Difference between revisions of "Example SQL for Stations"

From Earth Science Information Partners (ESIP)
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* Active monitoring sites: http://www.epa.gov/airnow/2011/20110705/active-sites.dat
 
* Active monitoring sites: http://www.epa.gov/airnow/2011/20110705/active-sites.dat
  
These constitute the raw input data in their native formats. These are then transferred to a SQL database. The schema to the database is totally up to the implementers. The WCS server uses specific, well defined database VIEWS that are created for WCS. See VIEW descriptions below.   
+
These constitute the raw input data in their native formats. These are then transferred to a SQL database. The schema to the database is totally up to the implementers. The WCS server uses specific, well defined SQL views that are created for WCS. See SQL view descriptions below.   
  
  
View in [http://webapps.datafed.net/Core.uFIND?dataset=airnow CORE data catalog] and [http://webapps.datafed.net/datafed.aspx?wcs=http://data1.datafed.net:8080/AIRNOW&coverage=AIRNOW&field=pmfine datafed browser]
+
View AIRNOW in the [http://webapps.datafed.net/Core.uFIND?dataset=airnow CORE data catalog] and [http://webapps.datafed.net/datafed.aspx?wcs=http://data1.datafed.net:8080/AIRNOW&coverage=AIRNOW&field=pmfine datafed browser]
  
 
== The Design of the SQL Database ==
 
== The Design of the SQL Database ==
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Fortunately, relational databases were invented just to solve this problem:
 
Fortunately, relational databases were invented just to solve this problem:
  
=== Physical Data Tables and Virtual Data Views ===
+
=== Physical Tables and Virtual SQL Views ===
  
 
The '''physical''' database on the left is the real data storage, which contains multiple tables and typical relationships of a fully normalized schema.  
 
The '''physical''' database on the left is the real data storage, which contains multiple tables and typical relationships of a fully normalized schema.  
  
On the right is the '''virtual''' WCS data access view, a flat, simple data source to select data from. The WCS does not have to understand the physical structure at all. The real configuration of the WCS system is in the making of these SQL views.  
+
On the right is the '''virtual''' WCS data access SQL view, a flat, simple data source to select data from. The WCS does not have to understand the physical structure at all. The real configuration of the WCS system is in the making of these SQL views.  
  
  
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*** data columns value and quality flag.  
 
*** data columns value and quality flag.  
  
* Virtual View
+
* Virtual SQL View
 
** loc_code, lat and lon is selected from the location table
 
** loc_code, lat and lon is selected from the location table
 
** datetime, pm10 and pm10 quality flag is selected from the pm10 data table
 
** datetime, pm10 and pm10 quality flag is selected from the pm10 data table
 
** loc_code is used to join location to the data
 
** loc_code is used to join location to the data
** the result is a flat view with six columns
+
** the result is a flat SQL view with six columns
  
  
In the particular case of AIRNOW encoding of PM 10 data in the datafed SQL Server, the view definition is below. The actual SQL operation that joins the location and data tables for any other implementation will depend on the local schema of that server.
+
In the particular case of AIRNOW encoding of PM 10 data in the datafed SQL Server, the SQL view definition is below. The actual SQL operation that joins the location and data tables for any other implementation will depend on the local schema of that server.
  
 
     CREATE VIEW pm10_data AS
 
     CREATE VIEW pm10_data AS
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     INNER JOIN pm10 ON location.loc_code = pm10.loc_code
 
     INNER JOIN pm10 ON location.loc_code = pm10.loc_code
  
The output of the view creation will need to follow the content and format shown below.
+
The output of the SQL view will need to follow the content and format shown below.
  
 
[[Image:Data_view.png]]
 
[[Image:Data_view.png]]
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longitude or längengrad, and then mark it to be the longitude column somewhere else, the field name is fixed to '''lon'''. On the other hand the data and metadata column names can be anything.
 
longitude or längengrad, and then mark it to be the longitude column somewhere else, the field name is fixed to '''lon'''. On the other hand the data and metadata column names can be anything.
  
=== Location View ===
+
=== Location SQL View ===
  
The Location view needs at least 3 columns: '''loc_code''', '''lat''' and '''lon'''. These columns are joined with the data table in the WCS data queries.
+
The Location SQL view needs at least 3 columns: '''loc_code''', '''lat''' and '''lon'''. These columns are joined with the data table in the WCS data queries.
  
 
The standard field names are ''loc_code'', ''loc_name'' (optional), ''lat'', ''lon'', ''elev'' (optional). It is important to use these names, since client software, like datafed browser, expects them. Any other fields can be added. The system will just copy the additional data to output.  
 
The standard field names are ''loc_code'', ''loc_name'' (optional), ''lat'', ''lon'', ''elev'' (optional). It is important to use these names, since client software, like datafed browser, expects them. Any other fields can be added. The system will just copy the additional data to output.  
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There are two reasons to use an SQL view also for the location table.
 
There are two reasons to use an SQL view also for the location table.
  
* The location data may be distributed over several tables. In this case, the view can hide the SQL joins.
+
* The location data may be distributed over several tables. In this case, the SQL view can hide the SQL joins.
* The location table most likely has different names for the columns. In this case, the view may do nothing else but rename ''SiteCode'' to ''loc_code'' and ''latitude'' to ''lat''.
+
* The location table most likely has different names for the columns. In this case, the SQL view may do nothing else but rename ''SiteCode'' to ''loc_code'' and ''latitude'' to ''lat''.
  
 
Once the SQL view is created, it can be published using Web Feature Service, WFS. Web Coverage Service, WCS, was originally designed to serve gridded data, and in the DescribeCoverage response there is no convenient way to encode the location dimension. WFS, on the other hand, was designed to serve static geographical features, and matches well for the job.  
 
Once the SQL view is created, it can be published using Web Feature Service, WFS. Web Coverage Service, WCS, was originally designed to serve gridded data, and in the DescribeCoverage response there is no convenient way to encode the location dimension. WFS, on the other hand, was designed to serve static geographical features, and matches well for the job.  
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There is no universally best solution, pros and conses must be weighted for your case.
 
There is no universally best solution, pros and conses must be weighted for your case.
  
The main point is '''Physical - Logical Separation!''' Whatever is your physical schema, the flat views are used for data access. The view system allows you to change the physical schema completely without changing the WCS configuration.
+
The main point is '''Physical - Logical Separation!''' Whatever is your physical schema, the flat SQL views are used for data access. The SQL view system allows you to change the physical schema completely without changing the WCS configuration.
  
== Client-side view of AIRNow WCS ==
+
== Client-side browser view of AIRNow WCS ==
  
 
[[image:AirNOw_WCS_Query.png|400px]]
 
[[image:AirNOw_WCS_Query.png|400px]]
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In the CF-NetCDF, the CF metadata is enough to do all the configuration. The configuration contains:
 
In the CF-NetCDF, the CF metadata is enough to do all the configuration. The configuration contains:
  
With SQL views in place, you only need to tell the WFS and WCS servers the view names. In the AIRNOW case, WFS configuration for locations.
+
With SQL views in place, you only need to tell the WFS and WCS servers the SQL view names. In the AIRNOW case, WFS configuration for locations.
  
 
     view_alias  : location_with_statistics
 
     view_alias  : location_with_statistics
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Since the location_with_statistics contains all the parameter statistics, the view must be filtered to include pm10 only.
 
Since the location_with_statistics contains all the parameter statistics, the view must be filtered to include pm10 only.
  
The data configuration needs the view and some basic metadata. Configuring the PM 10:
+
The data configuration needs the SQL view and some basic metadata. Configuring the PM 10:
  
 
     pm10:
 
     pm10:

Revision as of 13:21, July 12, 2011

Back to WCS Access to netCDF Files

This is an example on how to serve Station-Point data through WCS data access protocol. The basis of the use case is the [http:AIRNow.gov AIRNow] dataset that represents hourly near-real-time surface observations of PM10, PM2.5 and ozone over the US. This example use may be applicable to all datasets where:

  • Locations: the monitoring is performed a fixed geographic points, i.e. monitoring stations
  • Times: the sampling is over a fixed time range (e.g. hour or day) and fixed periodicity (every six hour or every third day)
  • Parameters: the number of observed Earth Observation parameters is finite and known

The Locations, Times and Parameters constitute the dimensions of the Station-Point data space.

Airnow data source

Daily PM 2.5, PM 10 and Ozone data can be downloaded from date encoded folders on the EPA web server. The data and location tables are in text files, one for each day.

These constitute the raw input data in their native formats. These are then transferred to a SQL database. The schema to the database is totally up to the implementers. The WCS server uses specific, well defined SQL views that are created for WCS. See SQL view descriptions below.


View AIRNOW in the CORE data catalog and datafed browser

The Design of the SQL Database

The most common and flexible method of storing station time series point data is an SQL database. The data collector designs the schema, which captures all the measured data and metadata. This very often requires a database with lots of tables, there is no one-size-fits-all design around because of differing requirements.

Fortunately, relational databases were invented just to solve this problem:

Physical Tables and Virtual SQL Views

The physical database on the left is the real data storage, which contains multiple tables and typical relationships of a fully normalized schema.

On the right is the virtual WCS data access SQL view, a flat, simple data source to select data from. The WCS does not have to understand the physical structure at all. The real configuration of the WCS system is in the making of these SQL views.


SQL views.png

  • Physical database:
    • The center of this database is the location table.
    • Each location has a unique loc_code as the key column.
    • Each measure parameter has its own data table
      • loc_code for the location of the station
      • datetime for the observation time
      • data columns value and quality flag.
  • Virtual SQL View
    • loc_code, lat and lon is selected from the location table
    • datetime, pm10 and pm10 quality flag is selected from the pm10 data table
    • loc_code is used to join location to the data
    • the result is a flat SQL view with six columns


In the particular case of AIRNOW encoding of PM 10 data in the datafed SQL Server, the SQL view definition is below. The actual SQL operation that joins the location and data tables for any other implementation will depend on the local schema of that server.

   CREATE VIEW pm10_data AS
   SELECT location.loc_code, lat, lon, datetime, pm10, pm10_qf
   FROM location 
   INNER JOIN pm10 ON location.loc_code = pm10.loc_code

The output of the SQL view will need to follow the content and format shown below.

Data view.png

The WCS point data is published using a fairly popular design philosophy called convention over configuration. Rather than allowing any name, longitude or längengrad, and then mark it to be the longitude column somewhere else, the field name is fixed to lon. On the other hand the data and metadata column names can be anything.

Location SQL View

The Location SQL view needs at least 3 columns: loc_code, lat and lon. These columns are joined with the data table in the WCS data queries.

The standard field names are loc_code, loc_name (optional), lat, lon, elev (optional). It is important to use these names, since client software, like datafed browser, expects them. Any other fields can be added. The system will just copy the additional data to output.

Location table.png

There are two reasons to use an SQL view also for the location table.

  • The location data may be distributed over several tables. In this case, the SQL view can hide the SQL joins.
  • The location table most likely has different names for the columns. In this case, the SQL view may do nothing else but rename SiteCode to loc_code and latitude to lat.

Once the SQL view is created, it can be published using Web Feature Service, WFS. Web Coverage Service, WCS, was originally designed to serve gridded data, and in the DescribeCoverage response there is no convenient way to encode the location dimension. WFS, on the other hand, was designed to serve static geographical features, and matches well for the job.

Example WFS request to get the WFS AIRNOW location table from the location SQL view.

The WFS standard is defined in WFS 1.0.0.

Notes on the Physical Database Schema Possibilities

There are numerous ways to organize the physical data tables, and all of these ways have pros and cons. The AIRNOW database is organized by creating a physical data table for each parameter separately. This arrangement has a some advantages: fastest to query single parameters and no need for NULLs

Other possibilies are:

Big Wide table: One big table with columns loc_code datetime pmfine pm10 super. In this case, querying multiple parameters at the same time requires no joins. Unfortunately, missing data must be expressed with NULLs.

Long and skinny table: loc_code datetime param_code param_value. In this case each row contains two data columns: the parameter code, telling what the measurement actually is, and the parameter value. In this case new parameter codes can be added any time without changing the database schema.

There is no universally best solution, pros and conses must be weighted for your case.

The main point is Physical - Logical Separation! Whatever is your physical schema, the flat SQL views are used for data access. The SQL view system allows you to change the physical schema completely without changing the WCS configuration.

Client-side browser view of AIRNow WCS

AirNOw WCS Query.png

Map Query for large area and single time instance: Identifier=AIRNOW RangeSubset=pmfine TimeSequence=2011-07-01T18:00:00 BoundingBox=-90,35,-70,45] . The actual WCS getCoverage call is:

http://data1.datafed.net:8080/AIRNOW?Service=WCS&Version=1.1.2&Request=GetCoverage&Identifier=AIRNOW&Format=text/csv&Store=true&TimeSequence=2011-07-01T18:00:00&RangeSubset=pmfine&BoundingBox=-90,35,-70,45,urn:ogc:def:crs:OGC:2:84

Time Series Query for a time range and a single location: Identifier=AIRNOW RangeSubset=pmfine[location[420010001]] TimeSequence=2005-06-01/2011-09-01/PT1H . The actual WCS getCoverage call is:

http://data1.datafed.net:8080/AIRNOW?Service=WCS&Version=1.1.2&Request=GetCoverage&Identifier=AIRNOW&Format=text/csv&Store=true&TimeSequence=2005-06-01/2011-09-01/PT1H&RangeSubset=pmfine[location[420010001]]

The syntax RangeSubset=pmfine[location[420010001]] is standard as documented in WCS 1.1.2 Standards. It specifies the pmfine parameter, filters by dimension location selecting loc_code = 420010001.

TimeSequence=2005-06-01/2011-09-01/PT1H has time_min/time_max/periodicity. PT1H is hourly, P1D is daily.

AIRNow registered in GEO Air Quality Community Catalog

Once the AIRNow WCS service is available as a tested and functioning web service, it is ready to be published in a service catalog(s) where the potential clients can find it and access (bind to) it. In other words, it is ready to be included in a network following the publish-find-bind triad of Service Oriented Architecture.

Unfortunately, a general registry and catalog of OGC W*S services does not exist. The task of identifying and organizing the available WCS data access services then falls on the communities within specific domains. For the Air Quality domain, a subset of the available interoperable services are gathered in the GEO Air Quality Community Catalog, which is harvested by the GEO Clearinghouse. The service can be registered in multiple catalogs.

View AIRNow in in the AQ Community Catalog.



and datafed browser

Configuring the WFS Service and WCS Service for point data

In the CF-NetCDF, the CF metadata is enough to do all the configuration. The configuration contains:

With SQL views in place, you only need to tell the WFS and WCS servers the SQL view names. In the AIRNOW case, WFS configuration for locations.

   view_alias  : location_with_statistics
   columns     : loc_code, loc_name, lat, lon
   view_filter : param_abbr='pm10' and data_count > 0

Since the location_with_statistics contains all the parameter statistics, the view must be filtered to include pm10 only.

The data configuration needs the SQL view and some basic metadata. Configuring the PM 10:

   pm10:
       title     : Particulate Matter 10
       datatype  : float
       units     : ug/cm^3
       data_view :
           view_alias : pm10_data,
           columns    : loc_code, lat, lon, datetime, pm10, pm10_qf

External Examples

TODO: separate page for CIRA, more infor about EBAS

NILU - EBAS

CIRA/VIEWS