def run_sigla_pipeline(master_spreadsheet_id: str,
google_api_credentials_path: str,
db_connection_url: str):
"""
Run the SIGLA ETL pipeline
Parameters
----------
google_api_credentials_path: str
The path to Google API credentials file needed to read Google Sheets.
db_connection_url: str
The DB's connection url str.
"""
# Delete all documents from db
_clean_up(db_connection_url)
# Create a connection to the google sheets reader
google_sheets_institution_extracter = GoogleSheetsInstitutionExtracter(
google_api_credentials_path)
# Get the list of spreadsheets ids from the master spreadsheet
spreadsheets_id = google_sheets_institution_extracter.get_spreadsheets_id(
master_spreadsheet_id)
log.info("Finished pipeline set up, start running pipeline")
log.info("=" * 80)
# Spawn local dask cluster
cluster = LocalCluster()
# Log the dashboard link
log.info(f"Dashboard available at: {cluster.dashboard_link}")
# Setup workflow
with Flow("Extract and transform") as flow:
# Extract sheets data.
# Get back list of list of SheetData
spreadsheets_data = _extract.map(
spreadsheets_id,
unmapped(google_api_credentials_path),
)
# Flatten the list of list of SheetData
flattened_spreadsheets_data = _flatten_list(spreadsheets_data)
# Transform list of SheetData into FormattedSheetData
_transform.map(flattened_spreadsheets_data)
# Run the extract and transform flow
state = flow.run(executor=DaskExecutor(cluster.scheduler_address))
formatted_sheets_data = state.result[flow.get_tasks(
name="_transform")[0]].result
log.info("=" * 80)
# Partion into institutions and non-institutions sheets
institutions = _filter_formatted_sheet_data(
formatted_sheets_data,
[
gs_format.standard_institution,
# gs_format.institution_by_rows,
gs_format.multiple_sigla_answer_variable,
],
)
non_institutions = _filter_formatted_sheet_data(
formatted_sheets_data,
[
gs_format.institution_and_composite_variable,
gs_format.composite_variable
],
)
# Run load institutions flow
with Flow("Load institutions") as load_institutions_flow:
_load.map(institutions, unmapped(db_connection_url))
load_institutions_flow.run(
executor=DaskExecutor(cluster.scheduler_address))
log.info("=" * 80)
# Run load non institutions flow
with Flow("Load non institutions") as load_non_institutions_flow:
_load.map(non_institutions, unmapped(db_connection_url))
load_non_institutions_flow.run(
executor=DaskExecutor(cluster.scheduler_address))
def test_no_raise_on_normal_flow(self):
flow = Flow("THIS IS A TEST")
assert healthchecks.environment_dependency_check([flow]) is None
from prefect import Flow, task
# latest.
@task
def extract():
"""Get a list of data"""
return [1, 2, 3]
@task
def transform(data):
"""Multiply the input by 10"""
return [i * 10 for i in data]
@task
def load(data):
"""Print the data to indicate it was received"""
print("Here's your data: {}".format(data))
with Flow("sample_workflow") as flow:
e = extract()
t = transform(e)
l = load(t)
# This would equivalent to:
# covid-world-scraper --cache-dir=$PWD/covid-cache bra
print(f"Processing {country}")
runner.run(cache_dir=cache_dir, headless_status=True, filter=[country])
@task(name="list_countries")
def list_countries():
"""Return list of all countries available, which will be mapped to the
process data function (to run in parallel if possible).
"""
runner = Runner(alert_manager=None)
return [x.split(' ')[0] for x in runner.list_countries()]
with Flow("dask-example") as flow:
cache_dir = create_cachedir()
countries = list_countries()
process_data.map(country=countries, cache_dir=unmapped(cache_dir))
# Run and visualize the flow!
flow.register(project_name="Covid-Data-Scraper")
flow_state = flow.run(executor=executor)
# GraphViz is required for this visualization
try:
flow.visualize(flow_state=flow_state)
except Exception as err:
print(err)
def run_qa_test(
db_connection_url: str,
google_api_credentials_path: str,
master_spreadsheet_id: Optional[str] = None,
spreadsheet_ids_str: Optional[str] = None,
):
"""
Run QA test
Parameters
----------
master_spreadsheet_id: str
The master spreadsheet id.
db_connection_url: str
The DB's connection url str.
google_api_credentials_path: str
The path to Google API credentials file needed to read Google Sheets.
spreadsheet_ids_str: Optional[str] = None
The list of spreadsheet ids.
"""
cluster = LocalCluster()
# Log the dashboard link
log.info(f"Dashboard available at: {cluster.dashboard_link}")
# Setup workflow
with Flow("QA Test") as flow:
# get a list of spreadsheet ids
spreadsheet_ids = _get_spreadsheet_ids(master_spreadsheet_id,
google_api_credentials_path,
spreadsheet_ids_str)
# list of list of db institutions
db_institutions_data = _gather_db_institutions.map(
spreadsheet_ids, unmapped(db_connection_url))
# db institutions with their db variables and composite variable data
db_institutions = _gather_db_variables.map(
flatten(db_institutions_data), unmapped(db_connection_url))
# group db institutions
db_institutions_group = _group_db_institutions(db_institutions)
# extract list of list of sheet data
spreadsheets_data = _extract.map(spreadsheet_ids,
unmapped(google_api_credentials_path))
# transform to list of formatted sheet data
formatted_spreadsheets_data = _transform.map(
flatten(spreadsheets_data))
# create institutional filter
gs_institution_filter = _create_filter_task([
GoogleSheetsFormat.standard_institution,
GoogleSheetsFormat.multiple_sigla_answer_variable,
GoogleSheetsFormat.institution_and_composite_variable,
])
# filter to list of institutional formatted sheet data
gs_institutions_data = gs_institution_filter(
formatted_spreadsheets_data)
# get list of list of gs institution
gs_institutions = _gather_gs_institutions.map(gs_institutions_data)
# create composite filter
gs_composite_filter = _create_filter_task([
GoogleSheetsFormat.composite_variable,
GoogleSheetsFormat.institution_and_composite_variable,
])
# filter to list of composite formatted sheet data
gs_composites = gs_composite_filter(formatted_spreadsheets_data)
# group gs institutions
gs_institutions_group = _group_gs_institutions(
flatten(gs_institutions))
# compare gs institutions against db
# get list of comparisons
gs_institution_comparisons = _compare_gs_institution.map(
flatten(gs_institutions), unmapped(db_institutions_group))
# compare gs composite variables against db institutions
# get list of list of comparisons
gs_composite_comparisons = _compare_gs_composite_variable.map(
gs_composites, unmapped(db_connection_url))
# write gs institution comparisons
_write_comparison.map(gs_institution_comparisons)
# write gs composite comparisons
_write_comparison.map(flatten(gs_composite_comparisons))
# write extra db institution
_write_extra_db_institutions(db_institutions, gs_institutions_group)
# Run the flow
state = flow.run(executor=DaskExecutor(cluster.scheduler_address))
if state.is_failed():
raise PrefectFlowFailure(ErrorInfo({"flow_name": flow.name}))
# get write comparison tasks
_write_comparison_tasks = flow.get_tasks(name="_write_comparison")
# get the comparisons
comparisons = [
*state.result[_write_comparison_tasks[0]].result,
*state.result[_write_comparison_tasks[1]].result,
]
# filter to error comparisons
gs_error_comparisons = [
comparison for comparison in comparisons if comparison.has_error()
]
# get extra db institution filename
extra_db_institutions_filename = state.result[flow.get_tasks(
name="_write_extra_db_institutions")[0]].result
# write zip file
with ZipFile("qa-test.zip", "w") as zip_file:
for comp in gs_error_comparisons:
zip_file.write(
comp.get_filename(),
f"{comp.spreadsheet_title}/{comp.sheet_title},{comp.name}",
)
if extra_db_institutions_filename:
zip_file.write(extra_db_institutions_filename,
"extra-institutions.csv")
retry_delay=timedelta(minutes=1),
nout=2,
trigger=triggers.all_finished,
)
def create_parquet(_success):
ts = prefect.context.scheduled_start_time
dt_str = pd.to_datetime(ts).strftime("%Y-%m-%dT%H")
vintage_fn = FN_STR.format(dt_str) + ".parquet"
fn = FN_STR.format("") + ".parquet"
df = pd.read_csv(CSV_FN, parse_dates=["dt"])
df.to_parquet(DATA_PATH / vintage_fn, index=False)
df.to_parquet(DATA_PATH / fn, index=False)
return vintage_fn, fn
@task
def get_gcs_cmd(fn):
return f"gsutil acl ch -u AllUsers:R gs://can-scrape-outputs/final/{fn}"
shell = ShellTask()
with Flow("UpdateParquetFiles", CronSchedule("10 */2 * * *")) as f:
connstr = EnvVarSecret("COVID_DB_CONN_URI")
success = export_to_csv(connstr)
vintage_fn, fn = create_parquet(success)
shell(get_gcs_cmd(vintage_fn))
shell(get_gcs_cmd(fn))
f.register(project_name="can-scrape")
] top_name = 'SramMinionRTL' clk_period = 2.0 #ns #------------------------------------------------------------------------- # Instantiate step instances #------------------------------------------------------------------------- block = Block( files, top_name, clk_period=clk_period, name='block' ) getadk = ADK( adk_config, name='adk' ) vcd2saif = Vcd2Saif( name='vcd2saif' ) synth = Synthesis( name='synth' ) pnr = PlaceAndRoute( name='pnr', aspect_ratio=0.6, core_density=0.65 ) pwr = PowerAnalysis( name='pwr' ) summary = Summary( name='summary' ) memgen = MemGen( name='memgen', inst_name='SRAM_32x128_1rw', word_size=32, num_words=128 ) #------------------------------------------------------------------------- # Instantiate the flow #------------------------------------------------------------------------- with Flow( 'ece5745-tut8-sram' ) as flow: sram = memgen() adk = getadk() rtl = block() saif = vcd2saif( vcd=rtl ) netlist = synth( adk=adk, verilog=rtl, saif=saif, macro=sram ) pnr_res = pnr( adk=adk, netlist=netlist, macro=sram ) pwr_rpt = pwr( adk=adk, saif=saif, namemap=netlist, verilog=pnr_res ) summary( post_pnr_reports=pnr_res, power_reports=pwr_rpt, saif=saif )
# create task templates
upload_template = GCSUpload(bucket=BUCKET,
blob=BLOB,
credentials_secret="GCS_CREDS")
download_template = GCSDownload(bucket=BUCKET,
blob=BLOB,
credentials_secret="GCS_CREDS")
copy_template = GCSCopy(
source_bucket=BUCKET,
source_blob=BLOB,
dest_bucket=BUCKET,
credentials_secret="GCS_CREDS",
)
with Flow("GCS Example") as flow:
# upload with default settings
upl = upload_template(data=DATA)
dwl = download_template(upstream_tasks=[upl])
# upload to new blob and download it
upl_new = upload_template(data=DATA, blob="another/blob")
dwl_new = download_template(blob=upl_new)
# copy the default blob twice
cp_1 = copy_template(dest_blob="yet/another/blob", upstream_tasks=[upl])
cp_2 = copy_template(source_blob=cp_1, dest_blob="one/last/blob")
# download the new blob
dwl_new = download_template(blob=cp_2)
def test_shell_task_raises_fail_if_cmd_fails():
with Flow(name="test") as f:
task = ShellTask()(command="ls surely_a_dir_that_doesnt_exist")
out = f.run()
assert out.is_failed()
assert "Command failed with exit code" in str(out.result[task].message)
def test_nested_collections(self, val):
with Flow("test") as f:
task = tasks.as_task(val)
f.add_task(task)
assert f.run().result[task].result == val
from prefect import Task, Flow, task
# @task
# def do_something():
# print("something")
class DoSomething(Task):
def run(self):
return "asdf"
class PrintVal(Task):
def run(self, val):
print(val)
do_something = DoSomething()
print_val = PrintVal()
flow = Flow("demo")
print_val.set_upstream(do_something, key="val", flow=flow)
flow.run()
def test_nested_collections_of_mixed_constants_are_not_constants(
self, val):
with Flow("test"):
task = tasks.as_task(val)
assert not isinstance(task, Constant)
def test_as_task_toggles_constants(self):
with Flow("test"):
t = tasks.as_task(4)
assert isinstance(t, Task)
assert t.name == "4"
def test_tasks_have_all_non_unmapped_constant_args_as_transitive_upstream_deps(
self, ):
def func(a, b, c, d):
m = inc.copy(name="m").bind(1)
n = inc.copy(name="n").bind(a)
o = inc.copy(name="o").bind(b)
p = add.copy(name="p").bind(n, o)
q = add.copy(name="q").bind(c, d)
r = add.copy(name="r").bind(q, m)
return m, n, o, p, q, r
with Flow("test") as flow:
a = ranged.copy(name="a").bind(3)
b = inc.copy(name="b").bind(1)
c = Constant(1, name="c")
d = Constant(range(3), name="d")
m, n, o, p, q, r = apply_map(func,
a,
edges.unmapped(b),
c=edges.unmapped(c),
d=d)
def edge_info(task):
"""Returns a map of {upstream: (is_data_dep, is_mapped)}"""
return {
e.upstream_task: (e.key is not None, e.mapped)
for e in flow.edges_to(task)
}
assert edge_info(m) == {
a: (False, True),
b: (False, False),
d: (False, True)
}
assert edge_info(n) == {
a: (True, True),
b: (False, False),
d: (False, True)
}
assert edge_info(o) == {
a: (False, True),
b: (True, False),
d: (False, True)
}
assert edge_info(p) == {n: (True, True), o: (True, True)}
assert edge_info(q) == {
a: (False, True),
b: (False, False),
d: (True, True)
}
assert edge_info(r) == {q: (True, True), m: (True, True)}
state = flow.run()
res = {t: state.result[t].result for t in [m, n, o, p, q, r]}
sol = {
m: [2, 2, 2],
n: [1, 2, 3],
o: [3, 3, 3],
p: [4, 5, 6],
q: [1, 2, 3],
r: [3, 4, 5],
}
assert res == sol
import prefect
from prefect import task, Flow
# from prefect.executors import LocalDaskExecutor
@task
def print_contexts():
print("foo = " + prefect.context.foo)
with Flow("test") as flow:
t1 = print_contexts()
t2 = print_contexts()
t1.set_downstream(t2)
# flow.executor = LocalDaskExecutor()
with prefect.context(foo="foo") as ctx:
flow.run(task_contexts=ctx)
def test_shell_initializes_with_basic_cmd():
with Flow(name="test") as f:
task = ShellTask(command="echo -n 'hello world'")()
out = f.run()
assert out.is_successful()
assert out.result[task].result == "hello world"
from prefect import task, Flow
@task
def say_hello():
print("Hello, world!")
with Flow("My First Flow") as flow:
say_hello()
flow.run() # "Hello, world!"
def test_shell_initializes_and_multiline_output_returns_last_line():
with Flow(name="test") as f:
task = ShellTask()(command="echo -n 'hello world\n42'")
out = f.run()
assert out.is_successful()
assert out.result[task].result == "42"
# Configurable value taken at runtime
length = Parameter(name="length", default=5, required=False)
# ETL Pipeline Tasks
@task(result=LocalResult(), target="{date:%A}/{task_name}.prefect")
def extract(length):
# Extract the data
return sample(range(100), length)
@task(max_retries=3, retry_delay=timedelta(seconds=5))
def transform(data):
# Transform the data
return data * 10
@task(trigger=some_successful(at_least=1, at_most=6))
def load(data):
# Load the data
print(f"\nHere's your data: {data}")
# Define Tasks in a Flow Context
with Flow('Evolving ETL', result=S3Result(bucket="flow-result-storage")) as flow:
with case(length, 5):
e = extract(length)
with case(length, 50):
e = extract(length)
t = transform.map(e)
l = load(t)
flow.run(parameters={'length': 50}) # Prints data
def test_shell_returns_none_if_empty_output():
with Flow(name="test") as f:
task = ShellTask()(command="ls > /dev/null")
out = f.run()
assert out.is_successful()
assert out.result[task].result is None
from prefect import Task, Flow
from prefect.environments.storage import GitHub
with Flow("hUGe fLow") as flow:
for i in range(2000):
flow.add_task(Task(name=f"{i}"))
flow.storage = GitHub(
repo="znicholasbrown/project-schematics",
path="flows/hUGe_fLow.py",
secrets=["GITHUB_AUTH_TOKEN"],
)
flow.register(project_name="Dev Straining")
def test_shell_initializes_and_multiline_output_optionally_returns_all_lines():
with Flow(name="test") as f:
task = ShellTask(return_all=True)(command="echo -n 'hello world\n42'")
out = f.run()
assert out.is_successful()
assert out.result[task].result == ["hello world", "42"]
def timestamper(task, old_state, new_state):
"""
Task state handler which timestamps new states
and logs the duration between state changes using
the task's logger.
"""
new_state.timestamp = pendulum.now("utc")
if hasattr(old_state, "timestamp"):
duration = (new_state.timestamp - old_state.timestamp).in_seconds()
task.logger.info(
"{} seconds passed in between state transitions".format(duration)
)
return new_state
@task(state_handlers=[timestamper])
def sleeper():
time.sleep(2)
f = Flow("log-task-duration", tasks=[sleeper])
f.run()
# INFO - prefect.FlowRunner | Beginning Flow run for 'log-task-duration'
# INFO - prefect.FlowRunner | Starting flow run.
# INFO - prefect.TaskRunner | Task 'sleeper': Starting task run...
# INFO - prefect.Task | 2 seconds passed in between state transitions
# INFO - prefect.TaskRunner | Task 'sleeper': finished task run for task with final state: 'Success'
# INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
def test_shell_raises_if_no_command_provided():
with Flow(name="test") as f:
ShellTask()()
with pytest.raises(TypeError):
with raise_on_exception():
assert f.run()
return response.text
@task
def load_file(filename: str) -> str:
with open(filename, "r", encoding="utf-8") as file:
return file.read()
@task
def printa(stuff):
print(stuff)
task = ShellTask(return_all=True)
with Flow("shell") as f:
translation_server_url = "http://localhost:1969"
bibtex = load_file("./workspace/aksw-short.bib")
zotero = import_translation(bibtex, translation_server_url)
rdf = export_translation(zotero, translation_server_url,
"rdf_bibliontology")
turtle = task(command="rapper - -o turtle -I www.test.com > tests.ttl")
printa(turtle)
f.run_config = DockerRun(image="prefecthq/prefect")
f.register(project_name="tutoriala")
# Configure extra environment variables for this flow,
# and set a custom image
# f.run()
def test_shell_runs_other_shells():
with Flow(name="test") as f:
task = ShellTask(shell="zsh")(command="echo -n $ZSH_NAME")
out = f.run()
assert out.is_successful()
assert out.result[task].result == "zsh"
from prefect import Flow
from prefect.backend import FlowView
from prefect.run_configs import UniversalRun
from prefect.storage import Local
FLOW_DATA_1 = {
"id": "id-1",
"name": "name-1",
"settings": {
"key-1": "value-1"
},
"run_config": UniversalRun(env={
"ENV-1": "VAL-1"
}).serialize(),
"serialized_flow": Flow("flow-1").serialize(),
"archived": False,
"project": {
"name": "project-1"
},
"flow_group": {
"labels": ["label-1"]
},
"core_version": "0.0.0",
"storage": Local(stored_as_script=True, path="fake-path-1.py").serialize(),
}
FLOW_DATA_2 = {
"id": "id-2",
"name": "name-2",
"settings": {
from prefect import task, Flow, Parameter
def run(flow, **parameters):
state = flow.run(**parameters)
terminal_task = list(flow.terminal_tasks())[0]
return state.result[terminal_task].result
with Flow('Add one') as flow:
result = Parameter('x') + 1
assert run(flow, x=1) == 2
assert run(flow, x=2) == 3
assert run(flow, x=-100) == -99
with Flow('Add x and y') as flow:
result = Parameter('x') + Parameter('y')
assert run(flow, x=1, y=1) == 2
assert run(flow, x=40, y=2) == 42
def flow(source: str,
relpath: str,
convert: bool = True,
output_dir: str = '/tmp/otpev',
version: str = '20.06',
created: Optional[str] = None,
n_partitions: Optional[int] = None) -> Flow:
"""Get OTP evidence import flow
Parameters
----------
source : str
OTP evidence source (e.g. eva, l2g, uniprot)
relpath : str
Path relative from `gs://open-targets-data-releases/$VERSION/input/evidence-files` to
data file or directory (e.g. "progeny-2018-07-23.json.gz" or "evidences_protein_fix/chembl_dataset")
output_dir : str
Directory in which temporary json/parquet files are stored
version : str
OTP release version
created: str, optional
Date at which OTP version was created. This should NOT
be a time at which data was collected -- it is intended to
reflect when OT created the release and should never change
for the same `version`. For this reason, `created` will
default to known release dates (see `OT_VERSION_RELEASE_DATES`).
n_partitions: int, optional
Number of partitions used to write parquet result.
Set as None to use default partitioning.
Raises
------
KeyError
If `created` is not provided and no known release date
was previously recorded for the specified `version`
Returns
-------
Flow
Prefect Flow
"""
version = str(version)
if created is None:
if version not in OT_VERSION_RELEASE_DATES:
raise KeyError(
f'No release date known for version "{version}" '
'(pass `created` explicitly or add date to `OT_VERSION_RELEASE_DATES`)'
)
created = OT_VERSION_RELEASE_DATES[version]
output_dir = Path(output_dir) / source
if not output_dir.exists():
output_dir.mkdir(parents=True, exist_ok=True)
is_file = relpath.endswith("json.gz")
src_url = OT_URL_FMT.format(version=version) + f'/{relpath.lstrip("/")}'
entry = get_entry(source,
version,
created,
format='parquet' if is_file else 'json.gz',
type='file' if is_file else 'directory',
properties=None if is_file else dict(compression='gzip'))
catalog_path = catalog.default_urlpath()
with Flow(f'otpev-{source}-v{version}') as flow:
# Add constants with important to DAG (all others are not visualized)
catalog_path = constant(catalog_path, name='catalog_path')
dst_url = next(iter(entry.resources.values()))
entry = constant(entry,
name=f'entry.key={entry_key_str(entry.key)}',
value=False)
n_partitions = constant(n_partitions, name='n_partitions')
if is_file:
filename = src_url.split('/')[-1]
src_url = constant(src_url, name='src_url')
dst_url = constant(dst_url, name='dst_url')
# Download and convert to parquet
json_path = constant(str(output_dir / filename), name='json_path')
parquet_path = constant(str(output_dir / filename.split('.')[0]) +
'.parquet',
name='parquet_path')
json_path = download(src_url, json_path)
info = convert_to_parquet(json_path,
parquet_path,
n_partitions=n_partitions)
# Upload results
# pylint:disable=unexpected-keyword-arg
status = upload(entry,
parquet_path,
dst_url,
upstream_tasks=[info])
add_entry(entry, info, catalog_path, upstream_tasks=[status])
else:
raise NotImplementedError(
'Integration of data directories (rather than single files) not yet implemented'
)
return flow
from prefect import Flow, task
from prefect.engine.results import LocalResult
@task
def a():
return None
@task
def b(foo):
print("noooo")
print(foo)
# return 1/0
result = LocalResult(location="{flow_name}/"
"{scheduled_start_time:%d-%m_%H-%M-%S}/"
"{task_full_name}-{task_run_id}.prefect_result", )
with Flow(
name="results_issue",
result=result,
storage=prefect.environments.storage.Local(
stored_as_script=True,
path="/Users/josh/Desktop/code/Dummy-Flows/res_issue.py"),
) as flow:
a = a()
b = b(a)
# flow.register("Demo")