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Code Issues Releases Activity

feat: versioned deployments for UDFs (#25121)

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Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
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Sandy Spicer 2024-09-26 11:30:26 -07:00 committed by GitHub
parent 5f39825513
commit 75d4658e59
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23 changed files with 1624 additions and 33 deletions
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2
.github/actions/run-backend-tests/action.yml vendored
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@ -31,11 +31,13 @@ runs:
steps:
# Pre-tests
# Copies the fully versioned UDF xml file for use in CI testing
- name: Stop/Start stack with Docker Compose
shell: bash
run: |
export CLICKHOUSE_SERVER_IMAGE=${{ inputs.clickhouse-server-image }}
export DOCKER_REGISTRY_PREFIX="us-east1-docker.pkg.dev/posthog-301601/mirror/"
cp posthog/user_scripts/latest_user_defined_function.xml docker/clickhouse/user_defined_function.xml
docker compose -f docker-compose.dev.yml down
docker compose -f docker-compose.dev.yml up -d

14
.github/workflows/ci-backend.yml vendored
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@ -147,6 +147,20 @@ jobs:
run: |
npm run schema:build:python && git diff --exit-code
- uses: actions/checkout@v4
with:
repository: 'PostHog/posthog-cloud-infra'
path: 'posthog-cloud-infra'
token: ${{ secrets.POSTHOG_BOT_GITHUB_TOKEN }}
- name: Assert user_defined_function.xml is deployed to US
run: |
cmp posthog/user_scripts/latest_user_defined_function.xml posthog-cloud-infra/ansible/us/clickhouse/config/common/user_defined_function.xml
- name: Assert user_defined_function.xml is deployed to EU
run: |
cmp posthog/user_scripts/latest_user_defined_function.xml posthog-cloud-infra/ansible/eu/clickhouse/config/common/user_defined_function.xml
check-migrations:
needs: changes
if: needs.changes.outputs.backend == 'true'

5
posthog/cloud_utils.py
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@ -1,3 +1,4 @@
import os
from typing import TYPE_CHECKING, Any, Optional
from django.conf import settings
@ -16,6 +17,10 @@ def is_cloud() -> bool:
return bool(settings.CLOUD_DEPLOYMENT)
def is_ci() -> bool:
return os.environ.get("GITHUB_ACTIONS") is not None
def get_cached_instance_license() -> Optional["License"]:
"""Returns the first valid license and caches the value for the lifetime of the instance, as it is not expected to change.
If there is no valid license, it returns None.

0
posthog/datetime.py → posthog/date_util.py
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31
posthog/hogql/functions/mapping.py
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@ -1,6 +1,8 @@
from dataclasses import dataclass
from itertools import chain
from typing import Optional
from posthog.cloud_utils import is_cloud, is_ci
from posthog.hogql import ast
from posthog.hogql.ast import (
ArrayType,
@ -834,15 +836,8 @@ HOGQL_CLICKHOUSE_FUNCTIONS: dict[str, HogQLFunctionMeta] = {
"leadInFrame": HogQLFunctionMeta("leadInFrame", 1, 1),
# table functions
"generateSeries": HogQLFunctionMeta("generate_series", 3, 3),
## UDFS
"aggregate_funnel": HogQLFunctionMeta("aggregate_funnel", 6, 6, aggregate=False),
"aggregate_funnel_array": HogQLFunctionMeta("aggregate_funnel_array", 6, 6, aggregate=False),
"aggregate_funnel_cohort": HogQLFunctionMeta("aggregate_funnel_cohort", 6, 6, aggregate=False),
"aggregate_funnel_trends": HogQLFunctionMeta("aggregate_funnel_trends", 7, 7, aggregate=False),
"aggregate_funnel_array_trends": HogQLFunctionMeta("aggregate_funnel_array_trends", 7, 7, aggregate=False),
"aggregate_funnel_cohort_trends": HogQLFunctionMeta("aggregate_funnel_cohort_trends", 7, 7, aggregate=False),
"aggregate_funnel_test": HogQLFunctionMeta("aggregate_funnel_test", 6, 6, aggregate=False),
}
# Permitted HogQL aggregations
HOGQL_AGGREGATIONS: dict[str, HogQLFunctionMeta] = {
# Standard aggregate functions
@ -1034,6 +1029,26 @@ HOGQL_POSTHOG_FUNCTIONS: dict[str, HogQLFunctionMeta] = {
"hogql_lookupOrganicMediumType": HogQLFunctionMeta("hogql_lookupOrganicMediumType", 1, 1),
}
UDFS: dict[str, HogQLFunctionMeta] = {
"aggregate_funnel": HogQLFunctionMeta("aggregate_funnel", 6, 6, aggregate=False),
"aggregate_funnel_array": HogQLFunctionMeta("aggregate_funnel_array", 6, 6, aggregate=False),
"aggregate_funnel_cohort": HogQLFunctionMeta("aggregate_funnel_cohort", 6, 6, aggregate=False),
"aggregate_funnel_trends": HogQLFunctionMeta("aggregate_funnel_trends", 7, 7, aggregate=False),
"aggregate_funnel_array_trends": HogQLFunctionMeta("aggregate_funnel_array_trends", 7, 7, aggregate=False),
"aggregate_funnel_cohort_trends": HogQLFunctionMeta("aggregate_funnel_cohort_trends", 7, 7, aggregate=False),
"aggregate_funnel_test": HogQLFunctionMeta("aggregate_funnel_test", 6, 6, aggregate=False),
}
# We want CI to fail if there is a breaking change and the version hasn't been incremented
if is_cloud() or is_ci():
from posthog.udf_versioner import augment_function_name
for v in UDFS.values():
v.clickhouse_name = augment_function_name(v.clickhouse_name)
HOGQL_CLICKHOUSE_FUNCTIONS.update(UDFS)
ALL_EXPOSED_FUNCTION_NAMES = [
name for name in chain(HOGQL_CLICKHOUSE_FUNCTIONS.keys(), HOGQL_AGGREGATIONS.keys()) if not name.startswith("_")
]

3
posthog/hogql/test/test_query.py
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@ -1,3 +1,5 @@
import datetime
import pytest
from uuid import UUID
@ -6,7 +8,6 @@ from django.test import override_settings
from django.utils import timezone
from freezegun import freeze_time
from posthog import datetime
from posthog.hogql import ast
from posthog.hogql.errors import QueryError
from posthog.hogql.property import property_to_expr

12
posthog/hogql_queries/insights/funnels/test/__snapshots__/test_funnel_strict_udf.ambr
View File

@ -1141,7 +1141,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1209,7 +1209,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'step_1', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'step_1', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1284,7 +1284,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1357,7 +1357,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1437,7 +1437,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1517,7 +1517,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'strict', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings

6
posthog/hogql_queries/insights/funnels/test/__snapshots__/test_funnel_trends_udf.ambr
View File

@ -7,7 +7,7 @@
if(ifNull(greater(reached_from_step_count, 0), 0), round(multiply(divide(reached_to_step_count, reached_from_step_count), 100), 2), 0) AS conversion_rate,
data.breakdown AS prop
FROM
(SELECT arrayJoin(aggregate_funnel_array_trends(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfDay(timestamp), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_trends_v0(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfDay(timestamp), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
toTimeZone(af_tuple.1, 'UTC') AS entrance_period_start,
af_tuple.2 AS success_bool,
af_tuple.3 AS breakdown
@ -58,7 +58,7 @@
if(ifNull(greater(reached_from_step_count, 0), 0), round(multiply(divide(reached_to_step_count, reached_from_step_count), 100), 2), 0) AS conversion_rate,
data.breakdown AS prop
FROM
(SELECT arrayJoin(aggregate_funnel_array_trends(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfDay(timestamp), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_trends_v0(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfDay(timestamp), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
toTimeZone(af_tuple.1, 'US/Pacific') AS entrance_period_start,
af_tuple.2 AS success_bool,
af_tuple.3 AS breakdown
@ -109,7 +109,7 @@
if(ifNull(greater(reached_from_step_count, 0), 0), round(multiply(divide(reached_to_step_count, reached_from_step_count), 100), 2), 0) AS conversion_rate,
data.breakdown AS prop
FROM
(SELECT arrayJoin(aggregate_funnel_array_trends(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfWeek(timestamp, 0), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_trends_v0(0, 3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), toStartOfWeek(timestamp, 0), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
toTimeZone(af_tuple.1, 'UTC') AS entrance_period_start,
af_tuple.2 AS success_bool,
af_tuple.3 AS breakdown

24
posthog/hogql_queries/insights/funnels/test/__snapshots__/test_funnel_udf.ambr
View File

@ -19,7 +19,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(3, 15, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(3, 15, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -195,7 +195,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -270,7 +270,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -330,7 +330,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(3, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -759,7 +759,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -814,7 +814,7 @@
rowNumberInBlock() AS row_number,
breakdown AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'ordered', [[]], arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), [], arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -866,7 +866,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -934,7 +934,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'step_1', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'step_1', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1009,7 +1009,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, ['Other']) AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel_array(2, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_array_v0(2, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1082,7 +1082,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1162,7 +1162,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings
@ -1242,7 +1242,7 @@
rowNumberInBlock() AS row_number,
if(ifNull(less(row_number, 25), 0), breakdown, 'Other') AS final_prop
FROM
(SELECT arrayJoin(aggregate_funnel(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
(SELECT arrayJoin(aggregate_funnel_v0(3, 1209600, 'first_touch', 'ordered', groupUniqArray(prop), arraySort(t -> t.1, groupArray(tuple(accurateCastOrNull(timestamp, 'Float64'), prop, arrayFilter(x -> ifNull(notEquals(x, 0), 1), [multiply(1, step_0), multiply(2, step_1), multiply(3, step_2)])))))) AS af_tuple,
af_tuple.1 AS af,
af_tuple.2 AS breakdown,
af_tuple.3 AS timings

2
posthog/models/utils.py
View File

@ -1,3 +1,4 @@
import datetime
import secrets
import string
import uuid
@ -12,7 +13,6 @@ from django.db.backends.ddl_references import Statement
from django.db.models.constraints import BaseConstraint
from django.utils.text import slugify
from posthog import datetime
from posthog.constants import MAX_SLUG_LENGTH
if TYPE_CHECKING:

2
posthog/test/test_datetime.py
View File

@ -1,6 +1,6 @@
from datetime import datetime, UTC
from posthog.datetime import (
from posthog.date_util import (
start_of_hour,
start_of_day,
end_of_day,

71
posthog/udf_versioner.py Normal file
View File

@ -0,0 +1,71 @@
import argparse
import os
import shutil
import glob
import datetime
import xml.etree.ElementTree as ET
from xml import etree
# For revertible cloud deploys:
# 1. Develop using the python files at the top level of `user_scripts`, with schema defined in `docker/clickhouse/user_defined_function.xml`
# 2. If you're made breaking changes to UDFs (likely involving changing type definitions), when ready to deploy, increment the version below and run this file
# 3. Copy the `user_defined_function.xml` file in the newly created version folder (e.g. `user_scripts/v4/user_defined_function.xml`) to the `posthog-cloud-infra` repo and deploy it
# 4. After that deploy goes out, it is safe to land and deploy the changes to the `posthog` repo
# If deploys aren't seamless, look into moving the action that copies the `user_scripts` folder to the clickhouse cluster earlier in the deploy process
UDF_VERSION = 0 # Last modified by: @aspicer, 2024-09-20
CLICKHOUSE_XML_FILENAME = "user_defined_function.xml"
ACTIVE_XML_CONFIG = "../../docker/clickhouse/user_defined_function.xml"
format_version_string = lambda version: f"v{version}"
VERSION_STR = format_version_string(UDF_VERSION)
LAST_VERSION_STR = format_version_string(UDF_VERSION - 1)
augment_function_name = lambda name: f"{name}_{VERSION_STR}"
def prepare_version(force=False):
os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), "user_scripts"))
if args.force:
shutil.rmtree(VERSION_STR)
try:
os.mkdir(VERSION_STR)
except FileExistsError:
if not args.force:
raise FileExistsError(
f"A directory already exists for this version at posthog/user_scripts/{VERSION_STR}. Did you forget to increment the version? If not, delete the folder and run this again, or run this script with a -f"
)
for file in glob.glob("*.py"):
shutil.copy(file, VERSION_STR)
base_xml = ET.parse(ACTIVE_XML_CONFIG)
if os.path.exists(LAST_VERSION_STR):
last_version_xml = ET.parse(os.path.join(LAST_VERSION_STR, CLICKHOUSE_XML_FILENAME))
else:
last_version_xml = ET.parse(ACTIVE_XML_CONFIG)
last_version_root = last_version_xml.getroot()
# We want to update the name and the command to include the version, and add it to last version
for function in list(base_xml.getroot()):
name = function.find("name")
name.text = augment_function_name(name.text)
command = function.find("command")
command.text = f"{VERSION_STR}/{command.text}"
last_version_root.append(function)
comment = etree.ElementTree.Comment(
f" Version: {VERSION_STR}. Generated at: {datetime.datetime.now(datetime.UTC).isoformat()}\nThis file is autogenerated by udf_versioner.py. Do not edit this, only edit the version at docker/clickhouse/user_defined_function.xml"
)
last_version_root.insert(0, comment)
last_version_xml.write(os.path.join(VERSION_STR, CLICKHOUSE_XML_FILENAME))
last_version_xml.write(f"latest_{CLICKHOUSE_XML_FILENAME}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Create a new version for UDF deployment.")
parser.add_argument("-f", "--force", action="store_true", help="override existing directories")
args = parser.parse_args()
prepare_version(args.force)

573
posthog/user_scripts/latest_user_defined_function.xml Normal file
View File

@ -0,0 +1,573 @@
<functions>
<!-- Version: v0. Generated at: 2024-09-23T23:01:41.610338+00:00
This file is autogenerated by udf_versioner.py. Do not edit this, only edit the version at docker/clickhouse/user_defined_function.xml--><function>
<type>executable</type>
<name>aggregate_funnel</name>
<return_type>Array(Tuple(Int8, Nullable(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort</name>
<return_type>Array(Tuple(Int8, UInt64, Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_cohort.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array</name>
<return_type>Array(Tuple(Int8, Array(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_test</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_trends</name>
<return_type>Array(Tuple(DateTime, Int8, Nullable(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends</name>
<return_type>Array(Tuple(DateTime, Int8, Array(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_trends</name>
<return_type>Array(Tuple(DateTime, Int8, UInt64))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_cohort_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_test</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array_trends_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_v0</name>
<return_type>Array(Tuple(Int8, Nullable(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_v0</name>
<return_type>Array(Tuple(Int8, UInt64, Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_cohort.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_v0</name>
<return_type>Array(Tuple(Int8, Array(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_test_v0</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, Nullable(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, Array(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, UInt64))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_cohort_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_test_v0</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array_trends_test.py</command>
</function>
</functions>

144
posthog/user_scripts/v0/aggregate_funnel.py Executable file
View File

@ -0,0 +1,144 @@
#!/usr/bin/python3
import json
import sys
from dataclasses import dataclass, replace
from itertools import groupby, permutations
from typing import Any, cast
from collections.abc import Sequence
def parse_args(line):
args = json.loads(line)
return [
int(args["num_steps"]),
int(args["conversion_window_limit"]),
str(args["breakdown_attribution_type"]),
str(args["funnel_order_type"]),
args["prop_vals"], # Array(Array(String))
args["value"], # Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))
]
@dataclass(frozen=True)
class EnteredTimestamp:
timestamp: Any
timings: Any
# each one can be multiple steps here
# it only matters when they entered the funnel - you can propagate the time from the previous step when you update
# This function is defined for Clickhouse in user_defined_functions.xml along with types
# num_steps is the total number of steps in the funnel
# conversion_window_limit is in seconds
# events is a array of tuples of (timestamp, breakdown, [steps])
# steps is an array of integers which represent the steps that this event qualifies for. it looks like [1,3,5,6].
# negative integers represent an exclusion on that step. each event is either all exclusions or all steps.
def calculate_funnel_from_user_events(
num_steps: int,
conversion_window_limit_seconds: int,
breakdown_attribution_type: str,
funnel_order_type: str,
prop_vals: list[Any],
events: Sequence[tuple[float, list[str] | int | str, list[int]]],
):
default_entered_timestamp = EnteredTimestamp(0, [])
max_step = [0, default_entered_timestamp]
# If the attribution mode is a breakdown step, set this to the integer that represents that step
breakdown_step = int(breakdown_attribution_type[5:]) if breakdown_attribution_type.startswith("step_") else None
# This function returns an Array. We build up an array of strings to return here.
results: list[tuple[int, Any, list[float]]] = []
# Process an event. If this hits an exclusion, return False, else return True.
def process_event(timestamp, breakdown, steps, *, entered_timestamp, prop_val) -> bool:
# iterate the steps in reverse so we don't count this event multiple times
for step in reversed(steps):
exclusion = False
if step < 0:
exclusion = True
step = -step
in_match_window = timestamp - entered_timestamp[step - 1].timestamp <= conversion_window_limit_seconds
already_reached_this_step_with_same_entered_timestamp = (
entered_timestamp[step].timestamp == entered_timestamp[step - 1].timestamp
)
if in_match_window and not already_reached_this_step_with_same_entered_timestamp:
if exclusion:
results.append((-1, prop_val, []))
return False
is_unmatched_step_attribution = (
breakdown_step is not None and step == breakdown_step - 1 and prop_val != breakdown
)
if not is_unmatched_step_attribution:
entered_timestamp[step] = replace(
entered_timestamp[step - 1], timings=[*entered_timestamp[step - 1].timings, timestamp]
)
if step > max_step[0]:
max_step[:] = (step, entered_timestamp[step])
if funnel_order_type == "strict":
for i in range(len(entered_timestamp)):
if i not in steps:
entered_timestamp[i] = default_entered_timestamp
return True
# We call this for each possible breakdown value.
def loop_prop_val(prop_val):
# an array of when the user entered the funnel
# entered_timestamp = [(0, "", [])] * (num_steps + 1)
max_step[:] = [0, default_entered_timestamp]
entered_timestamp: list[EnteredTimestamp] = [default_entered_timestamp] * (num_steps + 1)
def add_max_step():
i = cast(int, max_step[0])
final = cast(EnteredTimestamp, max_step[1])
results.append((i - 1, prop_val, [final.timings[i] - final.timings[i - 1] for i in range(1, i)]))
filtered_events = (
((timestamp, breakdown, steps) for (timestamp, breakdown, steps) in events if breakdown == prop_val)
if breakdown_attribution_type == "all_events"
else events
)
for timestamp, events_with_same_timestamp_iterator in groupby(filtered_events, key=lambda x: x[0]):
events_with_same_timestamp = tuple(events_with_same_timestamp_iterator)
entered_timestamp[0] = EnteredTimestamp(timestamp, [])
if len(events_with_same_timestamp) == 1:
if not process_event(
*events_with_same_timestamp[0], entered_timestamp=entered_timestamp, prop_val=prop_val
):
return
else:
# This is a special case for events with the same timestamp
# We play all of their permutations and most generously take the ones that advanced the furthest
# This has quite bad performance, and can probably be optimized through clever but annoying logic
# but shouldn't be hit too often
entered_timestamps = []
for events_group_perm in permutations(events_with_same_timestamp):
entered_timestamps.append(list(entered_timestamp))
for event in events_group_perm:
if not process_event(*event, entered_timestamp=entered_timestamps[-1], prop_val=prop_val):
# If any of the permutations hits an exclusion, we exclude this user.
# This isn't an important implementation detail and we could do something smarter here.
return
for i in range(len(entered_timestamp)):
entered_timestamp[i] = max((x[i] for x in entered_timestamps), key=lambda x: x.timestamp)
# If we have hit the goal, we can terminate early
if entered_timestamp[num_steps].timestamp > 0:
add_max_step()
return
# Find the furthest step we have made it to and print it
add_max_step()
return
[loop_prop_val(prop_val) for prop_val in prop_vals]
print(json.dumps({"result": results}), end="\n") # noqa: T201
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_from_user_events(*parse_args(line))
sys.stdout.flush()

9
posthog/user_scripts/v0/aggregate_funnel_array.py Executable file
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@ -0,0 +1,9 @@
#!/usr/bin/python3
import sys
from aggregate_funnel import parse_args, calculate_funnel_from_user_events
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_from_user_events(*parse_args(line))
sys.stdout.flush()

9
posthog/user_scripts/v0/aggregate_funnel_array_trends.py Executable file
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@ -0,0 +1,9 @@
#!/usr/bin/python3
import sys
from aggregate_funnel_trends import parse_args, calculate_funnel_trends_from_user_events
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_trends_from_user_events(*parse_args(line))
sys.stdout.flush()

13
posthog/user_scripts/v0/aggregate_funnel_array_trends_test.py Executable file
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#!/usr/bin/python3
from aggregate_funnel_trends import calculate_funnel_trends_from_user_events, parse_args
import sys
import json
if __name__ == "__main__":
for line in sys.stdin:
try:
calculate_funnel_trends_from_user_events(*parse_args(line))
except Exception as e:
print(json.dumps({"result": json.dumps(str(e))}), end="\n") # noqa: T201
sys.stdout.flush()

9
posthog/user_scripts/v0/aggregate_funnel_cohort.py Executable file
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@ -0,0 +1,9 @@
#!/usr/bin/python3
import sys
from aggregate_funnel import parse_args, calculate_funnel_from_user_events
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_from_user_events(*parse_args(line))
sys.stdout.flush()

9
posthog/user_scripts/v0/aggregate_funnel_cohort_trends.py Executable file
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@ -0,0 +1,9 @@
#!/usr/bin/python3
import sys
from aggregate_funnel_trends import parse_args, calculate_funnel_trends_from_user_events
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_trends_from_user_events(*parse_args(line))
sys.stdout.flush()

13
posthog/user_scripts/v0/aggregate_funnel_test.py Executable file
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@ -0,0 +1,13 @@
#!/usr/bin/python3
import json
from aggregate_funnel import calculate_funnel_from_user_events, parse_args
import sys
if __name__ == "__main__":
for line in sys.stdin:
try:
calculate_funnel_from_user_events(*parse_args(line))
except Exception as e:
print(json.dumps({"result": json.dumps(str(e))}), end="\n") # noqa: T201
sys.stdout.flush()

131
posthog/user_scripts/v0/aggregate_funnel_trends.py Executable file
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#!/usr/bin/python3
import sys
from dataclasses import dataclass, replace
from typing import Any
from collections.abc import Sequence
import json
def parse_args(line):
args = json.loads(line)
return [
int(args["from_step"]),
int(args["num_steps"]),
int(args["conversion_window_limit"]),
str(args["breakdown_attribution_type"]),
str(args["funnel_order_type"]),
args["prop_vals"], # Array(Array(String))
args["value"], # Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))
]
@dataclass(frozen=True)
class EnteredTimestamp:
timestamp: Any
timings: Any
# each one can be multiple steps here
# it only matters when they entered the funnel - you can propagate the time from the previous step when you update
# This function is defined for Clickhouse in user_defined_functions.xml along with types
# num_steps is the total number of steps in the funnel
# conversion_window_limit is in seconds
# events is a array of tuples of (timestamp, breakdown, [steps])
# steps is an array of integers which represent the steps that this event qualifies for. it looks like [1,3,5,6].
# negative integers represent an exclusion on that step. each event is either all exclusions or all steps.
def calculate_funnel_trends_from_user_events(
from_step: int,
num_steps: int,
conversion_window_limit_seconds: int,
breakdown_attribution_type: str,
funnel_order_type: str,
prop_vals: list[Any],
events: Sequence[tuple[float, int, list[str] | int | str, list[int]]],
):
default_entered_timestamp = EnteredTimestamp(0, [])
# If the attribution mode is a breakdown step, set this to the integer that represents that step
breakdown_step = int(breakdown_attribution_type[5:]) if breakdown_attribution_type.startswith("step_") else None
# Results is a map of start intervals to success or failure. If an interval isn't here, it means the
# user didn't enter
results = {}
# We call this for each possible breakdown value.
def loop_prop_val(prop_val):
# we need to track every distinct entry into the funnel through to the end
filtered_events = (
(
(timestamp, interval_start, breakdown, steps)
for (timestamp, interval_start, breakdown, steps) in events
if breakdown == prop_val
)
if breakdown_attribution_type == "all_events"
else events
)
list_of_entered_timestamps = []
for timestamp, interval_start, breakdown, steps in filtered_events:
for step in reversed(steps):
exclusion = False
if step < 0:
exclusion = True
step = -step
# Special code to handle the first step
# Potential Optimization: we could skip tracking here if the user has already completed the funnel for this interval
if step == 1:
entered_timestamp = [default_entered_timestamp] * (num_steps + 1)
# Set the interval start at 0, which is what we want to return if this works.
# For strict funnels, we need to track if the "from_step" has been hit
# Abuse the timings field on the 0th index entered_timestamp to have the elt True if we have
entered_timestamp[0] = EnteredTimestamp(interval_start, [True] if from_step == 0 else [])
entered_timestamp[1] = EnteredTimestamp(timestamp, [timestamp])
list_of_entered_timestamps.append(entered_timestamp)
else:
for entered_timestamp in list_of_entered_timestamps[:]:
in_match_window = (
timestamp - entered_timestamp[step - 1].timestamp <= conversion_window_limit_seconds
)
already_reached_this_step_with_same_entered_timestamp = (
entered_timestamp[step].timestamp == entered_timestamp[step - 1].timestamp
)
if in_match_window and not already_reached_this_step_with_same_entered_timestamp:
if exclusion:
# this is a complete failure, exclude this person, don't print anything, don't count
return False
is_unmatched_step_attribution = (
breakdown_step is not None and step == breakdown_step - 1 and prop_val != breakdown
)
if not is_unmatched_step_attribution:
entered_timestamp[step] = replace(
entered_timestamp[step - 1],
timings=[*entered_timestamp[step - 1].timings, timestamp],
)
# check if we have hit the goal. if we have, remove it from the list and add it to the successful_timestamps
if entered_timestamp[num_steps].timestamp > 0:
results[entered_timestamp[0].timestamp] = (1, prop_val)
list_of_entered_timestamps.remove(entered_timestamp)
# If we have hit the from_step threshold, record it (abuse the timings field)
elif step == from_step + 1:
entered_timestamp[0].timings.append(True)
# At the end of the event, clear all steps that weren't done by that event
if funnel_order_type == "strict":
for entered_timestamp in list_of_entered_timestamps[:]:
for i in range(1, len(entered_timestamp)):
if i not in steps:
entered_timestamp[i] = default_entered_timestamp
# At this point, everything left in entered_timestamps is a failure, if it has made it to from_step
for entered_timestamp in list_of_entered_timestamps:
if entered_timestamp[0].timestamp not in results and len(entered_timestamp[0].timings) > 0:
results[entered_timestamp[0].timestamp] = (-1, prop_val)
[loop_prop_val(prop_val) for prop_val in prop_vals]
result = [(interval_start, success_bool, prop_val) for interval_start, (success_bool, prop_val) in results.items()]
print(json.dumps({"result": result}), end="\n") # noqa: T201
if __name__ == "__main__":
for line in sys.stdin:
calculate_funnel_trends_from_user_events(*parse_args(line))
sys.stdout.flush()

573
posthog/user_scripts/v0/user_defined_function.xml Normal file
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@ -0,0 +1,573 @@
<functions>
<!-- Version: v0. Generated at: 2024-09-23T23:01:41.610338+00:00
This file is autogenerated by udf_versioner.py. Do not edit this, only edit the version at docker/clickhouse/user_defined_function.xml--><function>
<type>executable</type>
<name>aggregate_funnel</name>
<return_type>Array(Tuple(Int8, Nullable(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort</name>
<return_type>Array(Tuple(Int8, UInt64, Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_cohort.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array</name>
<return_type>Array(Tuple(Int8, Array(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_test</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_trends</name>
<return_type>Array(Tuple(DateTime, Int8, Nullable(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends</name>
<return_type>Array(Tuple(DateTime, Int8, Array(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_trends</name>
<return_type>Array(Tuple(DateTime, Int8, UInt64))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_cohort_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_test</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>aggregate_funnel_array_trends_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_v0</name>
<return_type>Array(Tuple(Int8, Nullable(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_v0</name>
<return_type>Array(Tuple(Int8, UInt64, Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_cohort.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_v0</name>
<return_type>Array(Tuple(Int8, Array(String), Array(Float64)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_test_v0</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_test.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, Nullable(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Nullable(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Nullable(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, Array(String)))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_cohort_trends_v0</name>
<return_type>Array(Tuple(DateTime, Int8, UInt64))</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(UInt64)</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), UInt64, Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_cohort_trends.py</command>
</function>
<function>
<type>executable</type>
<name>aggregate_funnel_array_trends_test_v0</name>
<return_type>String</return_type>
<return_name>result</return_name>
<argument>
<type>UInt8</type>
<name>from_step</name>
</argument>
<argument>
<type>UInt8</type>
<name>num_steps</name>
</argument>
<argument>
<type>UInt64</type>
<name>conversion_window_limit</name>
</argument>
<argument>
<type>String</type>
<name>breakdown_attribution_type</name>
</argument>
<argument>
<type>String</type>
<name>funnel_order_type</name>
</argument>
<argument>
<type>Array(Array(String))</type>
<name>prop_vals</name>
</argument>
<argument>
<type>Array(Tuple(Nullable(Float64), Nullable(DateTime), Array(String), Array(Int8)))</type>
<name>value</name>
</argument>
<format>JSONEachRow</format>
<command>v0/aggregate_funnel_array_trends_test.py</command>
</function>
</functions>

2
pytest.ini
View File

@ -3,7 +3,7 @@ env =
DEBUG=1
TEST=1
DJANGO_SETTINGS_MODULE = posthog.settings
addopts = -p no:warnings --reuse-db
addopts = -p no:warnings --reuse-db --ignore=posthog/user_scripts
markers =
ee