Encoding Relational Tables in NetCDF

Back to Questions and Comments about CF-1.6 Station Data Convention

Rational Behind this Proposal

The WCS service has potential to grow beyond regular, gridded coverages. Coverage types that need to be described are for example:

• station point data
• station trajectory data
• lidar data
• moving station data, like aeroplane or ship

With a relational system, it is easy to design a schema that captures all the data and the metadata.

The netCDF data format was designed to contain mainly gridded multidimensional data. The format does not have a special support for tables, which sounds like a severe restriction, but it's not.

• It's possible to agree about a simple way to encode tables in a netCDF file.
  • Tables with multiple fields.
  • Proper primary keys for tables.
  • Foreign keys for referential integrity.
  • Common relationships: many-to-many, 1-to-many, 1-to-1.

• It's possible to be efficient, the encoding does not have to be one size fits all.
  • Sparse tables can be encoded row-by-row manner.
  • Dense tables can be encoded as multidimensional hypercubes.
  • Mix and match the two above at will.

• It's possible to be elegant.
  • No mysterious indirections.
  • No complex offset calculations.
  • A reader-writer library can take care of following the rules, letting a programmer to operate on higher level.

Encoding Table Columns

Table is a collection of columns. These are indicated with table_name attribute.

Every table needs a primary key. It is indicated with primary_key = "T" attribute.

Example: A station table with three columns: station_code, lat and lon. There are ten rows with indexes 0..9

   dimensions:
       station = 10 ;  // station table dimension
       station_code_length = 4 ; // artificial dimension for station code, with max four character length.
   variables:
       char station_code(station, station_code_length) ;
           station_code:table_name = "station";
           station_code:primary_key = "T";
       float lon(station) ; 
           lon:table_name = "station";
       float lat(station) ; 
           lat:table_name = "station";

The dimension name station can be anything. It does not have to equal the table name station, but should be for readability. Same for station_code_length, it can be called anything.

A table can have multiple fields as a primary key.

Encoding Multiple Tables

Adding the second table is similar. Since new data is usually added by time, it can be made the unlimited dimension.

   dimensions:
       station = 10 ;  // station table dimension
       station_code_length = 4 ; // artificial dimension for station code, with max four character length.
       time = UNLIMITED ;
   variables:
       char station_code(station, station_code_length) ;
           station_code:table_name = "station";
           station_code:primary_key = "T";
       float lon(station) ; 
           lon:table_name = "station";
       float lat(station) ; 
           lat:table_name = "station";
       int time(time) ;
           time:table_name = "time";
           time:primary_key = "T";

Encoding Relations

A typical station data instance is a relation (station_code, time, temperature, humidity). The pair (station_code, time) is the primary key; data is recorded at a certain station, at a certain time. Temperature and humidity are the recorded parameters.

Encoding this into netCDF can be done in two ways, using either variable or a dimension.

Encoding Relation as a Record Array

So far we have the two unrelated tables for our two dimensions: location and time. Now let's add humidity and temperature, which refers to both of the dimensions:

   dimensions:
       station = 10 ;  // station table dimension
       station_code_length = 4 ; // artificial dimension for station code, with max four character length.
       data = UNLIMITED // each row in the data table. 
       time = 2000 ; we can't have two unlimited dimensions. 
   variables:
       char station_code(station, station_code_length) ;
           station_code:table_name = "station";
           station_code:primary_key = "T";
       float lon(station) ; 
           lon:table_name = "station";
       float lat(station) ; 
           lat:table_name = "station";
       int time(time) ;
           time:table_name = "time";
           time:primary_key = "T";
       int data_station_code(data) ;
           data_station_code:table_name = "data";
           data_station_code:foreign_key = "station_code";
           data_station_code:primary_key = "T";
       int data_time(data) ;
           data_time:table_name = "data";
           data_time:foreign_key = "time";
           data_time:primary_key = "T";
       float temperature(data) ; 
           temperature:table_name = "data";
       float humidity(data) ; 
           humidity:table_name = "data";

The variable data_station_code is marked foreign_key = "station_code", which means, that the variable is an index to the station_code variable. Similar for time. Both are marked primary_key = "T" which means, that the combination of (data_station_code, data_time) is unique.

   To read the full table (station_code, lat, lon, time, temperature, humidity), row by row:
   for idx in range of data
       read station:
           read data_station_code(idx)
           read variables station_code, lat and lon at that index
       read time:
           read data_time(idx)
           read variable time at that index
       read temperature(idx)
       read humidity(idx)

This is an space-efficient encoding for sparse tables.

Encoding Relation as a Multidimensional Array

This is identical to CF-1.6 encoding. Both variables are 2-dimensional cubes.

   dimensions:
       station = 10 ;  // station table dimension
       station_code_length = 4 ; // artificial dimension for station code, with max four character length.
       time = UNLIMITED ; // we can again add one time slice at a time
   variables:
       char station_code(station, station_code_length) ;
           station_code:table_name = "station";
           station_code:primary_key = "T";
       float lon(station) ; 
           lon:table_name = "station";
       float lat(station) ; 
           lat:table_name = "station";
       int time(time) ;
           time:table_name = "time";
           time:primary_key = "T";
       float temperature(time,station) ; 
           temperature:table_name = "data";
       float humidity(time,station) ; 
           humidity:table_name = "data";

This declares two arrays, one for temperature and one for humidity. Both are two-dimensional. The dimensions time and station refer to the time and station variables. They implicitly became part of the primary key.

   To find temperature at "ACAD" in "2004-17-22":
       Find index for "ACAD" from the "station_code" array. Let's say it's 2.
       Find index for "2004-17-22" from the time array. Lets say it's 317.
       Read temperature(317,2)

This is a very space-efficient encoding, if most of the stations have data most of the time.

Summa Summarum

• A typical schema of any relational database can be encoded.
• If the data is sparse, foreign keys can be expressed as variables.
• If the data is dense, foreign keys can be expressed as dimensions, resulting in vastly smaller files than flat CSV would be.
• There can be any combination of the two, enabling the best of both worlds whatever your data will look like.

More Real World Examples

These examples are not full real-life examples of real live databases, but rather examples how to encode a typical schema. The center is usually the data table, and the data record refers to the auxliary metadata tables. Typically cases are a star or a snowflake schema.

CIRA / VIEWS

All the data is stored at the center table AirData3. The

NILU / EBAS

The EBAS database schema is rather large, full schema outline looks like this:

The essential schema is not that big, only four tables.

The essential four tables are:

• EB_STATION: This contains the station information EB_STATION.
  • EB_STATION_CODE is the unique code.
  • lat, lon, alt; some miscellaneous fields about the history of the station.

• EC_COMPONENTS: This table contains the measured parameters. Fields EM_MATRIX_NAME and EC_COMP_NAME define what is being measured.
  • EM_MATRIX_NAME defines which medium the parameter is measured in (e.g. precipitation, air, pm10, ...)
  • EC_COMP_NAME is the name of the parameter (e.g. sulphate_total, ...)
  • Various other fields, like EC_UNIT define more metadata.

• DS_DATA_SET:
  • Table DS_DATA_SET describes what is called a dataset in EBAS: One parameter measured at a certain station. The full version has some more complex dependecies on instrument, method etc.
  • The columns EM_MATRIX_NAME and EC_COMP_NAME refer to the EC_COMPONENTS table.
  • DS_STARTDATE and DS_ENDDATE provide the timestamp of the first and last measurement in the timeseries.
  • There are many other fields and references to many other tables.

You can imagine a dataset as an instrument on a fixed location measuring one parameter. This leads us to the main data table.

• Table A1_TIME Contains all the observations, all the parameters from all the stations
  • DS_SETKEY refers to the dataset.
  • A1_STARTTIME and A1_ENDTIME define the measurement time.
  • A1_VALUE contains the data value
  • The rest of the fields are metadata.