Difference between revisions of "Encoding Relational Tables in NetCDF"
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=== NILU / EBAS === | === NILU / EBAS === | ||
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Revision as of 11:17, August 31, 2011
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, we're only presenting the essential central tables. Full schema outline looks like this: