Urgent Regarding Oracle Spatial

We are running arc SDe on Oracle 8.0.5. Is it advisable to modify the values in sdoindex and sdogeom table values thru triggers itself. If so, what is that SDO_CODE. How to calculate value for that column. Please advise.
Thanks in advance
Sudha
null

Dan,
Thanks For entertaining , can you please elobrate your explanation.Actually i am very new to the spatial Oracle.
Actually i know that there is a standard known as WGS84 but which are the valid values...e.g....
<BLOCKQUOTE><font size="1" face="Verdana, Arial">quote:</font><HR>Originally posted by dan abugov:
Hello Raju,
If you want to use the units parameter, you must be in wgs84 (long/lat).
The valid bounds for the coordinate system in long/lat are -180 to 180, and -90 to 90.
Hope this helps,
dan <HR></BLOCKQUOTE>
null

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    Hi All,
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    http://download-llnw.oracle.com/docs/html/E11829_01/
    I have searched for a similar one for 10g R2 but found only like
    http://download.oracle.com/docs/cd/B19306_01/appdev.102/b14373/toc.htm
    which misses the oracle.spatial.georaster package. Is there such a package and if so, where can I find docs for it?
    thank you in advance, best regards: Balázs Bámer

    Hi Balázs,
    the GeoRaster Java API was first released with 11gR2. Your first link points to that, or this link:
    http://download.oracle.com/docs/cd/E11882_01/appdev.112/e11829/toc.htm
    your second link points to 10g Oracle Spatial Java API, which doesn't include any GeoRaster related API. your tool might have been developed based on the old INTERNAL georaster jar file, which is not supposed to be used by your java program. But if you do want the api doc, you can run javadoc or jdeveloper to find out the api from the old sdogr.jar file.
    jeffrey

  • 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

  • Poor performance with Oracle Spatial when spatial query invoked remotely

    Is anyone aware of any problems with Oracle Spatial (10.2.0.4 with patches 6989483 and 7003151 on Red Hat Linux 4) which might explain why a spatial query (SDO_WITHIN_DISTANCE) would perform 20 times worse when it was invoked remotely from another computer (using SQLplus) vs. invoking the very same query from the database server itself (also using SQLplus)?
    Does Oracle Spatial have any known problems with servers which use SAN disk storage? That is the primary difference between a server in which I see this poor performance and another server where the performance is fine.
    Thank you in advance for any thoughts you might share.

    OK, that's clearer.
    Are you sure it is the SQL inside the procedure that is causing the problem? To check, try extracting the SQL from inside the procedure and run it in SQLPLUS with
    set autotrace on
    set timing on
    SELECT ....If the plans and performance are the same then it may be something inside the procedure itself.
    Have you profiled the procedure? Here is an example of how to do it:
    Prompt Firstly, create PL/SQL profiler table
    @$ORACLE_HOME/rdbms/admin/proftab.sql
    Prompt Secondly, use the profiler to gather stats on execution characteristics
    DECLARE
      l_run_num PLS_INTEGER := 1;
      l_max_num PLS_INTEGER := 1;
      v_geom    mdsys.sdo_geometry := mdsys.sdo_geometry(2002,null,null,sdo_elem_info_array(1,2,1),sdo_ordinate_array(0,0,45,45,90,0,135,45,180,0,180,-45,45,-45,0,0));
    BEGIN
      dbms_output.put_line('Start Profiler Result = ' || DBMS_PROFILER.START_PROFILER(run_comment => 'PARALLEL PROFILE'));  -- The comment name can be anything: here it is related to the Parallel procedure I am testing.
      v_geom := Parallel(v_geom,10,0.05,1);  -- Put your procedure call here
      dbms_output.put_line('Stop Profiler Result = ' || DBMS_PROFILER.STOP_PROFILER );
    END;
    SHOW ERRORS
    Prompt Finally, report activity
    COLUMN runid FORMAT 99999
    COLUMN run_comment FORMAT A40
    SELECT runid || ',' || run_date || ',' || run_comment || ',' || run_total_time
      FROM plsql_profiler_runs
      ORDER BY runid;
    COLUMN runid       FORMAT 99999
    COLUMN unit_number FORMAT 99999
    COLUMN unit_type   FORMAT A20
    COLUMN unit_owner  FORMAT A20
    COLUMN text        FORMAT A100
    compute sum label 'Total_Time' of total_time on runid
    break on runid skip 1
    set linesize 200
    SELECT u.runid || ',' ||
           u.unit_name,
           d.line#,
           d.total_occur,
           d.total_time,
           text
    FROM   plsql_profiler_units u
           JOIN plsql_profiler_data d ON u.runid = d.runid
                                         AND
                                         u.unit_number = d.unit_number
           JOIN all_source als ON ( als.owner = 'CODESYS'
                                   AND als.type = u.unit_type
                                   AND als.name = u.unit_name
                                AND als.line = d.line# )
    WHERE  u.runid = (SELECT max(runid) FROM plsql_profiler_runs)
    ORDER BY d.total_time desc;Run the profiler in both environments and see if you can see where the slowdown exists.
    regards
    Simon

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