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Crunching Data In GeoServer: Mastering Renderin...

Crunching Data In GeoServer: Mastering Rendering Transformations, WPS Processes And SQL Views - FOSS4G 2016

This presentation will provide the attendee with an introduction to data processing in GeoServer by means of WPS, rendering transformations and SQL views, describing real applications and how these facilities were used in them.

We'll start with the basic WPS capabilities, showing how to build processing request based on existing processes and how to build new processes leveraging scripting languages, and introducing unique GeoServer integration features, showing how processing can seamlessly integrate directly in the GeoServer data sources and complement existing services.

We'll also discuss how to integrate on the fly processing in WMS requests, achieving high performance data displays without having to pre-process the data in advance, and allowing the caller to interactively choose processing parameters.

While the above shows how to make GeoServer perform the work, the processing abilities of spatial databases should not be forgotten, so we’ll show how certain classes of processing can be achieved directly in the database.

At the end the attendee will be able to easily issue WPS requests both for Vectors and Rasters to GeoServer through the WPS Demo Builder, enrich SLDs with on-the-fly rendering transformations and play with SQL views in order to create dynamic layers.

Simone Giannecchini

September 02, 2018
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  1. Ing. Andrea Aime Ing. Simone Giannecchini Eng. Nuno Oliveira GeoSolutions

    Crunching Data In GeoServer: Mastering Rendering Transformations, WPS And SQL Views
  2. GeoSolutions ⚫ Founded in Italy in late 2006 ⚫ Expertise

    • Image Processing, GeoSpatial Data Fusion • Java, Java Enterprise, C++, Python • JPEG2000, JPIP, Advanced 2D visualization ⚫ Supporting/Developing FOSS4G projects ⚫ GeoServer, MapStore ⚫ GeoNetwork, GeoNode, Ckan ⚫ Clients ⚫ Public Agencies ⚫ Private Companies ⚫ http://www.geo-solutions.it FOSS4G 2018, August 29th/31st, Dar Es Salaam
  3. Web Processing Service ⚫ Wikipedia introduces OGC WPS as: ⚫

    [A service] designed to standardize the way that GIS calculations are made available to the Internet. ⚫ WPS can describe any calculation including all of its inputs and outputs, and trigger its execution ⚫ The specific processes served up by a WPS implementation are defined by the owner of that implementation. ⚫ Although WPS was designed to work with spatially referenced data, it can be used with any kind of data. FOSS4G 2018, August 29th/31st, Dar Es Salaam
  4. An Example ⚫ Buffer a L shaped geometry with distance

    “2” ⚫ Get the result back as GML FOSS4G 2018, August 29th/31st, Dar Es Salaam
  5. Synchronous vs asynchronous WPS client WPS Launch process Send back

    results Simple Suitable for fast executions Synchronous WPS client WPS Launch process Status URL Check progress 50% Check progress 100% Results inline Link to results More complex Suitable for longer computations Asynchronous FOSS4G 2018, August 29th/31st, Dar Es Salaam
  6. Common WPS setup WPS Remote WCS Remote WFS HTTP server

    WPS client Request + data or links to data Result data Fetch data FOSS4G 2018, August 29th/31st, Dar Es Salaam
  7. GeoServer WPS integration WPS Remote WCS Remote WFS HTTP server

    WPS client All GeoServer Layers WMS client WMS GeoServer UI FOSS4G 2018, August 29th/31st, Dar Es Salaam
  8. Demo request builder ⚫ List processes ⚫ Describe ⚫ Set

    parameters and execute ⚫ All in one form FOSS4G 2018, August 29th/31st, Dar Es Salaam
  9. Rendering transformations ⚫ On-the-fly data transformations inside rendering chain ⚫

    Calling WPS processes from SLD docs ⚫ Optimized for performance FOSS4G 2018, August 29th/31st, Dar Es Salaam
  10. Spatial DBMS! • Never under-estimate the processing power of your

    BDMS: • Designed to efficiently handle large quantities of data • Efficient spatial primitives (at least, in PostGIS) • Doesn’t get more local to your data than this! • Passing params down? • Parametric SQL views! FOSS4G 2018, August 29th/31st, Dar Es Salaam
  11. Parametric SQL views SELECT Date_part('year'::text, t1.obs_datetime) AS obs_year, t1.storm_num, t1.storm_name,

    t1.wind, t2.wind AS wind_end, t1.press, t2.press AS press_end, t1.obs_datetime, t2.obs_datetime AS obs_datetime_end, St_makeline(t1.geom, t2.geom) AS geom FROM storm_obs t1 join(SELECT storm_obs.id, storm_obs.storm_num, storm_obs.storm_name, storm_obs.wind, storm_obs.press, storm_obs.obs_datetime, storm_obs.geom FROM storm_obs) t2 ON(t1.obs_datetime + '06:00:00'::interval) = t2.obs_datetime AND t1.storm_name::text = t2.storm_name::text WHERE Date_part('year'::text, t1.obs_datetime) BETWEEN %min_obs_year% AND %max_obs_year% ORDER BY Date_part('year'::text, t1.obs_datetime), t1.storm_num, t1.obs_datetime • Building lines of the fly from point data: FOSS4G 2018, August 29th/31st, Dar Es Salaam
  12. Mapping Tuna Catches • Multiple Filtering • Aggregation • Joining

    quartely stats against the grid FOSS4G 2018, August 29th/31st, Dar Es Salaam
  13. Filtering, joining and aggregation Some example control regexps: • Y_INTERV:

    • Default: 1 • Regex: ^(\d)+$ • OP • Default: sum • Regex: ^[avg|sum]$ SELECT (T.TS_VALUE / %Y_INTERV%) AS TS_VALUE, T.CD_TA_OCEANAREA, G.GEOMETRY FROM (SELECT CD_TA_OCEANAREA, OP%(TS_VALUE) AS TS_VALUE FROM FIGIS.TS_FI_TA WHERE FIC_ITEM IN (%FIC_ITEM%) AND CD_GEAR IN (%CD_GEAR%) AND YR_TA IN (%YR_TA%) AND QTR_TA IN (%QTR_TA%) GROUP BY CD_TA_OCEANAREA ) t LEFT OUTER JOIN FIGIS_GIS.GRID_G5 g ON T.CD_TA_OCEANAREA = g.CD_OAREA ORDER BY T.CD_TA_OCEANAREA FOSS4G 2018, August 29th/31st, Dar Es Salaam
  14. Animation • Animator tool • GetMap + variying parameters +

    frame control => animated GIF! • http://docs.geoserver.org/stable/en/user/tutoria ls/animreflector.html FOSS4G 2018, August 29th/31st, Dar Es Salaam
  15. Advanced Clip and Ship • Community WPS module plus MapStore

    UI • Requirements • Download large amounts of data • Generic data filtering • Clip on polygon/bbox/circle, both vector and raster • Reproject to target CRS • Band selection in raster • Work in a cluster • Solution: new WPS processes, asynch WPS call FOSS4G 2018, August 29th/31st, Dar Es Salaam
  16. Download service architecture WPS GeoServer Layers MapStore WMS GetCapabilities List

    of layers Buffer DownloadEstimator Download GetStatus Fetch data Status database Shared Hazelcast/DBMS database FOSS4G 2018, August 29th/31st, Dar Es Salaam
  17. Identifying vegetation status • A sentinel2 dataset, picking 3 bands

    for false color display FOSS4G 2018, August 29th/31st, Dar Es Salaam B4, B3, B2
  18. Identifying vegetation status • NDVI, Normalized Difference Vegetation Index: https://en.wikipedia.org/wiki/Normalized_difference_veget

    ation_index • Done using Jiffle, allows to run map algebra on the bands of an input raster layer using the Jiffle language. • Called from SLD via «rendering transformation» • GeoServer rendering pipeline will be smart enough to only read the bands it needs ! • FOSS4G 2018, August 29th/31st, Dar Es Salaam
  19. Identifying vegetation status On the fly calculation and feeding results

    into color map On the fly WPS process call from SLD (aka rendering transformation)
  20. Identifying vegetation status • The output in a GetMap request:

    FOSS4G 2018, August 29th/31st, Dar Es Salaam
  21. WPS Remote • Use GeoServer as WPS Broker → Run

    Remote Processes Asynchronously • Support Python or command line tools • Relies on XMPP for discovery and messaging/logging • Supports Dismiss and basic load balancing for different executors • Automagic results ingestion in GeoServer FOSS4G 2018, August 29th/31st, Dar Es Salaam
  22. Intro Computing the risk of road accidents involving dangerous goods

    (chemicals, petrol, gases and so on) Road segments and stats about car accidents Human and environmental «targets» Involved area, depending on type of good and amount of damage FOSS4G 2018, August 29th/31st, Dar Es Salaam
  23. Large Data Volume • Road network of good part of

    northern Italy • Road divided into segments • 100m portions (500k of them) • 500m aggregation (120k of them) • 1 km square cells (few hundreds) • 51 buffer distances (depending on good, scenario, level of damage) • Several types of targets: schools, malls, hospitals, populated areas, superficial and underground acquifers, crops, woods, …. FOSS4G 2018, August 29th/31st, Dar Es Salaam
  24. The road arc risk formula (s) • Adding togheter the

    risk caused by the different • Arc own propension to accidentds • Types of goods • Human and enviromental targets • The system allows to compute partial views of the formula, either by selection of targets/goods or by computing portions of it • Has a number of coefficients that can be hand- tuned by the caller FOSS4G 2018, August 29th/31st, Dar Es Salaam
  25. The results, visually • Rendering transformation • Read the arcs/polys

    from the DB, compute their risk based on the chosen formula, scenario, targets, and coefficients FOSS4G 2018, August 29th/31st, Dar Es Salaam
  26. How to compute it efficiently? • Using SQL Views? No,

    the possible aggregations variants are too many • Using a pure Java process? No, too much data to transfer from the DBMS • Fully on the fly? No, too much data involved • ➔ Pre-compute all buffers and locate all involved targets before hand (pre-cooked per buffer risk) • ➔ Use a process that builds a final aggregation query on the fly (dynamic sql views) FOSS4G 2018, August 29th/31st, Dar Es Salaam
  27. Parametric queries on steroids • Find which queries are needed,

    replace params • Some queries have sub-queries as params FOSS4G 2018, August 29th/31st, Dar Es Salaam
  28. Efficient rendering tx Risk process Queries database Arcs/Buffer areas db

    Map renderer Build overall query, replace params Compute risk for a batch of arcs Raw arcs Arcs + risk • Compute risk on the fly in the viewing area • Batch requests to the BDMS to minimize round-trip overhead FOSS4G 2018, August 29th/31st, Dar Es Salaam
  29. Efficient cross layer filtering • Show only targets involved in

    the scenario under study, e.g., the ones crossing the buffer areas where there is significant risk • Limit query to the current bbox FOSS4G 2018, August 29th/31st, Dar Es Salaam
  30. Efficient cross layer filtering SELECT v_geo_popolazione_residente_pl.* FROM v_geo_popolazione_residente_pl WHERE v_geo_popolazione_residente_pl.fk_bersaglio_umano_pl

    in ( SELECT distinct bersaglio.fk_bersaglio_umano_pl FROM v_geo_popolazione_residente_pl bersaglio join siig_geo_ln_arco_1 on st_dwithin(bersaglio.geometria, siig_geo_ln_arco_1.geometria, %distanzaumano%) WHERE siig_geo_ln_arco_1.geometria && st_makeenvelope(%bounds%, 32632) ) This is a job for a parametric sql view FOSS4G 2018, August 29th/31st, Dar Es Salaam