def test_wf1_with_sql_with_patch():
sql = SQLTask(
"my-query",
query_template=
"SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds }}' LIMIT 10",
inputs=kwtypes(ds=datetime.datetime),
outputs=kwtypes(results=FlyteSchema),
metadata=TaskMetadata(retries=2),
)
@task
def t1() -> datetime.datetime:
return datetime.datetime.now()
@workflow
def my_wf() -> FlyteSchema:
dt = t1()
return sql(ds=dt)
@patch(sql)
def test_user_demo_test(mock_sql):
mock_sql.return_value = pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})
assert (my_wf().open().all() == pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})).all().all()
# Have to call because tests inside tests don't run
test_user_demo_test()
def test_wf1_with_sql():
sql = SQLTask(
"my-query",
query_template=
"SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds }}' LIMIT 10",
inputs=kwtypes(ds=datetime.datetime),
outputs=kwtypes(results=FlyteSchema),
metadata=TaskMetadata(retries=2),
)
@task
def t1() -> datetime.datetime:
return datetime.datetime.now()
@workflow
def my_wf() -> FlyteSchema:
dt = t1()
return sql(ds=dt)
with task_mock(sql) as mock:
mock.return_value = pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})
assert (my_wf().open().all() == pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})).all().all()
assert context_manager.FlyteContextManager.size() == 1
def dummy_node(node_id) -> Node:
n = Node(
node_id,
metadata=None,
bindings=[],
upstream_nodes=[],
flyte_entity=SQLTask(name="x", query_template="x", inputs={}),
)
n._id = node_id
return n
def test_sql_task():
sql = SQLTask(
"my-query",
query_template=
"SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds }}' LIMIT 10",
inputs=kwtypes(ds=datetime.datetime),
outputs=kwtypes(results=FlyteSchema),
metadata=TaskMetadata(retries=2, cache=True, cache_version="0.1"),
)
@task(cache=True, cache_version="0.1.2")
def t1() -> datetime.datetime:
global n_cached_task_calls
n_cached_task_calls += 1
return datetime.datetime.now()
@workflow
def my_wf() -> FlyteSchema:
dt = t1()
return sql(ds=dt)
with task_mock(sql) as mock:
mock.return_value = pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})
assert n_cached_task_calls == 0
assert (my_wf().open().all() == pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})).all().all()
assert n_cached_task_calls == 1
# The second and third calls hit the cache
assert (my_wf().open().all() == pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})).all().all()
assert n_cached_task_calls == 1
assert (my_wf().open().all() == pandas.DataFrame(data={
"x": [1, 2],
"y": ["3", "4"]
})).all().all()
assert n_cached_task_calls == 1
"""
import datetime
import pandas
from flytekit import SQLTask, TaskMetadata, kwtypes, task, workflow
from flytekit.testing import patch, task_mock
from flytekit.types.schema import FlyteSchema
# %%
# This is a generic SQL task (and is by default not hooked up to any datastore nor handled by any plugin), and must
# be mocked.
sql = SQLTask(
"my-query",
query_template=
"SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds }}' LIMIT 10",
inputs=kwtypes(ds=datetime.datetime),
outputs=kwtypes(results=FlyteSchema),
metadata=TaskMetadata(retries=2),
)
# %%
# This is a task that can run locally
@task
def t1() -> datetime.datetime:
return datetime.datetime.now()
# %%
# Declare a workflow that chains these two tasks together.
@workflow
