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Overview
Generic Data Transformation Tool
----------------------------------------------
```
+--------------+
|csv data |
+-----+--------+
|
|
v
+----web ui----+ +----func+----+ +---table----+
|import screen +------> |srce.sql +----------> |tps.srce | <-------------------+
+--------------+ +-------------+ +------------+ |
|p1:srce | |
|p2:file path | |
+-----web ui---+ +-------------+ +----table---+ |
|create map | |tps.map_rm | +--+--db proc-----+
|profile +---------------------------------> | | |update tps.trans |
+------+-------+ +-----+------+ |column allj to |
| ^ |contain map data |
| | +--+--------------+
v foreign key ^
+----web ui+----+ | |
|assign maps | + |
|for return | +---table----+ |
+values +--------------------------------> |tps.map_rv | |
+---------------+ | +---------------------+
+------------+
```
The goal is to:
1. house external data and prevent duplication on insert
2. apply mappings to the data to make it meaningful
3. be able to reference it from outside sources (no action required)
2. facilitate regular exression operations to extract meaningful data
3. be able to reference it from outside sources (no action required) and maintain reference to original data
There are 5 tables
* tps.srce : definition of source
* tps.trans : actual data
* tps.trans_log : log of inserts
* tps.map_rm : map profile
* tps.map_rv : profile associated values
# tps.srce schema
{
"name": "WMPD",
"descr": "Williams Paid File",
"type":"csv",
"schema": [
{
"key": "Carrier",
"type": "text"
},
{
"key": "Pd Amt",
"type": "numeric"
},
{
"key": "Pay Dt",
"type": "date"
}
],
"unique_constraint": {
"fields":[
"{Pay Dt}",
"{Carrier}"
]
}
}
It is well suited for data from outside systems that
* requires complex transformation (parsing and mapping)
* original data is retained for reference
# tps.map_rm schema
{
"name":"Strip Amount Commas",
"description":"the Amount field comes from PNC with commas embeded so it cannot be cast to numeric",
"defn": [
{
"key": "{Amount}", /*this is a Postgres text array stored in json*/
"field": "amount", /*key name assigned to result of regex/*
"regex": ",", /*regular expression/*
"flag":"g",
"retain":"y",
"map":"n"
}
],
"function":"replace",
"where": [
{
}
]
}
use cases:
* on-going bank feeds
* jumbled product lists
* storing api results
The data is converted to json by the importing program and inserted to the database.
Regex expressions are applied to specified json components and the results can be mapped to other values.
Major Interactions
------------------------
* Source Definitions (Maint/Inquire)
* Regex Instructions (Maint/Inquire)
* Cross Reference List (Maint/Inquire)
* Run Import (Run Job)
### Interaction Details
* Source Definitions (Maint/Inquire)
* display a list of existing sources with display detials/edit options
* create new option
* underlying function is `tps.srce_set(_name text, _defn jsonb)`
* Regex Instructions (Maint/Inquire)
* display a list of existing instruction sets with display details/edit options
* create new option
* underlying function is `tps.srce_map_def_set(_srce text, _map text, _defn jsonb, _seq int)` which takes a source "code" and a json
* Cross Reference List (Maint/Inquire)
* first step is to populate a list of values returned from the instructions (choose all or unmapped) `tps.report_unmapped(_srce text)`
* the list of rows facilitates additional named column(s) to be added which are used to assign values anytime the result occurs
* function to set the values of the cross reference `tps.srce_map_val_set_multi(_maps jsonb)`
* Run Import
Notes
======================================
pull various static files into postgres and do basic transformation without losing the original document
or getting into custom code for each scenario
the is an in-between for an foreign data wrapper & custom programming
## Storage
all records are jsonb
applied mappings are in associated jsonb documents
## Import
`COPY` function utilized
## Mappings
1. regular expressions are used to extract pieces of the json objects
2. the results of the regular expressions are bumped up against a list of basic mappings and written to an associated jsonb document
each regex expression within a targeted pattern can be set to map or not. then the mapping items should be joined to map_rv with an `=` as opposed to `@>` to avoid duplication of rows
## Transformation tools
* `COPY`
* `regexp_matches()`
## Difficulties
Non standard file formats will require additional logic
example: PNC loan balance and collateral CSV files
1. External: Anything not in CSV should be converted external to Postgres and then imported as CSV
2. Direct: Outside logic can be setup to push new records to tps.trans direct from non-csv fornmated sources or fdw sources
## Interface
maybe start out in excel until it gets firmed up
* list existing mappings
* apply mappings to see what results come back
* experiment with new mappings
* underlying function is `tps.srce_import(_path text, _srce text)`