Finding duplicate records in a books to prisoners database application

High on my list of neglected tech. projects is the Testament books to prisoners database web application.  This is the database program that projects like the Midwest Pages to Prisoners Project use to track packages sent and returned and books requested in the hopes avoiding delays in delivering books to incarcerated people and to provide metrics that grant providers like.

One of the design challenges has to do with duplicate records.  Recipients of books are identified by their state/federal department of correction (DOC) number (if they’re in a state or federal prison – most jails don’t use ID numbers), their state of incarceration and their name.  I assume that the database was designed originally to minimize barriers for the book project volunteers so both the name and DOC# are free text fields.  Javascript is used to match existing records based on the DOC#, but there is still a large possibility for duplicate records.

The reason for duplicate records is that both the person writing to request books and the volunteer may list their name and/or DOC# inconsistently.  For instance, the state may store the DOC# in their database as A-123456 but the incarcerated person may write it as A123456 A-123-456 or just 123456.  Volunteers who don’t know about this and aren’t careful may not check beforehand for an existing record.

This is probably preventable through more sophisticated validation, but we still need a way to find duplicates in the existing records.  As this application is written in the Django framework, I want to try to use the Django API to find matches.

At first thought, it seems like I will have to iterate through each inmate record and check if there is a duplicate record.  This seems pretty slow, but I can’t think of a better way to do this.  At this point, there aren’t so many records that this approach will fail, but it would be nice to do something slicker.

The other problem is how to match a duplicate.  One approach might be to build a regexp for the DOC# (for instance, match either the first character or omit it, allow dashes or spaces between all characters, …) and then use the iregexp field lookup to try to find matches. One challenge with this is that the current Testament codebase is using Django 0.97 (I think) and iregexp is only available starting in 1.0.  Maybe it’s time we updated our code anyway.

There is also the Python difflib module that can compute deltas between strings.  However, it seems like this would slow things down even further because you would have to load each inmate object and then use difflib to compare the DOC#s.  I assume that the previous approach would be faster because the regexp matching happens at the database level.

Importing relationships into CiviCRM

As part of my work at the Center for Research Libraries, I am investigating different Constituent Resource Management (CRM) systems.  One of the options is CiviCRM, a popular FLOSS CRM.  As CRL is, in large part, a membership organization, I wanted to see if it was possible to represent the basic information that we keep about our member organizations in the CRM.  I found that data entry through the web interface was pretty slow, so I wanted to experiment with CiviCRM’s contact import capabilities.

CiviCRM lets you define multiple, arbitrary relationships between contacts. This is how we can connect individual contacts with their institution (for instance the Librarian Councilor or Purchase Proposal Representative) or organizational sub-units (a particular library branch) with the parent organization.

Here is an example of part of our paper member information form that shows that sort of information that we collect about a member institution:

Screenshot of CRL's member information form

CiviCRM also lets you import contact information and relationship information through comma separated value (CSV) files. However, there are a number of things that need to be configured in order to get this working properly.

Need to have contact types configured correctly for the relationship

This is configured at Administer > Options List > Relationship Types

When you create a new relationship, it sets Contact Type A/Contact Type A to any contact type. This works fine if you are defining relationships within CiviCRM’s web interface, but doesn’t work well when importing contacts. This is because CiviCRM will not be able to correctly match the related contact if the contact type is not explicitly set.

In the case of our “Librarian Councillor of” relationship, Contact A is an Individual (the member organization librarian) and Contact B is an Organization (the member organization):

Configuring a relationship in CiviCRM

Need to update strict matching rules for individuals

CiviCRM has configurable matching criteria for identifying and merging existing duplicate contacts and for updated existing contacts based on import data. This feature is documented in the CiviCRM documentation page Find and Merge Duplicate Contacts.

The matching criteria can be configured at Administer > Manage > Find and Merge Duplicate Contacts. By default CiviCRM defines Strict and Fuzzy rules for each contact type. CiviCRM uses the strict rule when importing contact data. However, the default rules might not fit the data that you have. For instance, by default, the strict rule for matching individuals puts all the weight on e-mail address. For many of the contacts, however, there is not an e-mail address. So, I had to update the Strict rule for Individual contacts to also match on First Name, Last Name, and Phone Number. Note that I set the weight so that all three values must match for CiviCRM to consider the contact a duplicate:

Configuring the duplicate matching rules in CiviCRM

If you don’t configure these rules correctly, you will get duplicate entries when you try to import your contact relationships.

Need to only have one relationship per CSV import file

This is one of the most confusing aspects of the relationship import process. Initially, I tried to put all the relationships in the same CSV file that I used to import the individual contact:

First Name,Middle Name,Last Name,Job Title,Individual Prefix,Individual Suffix,Street Address,Supplemental Address 1,Supplemental Address 2,City,Postal Code Suffix,Postal Code,Address Name,County,State,Country,Phone,Email,Note(s),Employee Of, Librarian Councillor of
Jane,,Doe,Head Librarian,,,123 Fake St.,,,Springfield,,12345,,,Illinois,,123-456-7890,,,Sample University, Sample University

That is, in the last 2 columns, I specify that the individual contact (Jane Doe) is an Employee of and the Librarian Councillor of Sample University.

This doesn’t work! I can only specify a single Individual -> Organization relationship in each CSV file. So, I need to break out the Librarian Councillor of relationship into a separate CSV file:


First Name,Middle Name,Last Name,Job Title,Individual Prefix,Individual Suffix,Street Address,Supplemental Address 1,Supplemental Address 2,City,Postal Code Suffix,Postal Code,Address Name,County,State,Country,Phone,Email,Note(s),Employee Of
Jane,,Doe,Head Librarian,,,123 Fake St.,,,Springfield,,12345,,,Illinois,,123-456-7890,,,Sample University


First Name,Middle Name,Last Name,E-mail,Phone,Librarian Councillor for
Jane,,Doe,,,Sample University

I will first import the contact CSV (individual_import.csv), then the relationship CSV (librarian_councillor_import.csv).

Need to include fields in CSV so that matching rules will work

Note that in the above example, I have to be sure to include enough information for our matching rules that I defined before to match Jane Doe to her existing database entry. So, I need to have either an e-mail address or First Name, Last Name, and Phone number.

Need to tell import process how to handle duplicate contacts

When importing the relationships, we will already have imported the individual contact information. So, we just want to update the existing individual contact record to reflect their relationship with their organization. So, we need to set the For Duplicate Contacts option of the import settings to Update.

Configuring CiviCRM import settings

Need to set up relationship import field mappings correctly

The field import mapping setting that I needed for the relationship import file (in this example librarian_councillor_import.csv) wasn’t immediately obvious to me. Here is a screenshot of the configuration that worked:

Configuring import field mappings in CiviCRM

Note that the Librarian Councillor for field in the CSV if mapped to the Library Councillor of relationship (that I defined at Administer > Options List > Relationship Types) and that the option of this mapping is set to Organization Name so that it will try to relate the imported contact to the existing organization contact record with the name specified in the CSV file.


So, it is possible to import both individual and organizational contacts into CiviCRM as well as the relationships between them. However, this could be tedious because each relationship type must be imported in a separate file. One possible solution would be to have a master spreadsheet that is used to input contact and relationship data. Then the spreadsheet programs filters/macros could be used to export appropriate CSV files for importing the contacts and relationships into CiviCRM. The import process is still somewhat complicated, so it seems best to do have systems staff assist with an initial mass import and then have future contacts input manually through the web interface.

Boxcar Inventory Development Update

The top seller queries weren’t working properly so I re-wrote them.  The update implementation is contained in the ‘book top sellers’, ‘books sold in last 2 months’, ‘comic/zine top sellers’, and ‘comics/zines sold in last 2 months’ queries.