NAME

KEYWORDS

SYNOPSIS

DESCRIPTION

Output formats

Plain text

The individual fields (attribute values) are separated by a pipe (|) which can be customized using the separator option. The records (rows) are separated by newlines.

Example with a pipe as a separator (the default):

cat|road_name|multilane|year|length
1|NC-50|no|2001|4825.369405
2|NC-50|no|2002|14392.589058
3|NC-98|no|2003|3212.981242
4|NC-50|no|2004|13391.907552

No quoting or escaping is performed by default, so if these characters are in the output, they look just like the separators. This is usually not a problem for humans looking at the output to get a general idea about query result or attribute table content.

Consequently, this format is not recommended for computers, e.g., for reading attribute data in Python scripts. It works for further parsing in limited cases when the values don't contain separators or when the separators are set to one of the escaped characters.

CSV

All full CSV parsers such as the ones in LibreOffice or Python are able to parse this format when configured to the above specification.

Example with default settings:

cat,road_name,multilane,year,length
1,"NC-50","no",2001,4825.369405
2,"NC-50","no",2002,14392.589058
3,"NC-98","no",2003,3212.981242
4,"NC-50","no",2004,13391.907552

If desired, the separator can be customized and escaping can be enabled with the same characters being escaped as for the plain text. Notably, newlines and tabs are escaped, double quotes are not, and the separator is not escaped either (unless it is a tab). However, the format is guaranteed only for the commonly used separators such as comma, semicolon, pipe, and tab.

Note that using multi-character separator is allowed, but not recommended as it is not generally supported by CSV readers.

CSV is the recommended format for further use in another analytical applications, especially for use with spreadsheet applications. For scripting, it is advantageous when tabular data is needed (rather than key-value pairs).

JSON

Records which are the result of the query are stored under key records as an array (list) of objects (collections of key-value pairs). The keys for attributes are lowercase or uppercase depending on how the columns were defined in the database.

The JSON also contains information about columns stored under key info. Column names and types are under key columns. Each colum has SQL data type under sql_type in all caps. A boolean is_number specifies whether the value is a number, i.e., integer or floating point number. The is_number value is aded for convenience and it is recommended to rely on the types derived from the JSON representation or the SQL types. The definition of is_number may change in the future.

Example with added indentation:

{
  "info": {
    "columns": [
      {
        "name": "road_name",
        "sql_type": "CHARACTER",
        "is_number": false
      },
      {
        "name": "year",
        "sql_type": "INTEGER",
        "is_number": true
      },
      {
        "name": "length",
        "sql_type": "DOUBLE PRECISION",
        "is_number": true
      }
    ]
  },
  "records": [
    {
      "road_name": "NC-50",
      "year": 2001,
      "length": 4825.369405
    },
    {
      "road_name": "NC-50",
      "year": 2001,
      "length": 14392.589058
    }
  ]
}

JSON is the recommended format for reading the data in Python and for any uses and environments where convenient access to individual values is desired and JSON parser is available.

Vertical plain text

Example with (horizontal) separator = and vertical separator newline:

cat=1
road_name=NC-50
multilane=no
year=2001
length=4825.369405

cat=2
road_name=NC-50
multilane=no
year=2002
length=14392.589058

NOTES

• CSV and JSON were added in version 8.0 as new primary formats for further consumption by scripts and other applications.
• Escaping of plain and vertical formats was extended from just backslash and newlines to all escapes from JSON except for double quote character.

EXAMPLES

Select and show entire table

v.db.select map=roadsmajor
cat|MAJORRDS_|ROAD_NAME|MULTILANE|PROPYEAR|OBJECTID|SHAPE_LEN
1|1|NC-50|no|0|1|4825.369405
2|2|NC-50|no|0|2|14392.589058
3|3|NC-98|no|0|3|3212.981242
4|4|NC-50|no|0|4|13391.907552
...

Select and show single column from table

v.db.select map=roadsmajor column=ROAD_NAME
NC-50
NC-50
NC-98
NC-50
NC-98
...

Print region extent of selected vector features

v.db.select -r map=roadsmajor where="ROAD_NAME = 'NC-98'"
n=248425.389891
s=245640.640081
w=635906.517653
e=661979.801880

Select empty vector features (no data entries)

v.db.select geonames_wake where="ALTERNATEN IS NULL"
cat|GEONAMEID|NAME|ASCIINAME|ALTERNATEN|FEATURECLA|FEATURECOD|...
8|4498303|West Raleigh|West Raleigh||P|PPL|US||NC|338759|123|...
14|4459467|Cary|Cary||P|PPL|US||NC|103945|146|152|America/Iqaluit|...
31|4452808|Apex|Apex||P|PPL|US||NC|30873|167|134|America/Iqaluit|...
...

Select not empty vector features (no data entries)

v.db.select geonames_wake where="ALTERNATEN IS NOT NULL"
cat|GEONAMEID|NAME|ASCIINAME|ALTERNATEN|FEATURECLA|FEATURECOD|...
9|4487042|Raleigh|Raleigh|Raleigh,...
31299|4487056|Raleigh-Durham Airport|Raleigh-Durham Airport|...
...

Select features with distinct road names

v.db.select map=roadsmajor columns=ROAD_NAME group=ROAD_NAME
ROAD_NAME

I-40
I-440
I-540
NC-231
NC-39
NC-42
...

v.db.select map=roadsmajor columns=ROAD_NAME,MULTILANE group=ROAD_NAME where='ROAD_NAME is not null'
ROAD_NAME|MULTILANE
I-40|yes
I-440|yes
I-540|yes
NC-231|no
NC-39|no
NC-42|no
NC-50|no
NC-54|no
NC-55|no
NC-96|no
NC-97|no
NC-98|no
US-1|
US-401|no
US-64|yes
US-70|yes

v.db.select map=roadsmajor columns=ROAD_NAME,MULTILANE group=ROAD_NAME,MULTILANE where='ROAD_NAME is not null'
ROAD_NAME|MULTILANE
I-40|yes
I-440|yes
I-540|yes
NC-231|no
NC-39|no
NC-42|no
NC-50|no
NC-54|no
NC-55|no
NC-96|no
NC-97|no
NC-98|no
US-1|
US-1|yes
US-401|no
US-401|yes
US-64|yes
US-70|yes

Read results in Python

import json
import grass.script as gs

text = gs.read_command("v.db.select", map="roadsmajor", format="json")
data = json.loads(text)
for row in data["records"]:
    print(row["ROAD_NAME"])

SEE ALSO

GRASS SQL interface

AUTHORS

SOURCE CODE

Available at: v.db.select source code (history)

Latest change: Tuesday Dec 17 20:17:20 2024 in commit: d962e90c026708a4815ea2b9f46c0e84c17de22d

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© 2003-2024 GRASS Development Team, GRASS GIS 8.4.1dev Reference Manual