Migrate from Heap to PostHog

Prior to starting a historical data migration, ensure you do the following:
Create a project on our US or EU Cloud.
Sign up to a paid product analytics plan on the billing page (historic imports are free but this unlocks the necessary features).
Raise an in-app support request with the Data pipelines topic detailing where you are sending events from, how, the total volume, and the speed. For example, "we are migrating 30M events from a self-hosted instance to EU Cloud using the migration scripts at 10k events per minute."
Wait for the OK from our team before starting the migration process to ensure that it completes successfully and is not rate limited.
Set the historical_migration option to true when capturing events in the migration.

Migrating data from Heap is a three step process:

Exporting data via Heap Connect or a one-time export
Converting Heap event data to the PostHog schema and capturing in PostHog
Converting Heap user data to the PostHog schema and capturing in PostHog

1. Exporting data from Heap

Heap Connect enables you to export your data to S3, Redshift, BigQuery, or Snowflake. Data in each of these is much easier to access and capture into PostHog. See how to set this up in their docs.

This is only available on their Pro or Premier plans. The chart view CSV export doesn't provide the data we need.

The other option is a one-time JSON export. To do this, contact Heap support in-app. This requires a paid plan.

These exports come in the form of multiple tables including ones for users, pageviews, and each type of event you label.

2. Converting and capturing event data

The schema of Heap's exported event data is similar to PostHog's schema, but it does require conversion to work with the rest of PostHog's data. You can see details on Heap's schema in their docs and events and properties PostHog autocaptures in our docs.

To start, you need to query each of the production event tables because the all_events table doesn't contain any properties. For example, if you have a project named My Cool App and labeled an event My Cool Event, you query the my_cool_app.heap_main_production.my_cool_event table.

With this event data, you can then go through each row and convert it to PostHog's schema. This requires converting:

Event names like pageview to $pageview.
Properties like landing_page to $current_url
Event time to an ISO 8601 timestamp

Once done, you can capture the data into PostHog using the Python SDK or the capture API endpoint with historical_migration set to true.

Here's an example version of a Python script assuming the data is in .csv file:

Python

from posthog import Posthog
from datetime import datetime
import pandas as pd

posthog = Posthog(
  '<ph_project_api_key>', 
  host='https://us.i.posthog.com',
  debug=True,
  historical_migration=True
)

key_mapping = {
  'browser': '$browser',
  'browser_version': '$browser_version',
  'device_type': '$device_type',
  'library': '$lib',
  'ip': '$ip',
  'city': '$geoip_city_name',
  'country': '$geoip_country_name',
  'region': '$geoip_subdivision_1_name',
  'landing_page': '$current_url',
  'path': '$pathname',
  'referrer': '$referrer'
}

omitted_keys = ['session_time', 'user_id', 'time', 'event_id', 'session_id', 'domain']

file_name = 'my_cool_app.heap_main_production.my_cool_event.csv'
data = pd.read_csv(file_name)
# We can try to get the event name from the file name, 
# but it's not always the one you might want to use.
event_name = file_name.split('.')[-2]

for index, row in data.iterrows():
  line = row.to_dict()
  
  if "UTC" in line["time"]:
    ph_timestamp = datetime.strptime(line["time"], "%Y-%m-%d %H:%M:%S.%f %Z")
  else:
    ph_timestamp = datetime.strptime(line["time"], "%Y-%m-%d %H:%M:%S.%f")

  properties = {}
  for key, value in line.items():
    # Skip some properties
    if  key in omitted_keys:
      continue
    elif pd.isna(value):
      continue
    elif key in key_mapping:
      properties[key_mapping[key]] = value
    elif key == 'platform':
      # This doesn't handle Mac OS X well
      if value.startswith("Mac OS X"):
        ph_os = "Mac OS X"
        ph_os_version = value.split(' ')[2]
      else:
        ph_os = value.split(' ')[0]
        ph_os_version = value.split(' ')[1]
      properties['$os'] = ph_os
      properties['$os_version'] = ph_os_version
    else:
      # Custom properties, UTMs are the same
      properties[key] = value

  posthog.capture(
    event=event_name,
    distinct_id=line["user_id"],
    properties=properties,
    timestamp=ph_timestamp,
  )

3. Converting and capturing user data

Heap's user data is stored in the users table and connected to events using the user_id column. Some of these users have an identity column with a value we can use as a distinct_id.

Converting and capturing this data requires a similar process to the event data. The big difference is the need to call posthog.alias() with the user_id and identity values so we can query events from either as the same person.

Once we convert the properties to PostHog's schema, we can use the $set and $set_once to set the person's properties.

Together, this looks like this:

Python

from posthog import Posthog
from datetime import datetime
import pandas as pd

posthog = Posthog(
  '<ph_project_api_key>', 
  host='https://us.i.posthog.com',
  debug=True,
  historical_migration=True
)

key_mapping = {
  'browser': '$browser',
  'browser_version': '$browser_version',
  'device_type': '$device_type',
  'library': '$lib',
  'ip': '$ip',
  'city': '$geoip_city_name',
  'country': '$geoip_country_name',
  'region': '$geoip_subdivision_1_name',
  'landing_page': '$current_url',
  'path': '$pathname',
  'referrer': '$referrer',
  'initial_browser': '$initial_browser',
  'initial_browser_version': '$initial_browser_version',
  'initial_device_type': '$initial_device_type',
  'initial_landing_page': '$initial_current_url',
  'initial_path': '$initial_pathname',
  'initial_referrer': '$initial_referrer',
  'initial_city': '$initial_geoip_city_name',
  'initial_country': '$initial_geoip_country_name',
  'initial_region': '$initial_geoip_subdivision_1_name'
}

omitted_keys = ['id', 'user_id', 'full_initial_referrer']

file_name = 'my_cool_app.heap_main_production.users.csv'
data = pd.read_csv(file_name)
for index, row in data.iterrows():

  set_properties = {}
  set_once_properties = {}
  line = row.to_dict()
  user_id = line['user_id'] if 'user_id' in line else line['id']
  for key, value in line.items():
    if  key in omitted_keys:
      continue
    elif pd.isna(value):
      continue
    elif key == 'identity':
      # user_id is the anonymous value, identity is the identified one
      # An identity can have multiple user_id values, so we set it as the previous_id
      posthog.alias(previous_id=line['identity'], distinct_id=user_id)
    
    elif key.startswith('initial') and key in key_mapping:
      set_once_properties[key_mapping[key]] = value
    elif key.startswith('initial'):
      set_once_properties[key] = value
    elif key == 'initial_platform':
      if value.startswith("Mac OS X"):
        ph_os = "Mac OS X"
        ph_os_version = value.split(' ')[2]
      else:
        ph_os = value.split(' ')[0]
        ph_os_version = value.split(' ')[1]
      set_once_properties['$initial_os'] = ph_os
      set_once_properties['$initial_os_version'] = ph_os_version
    
    elif key in ['joindate', 'last_modified', 'lastseen', 'identity_time']:
      if value == 'Invalid date':
        continue
      # Dates must be converted to ISO 8601
      if isinstance(value, str):
        value = pd.to_datetime(value).isoformat()
      else:
        value = pd.to_datetime(value, unit='ms').isoformat()
      set_properties[key] = value
    else:
      # Custom properties stay the same
      set_properties[key] = value

  combined_properties = {
    "$set": set_properties,
    "$set_once": set_once_properties
  }
  distinct_id = line['identity'] if 'identity' in line and not pd.isna(line['identity']) else user_id

  posthog.capture(
    event="$set",
    distinct_id=distinct_id,
    properties=combined_properties
  )

This script may need modification depending on the structure of your Heap data, but it gives you a start.

1. Exporting data from Heap

2. Converting and capturing event data

3. Converting and capturing user data

Questions?

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