######################################################################
##  This file is part of SECONDO.
##
##  Copyright (C) 2008, University in Hagen, Faculty of Mathematics and
##  Computer Science, Database Systems for New Applications.
##
##  SECONDO is free software; you can redistribute it and/or modify
##  it under the terms of the GNU General Public License as published by
##  the Free Software Foundation; either version 2 of the License, or
##  (at your option) any later version.
##
##  SECONDO is distributed in the hope that it will be useful,
##  but WITHOUT ANY WARRANTY; without even the implied warranty of
##  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
##  GNU General Public License for more details.
##
##  You should have received a copy of the GNU General Public License
##  along with SECONDO; if not, write to the Free Software
##  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
######################################################################

######################################################################
# This file creates a set of auxiliary objects in parallel BerlinMOD database,
# to process the parallel benchmark queries. 
######################################################################

################################################################
#             Set the scale of Parallel Secondo                #
################################################################

# The number of slave Data Servers.
let CLUSTER_SIZE = 12;

# The number of tasks that can run in parallel, 
# usually for reduce tasks. 
let PS_SCALE     = 36;

################################################################
#                   Prepare the database                       #
################################################################

######################################
#    OBA & Compact Representation    #
######################################
# dataSCcar_List: flist ( rel{Licence: string, Type: string,
#                             Model: string, Journey: mpoint} )
let dataSCcar_List = dataScar_List 
  hadoopMap["PDataSCcar"; . projectextend[Licence, Type, Model; Journey: .Trip] consume];

# Create B-Tree based on licence
let dataSCcar_Licence_btree_List = dataSCcar_List hadoopMap[ ; . createbtree[Licence] ];

# Create temporal R-Tree based on units' definition time
let dataSCcar_Journey_tmpuni_List =
  dataSCcar_List hadoopMap[ ; . 
    feed projectextend[Journey ; TID: tupleid(.)] 
    projectextendstream[TID; MBR: 
      units(.Journey) use[fun(U: upoint) point2d(deftime(U)) ]]
    sortby[MBR asc]  bulkloadrtree[MBR]
];

# Create 2D Spatial R-Tree based units' bounding boxes
let dataSCcar_Journey_sptuni_List = 
  dataSCcar_List hadoopMap[ ; . 
    feed projectextend[Journey ; TID: tupleid(.)] 
    projectextendstream[TID; MBR: 
      units(.Journey) use[fun(U: upoint) bbox2d(U) ]] 
    sortby[MBR asc] bulkloadrtree[MBR] 
]; 

# Create 3D Spatio-temporal R-Tree based on units' bounding boxes
let dataSCcar_Journey_sptmpuni_List =
  dataSCcar_List hadoopMap[  ; . 
    feed projectextend[Journey ; TID: tupleid(.)] 
    projectextendstream[TID; MBR: 
      units(.Journey) use[fun(U: upoint) bbox(U) ]]
    sortby[MBR asc]  bulkloadrtree[MBR]
];


################################################################
#              Prepare the Global Cell-Grid                    #
################################################################

let SCAR_WORLD_CELL_NUM = real2int(sqrt(int2real(P_NUMALLCARS))); 

let SCAR_WORLD_CELL_SIZE = STAT_WORLD_MAXSIZE / SCAR_WORLD_CELL_NUM;

let SCAR_WORLD_GRID_LBP_X = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 1);

let SCAR_WORLD_GRID_LBP_Y = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 2);

let SCAR_WORLD_GRID_LBP_T = minD(SCAR_WORLD_SCALE_BOX(STAT_WOLRD_BBOX_rect3), 3);

let SCAR_WORLD_GRID_3D = createCellGrid3D( 
  SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y, SCAR_WORLD_GRID_LBP_T,
  SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE, 
  SCAR_WORLD_CELL_NUM, SCAR_WORLD_CELL_NUM ); 

let SCAR_WORLD_GRID_2D = createCellGrid2D( 
  SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y,
  SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_SIZE, SCAR_WORLD_CELL_NUM );

let SCAR_WORLD_LAYERS_3D = createCellGrid3D(
  SCAR_WORLD_GRID_LBP_X, SCAR_WORLD_GRID_LBP_Y, SCAR_WORLD_GRID_LBP_T,
  STAT_WORLD_MAXSIZE, STAT_WORLD_MAXSIZE, SCAR_WORLD_CELL_SIZE, 1, 1);

################################################################
#                  Prepare Distributed Samples                 #
################################################################

let QueryLicences_Dup_List = QueryLicences feed 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product
  spread[;SID,CLUSTER_SIZE,FALSE;];

let QueryLicences_Top10_Dup_List =
  QueryLicences feed head[10] 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] 
  product
  spread[;SID, CLUSTER_SIZE, TRUE;];

let QueryLicences_2Top10_Dup_List =
  QueryLicences feed head[20]  filter[.Id>10] 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] 
  product
  spread[;SID, CLUSTER_SIZE, TRUE;];

let QueryInstants_Top10_Dup_List = 
  QueryInstants feed head[10] 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] 
  product
  spread[;SID, CLUSTER_SIZE, TRUE;]
  hadoopMap[; . consume];

let QueryPoints_Dup_List =
  QueryPoints feed 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product 
  spread["QueryPoints_Dup",'';SID, CLUSTER_SIZE, FALSE;];

# * ???? Maybe I should use dup ???
let QueryPoints_Top10_List = 
  QueryPoints feed head[10] project[Pos] 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] 
  product 
  spread["QueryPoints_Top10_dup"; SID, CLUSTER_SIZE, FALSE;];

let QueryPeriods_Dup_List = 
  QueryPeriods feed 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product 
  spread["QueryPeriods_Dup",'';SID, CLUSTER_SIZE, FALSE;];

let QueryPeriods_Top10_Dup_List = 
  QueryPeriods feed head[10] 
  intstream(1, CLUSTER_SIZE) namedtransformstream[SID] product 
  spread["QueryPeriods_TOP10_Dup",'';SID, CLUSTER_SIZE, TRUE;] 
  hadoopMap[; . consume];