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a56714e1ab
gristlabs_grist-core/sandbox/grist/test_engine.py
Dmitry S a56714e1ab (core) Implement trigger formulas (generalizing default formulas) 
Summary:
Trigger formulas can be calculated for new records, or for new records and
updates to certain fields, or all fields. They do not recalculate on open,
and they MAY be set directly by the user, including for data-cleaning.

- Column metadata now includes recalcWhen and recalcDeps fields.
- Trigger formulas are NOT recalculated on open or on schema changes.
- When recalcWhen is "never", formula isn't calculated even for new records.
- When recalcWhen is "allupdates", formula is calculated for new records and
  any manual (non-formula) updates to the record.
- When recalcWhen is "", formula is calculated for new records, and changes to
  recalcDeps fields (which may be formula fields or column itself).
- A column whose recalcDeps includes itself is a "data-cleaning" column; a
  value set by the user will still trigger the formula.
- All trigger-formulas receive a "value" argument (to support the case above).

Small changes
- Update RefLists (used for recalcDeps) when target rows are deleted.
- Add RecordList.__contains__ (for `rec in refList` or `id in refList` checks)
- Clarify that Calculate action has replaced load_done() in practice,
  and use it in tests too, to better match reality.

Left for later:
- UI for setting recalcWhen / recalcDeps.
- Implementation of actions such as "Recalculate for all cells".
- Allowing trigger-formulas access to the current user's info.

Test Plan: Added a comprehensive python-side test for various trigger combinations

Reviewers: paulfitz, alexmojaki

Reviewed By: paulfitz

Differential Revision: https://phab.getgrist.com/D2872
2021-06-25 22:53:07 -04:00

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import difflib
import functools
import json
import unittest
from collections import namedtuple
import six
import actions
import column
import engine
import logger
import useractions
import testutil
log = logger.Logger(__name__, logger.DEBUG)
# These are for use in verifying metadata using assertTables/assertViews methods. E.g.
# self.assertViews([View(1, sections=[Section(1, parentKey="record", tableRef=1, fields=[
# Field(1, colRef=11) ]) ]) ])
Table = namedtuple('Table', ('id tableId primaryViewId summarySourceTable columns'))
Column = namedtuple('Column', ('id colId type isFormula formula summarySourceCol'))
View = namedtuple('View', 'id sections')
Section = namedtuple('Section', 'id parentKey tableRef fields')
Field = namedtuple('Field', 'id colRef')
unittest.TestCase.assertRaisesRegex = unittest.TestCase.assertRaisesRegexp
unittest.TestCase.assertRegex = unittest.TestCase.assertRegexpMatches
class EngineTestCase(unittest.TestCase):
"""
Provides functionality for verifying engine actions and data, which is general enough to be
useful for other tests. It is also used by TestEngine below.
"""
# Place to keep the original log handler (which we modify for the duration of the test).
# We can't use cls._orig_log_handler directly because then Python it's an actual class method.
_orig_log_handler = []
@classmethod
def setUpClass(cls):
cls._orig_log_handler.append(logger.set_handler(testutil.limit_log_stderr(logger.WARN)))
@classmethod
def tearDownClass(cls):
logger.set_handler(cls._orig_log_handler.pop())
def setUp(self):
"""
Initial setup for each test case.
"""
self.engine = engine.Engine()
self.engine.load_empty()
# Set up call tracing to count calls (formula evaluations) for each column for each table.
self.call_counts = {}
def trace_call(col_obj, _rec):
# Ignore formulas in metadata tables for simplicity. Such formulas are mostly private, and
# it would be annoying to fix tests every time we change them.
if not col_obj.table_id.startswith("_grist_"):
tmap = self.call_counts.setdefault(col_obj.table_id, {})
tmap[col_obj.col_id] = tmap.get(col_obj.col_id, 0) + 1
self.engine.formula_tracer = trace_call
# This is set when a test case is wrapped by `test_engine.test_undo`.
self._undo_state_tracker = None
@classmethod
def _getEngineDataLines(cls, engine_data, col_names=[]):
"""
Helper for assertEqualEngineData, which returns engine data represented as lines of text
suitable for diffing. If col_names is given, it determines the order of columns (columns not
found in this list are included in the end and sorted by name).
"""
sort_keys = {c: i for i, c in enumerate(col_names)}
ret = []
for table_id, table_data in sorted(engine_data.items()):
ret.append("TABLE %s\n" % table_id)
col_items = sorted(table_data.columns.items(),
key=lambda c: (sort_keys.get(c[0], float('inf')), c))
col_items.insert(0, ('id', table_data.row_ids))
table_rows = zip(*[[col_id] + values for (col_id, values) in col_items])
ret.extend(json.dumps(row) + "\n" for row in table_rows)
return ret
def assertEqualDocData(self, observed, expected, col_names=[]):
"""
Compare full engine data, as a mapping of table_ids to TableData objects, and reporting
differences with a customized diff (similar to the JSON representation in the test script).
"""
enc_observed = actions.encode_objects(observed)
enc_expected = actions.encode_objects(expected)
if enc_observed != enc_expected:
o_lines = self._getEngineDataLines(enc_observed, col_names)
e_lines = self._getEngineDataLines(enc_expected, col_names)
self.fail("Observed data not as expected:\n" +
"".join(difflib.unified_diff(e_lines, o_lines,
fromfile="expected", tofile="observed")))
def assertCorrectEngineData(self, expected_data):
"""
Verifies that the data engine contains the same data as the given expected data,
which should be a dictionary mapping table names to TableData objects.
"""
expected_output = actions.decode_objects(expected_data)
meta_tables = self.engine.fetch_table("_grist_Tables")
output = {t: self.engine.fetch_table(t) for t in meta_tables.columns["tableId"]}
output = testutil.replace_nans(output)
self.assertEqualDocData(output, expected_output)
def getFullEngineData(self):
return testutil.replace_nans({t: self.engine.fetch_table(t) for t in self.engine.tables})
def assertPartialData(self, table_name, col_names, row_data):
"""
Verifies that the data engine contains the right data for the given col_names (ignoring any
other columns).
"""
expected = testutil.table_data_from_rows(table_name, col_names, row_data)
observed = self.engine.fetch_table(table_name, private=True)
ignore = set(observed.columns) - set(expected.columns)
for col_id in ignore:
del observed.columns[col_id]
self.assertEqualDocData({table_name: observed}, {table_name: expected})
action_group_action_fields = ("stored", "undo", "calc", "direct")
@classmethod
def _formatActionGroup(cls, action_group, use_repr=False):
"""
Helper for assertEqualActionGroups below.
"""
lines = ["{"]
for (k, action_list) in sorted(action_group.items()):
if k in cls.action_group_action_fields:
for a in action_list:
rep = repr(a) if use_repr else json.dumps(a, sort_keys=True)
lines.append("%s: %s," % (k, rep))
else:
lines.append("%s: %s," % (k, json.dumps(action_list)))
lines.append("}")
return lines
def assertEqualActionGroups(self, observed, expected):
"""
Compare grouped doc actions, reporting differences with a customized diff
(a bit more readable than unittest's usual diff).
"""
# Do some clean up on the observed data.
observed = testutil.replace_nans(observed)
# Convert observed and expected actions into a comparable form.
for k in self.action_group_action_fields:
if k in observed:
observed[k] = [get_comparable_repr(v) for v in observed[k]]
if k in expected:
expected[k] = [get_comparable_repr(v) for v in expected[k]]
if observed != expected:
o_lines = self._formatActionGroup(observed)
e_lines = self._formatActionGroup(expected)
self.fail(("Observed out actions not as expected:\n") +
"\n".join(difflib.unified_diff(e_lines, o_lines, n=3, lineterm="",
fromfile="expected", tofile="observed")))
def assertOutActions(self, out_action_group, expected_group):
"""
Compares action group returned from engine.apply_user_actions() to expected actions as listed
in testscript. The array of retValues is only checked if present in expected_group.
"""
for k in self.action_group_action_fields:
# For comparing full actions, treat omitted groups (e.g. "calc") as expected to be empty.
expected_group.setdefault(k, [])
observed = {k: getattr(out_action_group, k) for k in self.action_group_action_fields }
if "retValue" in expected_group:
observed["retValue"] = out_action_group.retValues
self.assertEqualActionGroups(observed, expected_group)
def assertPartialOutActions(self, out_action_group, expected_group):
"""
Compares a single action group as returned from engine.apply_user_actions() to expected
actions, checking only those fields that are included in the expected_group dict.
"""
observed = {k: getattr(out_action_group, k) for k in expected_group}
self.assertEqualActionGroups(observed, expected_group)
def dump_data(self):
"""
Prints a dump of all engine data, for help in writing / debugging tests.
"""
output = {t: self.engine.fetch_table(t) for t in self.engine.schema}
output = testutil.replace_nans(output)
output = actions.encode_objects(output)
print(''.join(self._getEngineDataLines(output)))
def dump_actions(self, out_actions):
"""
Prints out_actions in human-readable format, for help in writing / debugging tets.
"""
print("\n".join(self._formatActionGroup(out_actions.__dict__)))
def assertTableData(self, table_name, data=[], cols="all", rows="all", sort=None):
"""
Verify some or all of the data in the table named `table_name`.
- data: an array of rows, with first row containing column names starting with "id", and
other rows also all starting with row_id.
- cols: may be "all" (default) to match all columns, or "subset" to match only those listed.
- rows: may be "all" (default) to match all rows, or "subset" to match only those listed,
or a function called with a Record to return whether to include it.
- sort: optionally a key function called with a Record, for sorting observed rows.
"""
assert data[0][0] == 'id', "assertRecords requires 'id' as the first column"
col_names = data[0]
row_data = data[1:]
expected = testutil.table_data_from_rows(table_name, col_names, row_data)
table = self.engine.tables[table_name]
columns = [c for c in table.all_columns.values()
if c.col_id != "id" and not column.is_virtual_column(c.col_id)]
if cols == "all":
pass
elif cols == "subset":
columns = [c for c in columns if c.col_id in col_names]
else:
raise ValueError("assertRecords: invalid value for cols: %s" % (cols,))
if rows == "all":
row_ids = list(table.row_ids)
elif rows == "subset":
row_ids = [row[0] for row in row_data]
elif callable(rows):
row_ids = [r.id for r in table.user_table.all if rows(r)]
else:
raise ValueError("assertRecords: invalid value for rows: %s" % (rows,))
if sort:
row_ids.sort(key=lambda r: sort(table.get_record(r)))
observed_col_data = {c.col_id: [c.raw_get(r) for r in row_ids] for c in columns if c.col_id != "id"}
observed = actions.TableData(table_name, row_ids, observed_col_data)
self.assertEqualDocData({table_name: observed}, {table_name: expected},
col_names=col_names)
def assertTables(self, list_of_tables):
"""
Verifies that the given Table test-records correspond to the metadata for tables/columns.
"""
self.assertPartialData('_grist_Tables',
["id", "tableId", "primaryViewId", "summarySourceTable"],
sorted((tbl.id, tbl.tableId, tbl.primaryViewId, tbl.summarySourceTable)
for tbl in list_of_tables))
self.assertPartialData('_grist_Tables_column',
["id", "parentId", "colId", "type",
"isFormula", "formula", "summarySourceCol"],
sorted((col.id, tbl.id, col.colId, col.type,
col.isFormula, col.formula, col.summarySourceCol)
for tbl in list_of_tables
for col in tbl.columns))
def assertViews(self, list_of_views):
"""
Verifies that the given View test-records correspond to the metadata for views/sections/fields.
"""
self.assertPartialData('_grist_Views', ["id"],
[[view.id] for view in list_of_views])
self.assertPartialData('_grist_Views_section', ["id", "parentId", "parentKey", "tableRef"],
sorted((sec.id, view.id, sec.parentKey, sec.tableRef)
for view in list_of_views
for sec in view.sections))
self.assertTableData('_grist_Views_section_field', sort=(lambda r: r.parentPos),
cols="subset",
data=[["id", "parentId", "colRef"]] + sorted(
((field.id, sec.id, field.colRef)
for view in list_of_views
for sec in view.sections
for field in sec.fields), key=lambda t: t[1])
)
def load_sample(self, sample):
"""
Load the data engine with given sample data. The sample is a dict with keys "SCHEMA" and
"DATA", each a dictionary mapping table names to actions.TableData objects. "SCHEMA" contains
"_grist_Tables" and "_grist_Tables_column" tables.
"""
schema = sample["SCHEMA"]
self.engine.load_meta_tables(schema['_grist_Tables'], schema['_grist_Tables_column'])
for data in six.itervalues(sample["DATA"]):
self.engine.load_table(data)
# We used to call load_done() at the end; in practice, Grist's ActiveDoc does not call
# load_done, but applies the "Calculate" user action. Do that for more realistic tests.
self.apply_user_action(['Calculate'])
# The following are convenience methods for tests deriving from EngineTestCase.
def add_column(self, table_name, col_name, **kwargs):
return self.apply_user_action(['AddColumn', table_name, col_name, kwargs])
def modify_column(self, table_name, col_name, **kwargs):
return self.apply_user_action(['ModifyColumn', table_name, col_name, kwargs])
def remove_column(self, table_name, col_name):
return self.apply_user_action(['RemoveColumn', table_name, col_name])
def update_record(self, table_name, row_id, **kwargs):
return self.apply_user_action(['UpdateRecord', table_name, row_id, kwargs])
def add_record(self, table_name, row_id=None, **kwargs):
return self.apply_user_action(['AddRecord', table_name, row_id, kwargs])
def remove_record(self, table_name, row_id):
return self.apply_user_action(['RemoveRecord', table_name, row_id])
def update_records(self, table_name, col_names, row_data):
return self.apply_user_action(
('BulkUpdateRecord',) + testutil.table_data_from_rows(table_name, col_names, row_data))
@classmethod
def add_records_action(cls, table_name, data):
"""
Creates a BulkAddRecord action; data should be an array of rows, with first row containing
column names, with "id" column optional.
"""
col_names, row_data = data[0], data[1:]
if "id" not in col_names:
col_names = ["id"] + col_names
row_data = [[None] + r for r in row_data]
return ('BulkAddRecord',) + testutil.table_data_from_rows(table_name, col_names, row_data)
def add_records(self, table_name, col_names, row_data):
return self.apply_user_action(self.add_records_action(table_name, [col_names] + row_data))
def apply_user_action(self, user_action_repr, is_undo=False):
if not is_undo:
log.debug("Applying user action %r" % (user_action_repr,))
if self._undo_state_tracker is not None:
doc_state = self.getFullEngineData()
self.call_counts.clear()
out_actions = self.engine.apply_user_actions([useractions.from_repr(user_action_repr)])
out_actions.calls = self.call_counts.copy()
if not is_undo and self._undo_state_tracker is not None:
self._undo_state_tracker.append((doc_state, out_actions.undo[:]))
return out_actions
def apply_undo_actions(self, undo_actions):
"""
Applies all doc_actions together (as happens e.g. for undo).
"""
action = ["ApplyUndoActions", [actions.get_action_repr(a) for a in undo_actions]]
return self.apply_user_action(action, is_undo=True)
def test_undo(test_method):
"""
If a test method is decorated with `@test_engine.test_undo`, then we will store the state before
each apply_user_action() call, and at the end of the test, undo each user-action and compare the
state. This makes for a fairly comprehensive test of undo.
"""
@functools.wraps(test_method)
def wrapped(self):
self._undo_state_tracker = []
test_method(self)
for (expected_engine_data, undo_actions) in reversed(self._undo_state_tracker):
log.debug("Applying undo actions %r" % (undo_actions,))
self.apply_undo_actions(undo_actions)
self.assertEqualDocData(self.getFullEngineData(), expected_engine_data)
return wrapped
class TestEngine(EngineTestCase):
samples = {}
#----------------------------------------------------------------------
# Implementations of the actual script steps.
#----------------------------------------------------------------------
def process_apply_step(self, data):
"""
Processes the "APPLY" step of a test script, applying a user action, and checking the
resulting action group's return value (if present)
"""
if "USER_ACTION" in data:
user_actions = [useractions.from_repr(data.pop("USER_ACTION"))]
else:
user_actions = [useractions.from_repr(u) for u in data.pop("USER_ACTIONS")]
expected_call_counts = data.pop("CHECK_CALL_COUNTS", None)
expected_actions = data.pop("ACTIONS", {})
expected_actions.setdefault("stored", [])
expected_actions.setdefault("calc", [])
expected_actions.setdefault("undo", [])
if data:
raise ValueError("Unrecognized key %s in APPLY step" % data.popitem()[0])
self.call_counts.clear()
out_actions = self.engine.apply_user_actions(user_actions)
self.assertOutActions(out_actions, expected_actions)
if expected_call_counts:
self.assertEqual(self.call_counts, expected_call_counts)
return out_actions
#----------------------------------------------------------------------
# The runner for scripted test cases.
#----------------------------------------------------------------------
def _run_test_body(self, _name, body):
"""
Runs the actual script defined in the JSON test-script file.
"""
undo_actions = []
loaded_sample = None
for line, step, data in body:
try:
if step == "LOAD_SAMPLE":
if loaded_sample:
# pylint: disable=unsubscriptable-object
self._verify_undo_all(undo_actions, loaded_sample["DATA"])
loaded_sample = self.samples[data]
self.load_sample(loaded_sample)
elif step == "APPLY":
action_group = self.process_apply_step(data)
undo_actions.extend(action_group.undo)
elif step == "CHECK_OUTPUT":
expected_data = {}
if "USE_SAMPLE" in data:
sample = self.samples[data.pop("USE_SAMPLE")]
expected_data = sample["DATA"].copy()
expected_data.update({t: testutil.table_data_from_rows(t, tdata[0], tdata[1:])
for (t, tdata) in six.iteritems(data)})
self.assertCorrectEngineData(expected_data)
else:
raise ValueError("Unrecognized step %s in test script" % step)
except Exception as e:
prefix = "LINE %s: " % line
e.args = (prefix + e.args[0],) + e.args[1:] if e.args else (prefix,)
raise
self._verify_undo_all(undo_actions, loaded_sample["DATA"])
def _verify_undo_all(self, undo_actions, expected_data):
"""
At the end of each test, undo all and verify we get back to the originally loaded sample.
"""
self.apply_undo_actions(undo_actions)
del undo_actions[:]
self.assertCorrectEngineData(expected_data)
# TODO We need several more tests.
# 1. After a bunch of schema actions, create a new engine from the resulting schema, ensure that
# modified engine and new engine produce the same results AND the same dep_graph.
# 2. Build up a table by adding one column at a time, in "good" order and in "bad" order (with
# references to columns that will be added later)
# 3. Tear down a table in both of the orders above.
# 4. At each intermediate state of 2 and 3, new engine should produce same results as the
# modified engine (and have the same state such as dep_graph).
sample1 = {
"SCHEMA": [
[1, "Address", [
[11, "city", "Text", False, "", "", ""],
[12, "state", "Text", False, "", "", ""],
[13, "amount", "Numeric", False, "", "", ""],
]]
],
"DATA": {
"Address": [
["id", "city", "state", "amount" ],
[ 21, "New York", "NY" , 1 ],
[ 22, "Albany", "NY" , 2 ],
]
}
}
def test_no_private_fields(self):
self.load_sample(testutil.parse_test_sample(self.sample1))
data = self.engine.fetch_table("_grist_Tables", private=True)
self.assertIn('tableId', data.columns)
self.assertIn('columns', data.columns)
self.assertIn('viewSections', data.columns)
data = self.engine.fetch_table("_grist_Tables")
self.assertIn('tableId', data.columns)
self.assertNotIn('columns', data.columns)
self.assertNotIn('viewSections', data.columns)
def test_fetch_table_query(self):
self.load_sample(testutil.parse_test_sample(self.sample1))
col_names = ["id", "city", "state", "amount" ]
data = self.engine.fetch_table('Address', query={'state': ['NY']})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [
[ 21, "New York", "NY" , 1 ],
[ 22, "Albany", "NY" , 2 ],
])})
data = self.engine.fetch_table('Address', query={'city': ['New York'], 'state': ['NY']})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [
[ 21, "New York", "NY" , 1 ],
])})
data = self.engine.fetch_table('Address', query={'amount': [2.0]})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [
[ 22, "Albany", "NY" , 2 ],
])})
data = self.engine.fetch_table('Address', query={'city': ['New York'], 'amount': [2.0]})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [])})
data = self.engine.fetch_table('Address', query={'city': ['New York'], 'amount': [1.0, 2.0]})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [
[ 21, "New York", "NY" , 1 ],
])})
# Ensure empty filter list works too.
data = self.engine.fetch_table('Address', query={'city': ['New York'], 'amount': []})
self.assertEqualDocData({'Address': data},
{'Address': testutil.table_data_from_rows('Address', col_names, [])})
def test_schema_restore_on_error(self):
# Simulate an error inside a DocAction, and make sure we restore the schema (don't leave it in
# inconsistent with metadata).
self.load_sample(testutil.parse_test_sample(self.sample1))
with self.assertRaisesRegex(AttributeError, r"'BAD'"):
self.add_column('Address', 'bad', isFormula=False, type="BAD")
self.engine.assert_schema_consistent()
def create_tests_from_script(samples, test_cases):
"""
Dynamically create tests from a file containing a JSON spec for test cases. The reason for doing
it this way is because the same JSON spec is used to test Python and JS code.
Tests are created as methods to a TestCase. It's done on import, so that python unittest feature
to run only particular test cases can apply to these cases too.
"""
TestEngine.samples = samples
for case in test_cases:
create_test_case("test_" + case["TEST_CASE"], case["BODY"])
def create_test_case(name, body):
"""
Helper for create_tests_from_script, which creates a single test case.
"""
def run(self):
self._run_test_body(name, body)
setattr(TestEngine, name, run)
# Convert observed/expected action into a comparable form.
def get_comparable_repr(a):
if isinstance(a, (list, int)):
return a
return actions.get_action_repr(a)
# Parse and create test cases on module load. This way the python unittest feature to run only
# particular test cases can apply to these cases too.
create_tests_from_script(*testutil.parse_testscript())
if __name__ == "__main__":
unittest.main()
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