Pair2 is a library for performing rules-based matches between records in two datasets. These datasets are typically from two different sources that pertain to the same or similar set of transactions. Matching allows you to compare the datasets and produces a list of matched records.
Pair2 is designed primarily for reconciliations. Example use cases:
-
Bank reconciliations, where input datasets come from an accounting system and an online bank statement.
-
Cellular commission reconciliation, where input datasets come from an independent retailer's Point Of Sale system and a carrier's commission statement.
NOTE: this library is not a replacement for database joins on a properly-designed RDBMS. It's designed for real-world situations where the programmer must handle data from different sources and find commonality between them.
If available in Hex, the package can be installed
by adding pair2 to your list of dependencies in mix.exs:
def deps do
[
{:pair2, "~> 0.1.1"}
]
endDocumentation can be generated with ExDoc and published on HexDocs. Once published, the docs can be found at https://hexdocs.pm/pair2.
To illustrate how Pair2 is useful in situations where a database join can lead to errors, take the example of reconciling a bank statement against an accounting system's transactions. In this example, the bookkeeper incorrectly recorded the Basecamp transaction twice and the two Github transactions have different dates.
| Date | Description | Amount |
|---|---|---|
| 2018-01-01 | Basecamp | 25.00 |
| 2018-01-01 | Basecamp | 25.00 |
| 2018-01-02 | Github | 25.00 |
| Date | Description | Amount |
|---|---|---|
| 2018-01-01 | Basecamp (37 signals) | 25.00 |
| 2018-01-03 | Github | 25.00 |
Using a SQL approach, you might load the datasets into two tables, "ledger" and "bank" then join on amount:
select * from ledger a join bank b on a.amount = b.amount;
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-01|Basecamp|25.0|2018-01-03|Github|25.0
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-01|Basecamp|25.0|2018-01-03|Github|25.0
2018-01-02|Github|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-02|Github|25.0|2018-01-03|Github|25.0 That's clearly not the right answer. Because amount was the only criterion used for joining, the query joins each record with a $25 value (3*2 pairs).
OK, how about adding in the date:
select * from ledger a join bank b on a.amount = b.amount and a.date = b.date;
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0 Still incorrect because the bookkeeper recorded the Github transaction on Jan. 2 and the bank shows the debit on Jan. 3. How about using description and amount?
select * from ledger a join bank b on a.amount = b.amount and a.description = b.description;
2018-01-02|Github|25.0|2018-01-03|Github|25.0Even worse. Because two different people or systems entered these records, they have slightly different descriptions. Now you might try some more complicated SQL:
select * from ledger a join bank b on a.amount = b.amount and (a.description = b.description or a.date = b.date);
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-02|Github|25.0|2018-01-03|Github|25.0 At first blush that might look right, but because there are two bank statement lines, a correctly matched result must not contain more than two records. What we want is this:
2018-01-01|Basecamp|25.0|2018-01-01|Basecamp (37 signals)|25.0
2018-01-02|Github|25.0|2018-01-03|Github|25.0 defmodule Mix.Tasks.Example do
use Mix.Task
alias Pair2.{
MatchRule,
Matcher,
}
@shortdoc "Simple example of matching"
def run(_) do
ledger_txns = [
%{id: "l1", name: "Basecamp", amount: 25.0, date: ~D[2018-01-01]},
%{id: "l2", name: "Basecamp", amount: 25.0, date: ~D[2018-01-01]},
%{id: "l3", name: "Github", amount: 25.0, date: ~D[2018-01-02]},
]
bank_txns = [
%{id: "r1", name: "Basecamp (37 signals)", amount: 25.0, date: ~D[2018-01-01]},
%{id: "r2", name: "Github", amount: 25.0, date: ~D[2018-01-03]}
]
rule_amount = %MatchRule{left_attr: :amount, right_attr: :amount, indexed: true}
rule_date = %MatchRule{left_attr: :date, right_attr: :date, min_match: 0.8}
{:ok, matches} = Matcher.match(ledger_txns, bank_txns, [rule_amount, rule_date], 1.0)
IO.inspect(matches)
end
endOutput:
bezell@argon ~/d/e/pair2_example> mix example
[{"l1", "r1", 2.0}, {"l3", "r2", 1.9666666666666668}]The tuples define {left_id, right_id, match_score}.
It correctly matched only one of the two duplicated Basecamp transactions to the bank statement and also matched the Github transactions despite the imperfect date match. Note that the weighting and minimum score required for a match can be adjusted by the developer. In your use of the library, you may want to accept all matches over a certain score (say 0.9) and manually review lower scoring matches (say between 0.7 and 0.9). The final score is arbitrary and the max score is determined by the rules you define. For instance, if you create three rules with the default score of 1.0, the max score for a perfect match is 3.0.
Looking at this example, how could we add more specificity to the match? We might want to compare the name strings to reduce the chance of false matches. This is where custom functions come into play. This example below uses a custom function that calls The_Fuzz library to compare the edit distance between two strings and return a similarity value between 0.0 and 1.0. Note that the dates and amounts are all the same so the system would otherwise make arbitrary assignments.
defmodule Mix.Tasks.ExampleCustomFunction do
use Mix.Task
alias Pair2.{
MatchRule,
Matcher,
}
@shortdoc "Simple example of matching"
def run(_) do
ledger_txns = [
%{id: "l1", name: "Basecamp", amount: 25.0, date: ~D[2018-01-01]},
%{id: "l2", name: "Basecamp", amount: 25.0, date: ~D[2018-01-01]},
%{id: "l3", name: "Github", amount: 25.0, date: ~D[2018-01-01]},
]
bank_txns = [
%{id: "r1", name: "Basecarp", amount: 25.0, date: ~D[2018-01-01]},
%{id: "r2", name: "Gitbulb", amount: 25.0, date: ~D[2018-01-01]}
]
rule_amount = %MatchRule{left_attr: :amount, right_attr: :amount, indexed: true}
rule_date = %MatchRule{left_attr: :date, right_attr: :date, min_match: 0.8}
# Returns a value between 0.0 and 1.0 based on the Levenshtein edit distance
# between strings a and b.
fuzzy_compare = fn(string_a, string_b) ->
distance = TheFuzz.Similarity.Levenshtein.compare(string_a, string_b)
shorter_length = [string_a, string_b]
|> Enum.map(&(String.length(&1)))
|> Enum.sort
|> List.first
(shorter_length - distance) / shorter_length
end
rule_edit_distance = %MatchRule{left_attr: :name, right_attr: :name, fun: fuzzy_compare, min_match: 0.3}
{:ok, matches} = Matcher.match(ledger_txns, bank_txns, [rule_amount, rule_date, rule_edit_distance], 1.5)
IO.inspect(matches)
end
endOutput:
bezell@argon ~/d/e/pair2_example> mix example_custom_function
[{"l1", "r1", 2.875}, {"l3", "r2", 2.6666666666666665}]- The two datasets being matches are referred to as "left" and "right".
- At least one rule must be indexed. This is needed to generate the superset of potential right matches for each left record. All non-indexed rules contribute to the score that determines matches.
- This is designed for 1:1 matching. You will need to fork and modify it for any other use. Check out fuzzy_match for a different approach to rich, rules-based searching: https://github.com/seamusabshere/fuzzy_match. FEBRL is another free data linking library written in Python: http://sourceforge.net/projects/febrl/.
- Fuzzy != magic. Every object from the left dataset will be matched with the highest-possible scoring match from the right dataset according to the rules you supply the matcher.
- You can use negative scores to decrease the likelihood of pairing.
- In cases where two or more left records match the same right record with the same score, the object chosen for final match assignment is arbitrary.
- Testing is your friend. Test your rules in the controlled environment of the test suite before deploying on production data.
- If you use it, I'd love to know what problem you're applying it to. Besides using it in my company, I also use it for reconciling my bank statement.
Copyright (c) 2018 Barry Ezell. MIT License:
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