(''' 360 CREATE TABLE events( 361 ev_event SMALLINT UNSIGNED NOT NULL, 362 ev_run SMALLINT UNSIGNED NOT NULL, 363 ev_tick BIGINT UNSIGNED NOT NULL, 364 INDEX(ev_event), 365 INDEX(ev_run), 366 INDEX(ev_tick), 367 UNIQUE(ev_event,ev_run,ev_tick) 368 ) TYPE=InnoDB''') 369 370 # COLUMNS: 371 # 'id' is the unique description id 372 # 'name' is the name of the event that occurred 373 # 374 # INDEXES: 375 # 'id' is indexed because it is the primary key and is what you use 376 # to look up the descriptions 377 # 'name' is indexed so one can find the event based on name 378 # 379 self.query(''' 380 CREATE TABLE event_names( 381 en_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT, 382 en_name VARCHAR(255) NOT NULL, 383 PRIMARY KEY (en_id), 384 UNIQUE (en_name) 385 ) TYPE=InnoDB''') 386 387 def clean(self): 388 self.query(''' 389 DELETE data 390 FROM data 391 LEFT JOIN runs ON dt_run=rn_id 392 WHERE rn_id IS NULL''') 393 394 self.query(''' 395 DELETE formula_ref 396 FROM formula_ref 397 LEFT JOIN runs ON fr_run=rn_id 398 WHERE rn_id IS NULL''') 399 400 self.query(''' 401 DELETE formulas 402 FROM formulas 403 LEFT JOIN formula_ref ON fm_stat=fr_stat 404 WHERE fr_stat IS NULL''') 405 406 self.query(''' 407 DELETE stats 408 FROM stats 409 LEFT JOIN data ON st_id=dt_stat 410 WHERE dt_stat IS NULL''') 411 412 self.query(''' 413 DELETE subdata 414 FROM subdata 415 LEFT JOIN data ON sd_stat=dt_stat 416 WHERE dt_stat IS NULL''') 417 418 self.query(''' 419 DELETE bins 420 FROM bins 421 LEFT JOIN data ON bn_id=dt_bin 422 WHERE dt_bin IS NULL''') 423 424 self.query(''' 425 DELETE events 426 FROM events 427 LEFT JOIN runs ON ev_run=rn_id 428 WHERE rn_id IS NULL''') 429 430 self.query(''' 431 DELETE event_names 432 FROM event_names 433 LEFT JOIN events ON en_id=ev_event 434 WHERE ev_event IS NULL''') 435