Skip to main content

A command-line tool (and Python library) to extract data from CommCare HQ into a SQL database or Excel workbook

Project description

CommCare Export

Build Status Test coverage PyPI version

A command-line tool (and Python library) to generate customized exports from the CommCare HQ REST API.

Installation & Quick Start

Following commands are to be run on a terminal or a command line.

Once on a terminal window or command line, for simplicity, run commands from the home directory.


Check which Python version is installed.

This tool is tested with Python versions from 3.8 to 3.12.

$ python3 --version

If Python is installed, its version will be shown.

If Python isn't installed, download and install a version of Python from 3.8 to 3.12.

Virtualenv (Optional)

It is recommended to set up a virtual environment for CommCare Export to avoid conflicts with other Python applications.

More about virtualenvs on

Setup a virtual environment using:

$ python3 -m venv venv

Activate virtual environment by running:

$ source venv/bin/activate

Note: virtualenv needs to be activated each time you start a new terminal session or command line prompt.

For convenience, to avoid doing that, you can create an alias to activate virtual environments in "venv" directory by adding the following to your .bashrc or .zshrc file:

$ alias venv='if [[ -d venv ]] ; then source venv/bin/activate ; fi'

Then you can activate virtual environments with simply typing

$ venv

Install CommCare Export

Install CommCare Export via pip

$ pip install commcare-export

CommCare HQ

  1. Sign up for CommCare HQ if you have not already.

  2. Create a project space and application.

  3. Visit the Release Manager, make a build, click the star to release it.

  4. Use Web Apps and fill out some forms.

  5. Modify one of example queries in the examples/ directory, modifying the "Filter Value" column to match your form XMLNS / case type. See this page to determine the XMLNS for your form.

Now you can run the following examples:

$ commcare-export \
     --query examples/demo-registration.xlsx \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-registration.json \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-deliveries.xlsx \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-deliveries.json \
     --project YOUR_PROJECT \
     --output-format markdown

You'll see the tables printed out. Change to --output-format sql --output URL_TO_YOUR_DB --since DATE to sync all forms submitted since that date.

Example query files are provided in both Excel and JSON format. It is recommended to use the Excel format as the JSON format may change upon future library releases.

Command-line Usage

The basic usage of the command-line tool is with a saved Excel or JSON query (see how to write these, below)

$ commcare-export --commcare-hq <URL or alias like "local" or "prod"> \
                  --username <username> \
                  --project <project> \
                  --api-version <api version, defaults to latest known> \
                  --version <print current version> \
                  --query <excel file, json file, or raw json> \
                  --output-format <csv, xls, xlsx, json, markdown, sql> \
                  --output <file name or SQL database URL> \
                  --users <export data about project's mobile workers> \
                  --locations <export data about project's location hierarchy> \
                  --with-organization <export users, locations and joinable form or case tables>

See commcare-export --help for the full list of options.

There are example query files for the CommCare Demo App (available on the CommCare HQ Exchange) in the examples/ directory.


CommCare Export uses SQLAlachemy's create_engine to establish a database connection. This is based off of the RFC-1738 protocol. Some common examples:

# Postgres



Excel Queries

An Excel query is any .xlsx workbook. Each sheet in the workbook represents one table you wish to create. There are two grouping of columns to configure the table:

  • Data Source: Set this to form to export form data, or case for case data.
  • Filter Name / Filter Value: These columns are paired up to filter the input cases or forms.
  • Field: The destination in your SQL database for the value.
  • Source Field: The particular field from the form you wish to extract. This can be any JSON path.

JSON Queries

JSON queries are a described in the table below. You build a JSON object that represents the query you have in mind. A good way to get started is to work from the examples, or you could make an Excel query and run the tool with --dump-query to see the resulting JSON query.

User and Location Data

The --users and --locations options export data from a CommCare project that can be joined with form and case data. The --with-organization option does all of that and adds a field to Excel query specifications to be joined on.

Specifying the --users option or --with-organization option will export an additional table named 'commcare_users' containing the following columns:

Column Type Note
id Text Primary key
default_phone_number Text
email Text
first_name Text
groups Text
last_name Text
phone_numbers Text
resource_uri Text
commcare_location_id Text Foreign key to commcare_locations
commcare_location_ids Text
commcare_primary_case_sharing_id Text
commcare_project Text
username Text

The data in the 'commcare_users' table comes from the List Mobile Workers API endpoint.

Specifying the --locations option or --with-organization options will export an additional table named 'commcare_locations' containing the following columns:

Column Type Note
id Text
created_at Date
domain Text
external_id Text
last_modified Date
latitude Text
location_data Text
location_id Text Primary key
location_type Text
longitude Text
name Text
parent Text Resource URI of parent location
resource_uri Text
site_code Text
location_type_administrative Text
location_type_code Text
location_type_name Text
location_type_parent Text
location level code Text Column name depends on project's organization
location level code Text Column name depends on project's organization

The data in the 'commcare_locations' table comes from the Location API endpoint along with some additional columns from the Location Type API endpoint. The last columns in the table exist if you have set up organization levels for your projects. One column is created for each organization level. The column name is derived from the Location Type that you specified. The column value is the location_id of the containing location at that level of your organization. Consider the example organization from the CommCare help page. A piece of the 'commcare_locations' table could look like this:

location_id location_type_name chw supervisor clinic district
939fa8 District NULL NULL NULL 939fa8
c4cbef Clinic NULL NULL c4cbef 939fa8
a9ca40 Supervisor NULL a9ca40 c4cbef 939fa8
4545b9 CHW 4545b9 a9ca40 c4cbef 939fa8

In order to join form or case data to 'commcare_users' and 'commcare_locations' the exported forms and cases need to contain a field identifying which user submitted them. The --with-organization option automatically adds a field called 'commcare_userid' to each query in an Excel specification for this purpose. Using that field, you can use a SQL query with a join to report data about any level of you organization. For example, to count the number of forms submitted by all workers in each clinic:

FROM form_table t
LEFT JOIN (commcare_users u
           LEFT JOIN commcare_locations l
           ON u.commcare_location_id = l.location_id)
ON t.commcare_userid =

Note that the table names 'commcare_users' and 'commcare_locations' are treated as reserved names and the export tool will produce an error if given a query specification that writes to either of them.

The export tool will write all users to 'commcare_users' and all locations to 'commcare_locations', overwriting existing rows with current data and adding rows for new users and locations. If you want to remove obsolete users or locations from your tables, drop them and the next export will leave only the current ones. If you modify your organization to add or delete levels, you will change the columns of the 'commcare_locations' table and it is very likely you will want to drop the table before exporting with the new organization.

Scheduling the DET

Scheduling the DET to run at regular intervals is a useful tactic to keep your database up to date with CommCare HQ.

A common approach to scheduling DET runs is making use of the operating systems' scheduling libraries to invoke a script to execute the commcare-export command. Sample scripts can be found in the examples/ directory for both Windows and Linux.


On Windows systems you can make use of the task scheduler to run scheduled scripts for you.

The examples/ directory contains a sample script file, scheduled_run_windows.bat, which can be used by the task scheduler to invoke the commcare-export command.

To set up the scheduled task you can follow the steps below.

  1. Copy the file scheduled_run_windows.bat to any desired location on your system (e.g. Documents)
  2. Edit the copied .bat file and populate your own details
  3. Follow the steps outlined here, using the .bat file when prompted for the Program/script.


On a Linux system you can make use of the crontab command to create scheduled actions (cron jobs) in the system.

The examples/ directory contains a sample script file,, which can be used by the cron job. To set up the cron job you can follow the steps below.

  1. Copy the example file to the home directory

cp ./examples/ ~/

  1. Edit the file to populate your own details

nano ~/

  1. Create a cron job by appending to the crontab file

crontab -e

Make an entry below any existing cron jobs. The example below executes the script file at the top of every 12th hour of every day

0 12 * * * bash ~/

You can consult the tool which is very useful to generate and interpret any custom cron schedules.

Python Library Usage

As a library, the various commcare_export modules make it easy to

  • Interact with the CommCare HQ REST API
  • Execute "Minilinq" queries against the API (a very simple query language, described below)
  • Load and save JSON representations of Minilinq queries
  • Compile Excel configurations to Minilinq queries

To directly access the CommCare HQ REST API:

from commcare_export.checkpoint import CheckpointManagerWithDetails
from commcare_export.commcare_hq_client import CommCareHqClient, AUTH_MODE_APIKEY
from commcare_export.commcare_minilinq import get_paginator, PaginationMode

username = ''
domain = 'your-awesome-domain'
hq_host = ''
API_KEY= 'your_secret_api_key'

api_client = CommCareHqClient(hq_host, domain, username, API_KEY, AUTH_MODE_APIKEY)
case_paginator=get_paginator(resource='case', pagination_mode=PaginationMode.date_modified)
checkpoint_manager=CheckpointManagerWithDetails(None, None, PaginationMode.date_modified)

cases = api_client.iterate('case', case_paginator, checkpoint_manager=checkpoint_manager)

for case in cases:

To issue a minilinq query against it, and then print out that query in a JSON serialization:

import json
import sys
from commcare_export.minilinq import *
from commcare_export.commcare_hq_client import CommCareHqClient
from commcare_export.commcare_minilinq import CommCareHqEnv
from commcare_export.env import BuiltInEnv, JsonPathEnv
from commcare_export.writers import StreamingMarkdownTableWriter

api_client = CommCareHqClient(

source = Map(
       Literal({"filter": {"term": {"app_id": "whatever"}}})

query = Emit(
       Literal('Received On'),

print(json.dumps(query.to_jvalue(), indent=2))

results = query.eval(BuiltInEnv() | CommCareHqEnv(api_client) | JsonPathEnv())

if len(list(env.emitted_tables())) > 0:
    with StreamingMarkdownTableWriter(sys.stdout) as writer:
        for table in env.emitted_tables():

Which will output JSON equivalent to this:

    "Emit": {
        "headings": [
                "Lit": "Received On"
                "Lit": "Gender"
        "source": {
            "Map": {
                "body": {
                    "List": [
                            "Ref": "received_on"
                            "Ref": "form.gender"
                "name": null,
                "source": {
                    "Apply": {
                        "args": [
                                "Lit": "form"
                                "Lit": {
                                    "filter": {
                                        "term": {
                                            "app_id": "whatever"
                        "fn": {
                            "Ref": "api_data"
        "table": "demo-table"

MiniLinq Reference

The abstract syntax can be directly inspected in the commcare_export.minilinq module. Note that the choice between functions and primitives is deliberately chosen to expose the structure of the MiniLinq for possible optimization, and to restrict the overall language.

Here is a description of the astract syntax and semantics

Python JSON Which is evaluates to
Literal(v) {"Lit": v} Just v
Reference(x) {"Ref": x} Whatever x resolves to in the environment
List([a, b, c, ...]) {"List": [a, b, c, ...} The list of what a, b, c evaluate to
Map(source, name, body) {"Map": {"source": ..., "name": ..., "body": ...} Evals body for each elem in source. If name is provided, the elem will be bound to it, otherwise it will replace the whole env.
FlatMap(source, name, body) {"FlatMap": {"source" ... etc}} Flattens after mapping, like nested list comprehensions
Filter(source, name, body) etc
Bind(value, name, body) etc Binds the result of value to name when evaluating body
Emit(table, headings, rows) etc Emits table with headings and rows. Note that table is a string, headings is a list of expressions, and rows is a list of lists of expressions. See explanation below for emitted output.
Apply(fn, args) etc Evaluates fn to a function, and all of args, then applies the function to the args.

Built in functions like api_data and basic arithmetic and comparison are provided via the environment, referred to be name using Ref, and utilized via Apply.

List of builtin functions:

Function Description Example Usage
+, -, *, //, /, >, <, >=, <= Standard Math
len Length
bool Bool
str2bool Convert string to boolean. True values are 'true', 't', '1' (case insensitive)
str2date Convert string to date
bool2int Convert boolean to integer (0, 1)
str2num Parse string as a number
format-uuid Parse a hex UUID, and format it into hyphen-separated groups
substr Returns substring indexed by [first arg, second arg), zero-indexed. substr(2, 5) of 'abcdef' = 'cde'
selected-at Returns the Nth word in a string. N is zero-indexed. selected-at(3) - return 4th word
selected Returns True if the given word is in the value. selected(fever)
count-selected Count the number of words
json2str Convert a JSON object to a string
template Render a string template (not robust) template({} on {}, state, date)
attachment_url Convert an attachment name into it's download URL
form_url Output the URL to the form view on CommCare HQ
case_url Output the URL to the case view on CommCare HQ
unique Ouptut only unique values in a list

Output Formats

Your MiniLinq may define multiple tables with headings in addition to their body rows by using Emit expressions, or may simply return the results of a single query.

If your MiniLinq does not contain any Emit expressions, then the results of the expression will be printed to standard output as pretty-printed JSON.

If your MiniLinq does contain Emit expressions, then there are many formats available, selected via the --output-format <format> option, and it can be directed to a file with the --output <file> command-line option.

  • csv: Each table will be a CSV file within a Zip archive.
  • xls: Each table will be a sheet in an old-format Excel spreadsheet.
  • xlsx: Each table will be a sheet in a new-format Excel spreadsheet.
  • json: The tables will each be a member of a JSON dictionary, printed to standard output
  • markdown: The tables will be streamed to standard output in Markdown format (very handy for debugging your queries)
  • sql: All data will be idempotently "upserted" into the SQL database you specify, including creating the needed tables and columns.


Required dependencies will be automatically installed via pip. But since you may not care about all export formats, the various dependencies there are optional. Here is how you might install them:

# To export "xlsx"
$ pip install "commcare-export[xlsx]"

# To export "xls"
$ pip install "commcare-export[xls]"

# To sync with a Postgres database
$ pip install "commcare-export[postgres]"

# To sync with a mysql database
$ pip install "commcare-export[mysql]"

# To sync with a database which uses odbc (e.g. mssql)
$ pip install "commcare-export[odbc]"

# To sync with another SQL database supported by SQLAlchemy
$ pip install "commcare-export[base_sql]"
# Then install the Python package for your database


0. Sign up for GitHub, if you have not already, at

1. Fork the repository at

2. Clone your fork, install into a virtualenv, and start a feature branch

$ git clone
$ cd commcare-export
$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -e ".[test]"
$ git checkout -b my-super-duper-feature

3. Make your edits.

4. Make sure the tests pass. The best way to test for all versions is to sign up for and turn on automatic continuous testing for your fork.

$ py.test
=============== test session starts ===============
platform darwin -- Python 2.7.3 -- pytest-2.3.4
collected 17 items

tests/ .
tests/ ....
tests/ ........
tests/ .
tests/ ...

============ 17 passed in 2.09 seconds ============

5. Type hints are used in the env and minilinq modules. Check that any changes in those modules adhere to those types:

$ mypy --install-types @mypy_typed_modules.txt

6. Push the feature branch up

$ git push -u origin my-super-duper-feature

7. Visit and submit a pull request.

8. Accept our gratitude for contributing: Thanks!

Release process

1. Create a tag for the release

$ git tag -a "X.YY.0" -m "Release X.YY.0"
$ git push --tags

2. Create the source distribution

$ python sdist

Ensure that the archive (dist/commcare-export-X.YY.0.tar.gz) has the correct version number (matching the tag name).

3. Upload to pypi

$ pip install twine
$ twine upload -u dimagi dist/commcare-export-X.YY.0.tar.gz

4. Verify upload

5. Create a release on github

Once the release is published a GitHub workflow is kicked off that compiles executables of the DET compatible with Linux and Windows machines, adding it to the release as assets.

[For Linux-based users] If you decide to download and use the executable file, please make sure the file has the executable permission enabled, after which it can be invoked like any other executable though the command line.

Testing and Test Databases

The following command will run the entire test suite (requires DB environment variables to be set as per below):

$ py.test

To run an individual test class or method you can run, e.g.:

$ py.test -k "TestExcelQuery"
$ py.test -k "test_get_queries_from_excel"

To exclude the database tests you can run:

$ py.test -m "not dbtest"

When running database tests, supported databases are PostgreSQL, MySQL, MSSQL.

To run tests against selected databases can be done using test marks as follows:

$ py.test -m [postgres,mysql,mssql]

Database URLs can be overridden via environment variables:



$ docker pull postgres:9.6
$ docker run --name ccexport-postgres -p 5432:5432 -e POSTGRES_PASSWORD=postgres -d postgres:9.6
$ export POSTGRES_URL=postgresql://postgres:postgres@localhost/

Docker postgres image docs


$ docker pull mysql
$ docker run --name ccexport-mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=pw -e MYSQL_USER=travis -e MYSQL_PASSWORD='' -d mysql

# create travis user
$ docker run -it --link ccexport-mysql:mysql --rm mysql sh -c 'exec mysql -h"$MYSQL_PORT_3306_TCP_ADDR" -P"$MYSQL_PORT_3306_TCP_PORT" -uroot -p"$MYSQL_ENV_MYSQL_ROOT_PASSWORD"'
mysql> CREATE USER 'travis'@'%';
mysql> GRANT ALL PRIVILEGES ON *.* TO 'travis'@'%';


$ docker pull 
$ docker run -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=Password@123" -p 1433:1433 --name mssql1 -d microsoft/mssql-server-linux:2017-latest

# install driver
$ curl | sudo apt-key add -
$ echo "deb [arch=amd64]$(lsb_release -rs)/prod $(lsb_release -rs) main" | sudo tee /etc/apt/sources.list.d/mssql-release.list

$ sudo apt-get update
$ sudo ACCEPT_EULA=Y apt-get install msodbcsql17
$ odbcinst -q -d

MSSQL for Mac OS

$ docker pull 
$ docker run -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=Password@123" -p 1433:1433 --name mssql1 -d microsoft/mssql-server-linux:2017-latest

# Install driver
$ brew install unixodbc freetds

# Add the following 5 lines to /usr/local/etc/odbcinst.ini
[ODBC Driver 17 for SQL Server]
Description=FreeTDS Driver for Linux & MSSQL

# Create a soft link from /etc/odbcinst.ini to actual file
$ sudo ln -s /usr/local/etc/odbcinst.ini /etc/odbcinst.ini

Integration Tests

Running the integration tests requires API credentials from CommCare HQ that have access to the corpora domain. This user should only have access to the corpora domain.

These need to be set as environment variables as follows:

$ export HQ_USERNAME=<username>
$ export HQ_API_KEY=<apikey>

For Travis builds these are included as encrypted vars in the travis config.

Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

commcare-export-1.13.0.tar.gz (150.5 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page