- Introduction
- Challenge summary
- Details of challenge
- Input file
- Output files
- Example
- Writing clean, scalable and well-tested code
- Repo directory structure
- Testing your directory structure and output format
You’re a data engineer working for political consultants and you’ve been asked to help identify possible donors for a variety of upcoming election campaigns.
The Federal Election Commission regularly publishes campaign contributions and while you don’t want to pull specific donors from those files — because using that information for fundraising or commercial purposes is illegal — you want to identify the areas (zip codes) that may be fertile ground for soliciting future donations for similar candidates.
Because those donations may come from specific events (e.g., high-dollar fundraising dinners) but aren’t marked as such in the data, you also want to identify which time periods are particularly lucrative so that an analyst might later correlate them to specific fundraising events.
For this challenge, we're asking you to take an input file that lists campaign contributions by individual donors and distill it into two output files:
-
medianvals_by_zip.txt
: contains a calculated running median, total dollar amount and total number of contributions by recipient and zip code -
medianvals_by_date.txt
: has the calculated median, total dollar amount and total number of contributions by recipient and date.
As part of the team working on the project, another developer has been placed in charge of building the graphical user interface, which consists of two dashboards. The first would show the zip codes that are particularly generous to a recipient over time while the second would display the days that were lucrative for each recipient.
Your role on the project is to work on the data pipeline that will hand off the information to the front-end. As the backend data engineer, you do not need to display the data or work on the dashboard but you do need to provide the information.
You can assume there is another process that takes what is written to both files and sends it to the front-end. If we were building this pipeline in real life, we’d probably have another mechanism to send the output to the GUI rather than writing to a file. However for the purposes of grading this challenge, we just want you to write the output to files.
You’re given one input file, itcont.txt
. Each line of the input file contains information about a campaign contribution that was made on a particular date from a donor to a political campaign, committee or other similar entity. Out of the many fields listed on the pipe-delimited line, you’re primarily interested in the zip code associated with the donor, amount contributed, date of the transaction and ID of the recipient.
Your code should process each line of the input file as if that record was sequentially streaming into your program. For each input file line, calculate the running median of contributions, total number of transactions and total amount of contributions streaming in so far for that recipient and zip code. The calculated fields should then be formatted into a pipe-delimited line and written to an output file named medianvals_by_zip.txt
in the same order as the input line appeared in the input file.
Your program also should write to a second output file named medianvals_by_date.txt
. Each line of this second output file should list every unique combination of date and recipient from the input file and then the calculated total contributions and median contribution for that combination of date and recipient.
The fields on each pipe-delimited line of medianvals_by_date.txt
should be date, recipient, total number of transactions, total amount of contributions and median contribution. Unlike the first output file, this second output file should have lines sorted alphabetical by recipient and then chronologically by date.
Also, unlike the first output file, every line in the medianvals_by_date.txt
file should be represented by a unique combination of day and recipient -- there should be no duplicates.
The Federal Election Commission provides data files stretching back years and is regularly updated
For the purposes of this challenge, we’re interested in individual contributions. While you're welcome to run your program using the data files found at the FEC's website, you should not assume that we'll be testing your program on any of those data files or that the lines will be in the same order as what can be found in those files. Our test data files, however, will conform to the data dictionary as described by the FEC.
Also, while there are many fields in the file that may be interesting, below are the ones that you’ll need to complete this challenge:
CMTE_ID
: identifies the flier, which for our purposes is the recipient of this contributionZIP_CODE
: zip code of the contributor (we only want the first five digits/characters)TRANSACTION_DT
: date of the transactionTRANSACTION_AMT
: amount of the transactionOTHER_ID
: a field that denotes whether contribution came from a person or an entity
Here are some considerations to keep in mind:
- Because we are only interested in individual contributions, we only want records that have the field,
OTHER_ID
, set to empty. If theOTHER_ID
field contains any other value, ignore the entire record and don't include it in any calculation - If
TRANSACTION_DT
is an invalid date (e.g., empty, malformed), you should still take the record into consideration when outputting the results ofmedianvals_by_zip.txt
but completely ignore the record when calculating values formedianvals_by_date.txt
- While the data dictionary has the
ZIP_CODE
occupying nine characters, for the purposes of the challenge, we only consider the first five characters of the field as the zip code - If
ZIP_CODE
is an invalid zipcode (i.e., empty, fewer than five digits), you should still take the record into consideration when outputting the results ofmedianvals_by_date.txt
but completely ignore the record when calculating values formedianvals_by_zip.txt
- If any lines in the input file contains empty cells in the
CMTE_ID
orTRANSACTION_AMT
fields, you should ignore and skip the record and not take it into consideration when making any calculations for the output files - Except for the considerations noted above with respect to
CMTE_ID
,ZIP_CODE
,TRANSACTION_DT
,TRANSACTION_AMT
,OTHER_ID
, data in any of the other fields (whether the data is valid, malformed, or empty) should not affect your processing. That is, as long as the four previously noted considerations apply, you should process the record as if it was a valid, newly arriving transaction. (For instance, campaigns sometimes retransmit transactions as amendments, however, for the purposes of this challenge, you can ignore that distinction and treat all of the lines as if they were new) - For the purposes of this challenge, you can assume the input file follows the data dictionary noted by the FEC for the 2015-current election years
- The transactions noted in the input file are not in any particular order, and in fact, can be out of order chronologically
For the two output files that your program will create, the fields on each line should be separated by a |
medianvals_by_zip.txt
The first output file medianvals_by_zip.txt
should contain the same number of lines or records as the input data file minus any records that were ignored as a result of the 'Input file considerations.'
Each line of this file should contain these fields:
- recipient of the contribution (or
CMTE_ID
from the input file) - 5-digit zip code of the contributor (or the first five characters of the
ZIP_CODE
field from the input file) - running median of contributions received by recipient from the contributor's zip code streamed in so far. Median calculations should be rounded to the whole dollar (drop anything below
$.50 and round anything from $ .50 and up to the next dollar) - total number of transactions received by recipient from the contributor's zip code streamed in so far
- total amount of contributions received by recipient from the contributor's zip code streamed in so far
When creating this output file, you can choose to process the input data file line by line, in small batches or all at once depending on which method you believe to be the best given the challenge description. However, when calculating the running median, total number of transactions and total amount of contributions, you should only take into account the input data that has streamed in so far -- in other words, from the top of the input file to the current line. See the below example for more guidance.
medianvals_by_date.txt
Each line of this file should contain these fields:
- recipeint of the contribution (or
CMTE_ID
from the input file) - date of the contribution (or
TRANSACTION_DT
from the input file) - median of contributions received by recipient on that date. Median calculations should be rounded to the whole dollar (drop anything below
$.50 and round anything from $ .50 and up to the next dollar) - total number of transactions received by recipient on that date
- total amount of contributions received by recipient on that date
This second output file does not depend on the order of the input file, and in fact should be sorted alphabetical by recipient and then chronologically by date.
Suppose your input file contained only the following few lines. Note that the fields we are interested in are in bold below but will not be like that in the input file. There's also an extra new line between records below, but the input file won't have that.
C00629618|N|TER|P|201701230300133512|15C|IND|PEREZ, JOHN A|LOS ANGELES|CA|90017|PRINCIPAL|DOUBLE NICKEL ADVISORS|01032017|40|H6CA34245|SA01251735122|1141239|||2012520171368850783
C00177436|N|M2|P|201702039042410894|15|IND|DEEHAN, WILLIAM N|ALPHARETTA|GA|300047357|UNUM|SVP, SALES, CL|01312017|384||PR2283873845050|1147350||P/R DEDUCTION ($192.00 BI-WEEKLY)|4020820171370029337
C00384818|N|M2|P|201702039042412112|15|IND|ABBOTT, JOSEPH|WOONSOCKET|RI|028956146|CVS HEALTH|VP, RETAIL PHARMACY OPS|01122017|250||2017020211435-887|1147467|||4020820171370030285
C00177436|N|M2|P|201702039042410893|15|IND|SABOURIN, JAMES|LOOKOUT MOUNTAIN|GA|307502818|UNUM|SVP, CORPORATE COMMUNICATIONS|01312017|230||PR1890575345050|1147350||P/R DEDUCTION ($115.00 BI-WEEKLY)|4020820171370029335
C00177436|N|M2|P|201702039042410895|15|IND|JEROME, CHRISTOPHER|FALMOUTH|ME|041051896|UNUM|EVP, GLOBAL SERVICES|01312017|384||PR2283905245050|1147350||P/R DEDUCTION ($192.00 BI-WEEKLY)|4020820171370029342
C00384818|N|M2|P|201702039042412112|15|IND|BAKER, SCOTT|WOONSOCKET|RI|028956146|CVS HEALTH|EVP, HEAD OF RETAIL OPERATIONS|01122017|333||2017020211435-910|1147467|||4020820171370030287
C00177436|N|M2|P|201702039042410894|15|IND|FOLEY, JOSEPH|FALMOUTH|ME|041051935|UNUM|SVP, CORP MKTG & PUBLIC RELAT.|01312017|384||PR2283904845050|1147350||P/R DEDUCTION ($192.00 BI-WEEKLY)|4020820171370029339
If we were to pick the relevant fields from each line, here is what we would record for each line.
1.
CMTE_ID: C00629618
ZIP_CODE: 90017
TRANSACTION_DT: 01032017
TRANSACTION_AMT: 40
OTHER_ID: H6CA34245
2.
CMTE_ID: C00177436
ZIP_CODE: 30004
TRANSACTION_DT: 01312017
TRANSACTION_AMT: 384
OTHER_ID: empty
3.
CMTE_ID: C00384818
ZIP_CODE: 02895
TRANSACTION_DT: 01122017
TRANSACTION_AMT: 250
OTHER_ID: empty
4.
CMTE_ID: C00177436
ZIP_CODE: 30750
TRANSACTION_DT: 01312017
TRANSACTION_AMT: 230
OTHER_ID: empty
5.
CMTE_ID: C00177436
ZIP_CODE: 04105
TRANSACTION_DT: 01312017
TRANSACTION_AMT: 384
OTHER_ID: empty
6.
CMTE_ID: C00384818
ZIP_CODE: 02895
TRANSACTION_DT: 01122017
TRANSACTION_AMT: 333
OTHER_ID: empty
7.
CMTE_ID: C00177436
ZIP_CODE: 04105
TRANSACTION_DT: 01312017
TRANSACTION_AMT: 384
OTHER_ID: empty
We would ignore the first record because the OTHER_ID
field contains data and is not empty. Moving to the next record, we would write out the first line of medianvals_by_zip.txt
to be:
C00177436|30004|384|1|384
Note that because we have only seen one record streaming in for that recipient and zip code, the running median amount of contribution and total amount of contribution is 384
.
Looking through the other lines, note that there are only two recipients for all of the records we're interested in our input file (minus the first line that was ignored due to non-null value of OTHER_ID
).
Also note that there are two records with the recipient C00177436
and zip code of 04105
totaling $768 in contributions while the recipient C00384818
and zip code 02895
has two contributions totaling $583 (250 + 333) and a median of $292 (583/2 = 291.5 or 292 when rounded up)
Processing all of the input lines, the entire contents of medianvals_by_zip.txt
would be:
C00177436|30004|384|1|384
C00384818|02895|250|1|250
C00177436|30750|230|1|230
C00177436|04105|384|1|384
C00384818|02895|292|2|583
C00177436|04105|384|2|768
If we drop the zip code, there are four records with the same recipient, C00177436
, and date of 01312017
. Their total amount of contributions is $1,382.
For the recipient, C00384818
, there are two records with the date 01122017
and total contribution of $583 and median of $292.
As a result, medianvals_by_date.txt
would contain these lines in this order:
C00177436|01312017|384|4|1382
C00384818|01122017|292|2|583
As a data engineer, it’s important that you write clean, well-documented code that scales for large amounts of data. For this reason, it’s important to ensure that your solution works well for a large number of records, rather than just the above example.
It's also important to use software engineering best practices like unit tests, especially since data is not always clean and predictable. For more details about the implementation, please refer to the FAQ below. If further clarification is necessary, email us at cc@insightdataengineering.com
Before submitting your solution you should summarize your approach, dependencies and run instructions (if any) in your README
.
You may write your solution in any mainstream programming language such as C, C++, C#, Clojure, Erlang, Go, Haskell, Java, Python, Ruby, or Scala. Once completed, submit a link to a Github repo with your source code.
In addition to the source code, the top-most directory of your repo must include the input
and output
directories, and a shell script named run.sh
that compiles and runs the program(s) that implement the required features.
If your solution requires additional libraries, environments, or dependencies, you must specify these in your README
documentation. See the figure below for the required structure of the top-most directory in your repo, or simply clone this repo.
The directory structure for your repo should look like this:
├── README.md
├── run.sh
├── src
│ └── find_political_donors.py
├── input
│ └── itcont.txt
├── output
| └── medianvals_by_zip.txt
| └── medianvals_by_date.txt
├── insight_testsuite
└── run_tests.sh
└── tests
└── test_1
| ├── input
| │ └── itcont.txt
| |__ output
| │ └── medianvals_by_zip.txt
| |__ └── medianvals_by_date.txt
├── your-own-test
├── input
│ └── your-own-input.txt
|── output
└── medianvals_by_zip.txt
└── medianvals_by_date.txt
To make sure that your code has the correct directory structure and the format of the output files are correct, we have included a test script called run_tests.sh
in the insight_testsuite
folder.
The tests are stored simply as text files under the insight_testsuite/tests
folder. Each test should have a separate folder with an input
folder for itcont.txt
and an output
folder for output corresponding to that test.
You can run the test with the following command from within the insight_testsuite
folder:
insight_testsuite~$ ./run_tests.sh
On a failed test, the output of run_tests.sh
should look like:
[FAIL]: test_1
[Thu Mar 30 16:28:01 PDT 2017] 0 of 1 tests passed
On success:
[PASS]: test_1
[Thu Mar 30 16:25:57 PDT 2017] 1 of 1 tests passed
One test has been provided as a way to check your formatting and simulate how we will be running tests when you submit your solution. We urge you to write your own additional tests. test_1
is only intended to alert you if the directory structure or the output for this test is incorrect.