Extrude 3D polygons in Oracle spatial

Dear Everyone,
I have two data sets of the city footprints imported in the Oracle Spatial already. One is a 2D polygon layer with only x and y, and the other is a 3D polygon layer with z values (the x and y are identical to the first one).
Now I am trying to extrude the 3D polygon to 3D composites based on the z value. I can successfully extrude the 2D polygons with specifying the ground heights (say sdo_ordinate_array(0)) and topheights (say, sdo_ordinate_array(20.0)).
However, how to extrude the 3D polygon taking each vertex's z value as the ground heights to an specified top heights is a problem for me. Since, I am very new to use PL/SQL, besides, I am not sure if it is possible to do this.
Here is the sample data extracted from the sample data:
select sample.geom.sdo_ordinates from sample2d_polygon sample
NUMBER(715202.739577727,733492.909091357,715206.759608945,733492.412077086,715208.666575034,733506.889047512,715204.72752625,733507.364066093,715202.739577727,733492.909091357)
select sample.geom.sdo_ordinates from sample3d_polygon sample
NUMBER(715202.739577727,733492.909091357,12.4890003204346,715206.759608945,733492.412077086,12.4890003204346,715208.666575034,733506.889047512,12.4540004730225,715204.72752625,733507.364066093,12.4320001602173,715202.739577727,733492.909091357,12.4890003204346)
Can anybody help me about this
select sdo_util.extrude(sample.geom, sdo_ordinate_array(), sdo_ordinate_array(20.0), 'true', 0.05) from sample2d_polygon sample, where the first sdo_ordinate_array is the array of corresponding vertex's z value?
Any suggestions is anticipated and highly appreciated.
Sincerely
Jun

Hi Jun,-
I hope the following code solves your problem.
Please also make sure your geometries are valid using sdo_geom.validate_geometry_with_context or
sdo_geom.validate_layer_with_context for 3D geometries.
Please let us know if you have questions.
Best
baris
drop table myGeoms;
drop table extruded_myGeoms;
create table myGeoms(i number, geom sdo_geometry);
insert into myGeoms(i, geom) values(1,
MDSYS.SDO_GEOMETRY(3003, null,null,
MDSYS.SDO_ELEM_INFO_ARRAY(1,1003,1),
MDSYS.sdo_ordinate_array(
715202.739577727,733492.909091357,12.4890003204346,
715206.759608945,733492.412077086,12.4890003204346,
715208.666575034,733506.889047512,12.4540004730225,
715204.72752625, 733507.364066093,12.4320001602173,
715202.739577727,733492.909091357,12.4890003204346
insert into myGeoms(i, geom) values(2,
MDSYS.SDO_GEOMETRY(3003, null,null,
MDSYS.SDO_ELEM_INFO_ARRAY(1,1003,1),
MDSYS.sdo_ordinate_array(
715202.739577727,733492.909091357,14.4890003204346,
715206.759608945,733492.412077086,14.4890003204346,
715208.666575034,733506.889047512,14.4540004730225,
715204.72752625, 733507.364066093,14.4320001602173,
715202.739577727,733492.909091357,14.4890003204346
insert into myGeoms(i, geom) values(3,
mdsys.sdo_geometry(3003, null, null,
mdsys.sdo_elem_info_array(1,1003,1),
mdsys.sdo_ordinate_array(1,1,10,  0,1,10, 0,0,10, 1,0,10, 1,1,10))
create table extruded_myGeoms(id number, geom sdo_geometry);
-- This program assumes that each geometry in myGeoms table
-- has only 1 ring per polygon ie, 1003 etype element.
set serveroutput on;
declare
type cursor_type is REF CURSOR;
query_crs cursor_type ;
-- For each extruded geometry (result), this array has the ground heights
ords_bottom_z_array sdo_number_array  := null;
-- For each extruded geometry (result), this array has the top heights which is set to 20 for each element.
-- Both arrays must have the same number of elements.
ords_top_z_array sdo_number_array  := null;
ords2d mdsys.sdo_ordinate_array  := null;
result sdo_geometry;
g1 sdo_geometry;
g2d sdo_geometry;
stmt  varchar2(100);
id1 number;
cnt integer;
k integer;
l integer;
the_dim        number;
gtype_2d       number;
begin
stmt := ' select i, geom from myGeoms ';
OPEN query_crs FOR stmt;
LOOP
  BEGIN
   FETCH query_crs into id1, g1;
   EXIT when query_crs%NOTFOUND ;
   ords2d := mdsys.sdo_ordinate_array();
   ords_bottom_z_array := SDO_NUMBER_ARRAY();
   ords_top_z_array := SDO_NUMBER_ARRAY();
   k:=ords_bottom_z_array.count;
   l:=ords2d.count;
   FOR cnt in 1..g1.sdo_ordinates.count LOOP
     if (mod(cnt, 3) = 0) then
       -- Get z values of polygon
       ords_bottom_z_array.extend(1);
       ords_top_z_array.extend(1);
       k:=k+1;
       ords_bottom_z_array(k) := g1.sdo_ordinates(cnt);
       ords_top_z_array(k) := 20.0; -- Constant
     else
       -- To have 2D geometry for input to sdo_util.extrude
       ords2d.extend(1);
       l := l+1;
       ords2d(l) := g1.sdo_ordinates(cnt);
     end if;
   end loop;
   the_dim := floor(g1.sdo_gtype / 1000);
   gtype_2d := (the_dim-1)*1000 + mod(g1.sdo_gtype, 10);
   g2d:= sdo_geometry(gtype_2d, null,  null,
           mdsys.sdo_elem_info_array(1,1003,1),
           ords2d);
   result:= sdo_util.extrude(g2d, ords_bottom_z_array, ords_top_z_array, 'FALSE', 0.05);
   -- insert the extruded geometry (result) into new table
   insert into extruded_myGeoms(id, geom) values(id1, result);
   dbms_output.put_line('id = '||id1);
  END;
END LOOP;
end;
/

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    INSERT
    INTO
    ta_3dgeb
    select
    SDO_UTIL.EXTRUDE(
              GEOM,
              SDO_NUMBER_ARRAY(0),
              SDO_NUMBER_ARRAY(10),
              'false', --validation
              0.005)
    from ta_geb;
    I got the following error message after executing this Code:
    ORA-29875: failed in the execution of the ODCIINDEXINSERT routine
    ORA-13365: layer SRID does not match geometry SRID
    ORA-06512: in "MDSYS.SDO_INDEX_METHOD_10I", line 709
    ORA-06512: in "MDSYS.SDO_INDEX_METHOD_10I", line 225
    Then I tried to change something on the metadata and spatial indices, wihtout success. Does anyone has advices to solve the problem??
    Afterwards I would like to export the 3d-geometry in a KML file to visualize it in Google Earth. Therefore oracle provides a tool. But I didn't managed it to export the geometry. Any hints for the KML export or are there any other tools alternatively??
    Thanks for any suggestions!
    Regards

    Extrude does not automatically populate the SRID for the resulting geometry as there may not be a 3D equivalent of the 2D SRID defined
    in the DB.
    So the extruded geometry comes out without the SRID. In your case, since you are creating an index on the ta_3dgeb table
    before inserting the extruded data, it is causing this failure.
    So drop the index on the ta_3dgeb table and do the inserts into it.
    Then you can manually update the geometries with the SRID if there is a corresponding 3D SRID for 31468.
    Otherwise, you can use 31468.
    siva

  • Oracle Spatial Performance with 10-20.000 users

    Does anyone have any experience when Oracle Spatial is used with say 20.000 concurrent users. I am not interested in MapViewer response time, but lets say there is:
    - an app using 800 different tables each having an sdo_geometry column
    - the app is configured with different tables visible on different view scales
    - let's say an average of 40-50 tables is visible at any given time
    - some tables will have only a few records, while other can hold millions.
    - there is no client side caching
    - clients can zoom in/out pan.
    Anwers I am interested in:
    - What sort of server would be required
    - How can Oracle serve all that data (each Refresh renders the map and retrieves the data over the wire as there is no client side caching).
    - What sort of network infrastructure would be required.
    - Can clients connect to different servers and hence use load balancing or does Oracle have an automatic mechanism for that?
    Thanks in advance,
    Patrick

    Patrick, et al.
    There are lots of things one can do to improve performance in mapping environments because of a lot of the visualisation is based on "background" or read-only data. Here are some "tips":
    1. Spatially sort read-only data.
    This tip makes sure that data that is close to each other in space are next to each other on disk! Dan gave a good suggestion when he referenced Chapter 14, "Reorganize the Table Data to Minimize I/O" pp 580- 582, Pro Oracle Spatial. But just as easily one can create a table as select ... where sdo_filter() where the filtering object is an optimized rectangle across the whole of the dataset. (This is quite quick on 10g and above but much slower on earlier releases.)
    When implementing this make sure that the created table is created such that its blocks are next to each other in the tablespace. (Consider tablespace defragmentation beforehand.) Also, if the data is READ ONLY set the PCTFREE to 0 in order to pack the data up into as small a number of blocks as possible.
    2. Generalise data
    Rendering spatial data can be expensive where the data is geometrically detailed (many vertices) esp where the data is being visualised at smaller scales than it was captured at. So, if your "zoom thresholds" allow 1:10,000 data to be used at 1:100,000 then you are going to have problems. Consider pre-generalising the data (see sdo_util.simplify) before deployment. You can add multiple columns to your base table to hold this data. Be careful with polygon data because generalising polygons that share boundaries will create gaps etc as the data is more generalised. Often it is better to export the data to a GIS which can maintain the boundary relationships when generalising (say via topological relationships).
    Oracle's MapViewer has excellent on-the-fly generalisation but here one needs to be careful. Application tier caching (cf Bryan's comments) can help here a lot.
    3. Don't draw data that is sub-pixel.
    As one zooms out objects become smaller and smaller until they reach a point where the whole object can be drawn within a single pixel. If you have control over your map visualisation application you might want to consider setting the SDO_FILTER parameter "min_resolution" flag dynamically so that its value is the same as the number of meters / pixel (eg min_resolution=10). If this is set Oracle Spatial will only include spatial objects in the returned search set if one side of a geometry's MBR is greater than or equal to this value. Thus any geometries smaller than a pixel will not be returned. Very useful for large scale data being drawn at small scales and for which no selection (eg identify) is required. With Oracle MapViewer this behaviour can be set via the generalized_pixels parameter.
    3. SDO_TOLERANCE, Clean Data
    If you are querying data other than via MBR (eg find all land parcels that touch each other) then make sure that your sdo_tolerance values are appropriate. I have seen sites where data captured to 1cm had an sdo_tolerance value set to a millionth of a meter!
    A corollary to this is make sure that all your data passes validation at the chosen sdo_tolerance value before deploying to visualisation. Run sdo_geom.validate_geometry()/validate_layer()...
    4. Rtree Spatial Indexing
    At 10g and above lots of great work went in to the RTree indexing. So, make sure you are using RTrees and not QuadTrees. Also, many GIS applications create sub-optimal RTrees by not using the additional parameters available at 10g and above.
    4.1 If your table/column sdo_geometry data contains only points, lines or polygons then let the RTree indexer know (via layer_gtype) as it can implement certain optimizations based on this knowledge.
    4.2 With 10g you can set the RTree's spatial index data block use via sdo_pct_free. Consider setting this parameter to 0 if the table/column sdo_geometry data is read only.
    4.3 If a table/column is in high demand (eg it is the most commonly used table in all visualisations) you can consider loading (a part of) the RTree index into memory. Now, with the RTree indexing, the sdo_non_leaf_tbl=true parameter will split the RTree index into its leaf (contains actual rowid reference) and non-leaf (the tree built on the leaves) components. Most RTrees are built without this so only the MDRT*** secondary tables are built. But if sdo_non_leaf_tbl is set to true you will see the creation of an additional MDNT*** secondary table (for the non_leaf part of the rtree index). Now, if appropriate, the non_leaf table can be loaded into memory via the following:
    ALTER TABLE MDNT*** STORAGE(BUFFER_AREA KEEP);
    This is NOT a general panacea for all performance problems. One should investigate other options before embarking on this (cf Tom Kyte's books such as Expert Oracle Database Architecture, 9i and 10g Programming Techniques and Solutions.)
    4.4 Don't forget to check your spatial index data quality regularly. Because many sites use GIS package GUI tools to create tables, load data and index them, there is a real tendency to not check what they have done or regularly monitor the objects. Check the SDO_RTREE_QUALITY column in USER_SDO_INDEX_METADATA and look for indexes with an SDO_RTREE_QUALITY setting that is > 2. If > 2 consider rebuilding or recreating the index.
    5. The rendering engine.
    Whatever rendering engine one uses make sure you try and understand fully what it can and cannot do. AutoDesk's MapGuide is an excellent product but I have seen it simply cache table/column data and never dynamically access it. Also, I have been at one site which was running Deegree and MapViewer and MapViewer was so fast in comparison to Deegree that I was called in to find out why. I discovered that Deegree was using SDO_RELATE(... ANYINTERACT ...) for all MBR queries while MapViewer was using SDO_FILTER. Just this difference was causing some queries to perform at < 10% of the speed of MapViewer!!!!
    6. Consider "denormalising" data
    There is an old adage in databases that is "normalise for edit, denormalise for performance". When we load spatial data we often get it from suppliers in a fairly flat or normalised form. In consort with spatial sorting, consider denormalising the data via aggregations based on a rendering attribute and some sort of spatial unit. For example, if you have 1 million points stored as single points in SDO_GEOMETRY.SDO_POINT which you want to render by a single attribute containing 20 values, consider aggregating the data using this attribute AND some sort of spatial BUCKET or BIN. So, consider using SDO_AGGR_UNION coupled with Spatial Analysis and Mining package functions to GROUP the data BY <<column_name>> and a set of spatial extents.
    6. Tablespace use
    Finally, talk to your DBA in order to find out how the oracle database's physical and logical storage is organised. Is a SAN being used or SAME arranged disk arrays? Knowing this you can organise your spatial data and indexes using more effective and efficient methods that will ensure greater scalability.
    7. Network fetch
    If your rendering engine (app server) and database are on separate machines you need to investigate what sort of fetch sizes are being used when returning data from queries to the middle-tier. Fetch sizes for attribute only data rows and rows containing spatial data can be, and normally are, radically different. Accepting the default settings for these sizes could be killing you (as could the sort_area_size of the Oracle session the application server has created on the database). For example I have been informed that MapInfo Pro uses a fixed value of 25 records per fetch when communicating with Oracle. I have done some testing to show that this value can be too small for certain types of spatial data. SQL Developer's GeoRaptor uses 100 which is generally better (but this one can modify this). Most programmers accept defaults for network properties when programming in ADO/ODBC/OLEDB/JDBC: just be careful as to what is being set here. (This is one of the great strengths of ArcSDE: its TCP/IP network transport is well written, tuneable and very efficient.)
    8. Physical Format
    Finally, while Oracle's excellent MapViewer requires data its spatial data to be in Oracle, other commercial rendering engines do not. So, consider using alternate, physical file formats that are more optimal for your rendering engine. For example, Google Earth Enterprise "compiles" all the source data into an optimal format which the server then serves to Google Earth Enterprise clients. Similarly, a shapefile on local disk to the application server (with spatial indexing) may be faster that storing the data back in Oracle on a database server that is being shared with other business databases (eg Oracle financials). If you don't like this approach and want to use Oracle only consider using a dedicated Oracle XE on the application server for the data that is read only and used in most of your generated maps eg contour or drainage data.
    Just some things to think about.
    regards
    Simon

  • Problem in Connection between MapServer and Oracle Spatial

    Hi Everybody,
    First i thanks Dan Abugov for quick reply fr my previous query.
    I am working on the Oracle Spatial and MapServer
    I want to clear that MapServer is not Oracle MapViewer, this is Open Source UMN MapServer.
    When I pass the query(shown below), the empty image is displayed in the browser without any map
    http://localhost/cgi-bin/mapserv.exe?map=/ms4w/apps/sdocountry/sdocountry.map&layers=country&mode=map
    I created .map file (shown below) with the Oracle Spatial Connection.
    MAP
    DEBUG ON
    STATUS ON
    SIZE 800 600
    UNITS dd
    IMAGECOLOR 255 255 255
    PROJECTION
    "proj=latlong"
    "ellps=WGS84"
    END
    EXTENT -180 -45 180 45
    LAYER
    DEBUG ON
    PROJECTION
    "proj=latlong"
    "ellps=WGS84"
    END
    NAME "CountryMap"
    TYPE POLYGON
    CONNECTIONTYPE oraclespatial
    CONNECTION "world/land@orcl"
    DATA "shape FROM country USING SRID 8307"
    CLASS
    NAME "Country_Class"
    STYLE
    COLOR 250 250 0
    OUTLINECOLOR 32 32 32
    END
    END
    END
    END
    And Set the environment variable in the apache/conf/httpd.conf file as
    SetEnv ORACLE_BASE "d:/oracle/product/10.2.0"
    SetEnv ORACLE_HOME "d:/oracle/product/10.2.0/db_1"
    SetEnv ORACLE_SID "orcl"
    SetEnv TNS_ADMIN "d:/oracle/product/10.2.0/db_1/network/admin"
    This is the my whole Sample code and configuration, I dont know why the map image is not generating.
    I am not receving any error message.
    MapServer for windows (ms4w) binaries is downloaded from "maptools.org"
    System configuration
    Windows 2000 Advanced Server
    Oracle 10g with Oracle Spatial
    MapServer 4.6.1
    Apache/2.0.55 (Win32)
    I am waiting for the some solution, from you to over come this problem.
    Thanks & Regards,
    Prema Selvam
    GISbiz.

    Prema,
    If you haven't already, you may want to post this same question to one of the MapServer mail lists (http://mapserver.gis.umn.edu/community/mailinglists/) - most MapServer users/developers are very quick to respond as well.
    -Justin

  • How to display the data store in Oracle Spatial?

    Hello everyone,
    I am totally newbie in Oracle Spatial and to GIS in general. I have started playing around with Oracle Spatial and did an example which is to store a couple of polygons in Oracle and my question is: how to view/display the stored data (in my case the polygons) using Oracle?
    Thanks in advance

    Latvian83,
    thing is, most of the GIS data out there are in SHAPE format so does it make sense to convert [between formats]Depends.
    If you're a data producer that wants to ensure a single version of the truth across your company, then maintaining a central repository makes imminent good sense. The alternative would be people working on likely outdated snap-shots of data – stored on their PCs as well as duplicated on file-servers -- bumbling along creating derivatives and passing on the results as if they were valid... Anyhow, spatial analysts would also benefit from a single version of truth.
    Data consumers who just want the latest mashups may not want to be bothered with the translation in/out of a database.
    Then there’s a grey area with transformations: I've seem instances where it was more expedient to pull data into ArcMap for a massive transformation than performing a similar operation within Oracle.
    Regards,
    Noel
    (I'm sure you've already found it, but you can right-click on a legend entry on ArcMap's Table of Contents and find the Export to ShapeFile option on the context-menu.)

  • Import Bently DGN Files into a oracle spatial 8.1.7-- ERRORS

    Hy
    I work with Oracle spatial 8.1.7, MapX, VB6 and Win2k.
    My Problem: I get a DGN File. When I translate this file into a MapInfo File(s), it works fine.
    Now I must have this data in a oracle spatial. On this part the problems start.
    I can only inport the geometry and no charakters strings into the DB.
    The Textlayers on the mapinfo file are converted th polygons (from DGN to MapInfo TAB).
    If I move these file to the oracle, the text Layers are empty. If I look into the MapInfo Layer, which could have the text, the visible table (on MapInfo) is empy.
    It is posible to transform the DGN to a other way (with FME). Then I have a smaler number of MapInfo files with text informations in the table(MapInfotable).
    Because it is not posible to load this file into the oracle.
    I get the errormessage, that the first row is Zero.....
    What can I do???
    Is the DGN FIle corrupt??? Have I a'n error on my Oracle???
    Please help me
    Peter

    Hi,
    Please, refer to Steve Rea's Web for DB Cloning examples:
    For Cloning using Datafile copy:
    http://uaex.edu/srea/Cloning_a_Database_using_Datafile_Copy.htm
    For Cloning using Export/Import:
    http://uaex.edu/srea/Cloning_a_Database_using_Export_Import.htm
    Cheers:
    Francisco Munoz Alvarez
    www.oraclenz.com

  • How to use Oracle Spatial in this scenario

    My scenario is like that:
    I'm very new to Oracle Spatial
    I'm building an application that will be based on asp.net.(I am
    confident about .net)
    As per my client requirement there are some kml file in one archive
    folder.
    Let me give an example:
    say there is a kml file for region A.(latitude say 36 n to 40 n and
    longitude is 110 w to 115 w) already in the archive folder.
    Now if a new kml file(say A1.kml) that has been created by the user
    and say its latitude and longitude are respectively 37n and 112w. As we clicked A.kml, google earth is opened up for the region A and as
    we move our mouse cursor to more deeper more polygons are visible.
    eventually polygon for A1.kml is also visible and definitely which is
    inside the polygon for region A.
    How can I achieve this thing by using oracle spatial 10g? ---(it's one of my senior's advice to use "oracle spatial 10g" in this scenario)
    I'm not too sure whether I can able to make it clear to u about my
    situation; plz xcuse me if I'm wasting ur valuable time.

    Hi,
    This link helped me a lot!
    http://www.oracle.com/technology/pub/articles/rubio-mashup.html
    Hope it could help you too.
    Best regards,
    Luiz

  • Simon Greener's Morton Key Clustering in Oracle Spatial

    Hi folks,
    Apologies for the rambling.  With mattyschell heading for greener open source big apple pastures I am looking for new folks to bounce ideas and code off.  I was thinking this week about the discussion last autumn over spatial clustering.
    https://community.oracle.com/thread/3617887
    During the course of the thread we all kind of pooh-poohed spatial clustering as not much of solution, myself being one of the primary poohers.  Yet the concept certainly remains as something to consider regardless of our opinions.  The yellow book, the Greener/Ravada book, Simon's recent treatise (http://download.oracle.com/otndocs/products/spatial/pdf/biwa_2015/biwa2015_uc_comparativeperformance_greener.pdf), they all put forward clustering such that at the very least we should consider it a technique we should be able as professionals to do - a tool in the toolbox whether or not it always is the right answer.  I am mildly (very mildly) curious to see if Kothuri, Godfrind and Beinat will recycle their section on spatial clustering with the locked-down MD.HHENCODE into their 12c revision out this summer.  If they don't then what is the replacement for this technique?  If they do then we return to all of our griping about this ancient routine that Simon implies may date back to the CHS and their hhcode indexes - at least its not written in Java! 
    Anyhow, so I've been in the midst this month of refreshing some of the datasets I manage and considering clustering the larger tables whilst I am at it.  Do I really expect to see huge performance gains?   Well... not really.  But it does seem like something that should be easy to accomplish, certainly something that "doesn't hurt" and shows that I am on top of things (e.g. "checks the box").  But returning to the discussion from last fall, just what is the best way to do this in Oracle Spatial?
    So if we agree to ignore poor old MD.HHENCODE, then what?  Hilbert curves look nifty but no one seems to be stepping up with the code for them.  And this reroutes us back around to Simon and his Morton key code.
    http://www.spatialdbadvisor.com/oracle_spatial_tips_tricks/138/spatial-sorting-of-data-via-morton-key
    So who all is using Simon's code currently?  If you read that discussion from last fall there does not seem to be anyone doing so and we never heard back from Cat Person on either what he decided to do or what his name is.
    I thought I could take a stab at streamlining Simon's process somewhat to make things easier for myself to roll this onto many tables.  I put together the following small package
    https://github.com/pauldzy/DZ_SDO_CLUSTER/tree/master/Packages
    In particular I wanted to bundle up the side issues of how to convert your lines and polygons into points, automate things somewhat and provide a little verification function to see what results look like.  So again nothing that Simon does not already walk through on his webpage, just make it bit easier to bang out on your tables without writing a separate long SQL process for each one.
    So for example to use Simon's Morton key logic, you need to know the extent envelope of the data (in order to define a proper grid).  So if its a large table, you'd want to stash the envelope info in the metadata.  You can do this with the update_metadata_envelope procedure or just suffer through the sdo_aggr_mbr each time if you don't want to go that route (I have one table of small watershed polygons that takes about 9 hours to run sdo_aggr_mbr upon).  So just run things at the sql prompt
    SELECT
    DZ_SDO_CLUSTER.MORTON_UPDATE(
        p_table_name => 'CATCHMENT_NP21'
       ,p_column_name => 'SHAPE'
       ,p_grid_size => 1000
    FROM dual;
    This will return the update clause populated with the values to use with the morton_key wrapper function, e.g. "morton_key(SHAPE,160.247133275879,-17.673722530871,.0956820001136141,.0352063207508021)".  So then just paste that into an update statement
    UPDATE foo
    SET my_morton_key = dz_sdo_cluster.morton_key(
        SHAPE
       ,160.247133275879
       ,-17.673722530871
       ,.0956820001136141
       ,.0352063207508021
    Then rebuild your table sorting on the morton_key.  I just use the TOAD rebuild table tool and manually add the order by clause to the rebuild script.  I let TOAD do all the work of moving the indexes, constraints and grants to the new table.  I imagine there are other ways to do this.
    The final function is meant to be popped into Oracle mapviewer or something similar to show your family and friends the results.
    SELECT
    dz_sdo_cluster.morton_visualize(
        'NHDPLUS'
       ,'NHDFLOWLINE_NP21_ACU'
       ,'SHAPE'
       ,'OBJECTID'
       ,'100'
       ,10000
       ,'MORTON_KEY'
    FROM dual;
    Look Mom, there it is!
    So anyhow this is first stab at things and interested in feedback or suggestions for improvement.  Did I get the logic correct?  Don't spare my feelings if I botched something.  Note that like Simon I passed on the matter of just how to determine the proper grid size.  I've been using 1000 for the continental US + Hawaii/PR/VI and sitting here this morning I think that probably is too large.  Of course it depends on the size of the geometries and thus the density of the resulting points.  With water features this can vary a lot from place to place, so perhaps 1000 is okay.  What would the algorithm be to determine a decent grid size?  It occurs to me I could tell you the average feature count per morton key value, okay well its about 10.  That seems small to me.  So I could see another function in this package that returns some kind of summary on the results of the keying to tell you if your grid size estimate was reasonable.
    Cheers and Happy Saturday,
    Paul

    I've done some spatial clustering testing this week.
    Firstly, to reiterate the purpose of spatial clustering as I see it:  spatial clustering can be of benefit in situations where frequent window based spatial queries are made.  In particular it can be very useful in web mapping scenarios where a map server is requesting data using SDO_FILTER or SDO_ANYINTERACT and there is a need to return the data as quickly as possible.  If the data required to satisfy the query can be squeezed into as few blocks as possible, then the IO overhead is clearly reduced.
    As Bryan mentioned above, once the data is in the buffer cache, then the advantage of spatial clustering is reduced.  However it is not always possible to get/keep enough of the data in the buffer cache, so I believe spatial clustering still has merits, particularly if it can be implemented alongside spatial partitioning.
    I ran the tests using an 11.2.0.4 database on my laptop.  I have a hard disk rather than SSD, so the effects of excessive IO are exaggerated.  The database is configured with the default 8kb block size.
    Initially, I created a table PARCELS:
    create table parcels (
    id            integer,
    created_date  date,
    x            number,
    y            number,
    val1          varchar2(20),
    val2          varchar2(100),
    val3          varchar2(200),
    geometry      mdsys.sdo_geometry,
    hilbert_key  number);
    I inserted 2.8 million polygons into this table.  The CREATED_DATE is the actual date the polygons were captured.  I populated val1, val2 and val3 with string values to pad the rows out to simulate some business data sitting alongside the sdo_geometry.
    I set X,Y to the first ordinate of the polygon and then set hilbert_key = sdo_pc_pkg.hilbert_xy2d(power(2,31), x, y).
    I then created 4 tables to base the tests upon:
    PARCELS_RANDOM:  Ordered by dbms_random.random - an absolute worst case scenario.  Unrealistic, but worthwhile as a benchmark.
    PARCELS_BASE_DATE:  Ordered by CREATED_DATE.  This is probably pretty close to how the original source data is structured on disk.
    PARCELS_RTREE:  Ordered by RTree.  Achieved by inserting based on an SDO_FILTER query
    PARCELS_HILBERT:  Ordered by the hilbert_key attribute
    As a first test, I counted the number of blocks required to satisfy an SDO_FILTER query.  E.g.
    select count(distinct(dbms_rowid.rowid_block_number(rowid)))
    from parcels_rtree
    where sdo_filter(geometry,
                    sdo_geometry(2003, 2157, null, sdo_elem_info_array(1, 1003, 3),
                                    sdo_ordinate_array(644232,773809, 651523,780200))) = 'TRUE';
    I'm assuming dbms_rowid.rowid_block_number(rowid) is suitable for this.
    I ran this on each table and repeated it over three windows.
    Results:
    So straight off we can see that the random ordering gave pretty horrific results as the data required to satisfy the query is spread over a large number of blocks.  The natural date based clustering was far better. RTree and Hilbert based clustering reduced this by a further 50% with Hilbert just nosing out RTree.
    Since web mapping is the use case I am most likely to target, I then setup a test case as follows:
    Setup layers in GeoServer for each of the tables
    Used a script to generate 1,000 random squares over the extent of the data, ranging from 200m to 500m in width and height.
    Used JMeter to make a WMS request for a png of the each of the 1,000 windows.  JMeter was run sequentially with just one thread, so it waited for each request to complete before starting the next.  I ran these tests 3 times to balance out the results, flushing the buffer cache before each run.
    Results:
    Again the random ordering performed woefully bad - somewhat exacerbated by the quality of the disk on my laptop.  The natural date based clustering performed far better.  RTree and hilbert based clustering further reduced the time by more than half.
    In summary, the results suggest that spatial clustering is worth the effort if:
    the data is not already reasonably well clustered
    you've got a decent quantity of data
    you're expecting a lot of window based queries which need to be returned as quickly as possible
    you don’t expect to be able to fit all the data in the buffer cache
    When it comes to deciding between RTree and Hilbert (or Morton/z-order or any other space filling curve method).... I found that the RTree method can be a bit slow on large datasets, although this may not matter as a one off task.  Plus it requires a spatial index on the source table to start off with.  The key based methods are based on an xy, so for lines and polygons there is an intermediate step to extract an xy.  I would tend to recommend this approach if you also partition the data based on a subset of the cluster key.
    Scripts are available here: https://github.com/john-otoole/oracle_spatial_cluster_test
    John

  • Oracle Spatial 10g

    Hello all
    I would like to know if is it possible to install the 10g Spatial functionality in a previous version of the database (8.1.7).
    We want to work with Oracle Spatial + ArcSDE (with versioning and Spatial as Geometry Storage) + ArcGIS-ArcIMS, but the editing software will be AutoCAD.
    We will get from the database those elements whe want to edit and put it then in AutoCAD layers . To limit the traffic, we only need the geographic primitives (points and arc, but no polygons), but as we are editing polygons, we need a process that rebuild topology. I think that the new Oracle Spatial 10 g topological functionalities could help us (so we can insert arcs in a table and get the polygons they are building, and dangle arcs).
    I suppose it will not be possible to edit directly to the geodatabase feature class if it is versioned (so all the changes will appear as consolidated), but, is it possible (and of course, easy) to rebuild via SQL an ArcSDE version?
    And finally:
    Can (and how) we use Oracle WorkSpace Manager whit ArcSDE??? , and only with AutoCAD? Any experience? I´m thinking about spatial index and mdsys metadata table. Must be these tables registered as "versioned" with Oracle WorkSpace Manager to work with spatial data?
    Thank you for all
    Jesús de Diego

    Hello all again
    First of all, thank you for your quick reply.
    I can explain a bit more what we wanna do:
    we're in a multi-platform environ. We've ArcGIS users, AutoCAD users and a ArcSDE DB. We don't want AutoCAD users to migrate to ArcEditor, but they need to edit corporative (DB) data, and, in the other hand, there will be others editors using ArcEditor capabilities, so it is possible we will need versioned (ArcSDE) data.
    I think we can edit an ArcSDE version vía AutoCAD:
    1º Data storage will be Oracle Spatial.
    2º A new version over the default will be created.
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