# Copyright (c) 2003, 2004# The Regents of The University of Michigan# All Rights Reserved## This code is part of the M5 simulator, developed by Nathan Binkert,# Erik Hallnor, Steve Raasch, and Steve Reinhardt, with contributions# from Ron Dreslinski, Dave Greene, Lisa Hsu, Kevin Lim, Ali Saidi,# and Andrew Schultz.## Permission is granted to use, copy, create derivative works and# redistribute this software and such derivative works for any purpose,# so long as the copyright notice above, this grant of permission, and# the disclaimer below appear in all copies made; and so long as the# name of The University of Michigan is not used in any advertising or# publicity pertaining to the use or distribution of this software# without specific, written prior authorization.## THIS SOFTWARE IS PROVIDED AS IS, WITHOUT REPRESENTATION FROM THE# UNIVERSITY OF MICHIGAN AS TO ITS FITNESS FOR ANY PURPOSE, AND WITHOUT# WARRANTY BY THE UNIVERSITY OF MICHIGAN OF ANY KIND, EITHER EXPRESS OR# IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE REGENTS OF# THE UNIVERSITY OF MICHIGAN SHALL NOT BE LIABLE FOR ANY DAMAGES,# INCLUDING DIRECT, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL# DAMAGES, WITH RESPECT TO ANY CLAIM ARISING OUT OF OR IN CONNECTION# WITH THE USE OF THE SOFTWARE, EVEN IF IT HAS BEEN OR IS HEREAFTER# ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.import MySQLdbclass MyDB(object):    def __init__(self, options):        self.name = options.db        self.host = options.host        self.user = options.user        self.passwd = options.passwd        self.mydb = None        self.cursor = None    def admin(self):        self.close()        self.mydb = MySQLdb.connect(db='mysql', host=self.host, user=self.user,                                    passwd=self.passwd)        self.cursor = self.mydb.cursor()    def connect(self):        self.close()        self.mydb = MySQLdb.connect(db=self.name, host=self.host,                                    user=self.user, passwd=self.passwd)        self.cursor = self.mydb.cursor()    def close(self):        if self.mydb is not None:            self.mydb.close()        self.cursor = None    def query(self, sql):        self.cursor.execute(sql)    def drop(self):        self.query('DROP DATABASE IF EXISTS %s' % self.name)    def create(self):        self.query('CREATE DATABASE %s' % self.name)    def populate(self):        #        # Each run (or simulation) gets its own entry in the runs table to        # group stats by where they were generated        #        # COLUMNS:        #   'id' is a unique identifier for each run to be used in other        #       tables.        #   'name' is the user designated name for the data generated.  It is        #       configured in the simulator.        #   'user' identifies the user that generated the data for the given        #       run.        #   'project' another name to identify runs for a specific goal        #   'date' is a timestamp for when the data was generated.  It can be        #       used to easily expire data that was generated in the past.        #   'expire' is a timestamp for when the data should be removed from        #       the database so we don't have years worth of junk.        #          # INDEXES:        #   'run' is indexed so you can find out details of a run if the run        #       was retreived from the data table.         #   'name' is indexed so that two all run names are forced to be unique        #        self.query('''        CREATE TABLE runs(	    rn_id	SMALLINT UNSIGNED	NOT NULL AUTO_INCREMENT,	    rn_name	VARCHAR(200)		NOT NULL,	    rn_sample	VARCHAR(32)		NOT NULL,	    rn_user	VARCHAR(32)		NOT NULL,            rn_project	VARCHAR(100)            NOT NULL,	    rn_date	TIMESTAMP		NOT NULL,            rn_expire	TIMESTAMP               NOT NULL,	    PRIMARY KEY (rn_id),	    UNIQUE (rn_name,rn_sample)        ) TYPE=InnoDB''')        #        # We keep the bin names separate so that the data table doesn't get        # huge since bin names are frequently repeated.        #        # COLUMNS:        #   'id' is the unique bin identifer.        #   'name' is the string name for the bin.        #        # INDEXES:        #   'bin' is indexed to get the name of a bin when data is retrieved        #       via the data table.        #   'name' is indexed to get the bin id for a named bin when you want        #       to search the data table based on a specific bin.        #        self.query('''        CREATE TABLE bins(            bn_id	SMALLINT UNSIGNED	NOT NULL AUTO_INCREMENT,            bn_name	VARCHAR(255)		NOT NULL,            PRIMARY KEY(bn_id),            UNIQUE (bn_name)        ) TYPE=InnoDB''')        #        # The stat table gives us all of the data for a particular stat.        #        # COLUMNS:        #   'stat' is a unique identifier for each stat to be used in other        #       tables for references.        #   'name' is simply the simulator derived name for a given        #       statistic.         #   'descr' is the description of the statistic and what it tells        #       you.         #   'type' defines what the stat tells you.  Types are:        #       SCALAR: A simple scalar statistic that holds one value        #       VECTOR: An array of statistic values.  Such a something that        #           is generated per-thread.  Vectors exist to give averages,        #	     pdfs, cdfs, means, standard deviations, etc across the        #           stat values.         #       DIST: Is a distribution of data.  When the statistic value is        #	     sampled, its value is counted in a particular bucket.        #           Useful for keeping track of utilization of a resource.        #           (e.g. fraction of time it is 25% used vs. 50% vs. 100%)        #       VECTORDIST: Can be used when the distribution needs to be        #	     factored out into a per-thread distribution of data for        #	     example.  It can still be summed across threads to find        #           the total distribution.        #       VECTOR2D: Can be used when you have a stat that is not only        #           per-thread, but it is per-something else.  Like        #           per-message type.        #       FORMULA: This statistic is a formula, and its data must be        #	     looked up in the formula table, for indicating how to        #           present its values.        #   'subdata' is potentially used by any of the vector types to         #       give a specific name to all of the data elements within a        #       stat.        #   'print' indicates whether this stat should be printed ever.        #       (Unnamed stats don't usually get printed)          #   'prereq' only print the stat if the prereq is not zero.        #   'prec' number of decimal places to print        #   'nozero' don't print zero values        #   'nonan' don't print NaN values        #   'total' for vector type stats, print the total.        #   'pdf' for vector type stats, print the pdf.        #   'cdf' for vector type stats, print the cdf.        #        #   The Following are for dist type stats:        #   'min' is the minimum bucket value. Anything less is an underflow.         #   'max' is the maximum bucket value. Anything more is an overflow.        #   'bktsize' is the approximate number of entries in each bucket.        #   'size' is the number of buckets. equal to (min/max)/bktsize.        #        # INDEXES:        #   'stat' is indexed so that you can find out details about a stat        #       if the stat id was retrieved from the data table.        #   'name' is indexed so that you can simply look up data about a        #       named stat.        #        self.query('''        CREATE TABLE stats(            st_id	SMALLINT UNSIGNED	NOT NULL AUTO_INCREMENT,            st_name	VARCHAR(255)		NOT NULL,            st_descr	TEXT			NOT NULL,            st_type	ENUM("SCALAR", "VECTOR", "DIST", "VECTORDIST",                "VECTOR2D", "FORMULA")	NOT NULL,            st_print	BOOL			NOT NULL,            st_prereq	SMALLINT UNSIGNED	NOT NULL,            st_prec	TINYINT			NOT NULL,            st_nozero	BOOL			NOT NULL,            st_nonan	BOOL			NOT NULL,            st_total	BOOL			NOT NULL,            st_pdf	BOOL			NOT NULL,            st_cdf	BOOL			NOT NULL,            st_min	DOUBLE			NOT NULL,            st_max	DOUBLE			NOT NULL,            st_bktsize	DOUBLE			NOT NULL,            st_size	SMALLINT UNSIGNED	NOT NULL,            PRIMARY KEY (st_id),            UNIQUE (st_name)        ) TYPE=InnoDB''')        #        # This is the main table of data for stats.        #        # COLUMNS:        #   'stat' refers to the stat field given in the stat table.        #        #   'x' referrs to the first dimension of a multi-dimensional stat. For        #       a vector, x will start at 0 and increase for each vector