def update_table(table_name: str = None,
job_group: str = None,
parallel_jobs: int = 8) -> Response:
table_name = _get_request_param("table", table_name)
job_group = _get_request_param("job_group", job_group)
process_count = _get_request_param("parallel_jobs", parallel_jobs)
# Default to 1 if invalid process count is given
process_count = safe_int_cast(process_count) or 1
# Early exit: table name not found
if table_name not in list(get_table_names()):
return Response(f"Invalid table name {table_name}", status=400)
with temporary_directory() as workdir:
(workdir / "snapshot").mkdir(parents=True, exist_ok=True)
(workdir / "intermediate").mkdir(parents=True, exist_ok=True)
# Load the pipeline configuration given its name
pipeline_name = table_name.replace("-", "_")
data_pipeline = DataPipeline.load(pipeline_name)
# Limit the sources to only the job_group provided
if job_group is not None:
data_pipeline.data_sources = [
data_source for data_source in data_pipeline.data_sources
if data_source.config.get("automation", {}).get("job_group") ==
job_group
]
# Early exit: job group contains no data sources
if not data_pipeline.data_sources:
return Response(
f"No data sources matched job group {job_group} for table {table_name}",
status=400,
)
# Log the data sources being extracted
data_source_names = [
src.config.get("name") for src in data_pipeline.data_sources
]
logger.log_info(f"Updating data sources: {data_source_names}")
# When running the data pipeline, use as many parallel processes as allowed and avoid
# downloading files multiple times.
run_options = dict(process_count=process_count, skip_existing=True)
# Produce the intermediate files from the data source
intermediate_results = data_pipeline.parse(workdir, **run_options)
data_pipeline._save_intermediate_results(workdir / "intermediate",
intermediate_results)
intermediate_files = list(
map(str, (workdir / "intermediate").glob("*.csv")))
logger.log_info(f"Created intermediate tables: {intermediate_files}")
# Upload results to the test bucket because these are not prod files
upload_folder(GCS_BUCKET_TEST, "snapshot", workdir / "snapshot")
upload_folder(GCS_BUCKET_TEST, "intermediate",
workdir / "intermediate")
return Response("OK", status=200)
def publish_sources(prod_folder: str = "v3") -> Response:
"""Publishes a table with the source of each datapoint."""
prod_folder = _get_request_param("prod_folder", prod_folder)
with temporary_directory() as workdir:
# Get the data sources and write a JSON file summarizing them to disk
metadata = create_metadata_dict()
with open(workdir / "metadata.json", "w") as fh:
json.dump(metadata, fh)
# Iterate over the individual tables and build their sources file
# TODO: create source map for all tables, not just a hand-picked subset
for table_name in ("epidemiology", "hospitalizations", "vaccinations",
"by-age"):
data_sources = metadata["sources"]
pipeline = DataPipeline.load(table_name.replace("-", "_"))
source_map = map_table_sources_to_index(data_sources,
pipeline,
prod_folder=prod_folder)
output_table_sources(source_map,
workdir / f"{table_name}.sources.csv")
# Upload to root folder
upload_folder(GCS_BUCKET_PROD, prod_folder + "/", workdir)
return Response("OK", status=200)
def _test_data_source(pipeline_name: DataPipeline,
data_source_idx: DataSource,
random_seed: int = 0):
# Re-load the data pipeline and data source
# It seems inefficient but it's necessary because we can't move these objects across
# processes
pipeline = DataPipeline.load(pipeline_name)
data_source = pipeline.data_sources[data_source_idx]
# Replace the error logging function to keep logs cleaner during tests
data_source.log_error = _log_nothing
data_source.log_warning = _log_nothing
# Load the real cache files
cache = requests.get("{}/sitemap.json".format(CACHE_URL)).json()
data_source_name = data_source.__class__.__name__
data_source_opts = data_source.config
if data_source_opts.get("test", {}).get("skip"):
return
# Make a copy of all auxiliary files
aux = {
name: table.copy()
for name, table in pipeline.auxiliary_tables.items()
}
# If we have a hint for the expected keys, use only those from metadata
metadata_query = data_source_opts.get("test", {}).get("metadata_query")
if metadata_query:
aux["metadata"] = aux["metadata"].query(metadata_query)
# Get a small sample of metadata, since we are testing for whether a source produces
# _any_ output, not if the output is exhaustive
sample_size = min(len(aux["metadata"]), METADATA_SAMPLE_SIZE)
aux["metadata"] = aux["metadata"].sample(sample_size,
random_state=random_seed)
# Build the failure message to log the config of this data source
failure_message = (
f"{data_source_name} from {pipeline_name} pipeline failed with options {data_source_opts} "
f"and using metadata keys {aux['metadata']['key'].values.tolist()}")
# Use a different temporary working directory for each data source
with temporary_directory() as workdir:
(workdir / "snapshot").mkdir(parents=True, exist_ok=True)
try:
output_data = data_source.run(workdir, cache, aux)
except Exception as exc:
traceback.print_exc()
raise RuntimeError(failure_message)
# Run our battery of tests against the output data to ensure it looks correct
# Data source has at least one row in output
assert len(output_data) >= 1, failure_message
def combine_table(table_name: str = None) -> Response:
table_name = _get_request_param("table", table_name)
logger.log_info(f"Combining data sources for {table_name}")
# Early exit: table name not found
if table_name not in list(get_table_names()):
return Response(f"Invalid table name {table_name}", status=400)
with temporary_directory() as workdir:
(workdir / "tables").mkdir(parents=True, exist_ok=True)
# Load the pipeline configuration given its name
pipeline_name = table_name.replace("-", "_")
data_pipeline = DataPipeline.load(pipeline_name)
# Get a list of the intermediate files used by this data pipeline
intermediate_file_names = []
for data_source in data_pipeline.data_sources:
intermediate_file_names.append(
f"{data_source.uuid(data_pipeline.table)}.csv")
logger.log_info(
f"Downloading intermediate tables {intermediate_file_names}")
# Download only the necessary intermediate files
download_folder(
GCS_BUCKET_TEST,
"intermediate",
workdir / "intermediate",
lambda x: x.name in intermediate_file_names,
)
# Re-load all intermediate results
intermediate_results = data_pipeline._load_intermediate_results(
workdir / "intermediate")
logger.log_info(
f"Loaded intermediate tables {intermediate_file_names}")
# Limit the number of processes to avoid OOM in big datasets
process_count = 4
# Combine all intermediate results into a single dataframe
pipeline_output = data_pipeline.combine(intermediate_results,
process_count=process_count)
logger.log_info(f"Combined intermediate tables into {table_name}")
# Output combined data to disk
output_path = workdir / "tables" / f"{table_name}.csv"
export_csv(pipeline_output, output_path, schema=data_pipeline.schema)
logger.log_info(f"Exported combined {table_name} to CSV")
# Upload results to the test bucket because these are not prod files
# They will be copied to prod in the publish step, so main.csv is in sync
logger.log_info(f"Uploading combined {table_name}...")
upload_folder(GCS_BUCKET_TEST, "tables", workdir / "tables")
return Response("OK", status=200)
def _test_data_pipeline(pipeline_name: str, random_seed: int = 0):
# Load the data pipeline to get the number of data sources
data_pipeline = DataPipeline.load(pipeline_name)
# Load the data pipeline, iterate over each data source and run it to get its output
pipeline_count = len(data_pipeline.data_sources)
map_func = partial(_test_data_source, pipeline_name, random_seed=random_seed)
_ = thread_map(map_func, range(pipeline_count), total=pipeline_count, max_workers=4)
# Consume the results
list(_)
def update_table(table_name: str = None, job_group: int = None) -> Response:
table_name = _get_request_param("table", table_name)
job_group = _get_request_param("job_group", job_group)
# Early exit: table name not found
if table_name not in list(get_table_names()):
return Response(f"Invalid table name {table_name}", status=400)
with temporary_directory() as workdir:
(workdir / "snapshot").mkdir(parents=True, exist_ok=True)
(workdir / "intermediate").mkdir(parents=True, exist_ok=True)
# Load the pipeline configuration given its name
pipeline_name = table_name.replace("-", "_")
data_pipeline = DataPipeline.load(pipeline_name)
# Limit the sources to only the job_group provided
if job_group is not None:
data_pipeline.data_sources = [
data_source for data_source in data_pipeline.data_sources
if data_source.config.get("automation", {}).get("job_group") ==
job_group
]
# Early exit: job group contains no data sources
if not data_pipeline.data_sources:
return Response(
f"No data sources matched job group {job_group} for table {table_name}",
status=400,
)
# Log the data sources being extracted
data_source_names = [
src.config.get("name") for src in data_pipeline.data_sources
]
logger.log_info(f"Updating data sources: {data_source_names}")
# Produce the intermediate files from the data source
intermediate_results = data_pipeline.parse(workdir, process_count=1)
data_pipeline._save_intermediate_results(workdir / "intermediate",
intermediate_results)
# Upload results to the test bucket because these are not prod files
upload_folder(GCS_BUCKET_TEST, "snapshot", workdir / "snapshot")
upload_folder(GCS_BUCKET_TEST, "intermediate",
workdir / "intermediate")
return Response("OK", status=200)
def combine_table(table_name: str = None) -> Response:
table_name = _get_request_param("table", table_name)
# Early exit: table name not found
if table_name not in list(get_table_names()):
return Response(f"Invalid table name {table_name}", status=400)
with TemporaryDirectory() as output_folder:
output_folder = Path(output_folder)
(output_folder / "tables").mkdir(parents=True, exist_ok=True)
# Load the pipeline configuration given its name
pipeline_name = table_name.replace("-", "_")
data_pipeline = DataPipeline.load(pipeline_name)
# Get a list of the intermediate files used by this data pipeline
intermediate_file_names = []
for data_source in data_pipeline.data_sources:
intermediate_file_names.append(f"{data_source.uuid(data_pipeline.table)}.csv")
# Download only the necessary intermediate files
download_folder(
GCS_BUCKET_TEST,
"intermediate",
output_folder / "intermediate",
lambda x: x.name in intermediate_file_names,
)
# Re-load all intermediate results
intermediate_results = data_pipeline._load_intermediate_results(
output_folder / "intermediate"
)
# Combine all intermediate results into a single dataframe
pipeline_output = data_pipeline.combine(intermediate_results)
# Output combined data to disk
export_csv(
pipeline_output,
output_folder / "tables" / f"{table_name}.csv",
schema=data_pipeline.schema,
)
# Upload results to the test bucket because these are not prod files
# They will be copied to prod in the publish step, so main.csv is in sync
upload_folder(GCS_BUCKET_TEST, "tables", output_folder / "tables")
return Response("OK", status=200)
def update_table(table_name: str = None, job_group: int = None) -> str:
table_name = table_name or request.args.get("table")
assert table_name in list(get_table_names())
try:
job_group = request.args.get("job_group")
except:
pass
with TemporaryDirectory() as output_folder:
output_folder = Path(output_folder)
(output_folder / "snapshot").mkdir(parents=True, exist_ok=True)
(output_folder / "intermediate").mkdir(parents=True, exist_ok=True)
# Load the pipeline configuration given its name
pipeline_name = table_name.replace("-", "_")
data_pipeline = DataPipeline.load(pipeline_name)
# Limit the sources to only the job_group provided
if job_group is not None:
data_pipeline.data_sources = [
data_source for data_source in data_pipeline.data_sources
if data_source.config.get("automation", {}).get("job_group") ==
job_group
]
assert (
data_pipeline.data_sources
), f"No data sources matched job group {job_group} for table {table_name}"
# Log the data sources being extracted
data_source_names = [
src.config.get("name") for src in data_pipeline.data_sources
]
print(f"Data sources: {data_source_names}")
# Produce the intermediate files from the data source
intermediate_results = data_pipeline.parse(output_folder,
process_count=1)
data_pipeline._save_intermediate_results(
output_folder / "intermediate", intermediate_results)
# Upload results to the test bucket because these are not prod files
upload_folder(GCS_BUCKET_TEST, "snapshot", output_folder / "snapshot")
upload_folder(GCS_BUCKET_TEST, "intermediate",
output_folder / "intermediate")
return "OK"
def get_source_configs(pipeline_names: List[str]) -> Iterator[Dict]:
"""Map a list of pipeline names to their source configs."""
for pipeline_name in pipeline_names:
data_pipeline = DataPipeline.load(pipeline_name)
for data_source in data_pipeline.data_sources:
data_source_config = data_source.config
data_source_name = data_source_config.get("name")
data_source_fetch_params = data_source_config.get("fetch", [])
for fetch_param in data_source_fetch_params:
file_ext = fetch_param.get("opts", {}).get("ext", "")
data_source_url = fetch_param.get("url")
yield {
"pipeline_name": pipeline_name,
"source_name": data_source_name,
"url": data_source_url,
"ext": file_ext,
}
# Create a schema to output integers and avoid pandas converting to floating point
schema_map = dict(date="str", location_key="str")
schema = {col: schema_map.get(col, "int") for col in source_table.columns}
# Output the table at the requested location
export_csv(source_table, output_path, schema)
if __name__ == "__main__":
# To authenticate with Cloud locally, run the following commands:
# > $env:GOOGLE_CLOUD_PROJECT = "github-open-covid-19"
# > $env:GCS_SERVICE_ACCOUNT = "*****@*****.**"
# > $env:GCP_TOKEN = $(gcloud auth application-default print-access-token)
# Create the output directory where the sources will go
output_directory = SRC / ".." / "output" / "sources"
output_directory.mkdir(exist_ok=True, parents=True)
# Get the data sources and write a JSON file summarizing them to disk
metadata = create_metadata_dict()
with open(output_directory / "metadata.json", "w") as fh:
json.dump(metadata, fh)
# Iterate over the individual tables and build their sources file
for table_name in ("epidemiology", "hospitalizations", "vaccinations",
"by-age"):
pipeline = DataPipeline.load(table_name.replace("-", "_"))
source_map = map_table_sources_to_index(metadata["sources"], pipeline)
output_table_sources(source_map,
output_directory / f"{table_name}.sources.csv")
def main(
output_folder: Path,
verify: str = None,
only: List[str] = None,
exclude: List[str] = None,
process_count: int = cpu_count(),
show_progress: bool = True,
) -> None:
"""
Executes the data pipelines and places all outputs into `output_folder`. This is typically
followed by publishing of the contents of the output folder to a server.
Args:
output_folder: Root folder where snapshot, intermediate and tables will be placed.
verify: Run anomaly detection on the outputs using this strategy. Value must be one of:
- None: (default) perform no anomaly detection
- "simple": perform only fast anomaly detection
- "full": perform exhaustive anomaly detection (can be very slow)
only: If provided, only pipelines with a name appearing in this list will be run.
exclude: If provided, pipelines with a name appearing in this list will not be run.
process_count: Maximum number of processes to use during the data pipeline execution.
show_progress: Display progress for the execution of individual DataSources within this
pipeline.
"""
assert not (only is not None and exclude is not None
), "--only and --exclude options cannot be used simultaneously"
# Ensure that there is an output folder to put the data in
(output_folder / "snapshot").mkdir(parents=True, exist_ok=True)
(output_folder / "intermediate").mkdir(parents=True, exist_ok=True)
(output_folder / "tables").mkdir(parents=True, exist_ok=True)
# A pipeline chain is any subfolder not starting with "_" in the pipelines folder
all_pipeline_names = []
for item in (ROOT / "src" / "pipelines").iterdir():
if not item.name.startswith("_") and not item.is_file():
all_pipeline_names.append(item.name)
# Verify that all of the provided pipeline names exist as pipelines
for pipeline_name in (only or []) + (exclude or []):
module_name = pipeline_name.replace("-", "_")
assert module_name in all_pipeline_names, f'"{pipeline_name}" pipeline does not exist'
# Run all the pipelines and place their outputs into the output folder
# The output name for each pipeline chain will be the name of the directory that the chain is in
for pipeline_name in all_pipeline_names:
table_name = pipeline_name.replace("_", "-")
# Skip if `exclude` was provided and this table is in it
if exclude is not None and table_name in exclude:
continue
# Skip is `only` was provided and this table is not in it
if only is not None and not table_name in only:
continue
data_pipeline = DataPipeline.load(pipeline_name)
pipeline_output = data_pipeline.run(
pipeline_name,
output_folder,
verify=verify,
process_count=process_count,
progress=show_progress,
)
export_csv(pipeline_output,
output_folder / "tables" / f"{table_name}.csv")
def main(
output_folder: Path,
verify: str = None,
only: List[str] = None,
exclude: List[str] = None,
location_key: str = None,
strict_match: bool = False,
process_count: int = cpu_count(),
skip_download: bool = False,
) -> None:
"""
Executes the data pipelines and places all outputs into `output_folder`. This is typically
followed by publishing of the contents of the output folder to a server.
Args:
output_folder: Root folder where snapshot, intermediate and tables will be placed.
verify: Run anomaly detection on the outputs using this strategy. Value must be one of:
- None: (default) perform no anomaly detection
- "simple": perform only fast anomaly detection
- "full": perform exhaustive anomaly detection (can be very slow)
only: If provided, only pipelines with a name appearing in this list will be run.
exclude: If provided, pipelines with a name appearing in this list will not be run.
location_key: If present, only run data sources which output data for this location.
strict_match: In combination with `location_key`, filter data to only output `location_key`.
process_count: Maximum number of processes to use during the data pipeline execution.
skip_download: Skip downloading data sources if a cached version is available.
"""
assert not (only is not None and exclude is not None
), "--only and --exclude options cannot be used simultaneously"
# Ensure that there is an output folder to put the data in
(output_folder / "snapshot").mkdir(parents=True, exist_ok=True)
(output_folder / "intermediate").mkdir(parents=True, exist_ok=True)
(output_folder / "tables").mkdir(parents=True, exist_ok=True)
# A pipeline chain is any subfolder not starting with "_" in the pipelines folder
all_pipeline_names = []
for item in (SRC / "pipelines").iterdir():
if not item.name.startswith("_") and not item.is_file():
all_pipeline_names.append(item.name)
# Verify that all of the provided pipeline names exist as pipelines
for pipeline_name in (only or []) + (exclude or []):
module_name = pipeline_name.replace("-", "_")
assert module_name in all_pipeline_names, f'"{pipeline_name}" pipeline does not exist'
# Run all the pipelines and place their outputs into the output folder. The output name for
# each pipeline chain will be the name of the directory that the chain is in.
for pipeline_name in all_pipeline_names:
table_name = pipeline_name.replace("_", "-")
# Skip if `exclude` was provided and this table is in it
if exclude is not None and table_name in exclude:
continue
# Skip is `only` was provided and this table is not in it
if only is not None and not table_name in only:
continue
# Load data pipeline and get rid of data sources if requested
data_pipeline = DataPipeline.load(pipeline_name)
if location_key is not None:
exprs = [
src.config.get("test", {}).get("location_key_match", ".*")
for src in data_pipeline.data_sources
]
exprs = [
expr if isinstance(expr, list) else [expr] for expr in exprs
]
data_pipeline.data_sources = [
src for src, expr in zip(data_pipeline.data_sources, exprs)
if any(re.match(expr_, location_key) for expr_ in expr)
]
# Run the data pipeline to retrieve live data
pipeline_output = data_pipeline.run(
output_folder,
process_count=process_count,
verify_level=verify,
skip_existing=skip_download,
)
# Filter out data output if requested
if location_key is not None and strict_match:
pipeline_output = pipeline_output[pipeline_output["key"] ==
location_key]
# Export the data output to disk as a CSV file
export_csv(
pipeline_output,
output_folder / "tables" / f"{table_name}.csv",
schema=data_pipeline.schema,
)
def get_pipelines() -> Iterator[DataPipeline]:
""" Iterator with all the available data pipelines """
for pipeline_name in get_pipeline_names():
yield DataPipeline.load(pipeline_name)
profiler.enable()
# A pipeline chain is any subfolder not starting with "_" in the pipelines folder
all_pipeline_chains = []
for item in (ROOT / "src" / "pipelines").iterdir():
if not item.name.startswith("_") and not item.is_file():
all_pipeline_chains.append(item.name)
# Run all the pipelines and place their outputs into the output folder
# The output name for each pipeline chain will be the name of the directory that the chain is in
for pipeline_name in all_pipeline_chains:
table_name = pipeline_name.replace("_", "-")
if args.only and not table_name in args.only.split(","):
continue
if args.exclude and table_name in args.exclude.split(","):
continue
pipeline_chain = DataPipeline.load(pipeline_name)
show_progress = not args.no_progress
pipeline_output = pipeline_chain.run(pipeline_name,
verify=args.verify,
process_count=args.process_count,
progress=show_progress)
export_csv(pipeline_output,
ROOT / "output" / "tables" / f"{table_name}.csv")
if args.profile:
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats("cumtime")
stats.print_stats(20)