A Singer tap for extracting data from a CSV file.
This is a fairly brittle implementation of a CSV reader, but it gets the job done for tasks where you file structure is highly predictable.
The input files must be a traditionally-delimited CSV (commas separated
columns, newlines indicate new rows, double quoted values) as defined
by the defaults to the python csv
library.
Paths to local files and the names of their corresponding entities are specified in the config file, and each file must contain a header row including the names of the columns that follow.
Perhaps the greatest limitation of this implementation is that it assumes all incoming data is a string. Future iterations could intelligently identify data types based on a sampling of rows or allow the user to provide that information.
Clone this repository, and then:
› python setup.py install
python tap_csv.py -c config.json
Where config.json
contains an array called files
that consists of dictionary objects detailing each destination table to be passed to Singer. Each of those entries contains:
entity
: The entity name to be passed to singer (i.e. the table)path
: Local path to the file to be ingested. Note that this may be a directory, in which case all files in that directory and any of its subdirectories will be recursively processedkeys
: The names of the columns that constitute the unique keys for that entity
Example:
{
"files": [
{ "entity" : "leads",
"file" : "/path/to/leads.csv",
"keys" : ["Id"]
},
{ "entity" : "opportunities",
"file" : "/path/to/opportunities.csv",
"keys" : ["Id"]
}
]
}
Optionally, the files definition can be provided by an external json file:
config.json
{
"csv_files_definition": "files_def.json"
}
files_def.json
[
{ "entity" : "leads",
"file" : "/path/to/leads.csv",
"keys" : ["Id"]
},
{ "entity" : "opportunities",
"file" : "/path/to/opportunities.csv",
"keys" : ["Id"]
}
]
This tap is based on the following tap-csv
project: https://github.com/robertjmoore/tap-csv