ftmq

An attempt towards a followthemoney query dsl.

This library provides methods to query and filter entities formatted as followthemoney data, either from a json file/stream or using a SQL backend via followthemoney-store

It also provides a Query class that can be used in other libs to work with SQL queries or api queries.

Minimum Python version: 3.11

Installation

pip install ftmq

Usage

ftmq accepts either a line-based input stream or an argument with a file uri. (For integration with followthemoney-store, see below)

Input stream:

cat entities.ftm.json | ftmq <filter expression> > output.ftm.json

URI argument:

Under the hood, ftmq uses smart_open to be able to interpret arbitrary file uris as argument -i:

ftmq <filter expression> -i ~/Data/entities.ftm.json
ftmq <filter expression> -i https://example.org/data.json.gz
ftmq <filter expression> -i s3://data-bucket/entities.ftm.json
ftmq <filter expression> -i webhdfs://host:port/path/file

...and so on

Of course, the same is possible for output -o:

cat data.json | ftmq <filter expression> -o s3://data-bucket/output.json

Filter for a dataset:

cat entities.ftm.json | ftmq -d ec_meetings

Filter for a schema:

cat entities.ftm.json | ftmq -s Person

Filter for a schema and all it's descendants or ancestors:

cat entities.ftm.json | ftmq -s LegalEntity --schema-include-descendants
cat entities.ftm.json | ftmq -s LegalEntity --schema-include-ancestors

Filter for properties:

Properties are options via --<prop>=<value>

cat entities.ftm.json | ftmq -s Company --country=de

Comparison lookups for properties:

cat entities.ftm.json | ftmq -s Company --incorporationDate__gte=2020 --address__ilike=berlin

Possible lookups:

• gt - greater than
• lt - lower than
• gte - greater or equal
• lte - lower or equal
• like - SQLish LIKE (use % placeholders)
• ilike - SQLish ILIKE, case-insensitive (use % placeholders)
• [] - usage: prop[]=foo evaluates if foo is member of array prop

ftmq apply

"Uplevel" an entity input stream to nomenklatura.entity.CompositeEntity and optionally apply a dataset.

ftmq apply -i ./entities.ftm.json -d <aditional_dataset>

Overwrite datasets:

ftmq apply -i ./entities.ftm.json -d <aditional_dataset> --replace-dataset

Coverage / Statistics

Often in ftm scripting, we are iterating through all the proxies (e.g. during aggregation). Why not use this to collect statistics on the way? There is a context manager for this, which turns into the Coverage model:

Print coverage to stdout (and filtered entities to nowhere):

cat entities.ftm.json | ftmq -s Event -o /dev/null --coverage-uri -

Within code:

from ftmq.coverage import Collector

fragments = [...]
buffer = {}

c = Collector()
for proxy in fragments:
    if proxy.id in buffer:
        buffer[proxy.id].merge(proxy)
    else:
        buffer[proxy.id] = proxy
        # here collect stats:
        c.collect(proxy)

coverage = c.export()

ftmstore (database read)

NOT IMPLEMENTED YET

The same cli logic applies:

ftmq store iterate -d ec_meetings -s Event --date__gte=2019 --date__lte=2020

Python Library

from ftmq import Query

q = Query() \
    .where(dataset="ec_meetings", date__lte=2020) \
    .where(schema="Event") \
    .order_by("date", ascending=False)

assert q.apply(proxy)

support

This project is part of investigraph

Media Tech Lab Bayern batch #3