From fd6ec71dabae91f4f243362acbcda6dc1beccec6 Mon Sep 17 00:00:00 2001 From: Michelle Ark Date: Thu, 21 Nov 2024 00:31:47 -0500 Subject: [PATCH] Microbatch parallelism (#10958) --- .../unreleased/Features-20241118-160038.yaml | 6 + core/dbt/artifacts/resources/v1/config.py | 1 + core/dbt/artifacts/schemas/batch_results.py | 3 + core/dbt/clients/jinja_static.py | 12 + core/dbt/contracts/graph/nodes.py | 14 + core/dbt/events/core_types.proto | 10 + core/dbt/events/core_types_pb2.py | 346 ++++++------ core/dbt/events/types.py | 8 + core/dbt/parser/manifest.py | 7 + core/dbt/task/build.py | 24 +- core/dbt/task/run.py | 534 +++++++++++------- core/dbt/task/runnable.py | 5 + core/setup.py | 2 +- schemas/dbt/manifest/v12.json | 58 +- .../functional/artifacts/expected_manifest.py | 3 + tests/functional/list/test_list.py | 9 + .../functional/microbatch/test_microbatch.py | 86 ++- tests/unit/clients/test_jinja_static.py | 16 + tests/unit/task/test_run.py | 133 ++--- tests/unit/test_events.py | 1 + 20 files changed, 816 insertions(+), 462 deletions(-) create mode 100644 .changes/unreleased/Features-20241118-160038.yaml diff --git a/.changes/unreleased/Features-20241118-160038.yaml b/.changes/unreleased/Features-20241118-160038.yaml new file mode 100644 index 00000000000..b1a52fec837 --- /dev/null +++ b/.changes/unreleased/Features-20241118-160038.yaml @@ -0,0 +1,6 @@ +kind: Features +body: Allow microbatch batches to run in parallel +time: 2024-11-18T16:00:38.895449-06:00 +custom: + Author: QMalcolm MichelleArk + Issue: 10853 10855 diff --git a/core/dbt/artifacts/resources/v1/config.py b/core/dbt/artifacts/resources/v1/config.py index b759f6bdf83..f253f091b89 100644 --- a/core/dbt/artifacts/resources/v1/config.py +++ b/core/dbt/artifacts/resources/v1/config.py @@ -126,6 +126,7 @@ class NodeConfig(NodeAndTestConfig): metadata=MergeBehavior.Update.meta(), ) event_time: Any = None + concurrent_batches: Any = None def __post_init__(self): # we validate that node_color has a suitable value to prevent dbt-docs from crashing diff --git a/core/dbt/artifacts/schemas/batch_results.py b/core/dbt/artifacts/schemas/batch_results.py index e910c2916f9..b4f4ae90292 100644 --- a/core/dbt/artifacts/schemas/batch_results.py +++ b/core/dbt/artifacts/schemas/batch_results.py @@ -19,3 +19,6 @@ def __add__(self, other: BatchResults) -> BatchResults: successful=self.successful + other.successful, failed=self.failed + other.failed, ) + + def __len__(self): + return len(self.successful) + len(self.failed) diff --git a/core/dbt/clients/jinja_static.py b/core/dbt/clients/jinja_static.py index a812c3fb7cb..a89cbef9b26 100644 --- a/core/dbt/clients/jinja_static.py +++ b/core/dbt/clients/jinja_static.py @@ -17,6 +17,18 @@ _TESTING_MACRO_CACHE: Dict[str, Any] = {} +def statically_extract_has_name_this(source: str) -> bool: + """Checks whether the raw jinja has any references to `this`""" + env = get_environment(None, capture_macros=True) + parsed = env.parse(source) + names = tuple(parsed.find_all(jinja2.nodes.Name)) + + for name in names: + if hasattr(name, "name") and name.name == "this": + return True + return False + + def statically_extract_macro_calls( source: str, ctx: Dict[str, Any], db_wrapper: Optional["ParseDatabaseWrapper"] = None ) -> List[str]: diff --git a/core/dbt/contracts/graph/nodes.py b/core/dbt/contracts/graph/nodes.py index 262ec83735e..d341b167ab9 100644 --- a/core/dbt/contracts/graph/nodes.py +++ b/core/dbt/contracts/graph/nodes.py @@ -61,6 +61,7 @@ from dbt.artifacts.resources import TimeSpine from dbt.artifacts.resources import UnitTestDefinition as UnitTestDefinitionResource from dbt.artifacts.schemas.batch_results import BatchResults +from dbt.clients.jinja_static import statically_extract_has_name_this from dbt.contracts.graph.model_config import UnitTestNodeConfig from dbt.contracts.graph.node_args import ModelNodeArgs from dbt.contracts.graph.unparsed import ( @@ -444,6 +445,13 @@ def resource_class(cls) -> Type[HookNodeResource]: @dataclass class ModelNode(ModelResource, CompiledNode): batch_info: Optional[BatchResults] = None + _has_this: Optional[bool] = None + + def __post_serialize__(self, dct: Dict, context: Optional[Dict] = None): + dct = super().__post_serialize__(dct, context) + if "_has_this" in dct: + del dct["_has_this"] + return dct @classmethod def resource_class(cls) -> Type[ModelResource]: @@ -520,6 +528,12 @@ def all_constraints(self) -> List[Union[ModelLevelConstraint, ColumnLevelConstra return constraints + @property + def has_this(self) -> bool: + if self._has_this is None: + self._has_this = statically_extract_has_name_this(self.raw_code) + return self._has_this + def infer_primary_key(self, data_tests: List["GenericTestNode"]) -> List[str]: """ Infers the columns that can be used as primary key of a model in the following order: diff --git a/core/dbt/events/core_types.proto b/core/dbt/events/core_types.proto index ea438cf12fc..2938ebb554f 100644 --- a/core/dbt/events/core_types.proto +++ b/core/dbt/events/core_types.proto @@ -1680,6 +1680,16 @@ message SnapshotTimestampWarningMsg { SnapshotTimestampWarning data = 2; } +// Q044 +message MicrobatchExecutionDebug { + string msg = 1; +} + +message MicrobatchExecutionDebugMsg { + CoreEventInfo info = 1; + MicrobatchExecutionDebug data = 2; +} + // W - Node testing // Skipped W001 diff --git a/core/dbt/events/core_types_pb2.py b/core/dbt/events/core_types_pb2.py index 17bbef5dec3..1e8444f4833 100644 --- a/core/dbt/events/core_types_pb2.py +++ b/core/dbt/events/core_types_pb2.py @@ -26,7 +26,7 @@ from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x63ore_types.proto\x12\x0bproto_types\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1cgoogle/protobuf/struct.proto\"\x99\x02\n\rCoreEventInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04\x63ode\x18\x02 \x01(\t\x12\x0b\n\x03msg\x18\x03 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_globals['_RUNRESULTWARNINGMESSAGEMSG']._serialized_end=35776 + _globals['_DEBUGCMDOUT']._serialized_start=35778 + _globals['_DEBUGCMDOUT']._serialized_end=35804 + _globals['_DEBUGCMDOUTMSG']._serialized_start=35806 + _globals['_DEBUGCMDOUTMSG']._serialized_end=35904 + _globals['_DEBUGCMDRESULT']._serialized_start=35906 + _globals['_DEBUGCMDRESULT']._serialized_end=35935 + _globals['_DEBUGCMDRESULTMSG']._serialized_start=35937 + _globals['_DEBUGCMDRESULTMSG']._serialized_end=36041 + _globals['_LISTCMDOUT']._serialized_start=36043 + _globals['_LISTCMDOUT']._serialized_end=36068 + _globals['_LISTCMDOUTMSG']._serialized_start=36070 + _globals['_LISTCMDOUTMSG']._serialized_end=36166 + _globals['_RESOURCEREPORT']._serialized_start=36169 + _globals['_RESOURCEREPORT']._serialized_end=36405 + _globals['_RESOURCEREPORTMSG']._serialized_start=36407 + _globals['_RESOURCEREPORTMSG']._serialized_end=36511 # @@protoc_insertion_point(module_scope) diff --git a/core/dbt/events/types.py b/core/dbt/events/types.py index 2a49a8ad047..6cb9b688484 100644 --- a/core/dbt/events/types.py +++ b/core/dbt/events/types.py @@ -1702,6 +1702,14 @@ def message(self) -> str: ) +class MicrobatchExecutionDebug(DebugLevel): + def code(self) -> str: + return "Q044" + + def message(self) -> str: + return self.msg + + # ======================================================= # W - Node testing # ======================================================= diff --git a/core/dbt/parser/manifest.py b/core/dbt/parser/manifest.py index fbfc83cd6c5..98e7cea6257 100644 --- a/core/dbt/parser/manifest.py +++ b/core/dbt/parser/manifest.py @@ -1461,6 +1461,13 @@ def check_valid_microbatch_config(self): f"Microbatch model '{node.name}' must provide the optional 'lookback' config as type int, but got: {type(lookback)})." ) + # optional config: concurrent_batches (bool) + concurrent_batches = node.config.concurrent_batches + if not isinstance(concurrent_batches, bool) and concurrent_batches is not None: + raise dbt.exceptions.ParsingError( + f"Microbatch model '{node.name}' optional 'concurrent_batches' config must be of type `bool` if specified, but got: {type(concurrent_batches)})." + ) + # Validate upstream node event_time (if configured) has_input_with_event_time_config = False for input_unique_id in node.depends_on.nodes: diff --git a/core/dbt/task/build.py b/core/dbt/task/build.py index 490d6627b33..ff68d976744 100644 --- a/core/dbt/task/build.py +++ b/core/dbt/task/build.py @@ -11,6 +11,7 @@ from dbt.graph import Graph, GraphQueue, ResourceTypeSelector from dbt.node_types import NodeType from dbt.task.base import BaseRunner, resource_types_from_args +from dbt.task.run import MicrobatchModelRunner from dbt_common.events.functions import fire_event from .run import ModelRunner as run_model_runner @@ -141,13 +142,13 @@ def handle_job_queue(self, pool, callback): def handle_model_with_unit_tests_node(self, node, pool, callback): self._raise_set_error() - args = [node] + args = [node, pool] if self.config.args.single_threaded: callback(self.call_model_and_unit_tests_runner(*args)) else: pool.apply_async(self.call_model_and_unit_tests_runner, args=args, callback=callback) - def call_model_and_unit_tests_runner(self, node) -> RunResult: + def call_model_and_unit_tests_runner(self, node, pool) -> RunResult: assert self.manifest for unit_test_unique_id in self.model_to_unit_test_map[node.unique_id]: unit_test_node = self.manifest.unit_tests[unit_test_unique_id] @@ -166,6 +167,10 @@ def call_model_and_unit_tests_runner(self, node) -> RunResult: if runner.node.unique_id in self._skipped_children: cause = self._skipped_children.pop(runner.node.unique_id) runner.do_skip(cause=cause) + + if isinstance(runner, MicrobatchModelRunner): + return self.handle_microbatch_model(runner, pool) + return self.call_runner(runner) # handle non-model-plus-unit-tests nodes @@ -177,11 +182,12 @@ def handle_job_queue_node(self, node, pool, callback): if runner.node.unique_id in self._skipped_children: cause = self._skipped_children.pop(runner.node.unique_id) runner.do_skip(cause=cause) - args = [runner] - if self.config.args.single_threaded: - callback(self.call_runner(*args)) + + if isinstance(runner, MicrobatchModelRunner): + callback(self.handle_microbatch_model(runner, pool)) else: - pool.apply_async(self.call_runner, args=args, callback=callback) + args = [runner] + self._submit(pool, args, callback) # Make a map of model unique_ids to selected unit test unique_ids, # for processing before the model. @@ -210,6 +216,12 @@ def get_node_selector(self, no_unit_tests=False) -> ResourceTypeSelector: ) def get_runner_type(self, node) -> Optional[Type[BaseRunner]]: + if ( + node.resource_type == NodeType.Model + and super().get_runner_type(node) == MicrobatchModelRunner + ): + return MicrobatchModelRunner + return self.RUNNER_MAP.get(node.resource_type) # Special build compile_manifest method to pass add_test_edges to the compiler diff --git a/core/dbt/task/run.py b/core/dbt/task/run.py index 9c65a7d8438..1b4be463e78 100644 --- a/core/dbt/task/run.py +++ b/core/dbt/task/run.py @@ -2,12 +2,14 @@ import threading import time from copy import deepcopy -from dataclasses import asdict +from dataclasses import asdict, field from datetime import datetime +from multiprocessing.pool import ThreadPool from typing import AbstractSet, Any, Dict, Iterable, List, Optional, Set, Tuple, Type from dbt import tracking, utils from dbt.adapters.base import BaseAdapter, BaseRelation +from dbt.adapters.capability import Capability from dbt.adapters.events.types import FinishedRunningStats from dbt.adapters.exceptions import MissingMaterializationError from dbt.artifacts.resources import Hook @@ -32,6 +34,7 @@ LogHookStartLine, LogModelResult, LogStartLine, + MicrobatchExecutionDebug, ) from dbt.exceptions import CompilationError, DbtInternalError, DbtRuntimeError from dbt.graph import ResourceTypeSelector @@ -198,21 +201,7 @@ def get_node_representation(self): def describe_node(self) -> str: # TODO CL 'language' will be moved to node level when we change representation - materialization_strategy = self.node.config.get("incremental_strategy") - materialization = ( - "microbatch" - if materialization_strategy == "microbatch" - else self.node.get_materialization() - ) - return f"{self.node.language} {materialization} model {self.get_node_representation()}" - - def describe_batch(self, batch_start: Optional[datetime]) -> str: - # Only visualize date if batch_start year/month/day - formatted_batch_start = MicrobatchBuilder.format_batch_start( - batch_start, self.node.config.batch_size - ) - - return f"batch {formatted_batch_start} of {self.get_node_representation()}" + return f"{self.node.language} {self.node.get_materialization()} model {self.get_node_representation()}" def print_start_line(self): fire_event( @@ -246,59 +235,6 @@ def print_result_line(self, result): level=level, ) - def print_batch_result_line( - self, - result: RunResult, - batch_start: Optional[datetime], - batch_idx: int, - batch_total: int, - exception: Optional[Exception], - ): - description = self.describe_batch(batch_start) - group = group_lookup.get(self.node.unique_id) - if result.status == NodeStatus.Error: - status = result.status - level = EventLevel.ERROR - else: - status = result.message - level = EventLevel.INFO - fire_event( - LogModelResult( - description=description, - status=status, - index=batch_idx, - total=batch_total, - execution_time=result.execution_time, - node_info=self.node.node_info, - group=group, - ), - level=level, - ) - if exception: - fire_event( - GenericExceptionOnRun( - unique_id=self.node.unique_id, - exc=f"Exception on worker thread. {str(exception)}", - node_info=self.node.node_info, - ) - ) - - def print_batch_start_line( - self, batch_start: Optional[datetime], batch_idx: int, batch_total: int - ) -> None: - if batch_start is None: - return - - batch_description = self.describe_batch(batch_start) - fire_event( - LogStartLine( - description=batch_description, - index=batch_idx, - total=batch_total, - node_info=self.node.node_info, - ) - ) - def before_execute(self) -> None: self.print_start_line() @@ -325,81 +261,6 @@ def _build_run_model_result(self, model, context, elapsed_time: float = 0.0): batch_results=None, ) - def _build_run_microbatch_model_result( - self, model: ModelNode, batch_run_results: List[RunResult] - ) -> RunResult: - batch_results = BatchResults() - for result in batch_run_results: - if result.batch_results is not None: - batch_results += result.batch_results - else: - raise DbtInternalError( - "Got a run result without batch results for a batch run, this should be impossible" - ) - - num_successes = len(batch_results.successful) - num_failures = len(batch_results.failed) - - if num_failures == 0: - status = RunStatus.Success - msg = "SUCCESS" - elif num_successes == 0: - status = RunStatus.Error - msg = "ERROR" - else: - status = RunStatus.PartialSuccess - msg = f"PARTIAL SUCCESS ({num_successes}/{num_successes + num_failures})" - - if model.batch_info is not None: - new_batch_results = deepcopy(model.batch_info) - new_batch_results.failed = [] - new_batch_results = new_batch_results + batch_results - else: - new_batch_results = batch_results - - return RunResult( - node=model, - status=status, - timing=[], - thread_id=threading.current_thread().name, - # The execution_time here doesn't get propagated to logs because - # `safe_run_hooks` handles the elapsed time at the node level - execution_time=0, - message=msg, - adapter_response={}, - failures=num_failures, - batch_results=new_batch_results, - ) - - def _build_succesful_run_batch_result( - self, - model: ModelNode, - context: Dict[str, Any], - batch: BatchType, - elapsed_time: float = 0.0, - ) -> RunResult: - run_result = self._build_run_model_result(model, context, elapsed_time) - run_result.batch_results = BatchResults(successful=[batch]) - return run_result - - def _build_failed_run_batch_result( - self, - model: ModelNode, - batch: BatchType, - elapsed_time: float = 0.0, - ) -> RunResult: - return RunResult( - node=model, - status=RunStatus.Error, - timing=[], - thread_id=threading.current_thread().name, - execution_time=elapsed_time, - message="ERROR", - adapter_response={}, - failures=1, - batch_results=BatchResults(failed=[batch]), - ) - def _materialization_relations(self, result: Any, model) -> List[BaseRelation]: if isinstance(result, str): msg = ( @@ -422,6 +283,7 @@ def _execute_model( hook_ctx: Any, context_config: Any, model: ModelNode, + manifest: Manifest, context: Dict[str, Any], materialization_macro: MacroProtocol, ) -> RunResult: @@ -437,28 +299,6 @@ def _execute_model( return self._build_run_model_result(model, context) - def _execute_microbatch_model( - self, - hook_ctx: Any, - context_config: Any, - model: ModelNode, - manifest: Manifest, - context: Dict[str, Any], - materialization_macro: MacroProtocol, - ) -> RunResult: - batch_results = None - try: - batch_results = self._execute_microbatch_materialization( - model, manifest, context, materialization_macro - ) - finally: - self.adapter.post_model_hook(context_config, hook_ctx) - - if batch_results is not None: - return self._build_run_microbatch_model_result(model, batch_results) - else: - return self._build_run_model_result(model, context) - def execute(self, model, manifest): context = generate_runtime_model_context(model, self.config, manifest) @@ -487,18 +327,167 @@ def execute(self, model, manifest): ) hook_ctx = self.adapter.pre_model_hook(context_config) - if ( - model.config.materialized == "incremental" - and model.config.incremental_strategy == "microbatch" - and manifest.use_microbatch_batches(project_name=self.config.project_name) - ): - return self._execute_microbatch_model( - hook_ctx, context_config, model, manifest, context, materialization_macro - ) + + return self._execute_model( + hook_ctx, context_config, model, manifest, context, materialization_macro + ) + + +class MicrobatchModelRunner(ModelRunner): + batch_idx: Optional[int] = None + batches: Dict[int, BatchType] = field(default_factory=dict) + relation_exists: bool = False + + def set_batch_idx(self, batch_idx: int) -> None: + self.batch_idx = batch_idx + + def set_relation_exists(self, relation_exists: bool) -> None: + self.relation_exists = relation_exists + + def set_batches(self, batches: Dict[int, BatchType]) -> None: + self.batches = batches + + def describe_node(self) -> str: + return f"{self.node.language} microbatch model {self.get_node_representation()}" + + def describe_batch(self, batch_start: Optional[datetime]) -> str: + # Only visualize date if batch_start year/month/day + formatted_batch_start = MicrobatchBuilder.format_batch_start( + batch_start, self.node.config.batch_size + ) + return f"batch {formatted_batch_start} of {self.get_node_representation()}" + + def print_batch_result_line( + self, + result: RunResult, + ): + if self.batch_idx is None: + return + + batch_start = self.batches[self.batch_idx][0] + description = self.describe_batch(batch_start) + group = group_lookup.get(self.node.unique_id) + if result.status == NodeStatus.Error: + status = result.status + level = EventLevel.ERROR else: - return self._execute_model( - hook_ctx, context_config, model, context, materialization_macro + status = result.message + level = EventLevel.INFO + fire_event( + LogModelResult( + description=description, + status=status, + index=self.batch_idx + 1, + total=len(self.batches), + execution_time=result.execution_time, + node_info=self.node.node_info, + group=group, + ), + level=level, + ) + + def print_batch_start_line(self) -> None: + if self.batch_idx is None: + return + + batch_start = self.batches[self.batch_idx][0] + if batch_start is None: + return + + batch_description = self.describe_batch(batch_start) + fire_event( + LogStartLine( + description=batch_description, + index=self.batch_idx + 1, + total=len(self.batches), + node_info=self.node.node_info, ) + ) + + def before_execute(self) -> None: + if self.batch_idx is None: + self.print_start_line() + else: + self.print_batch_start_line() + + def after_execute(self, result) -> None: + if self.batch_idx is not None: + self.print_batch_result_line(result) + + def merge_batch_results(self, result: RunResult, batch_results: List[RunResult]): + """merge batch_results into result""" + if result.batch_results is None: + result.batch_results = BatchResults() + + for batch_result in batch_results: + if batch_result.batch_results is not None: + result.batch_results += batch_result.batch_results + result.execution_time += batch_result.execution_time + + num_successes = len(result.batch_results.successful) + num_failures = len(result.batch_results.failed) + if num_failures == 0: + status = RunStatus.Success + msg = "SUCCESS" + elif num_successes == 0: + status = RunStatus.Error + msg = "ERROR" + else: + status = RunStatus.PartialSuccess + msg = f"PARTIAL SUCCESS ({num_successes}/{num_successes + num_failures})" + result.status = status + result.message = msg + + result.batch_results.successful = sorted(result.batch_results.successful) + result.batch_results.failed = sorted(result.batch_results.failed) + + # # If retrying, propagate previously successful batches into final result, even thoguh they were not run in this invocation + if self.node.batch_info is not None: + result.batch_results.successful += self.node.batch_info.successful + + def _build_succesful_run_batch_result( + self, + model: ModelNode, + context: Dict[str, Any], + batch: BatchType, + elapsed_time: float = 0.0, + ) -> RunResult: + run_result = self._build_run_model_result(model, context, elapsed_time) + run_result.batch_results = BatchResults(successful=[batch]) + return run_result + + def _build_failed_run_batch_result( + self, + model: ModelNode, + batch: BatchType, + elapsed_time: float = 0.0, + ) -> RunResult: + return RunResult( + node=model, + status=RunStatus.Error, + timing=[], + thread_id=threading.current_thread().name, + execution_time=elapsed_time, + message="ERROR", + adapter_response={}, + failures=1, + batch_results=BatchResults(failed=[batch]), + ) + + def _build_run_microbatch_model_result(self, model: ModelNode) -> RunResult: + return RunResult( + node=model, + status=RunStatus.Success, + timing=[], + thread_id=threading.current_thread().name, + # The execution_time here doesn't get propagated to logs because + # `safe_run_hooks` handles the elapsed time at the node level + execution_time=0, + message="", + adapter_response={}, + failures=0, + batch_results=BatchResults(), + ) def _execute_microbatch_materialization( self, @@ -506,8 +495,7 @@ def _execute_microbatch_materialization( manifest: Manifest, context: Dict[str, Any], materialization_macro: MacroProtocol, - ) -> List[RunResult]: - batch_results: List[RunResult] = [] + ) -> RunResult: microbatch_builder = MicrobatchBuilder( model=model, is_incremental=self._is_incremental(model), @@ -515,28 +503,28 @@ def _execute_microbatch_materialization( event_time_end=getattr(self.config.args, "EVENT_TIME_END", None), default_end_time=self.config.invoked_at, ) - # Indicates whether current batch should be run incrementally - incremental_batch = False - - # Note currently (9/30/2024) model.batch_info is only ever _not_ `None` - # IFF `dbt retry` is being run and the microbatch model had batches which - # failed on the run of the model (which is being retried) - if model.batch_info is None: - end = microbatch_builder.build_end_time() - start = microbatch_builder.build_start_time(end) - batches = microbatch_builder.build_batches(start, end) - else: - batches = model.batch_info.failed - # If there is batch info, then don't run as full_refresh and do force is_incremental - # not doing this risks blowing away the work that has already been done - if self._has_relation(model=model): - incremental_batch = True - # iterate over each batch, calling materialization_macro to get a batch-level run result - for batch_idx, batch in enumerate(batches): - self.print_batch_start_line(batch[0], batch_idx + 1, len(batches)) + if self.batch_idx is None: + # Note currently (9/30/2024) model.batch_info is only ever _not_ `None` + # IFF `dbt retry` is being run and the microbatch model had batches which + # failed on the run of the model (which is being retried) + if model.batch_info is None: + end = microbatch_builder.build_end_time() + start = microbatch_builder.build_start_time(end) + batches = microbatch_builder.build_batches(start, end) + else: + batches = model.batch_info.failed + # If there is batch info, then don't run as full_refresh and do force is_incremental + # not doing this risks blowing away the work that has already been done + if self._has_relation(model=model): + self.relation_exists = True + + batch_result = self._build_run_microbatch_model_result(model) + self.batches = {batch_idx: batches[batch_idx] for batch_idx in range(len(batches))} - exception = None + else: + batch = self.batches[self.batch_idx] + # call materialization_macro to get a batch-level run result start_time = time.perf_counter() try: # Set start/end in context prior to re-compiling @@ -554,7 +542,7 @@ def _execute_microbatch_materialization( ) # Update jinja context with batch context members batch_context = microbatch_builder.build_batch_context( - incremental_batch=incremental_batch + incremental_batch=self.relation_exists ) context.update(batch_context) @@ -569,24 +557,30 @@ def _execute_microbatch_materialization( batch_run_result = self._build_succesful_run_batch_result( model, context, batch, time.perf_counter() - start_time ) + batch_result = batch_run_result + # At least one batch has been inserted successfully! - incremental_batch = True + # Can proceed incrementally + in parallel + self.relation_exists = True except (KeyboardInterrupt, SystemExit): # reraise it for GraphRunnableTask.execute_nodes to handle raise except Exception as e: - exception = e + fire_event( + GenericExceptionOnRun( + unique_id=self.node.unique_id, + exc=f"Exception on worker thread. {str(e)}", + node_info=self.node.node_info, + ) + ) batch_run_result = self._build_failed_run_batch_result( model, batch, time.perf_counter() - start_time ) - self.print_batch_result_line( - batch_run_result, batch[0], batch_idx + 1, len(batches), exception - ) - batch_results.append(batch_run_result) + batch_result = batch_run_result - return batch_results + return batch_result def _has_relation(self, model) -> bool: relation_info = self.adapter.Relation.create_from(self.config, model) @@ -595,6 +589,25 @@ def _has_relation(self, model) -> bool: ) return relation is not None + def _should_run_in_parallel( + self, + relation_exists: bool, + ) -> bool: + if not self.adapter.supports(Capability.MicrobatchConcurrency): + run_in_parallel = False + elif not relation_exists: + # If the relation doesn't exist, we can't run in parallel + run_in_parallel = False + elif self.node.config.concurrent_batches is not None: + # If the relation exists and the `concurrent_batches` config isn't None, use the config value + run_in_parallel = self.node.config.concurrent_batches + else: + # If the relation exists, the `concurrent_batches` config is None, check if the model self references `this`. + # If the model self references `this` then we assume the model batches _can't_ be run in parallel + run_in_parallel = not self.node.has_this + + return run_in_parallel + def _is_incremental(self, model) -> bool: # TODO: Remove. This is a temporary method. We're working with adapters on # a strategy to ensure we can access the `is_incremental` logic without drift @@ -614,6 +627,37 @@ def _is_incremental(self, model) -> bool: else: return False + def _execute_microbatch_model( + self, + hook_ctx: Any, + context_config: Any, + model: ModelNode, + manifest: Manifest, + context: Dict[str, Any], + materialization_macro: MacroProtocol, + ) -> RunResult: + try: + batch_result = self._execute_microbatch_materialization( + model, manifest, context, materialization_macro + ) + finally: + self.adapter.post_model_hook(context_config, hook_ctx) + + return batch_result + + def _execute_model( + self, + hook_ctx: Any, + context_config: Any, + model: ModelNode, + manifest: Manifest, + context: Dict[str, Any], + materialization_macro: MacroProtocol, + ) -> RunResult: + return self._execute_microbatch_model( + hook_ctx, context_config, model, manifest, context, materialization_macro + ) + class RunTask(CompileTask): def __init__( @@ -639,6 +683,70 @@ def get_hook_sql(self, adapter, hook, idx, num_hooks, extra_context) -> str: hook_obj = get_hook(statement, index=hook_index) return hook_obj.sql or "" + def handle_job_queue(self, pool, callback): + node = self.job_queue.get() + self._raise_set_error() + runner = self.get_runner(node) + # we finally know what we're running! Make sure we haven't decided + # to skip it due to upstream failures + if runner.node.unique_id in self._skipped_children: + cause = self._skipped_children.pop(runner.node.unique_id) + runner.do_skip(cause=cause) + + if isinstance(runner, MicrobatchModelRunner): + callback(self.handle_microbatch_model(runner, pool)) + else: + args = [runner] + self._submit(pool, args, callback) + + def handle_microbatch_model( + self, + runner: MicrobatchModelRunner, + pool: ThreadPool, + ) -> RunResult: + # Initial run computes batch metadata + result = self.call_runner(runner) + batch_results: List[RunResult] = [] + + # Execute batches serially until a relation exists, at which point future batches are run in parallel + relation_exists = runner.relation_exists + batch_idx = 0 + while batch_idx < len(runner.batches): + batch_runner = MicrobatchModelRunner( + self.config, runner.adapter, deepcopy(runner.node), self.run_count, self.num_nodes + ) + batch_runner.set_batch_idx(batch_idx) + batch_runner.set_relation_exists(relation_exists) + batch_runner.set_batches(runner.batches) + + if runner._should_run_in_parallel(relation_exists): + fire_event( + MicrobatchExecutionDebug( + msg=f"{batch_runner.describe_batch} is being run concurrently" + ) + ) + self._submit(pool, [batch_runner], batch_results.append) + else: + fire_event( + MicrobatchExecutionDebug( + msg=f"{batch_runner.describe_batch} is being run sequentially" + ) + ) + batch_results.append(self.call_runner(batch_runner)) + relation_exists = batch_runner.relation_exists + + batch_idx += 1 + + # Wait until all batches have completed + while len(batch_results) != len(runner.batches): + pass + + runner.merge_batch_results(result, batch_results) + track_model_run(runner.node_index, runner.num_nodes, result, adapter=runner.adapter) + runner.print_result_line(result) + + return result + def _hook_keyfunc(self, hook: HookNode) -> Tuple[str, Optional[int]]: package_name = hook.package_name if package_name == self.config.project_name: @@ -832,8 +940,18 @@ def get_node_selector(self) -> ResourceTypeSelector: resource_types=[NodeType.Model], ) - def get_runner_type(self, _) -> Optional[Type[BaseRunner]]: - return ModelRunner + def get_runner_type(self, node) -> Optional[Type[BaseRunner]]: + if self.manifest is None: + raise DbtInternalError("manifest must be set prior to calling get_runner_type") + + if ( + node.config.materialized == "incremental" + and node.config.incremental_strategy == "microbatch" + and self.manifest.use_microbatch_batches(project_name=self.config.project_name) + ): + return MicrobatchModelRunner + else: + return ModelRunner def task_end_messages(self, results) -> None: if results: diff --git a/core/dbt/task/runnable.py b/core/dbt/task/runnable.py index 492fafff84d..b889558d048 100644 --- a/core/dbt/task/runnable.py +++ b/core/dbt/task/runnable.py @@ -232,7 +232,12 @@ def call_runner(self, runner: BaseRunner) -> RunResult: ) try: result = runner.run_with_hooks(self.manifest) + except (KeyboardInterrupt, SystemExit) as exe: + result = None + thread_exception: Union[KeyboardInterrupt, SystemExit, Exception] = exe + raise except Exception as e: + result = None thread_exception = e finally: if result is not None: diff --git a/core/setup.py b/core/setup.py index b7345621b3e..62b7383a150 100644 --- a/core/setup.py +++ b/core/setup.py @@ -72,7 +72,7 @@ "dbt-semantic-interfaces>=0.7.4,<0.8", # Minor versions for these are expected to be backwards-compatible "dbt-common>=1.13.0,<2.0", - "dbt-adapters>=1.9.0,<2.0", + "dbt-adapters>=1.10.1,<2.0", # ---- # Expect compatibility with all new versions of these packages, so lower bounds only. "packaging>20.9", diff --git a/schemas/dbt/manifest/v12.json b/schemas/dbt/manifest/v12.json index 4d66f65f234..8a43f2e978b 100644 --- a/schemas/dbt/manifest/v12.json +++ b/schemas/dbt/manifest/v12.json @@ -13,7 +13,7 @@ }, "dbt_version": { "type": "string", - "default": "1.9.0b2" + "default": "1.9.0b4" }, "generated_at": { "type": "string" @@ -446,6 +446,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "delimiter": { "type": "string", "default": "," @@ -1113,6 +1116,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -1499,6 +1505,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -2944,6 +2953,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -3747,6 +3759,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "access": { "enum": [ "private", @@ -4675,6 +4690,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -5115,6 +5133,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -6622,6 +6643,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "strategy": { "anyOf": [ { @@ -7542,6 +7566,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -10337,6 +10364,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "delimiter": { "type": "string", "default": "," @@ -11004,6 +11034,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -11390,6 +11423,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -12835,6 +12871,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -13638,6 +13677,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "access": { "enum": [ "private", @@ -14566,6 +14608,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -15006,6 +15051,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -16513,6 +16561,9 @@ "event_time": { "default": null }, + "concurrent_batches": { + "default": null + }, "strategy": { "anyOf": [ { @@ -17433,6 +17484,9 @@ }, "event_time": { "default": null + }, + "concurrent_batches": { + "default": null } }, "additionalProperties": true @@ -22648,4 +22702,4 @@ "unit_tests" ], "$id": "https://schemas.getdbt.com/dbt/manifest/v12.json" -} +} \ No newline at end of file diff --git a/tests/functional/artifacts/expected_manifest.py b/tests/functional/artifacts/expected_manifest.py index f8091c1ddd2..aafe093d86e 100644 --- a/tests/functional/artifacts/expected_manifest.py +++ b/tests/functional/artifacts/expected_manifest.py @@ -43,6 +43,7 @@ def get_rendered_model_config(**updates): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, } result.update(updates) return result @@ -82,6 +83,7 @@ def get_rendered_seed_config(**updates): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, } result.update(updates) return result @@ -133,6 +135,7 @@ def get_rendered_snapshot_config(**updates): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, } result.update(updates) return result diff --git a/tests/functional/list/test_list.py b/tests/functional/list/test_list.py index 0775687788c..5442436271e 100644 --- a/tests/functional/list/test_list.py +++ b/tests/functional/list/test_list.py @@ -90,6 +90,7 @@ def expect_snapshot_output(self, happy_path_project): # noqa: F811 "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "unique_id": "snapshot.test.my_snapshot", "original_file_path": normalize("snapshots/snapshot.sql"), @@ -136,6 +137,7 @@ def expect_analyses_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "unique_id": "analysis.test.a", "original_file_path": normalize("analyses/a.sql"), @@ -201,6 +203,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/ephemeral.sql"), "unique_id": "model.test.ephemeral", @@ -243,6 +246,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/incremental.sql"), "unique_id": "model.test.incremental", @@ -285,6 +289,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/sub/inner.sql"), "unique_id": "model.test.inner", @@ -327,6 +332,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/metricflow_time_spine.sql"), "unique_id": "model.test.metricflow_time_spine", @@ -369,6 +375,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/metricflow_time_spine_second.sql"), "unique_id": "model.test.metricflow_time_spine_second", @@ -411,6 +418,7 @@ def expect_model_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "original_file_path": normalize("models/outer.sql"), "unique_id": "model.test.outer", @@ -534,6 +542,7 @@ def expect_seed_output(self): "lookback": 1, "batch_size": None, "begin": None, + "concurrent_batches": None, }, "depends_on": {"macros": []}, "unique_id": "seed.test.seed", diff --git a/tests/functional/microbatch/test_microbatch.py b/tests/functional/microbatch/test_microbatch.py index 0ef12367a07..c9b9db2ff4e 100644 --- a/tests/functional/microbatch/test_microbatch.py +++ b/tests/functional/microbatch/test_microbatch.py @@ -1,10 +1,14 @@ from unittest import mock import pytest +from pytest_mock import MockerFixture from dbt.events.types import ( + ArtifactWritten, + EndOfRunSummary, GenericExceptionOnRun, LogModelResult, + MicrobatchExecutionDebug, MicrobatchMacroOutsideOfBatchesDeprecation, MicrobatchModelNoEventTimeInputs, ) @@ -259,12 +263,14 @@ def assert_row_count(self, project, relation_name: str, expected_row_count: int) class TestMicrobatchCLI(BaseMicrobatchTest): + CLI_COMMAND_NAME = "run" + def test_run_with_event_time(self, project): # run without --event-time-start or --event-time-end - 3 expected rows in output catcher = EventCatcher(event_to_catch=LogModelResult) with patch_microbatch_end_time("2020-01-03 13:57:00"): - run_dbt(["run"], callbacks=[catcher.catch]) + run_dbt([self.CLI_COMMAND_NAME], callbacks=[catcher.catch]) self.assert_row_count(project, "microbatch_model", 3) assert len(catcher.caught_events) == 5 @@ -280,7 +286,7 @@ def test_run_with_event_time(self, project): # build model between 2020-01-02 >= event_time < 2020-01-03 run_dbt( [ - "run", + self.CLI_COMMAND_NAME, "--event-time-start", "2020-01-02", "--event-time-end", @@ -291,6 +297,10 @@ def test_run_with_event_time(self, project): self.assert_row_count(project, "microbatch_model", 1) +class TestMicrobatchCLIBuild(TestMicrobatchCLI): + CLI_COMMAND_NAME = "build" + + class TestMicroBatchBoundsDefault(BaseMicrobatchTest): def test_run_with_event_time(self, project): # initial run -- backfills all data @@ -791,3 +801,75 @@ def test_microbatch(self, project) -> None: # they should have the same last batch because they are using the _same_ "current_time" assert microbatch_model_last_batch == second_microbatch_model_last_batch + + +class TestMicrobatchModelStoppedByKeyboardInterrupt(BaseMicrobatchTest): + @pytest.fixture + def catch_eors(self) -> EventCatcher: + return EventCatcher(EndOfRunSummary) + + @pytest.fixture + def catch_aw(self) -> EventCatcher: + return EventCatcher( + event_to_catch=ArtifactWritten, + predicate=lambda event: event.data.artifact_type == "RunExecutionResult", + ) + + def test_microbatch( + self, + mocker: MockerFixture, + project, + catch_eors: EventCatcher, + catch_aw: EventCatcher, + ) -> None: + mocked_fbs = mocker.patch( + "dbt.materializations.incremental.microbatch.MicrobatchBuilder.format_batch_start" + ) + mocked_fbs.side_effect = KeyboardInterrupt + try: + run_dbt(["run"], callbacks=[catch_eors.catch, catch_aw.catch]) + assert False, "KeyboardInterrupt failed to stop batch execution" + except KeyboardInterrupt: + assert len(catch_eors.caught_events) == 1 + assert "Exited because of keyboard interrupt" in catch_eors.caught_events[0].info.msg + assert len(catch_aw.caught_events) == 1 + + +class TestMicrobatchCanRunParallelOrSequential(BaseMicrobatchTest): + @pytest.fixture + def batch_exc_catcher(self) -> EventCatcher: + return EventCatcher(MicrobatchExecutionDebug) # type: ignore + + def test_microbatch( + self, mocker: MockerFixture, project, batch_exc_catcher: EventCatcher + ) -> None: + mocked_srip = mocker.patch("dbt.task.run.MicrobatchModelRunner._should_run_in_parallel") + + # Should be run in parallel + mocked_srip.return_value = True + with patch_microbatch_end_time("2020-01-03 13:57:00"): + _ = run_dbt(["run"], callbacks=[batch_exc_catcher.catch]) + + assert len(batch_exc_catcher.caught_events) > 1 + some_batches_run_concurrently = False + for caugh_event in batch_exc_catcher.caught_events: + if "is being run concurrently" in caugh_event.data.msg: # type: ignore + some_batches_run_concurrently = True + break + assert some_batches_run_concurrently, "Found no batches being run concurrently!" + + # reset caught events + batch_exc_catcher.caught_events = [] + + # Should _not_ run in parallel + mocked_srip.return_value = False + with patch_microbatch_end_time("2020-01-03 13:57:00"): + _ = run_dbt(["run"], callbacks=[batch_exc_catcher.catch]) + + assert len(batch_exc_catcher.caught_events) > 1 + some_batches_run_concurrently = False + for caugh_event in batch_exc_catcher.caught_events: + if "is being run concurrently" in caugh_event.data.msg: # type: ignore + some_batches_run_concurrently = True + break + assert not some_batches_run_concurrently, "Found a batch being run concurrently!" diff --git a/tests/unit/clients/test_jinja_static.py b/tests/unit/clients/test_jinja_static.py index 42264f24331..fb48c117b47 100644 --- a/tests/unit/clients/test_jinja_static.py +++ b/tests/unit/clients/test_jinja_static.py @@ -2,6 +2,7 @@ from dbt.artifacts.resources import RefArgs from dbt.clients.jinja_static import ( + statically_extract_has_name_this, statically_extract_macro_calls, statically_parse_ref_or_source, statically_parse_unrendered_config, @@ -118,3 +119,18 @@ class TestStaticallyParseUnrenderedConfig: def test_statically_parse_unrendered_config(self, expression, expected_unrendered_config): unrendered_config = statically_parse_unrendered_config(expression) assert unrendered_config == expected_unrendered_config + + +@pytest.mark.parametrize( + "raw_code,expected_result", + [ + ("{{ this }}", True), + ("{{ this.variable }}", True), + ("{{ log(this) }}", True), + ("{{ some_other_this }}", False), + ("this", False), + ("{{ some_object.this }}", False), + ], +) +def test_statically_extract_has_name_this(raw_code: str, expected_result: bool) -> None: + assert statically_extract_has_name_this(raw_code) == expected_result diff --git a/tests/unit/task/test_run.py b/tests/unit/task/test_run.py index 8f239ccfc3a..b33e6f57ffe 100644 --- a/tests/unit/task/test_run.py +++ b/tests/unit/task/test_run.py @@ -1,7 +1,5 @@ -import threading from argparse import Namespace from dataclasses import dataclass -from datetime import datetime, timedelta from importlib import import_module from typing import Optional, Type, Union from unittest import mock @@ -14,11 +12,10 @@ from dbt.adapters.contracts.connection import AdapterResponse from dbt.adapters.postgres import PostgresAdapter from dbt.artifacts.resources.base import FileHash -from dbt.artifacts.resources.types import BatchSize, NodeType, RunHookType +from dbt.artifacts.resources.types import NodeType, RunHookType from dbt.artifacts.resources.v1.components import DependsOn from dbt.artifacts.resources.v1.config import NodeConfig from dbt.artifacts.resources.v1.model import ModelConfig -from dbt.artifacts.schemas.batch_results import BatchResults from dbt.artifacts.schemas.results import RunStatus from dbt.artifacts.schemas.run import RunResult from dbt.config.runtime import RuntimeConfig @@ -27,8 +24,7 @@ from dbt.events.types import LogModelResult from dbt.exceptions import DbtRuntimeError from dbt.flags import get_flags, set_from_args -from dbt.materializations.incremental.microbatch import MicrobatchBuilder -from dbt.task.run import ModelRunner, RunTask, _get_adapter_info +from dbt.task.run import MicrobatchModelRunner, ModelRunner, RunTask, _get_adapter_info from dbt.tests.util import safe_set_invocation_context from dbt_common.events.base_types import EventLevel from dbt_common.events.event_manager_client import add_callback_to_manager @@ -161,50 +157,23 @@ def test_execute( model_runner.execute(model=table_model, manifest=manifest) # TODO: Assert that the model was executed - def test__build_run_microbatch_model_result( - self, table_model: ModelNode, model_runner: ModelRunner - ) -> None: - batch = (datetime.now() - timedelta(days=1), datetime.now()) - only_successes = [ - RunResult( - node=table_model, - status=RunStatus.Success, - timing=[], - thread_id=threading.current_thread().name, - execution_time=0, - message="SUCCESS", - adapter_response={}, - failures=0, - batch_results=BatchResults(successful=[batch]), - ) - ] - only_failures = [ - RunResult( - node=table_model, - status=RunStatus.Error, - timing=[], - thread_id=threading.current_thread().name, - execution_time=0, - message="ERROR", - adapter_response={}, - failures=1, - batch_results=BatchResults(failed=[batch]), - ) - ] - mixed_results = only_failures + only_successes - expect_success = model_runner._build_run_microbatch_model_result( - table_model, only_successes - ) - expect_error = model_runner._build_run_microbatch_model_result(table_model, only_failures) - expect_partial_success = model_runner._build_run_microbatch_model_result( - table_model, mixed_results +class TestMicrobatchModelRunner: + @pytest.fixture + def model_runner( + self, + postgres_adapter: PostgresAdapter, + table_model: ModelNode, + runtime_config: RuntimeConfig, + ) -> MicrobatchModelRunner: + return MicrobatchModelRunner( + config=runtime_config, + adapter=postgres_adapter, + node=table_model, + node_index=1, + num_nodes=1, ) - assert expect_success.status == RunStatus.Success - assert expect_error.status == RunStatus.Error - assert expect_partial_success.status == RunStatus.PartialSuccess - @pytest.mark.parametrize( "has_relation,relation_type,materialized,full_refresh_config,full_refresh_flag,expectation", [ @@ -228,7 +197,7 @@ def test__build_run_microbatch_model_result( def test__is_incremental( self, mocker: MockerFixture, - model_runner: ModelRunner, + model_runner: MicrobatchModelRunner, has_relation: bool, relation_type: str, materialized: str, @@ -266,32 +235,52 @@ class Relation: # Assert result of _is_incremental assert model_runner._is_incremental(model) == expectation - def test_keyboard_breaks__execute_microbatch_materialization( + @pytest.mark.parametrize( + "adapter_microbatch_concurrency,has_relation,concurrent_batches,has_this,expectation", + [ + (True, True, None, False, True), + (True, True, None, True, False), + (True, True, True, False, True), + (True, True, True, True, True), + (True, True, False, False, False), + (True, True, False, True, False), + (True, False, None, False, False), + (True, False, None, True, False), + (True, False, True, False, False), + (True, False, True, True, False), + (True, False, False, False, False), + (True, False, False, True, False), + (False, True, None, False, False), + (False, True, None, True, False), + (False, True, True, False, False), + (False, True, True, True, False), + (False, True, False, False, False), + (False, True, False, True, False), + (False, False, None, False, False), + (False, False, None, True, False), + (False, False, True, False, False), + (False, False, True, True, False), + (False, False, False, False, False), + (False, False, False, True, False), + ], + ) + def test__should_run_in_parallel( self, - table_model: ModelNode, - manifest: Manifest, - model_runner: ModelRunner, + mocker: MockerFixture, + model_runner: MicrobatchModelRunner, + adapter_microbatch_concurrency: bool, + has_relation: bool, + concurrent_batches: Optional[bool], + has_this: bool, + expectation: bool, ) -> None: - def mock_build_batch_context(*args, **kwargs): - raise KeyboardInterrupt("Test exception") - - def mock_is_incremental(*args, **kwargs): - return True - - table_model.config.materialized = "incremental" - table_model.config.incremental_strategy = "microbatch" - table_model.config.batch_size = BatchSize.day - - with patch.object( - MicrobatchBuilder, "build_batch_context", mock_build_batch_context - ), patch.object(ModelRunner, "_is_incremental", mock_is_incremental): - try: - model_runner._execute_microbatch_materialization( - table_model, manifest, {}, MagicMock() - ) - assert False, "KeybaordInterrupt failed to escape" - except KeyboardInterrupt: - assert True + model_runner.node._has_this = has_this + model_runner.node.config = ModelConfig(concurrent_batches=concurrent_batches) + mocked_supports = mocker.patch.object(model_runner.adapter, "supports") + mocked_supports.return_value = adapter_microbatch_concurrency + + # Assert result of _should_run_in_parallel + assert model_runner._should_run_in_parallel(has_relation) == expectation class TestRunTask: diff --git a/tests/unit/test_events.py b/tests/unit/test_events.py index d6454da213c..038a228022c 100644 --- a/tests/unit/test_events.py +++ b/tests/unit/test_events.py @@ -421,6 +421,7 @@ def test_event_codes(self): core_types.SnapshotTimestampWarning( snapshot_time_data_type="DATETIME", updated_at_data_type="DATETIMEZ" ), + core_types.MicrobatchExecutionDebug(msg=""), # W - Node testing ====================== core_types.CatchableExceptionOnRun(exc=""), core_types.InternalErrorOnRun(build_path="", exc=""),