157 lines
6.0 KiB
Plaintext
157 lines
6.0 KiB
Plaintext
######################################################################
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## This file is part of SECONDO.
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##
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## Copyright (C) 2008, University in Hagen, Faculty of Mathematics and
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## Computer Science, Database Systems for New Applications.
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##
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## SECONDO is free software; you can redistribute it and/or modify
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## it under the terms of the GNU General Public License as published by
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## the Free Software Foundation; either version 2 of the License, or
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## (at your option) any later version.
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##
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## SECONDO is distributed in the hope that it will be useful,
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## but WITHOUT ANY WARRANTY; without even the implied warranty of
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## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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## GNU General Public License for more details.
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##
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## You should have received a copy of the GNU General Public License
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## along with SECONDO; if not, write to the Free Software
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## Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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######################################################################
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######################################################################
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# This file creates a set of auxiliary objects in parallel BerlinMOD database,
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# to process the parallel benchmark queries.
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######################################################################
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################################################################
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# Set the scale of Parallel Secondo #
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################################################################
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# The number of slave Data Servers.
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let CLUSTER_SIZE = 12;
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# The number of tasks that can run in parallel,
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# usually for reduce tasks.
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let PS_SCALE = 36;
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################################################################
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# Prepare the database #
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################################################################
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######################################
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# OBA & Compact Representation #
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######################################
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# dataSCcar_List: flist ( rel{Licence: string, Type: string,
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# Model: string, Journey: mpoint} )
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let dataSCcar_List = dataScar_List
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hadoopMap["PDataSCcar"; . projectextend[Licence, Type, Model; Journey: .Trip] consume];
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# Create B-Tree based on licence
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let dataSCcar_Licence_btree_List = dataSCcar_List hadoopMap[ ; . createbtree[Licence] ];
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# Create temporal R-Tree based on units' definition time
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let dataSCcar_Journey_tmpuni_List =
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dataSCcar_List hadoopMap[ ; .
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feed projectextend[Journey ; TID: tupleid(.)]
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projectextendstream[TID; MBR:
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units(.Journey) use[fun(U: upoint) point2d(deftime(U)) ]]
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sortby[MBR asc] bulkloadrtree[MBR]
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];
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# Create 2D Spatial R-Tree based units' bounding boxes
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let dataSCcar_Journey_sptuni_List =
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dataSCcar_List hadoopMap[ ; .
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feed projectextend[Journey ; TID: tupleid(.)]
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projectextendstream[TID; MBR:
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units(.Journey) use[fun(U: upoint) bbox2d(U) ]]
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sortby[MBR asc] bulkloadrtree[MBR]
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];
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# Create 3D Spatio-temporal R-Tree based on units' bounding boxes
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let dataSCcar_Journey_sptmpuni_List =
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dataSCcar_List hadoopMap[ ; .
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feed projectextend[Journey ; TID: tupleid(.)]
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projectextendstream[TID; MBR:
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units(.Journey) use[fun(U: upoint) bbox(U) ]]
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sortby[MBR asc] bulkloadrtree[MBR]
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];
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################################################################
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# Prepare the Global Cell-Grid #
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################################################################
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let SCAR_WORLD_CELL_NUM = real2int(sqrt(int2real(P_NUMALLCARS)));
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let SCAR_WORLD_CELL_SIZE = STAT_WORLD_MAXSIZE / SCAR_WORLD_CELL_NUM;
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let SCAR_WORLD_GRID_LBP_X = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 1);
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let SCAR_WORLD_GRID_LBP_Y = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 2);
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let SCAR_WORLD_GRID_LBP_T = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 3);
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let SCAR_WORLD_GRID_3D = createCellGrid3D(
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SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y, SCAR_WORLD_GRID_LBP_T,
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SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE,
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SCAR_WORLD_CELL_NUM, SCAR_WORLD_CELL_NUM );
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let SCAR_WORLD_GRID_2D = createCellGrid2D(
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SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y,
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SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_NUM );
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let SCAR_WORLD_LAYERS_3D = createCellGrid3D(
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SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y, SCAR_WORLD_GRID_LBP_T,
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STAT_WORLD_MAXSIZE, STAT_WORLD_MAXSIZE, SCAR_WORLD_CELL_SIZE, 1, 1);
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################################################################
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# Prepare Distributed Samples #
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################################################################
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let QueryLicences_Dup_List = QueryLicences feed
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product
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spread[;SID,CLUSTER_SIZE,FALSE;];
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let QueryLicences_Top10_Dup_List =
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QueryLicences feed head[10]
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID]
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product
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spread[;SID, CLUSTER_SIZE, TRUE;];
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let QueryLicences_2Top10_Dup_List =
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QueryLicences feed head[20] filter[.Id>10]
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID]
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product
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spread[;SID, CLUSTER_SIZE, TRUE;];
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let QueryInstants_Top10_Dup_List =
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QueryInstants feed head[10]
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID]
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product
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spread[;SID, CLUSTER_SIZE, TRUE;]
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hadoopMap[; . consume];
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let QueryPoints_Dup_List =
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QueryPoints feed
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product
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spread["QueryPoints_Dup",'';SID, CLUSTER_SIZE, FALSE;];
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# * ???? Maybe I should use dup ???
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let QueryPoints_Top10_List =
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QueryPoints feed head[10] project[Pos]
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID]
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product
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spread["QueryPoints_Top10_dup"; SID, CLUSTER_SIZE, FALSE;];
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let QueryPeriods_Dup_List =
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QueryPeriods feed
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product
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spread["QueryPeriods_Dup",'';SID, CLUSTER_SIZE, FALSE;];
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let QueryPeriods_Top10_Dup_List =
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QueryPeriods feed head[10]
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intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product
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spread["QueryPeriods_TOP10_Dup",'';SID, CLUSTER_SIZE, TRUE;]
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hadoopMap[; . consume];
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