Crunching Data In GeoServer: Mastering Rendering Transformations, WPS And SQL Views

Transcript

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 2019, August 26th/30th, Bucharest
3. WPS: quick intro
4. 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 2019, August 26th/30th, Bucharest
5. An Example ⚫ Buffer a L shaped geometry with distance “2” ⚫ Get the result back as GML FOSS4G 2019, August 26th/30th, Bucharest
6. 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 2019, August 26th/30th, Bucharest
7. Common WPS setup WPS Remote WCS Remote WFS HTTP server WPS client Request + data or links to data Result data Fetch data FOSS4G 2019, August 26th/30th, Bucharest
8. GeoServer WPS integration WPS Remote WCS Remote WFS HTTP server WPS client All GeoServer Layers WMS client WMS GeoServer UI FOSS4G 2019, August 26th/30th, Bucharest
9. Demo request builder ⚫ List processes ⚫ Describe ⚫ Set parameters and execute ⚫ All in one form FOSS4G 2019, August 26th/30th, Bucharest
10. Rendering transformations ⚫ On-the-fly data transformations inside rendering chain ⚫ Calling WPS processes from SLD docs ⚫ Optimized for performance FOSS4G 2019, August 26th/30th, Bucharest
11. But you don’t always need a WPS
12. 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 2019, August 26th/30th, Bucharest
13. Parametric SQL views WMS/WFS client GeoServer Spatial database &viewparams=from:2000000;high:5000000 Expanded Query FOSS4G 2019, August 26th/30th, Bucharest
14. 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 2019, August 26th/30th, Bucharest
15. Pure SQL views: Tuna Atlas
16. Mapping Tuna Catches • Multiple Filtering • Diferent aggregations • Joining quartely stats against the grid FOSS4G 2019, August 26th/30th, Bucharest
17. 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 2019, August 26th/30th, Bucharest
18. Compunding WFS/WCS deficiencies: Clips and ship services
19. 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 2019, August 26th/30th, Bucharest
20. MapStore GUI Buffer process called synchronously when buffer size changes FOSS4G 2019, August 26th/30th, Bucharest
21. MapStore GUI Tracking download status (asynch WPS) FOSS4G 2019, August 26th/30th, Bucharest
22. 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 2019, August 26th/30th, Bucharest
23. Dashboards and widgets
24. Widgets • Community WPS module plus MapStore UI • Aggregate data to show synthetic information from map layers • Widgets are floating on the map • Automatically filtered on current map view FOSS4G 2019, August 26th/30th, Bucharest
25. Widgets • Each widget is updated using a synchronous WPS (or WFS) request • You can create / configure your own widgets through a simple wizard FOSS4G 2019, August 26th/30th, Bucharest
26. Widgets • You can create / configure your own widgets through a simple wizard • You can filter the data FOSS4G 2019, August 26th/30th, Bucharest
27. Dashboards • In Dashboards there is not a single map • Multiple maps can be configured and connected to widgets to create a dynamic dashboard FOSS4G 2019, August 26th/30th, Bucharest
28. Dashboards • Another point of view on your data FOSS4G 2019, August 26th/30th, Bucharest
29. Rendering transformations: Precision Agriculture with Sentinel-2 Data
30. Identifying vegetation status • A sentinel2 dataset, picking 3 bands for false color display B4, B3, B2 FOSS4G 2019, August 26th/30th, Bucharest
31. 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 2019, August 26th/30th, Bucharest
32. Identifying vegetation status On the fly calculation and feeding results into color map On the fly WPS process call from SLD (aka rendering transformation) FOSS4G 2019, August 26th/30th, Bucharest
33. Identifying vegetation status • The output in a GetMap request: FOSS4G 2019, August 26th/30th, Bucharest
34. Pure WPS calling to other computing nodes: WPS Remote
35. 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 2019, August 26th/30th, Bucharest
36. WPS Remote FOSS4G 2019, August 26th/30th, Bucharest
37. Mixing parametric SQL views and WPS: The Destination Project
38. 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 2019, August 26th/30th, Bucharest
39. 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 2019, August 26th/30th, Bucharest
40. 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 2019, August 26th/30th, Bucharest
41. 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 2019, August 26th/30th, Bucharest
42. 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 2019, August 26th/30th, Bucharest
43. Parametric queries on steroids • Find which queries are needed, replace params • Some queries have sub-queries as params FOSS4G 2019, August 26th/30th, Bucharest
44. 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 DBMS to minimize round-trip overhead FOSS4G 2019, August 26th/30th, Bucharest
45. 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 2019, August 26th/30th, Bucharest
46. 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 2019, August 26th/30th, Bucharest
47. The End Questions? andrea.aime@geo-solutions.it simone.giannecchini@geo-solutions.it alessio.fabiani@geo-solutions.it FOSS4G 2019, August 26th/30th, Bucharest