def euro_exchange_rates_pipeline(db_alias: str):
pipeline = Pipeline(
id="load_euro_exchange_rates",
description=
"Loads daily Euro exchange rates since 1999 from the European central bank",
base_path=pathlib.Path(__file__).parent)
pipeline.add(
Task(id="create_schema_and_table",
description="Re-creates currency exchange rate schema",
commands=[
ExecuteSQL(sql_file_name='create_schema_and_table.sql',
echo_queries=False)
]))
pipeline.add(Task(
id='load_exchange_rate',
description='Loads exchange rates from the European central bank',
commands=[
ReadScriptOutput(file_name='load_exchange_rate.py',
target_table='euro_fx.exchange_rate',
db_alias=db_alias)
]),
upstreams=['create_schema_and_table'])
pipeline.add(Task(id="postprocess_exchange_rate",
description="Adds values for missing days",
commands=[
ExecuteSQL(
sql_file_name='postprocess_exchange_rate.sql',
echo_queries=False)
]),
upstreams=['load_exchange_rate'])
return pipeline
def utils_pipeline(with_hll=False, with_cstore_fdw=False):
pipeline = Pipeline(
id="initialize_utils",
description=
"Creates an utils schema with a number of functions around the ETL best practices of Project A",
base_path=pathlib.Path(__file__).parent)
pipeline.add_initial(
Task(id="create_utils_schema",
description="Re-creates the utils schema",
commands=[
ExecuteSQL(
sql_statement=
"DROP SCHEMA IF EXISTS util CASCADE; CREATE SCHEMA util;")
]))
pipeline.add(
Task(id='chunking',
description='Runs file chunking.sql',
commands=[
ExecuteSQL(sql_file_name='chunking.sql',
echo_queries=False,
replace={
'number_of_chunks':
lambda: config.number_of_chunks()
})
]))
def add_task_for_file(file_name_without_extension):
pipeline.add(
Task(id=file_name_without_extension,
description=f'Runs file "{file_name_without_extension}.sql"',
commands=[
ExecuteSQL(sql_file_name=file_name_without_extension +
'.sql',
echo_queries=False)
]))
for file_name_without_extension in [
'consistency_checks', 'data_sets', 'partitioning',
'indexes_and_constraints', 'schema_switching', 'enums'
]:
add_task_for_file(file_name_without_extension)
if with_hll:
add_task_for_file('hll')
if with_cstore_fdw:
add_task_for_file('cstore_fdw')
return pipeline
def add_task_for_file(file_name_without_extension):
pipeline.add(
Task(id=file_name_without_extension,
description=f'Runs file "{file_name_without_extension}.sql"',
commands=[
ExecuteSQL(sql_file_name=file_name_without_extension +
'.sql',
echo_queries=False)
]))
import mara_mondrian.connection
import mara_schema.config
from mara_pipelines.commands.python import RunFunction
from mara_pipelines.logging import logger
from mara_pipelines.pipelines import Pipeline, Task
import mara_metabase.metadata
pipeline = Pipeline(id="update_frontends",
description="Updates Metabase & Mondrian",
base_path=pathlib.Path(__file__).parent)
pipeline.add(
Task(id='update_metabase_metadata',
description=
'Flushes all field value caches in Metabase and updates metadata',
commands=[RunFunction(mara_metabase.metadata.update_metadata)]))
def write_mondrian_schema():
import mara_mondrian.schema_generation
file_name = pathlib.Path('.mondrian-schema.xml')
logger.log(f'Writing {file_name}', logger.Format.ITALICS)
mara_mondrian.schema_generation.write_mondrian_schema(
file_name=pathlib.Path('.mondrian-schema.xml'),
data_set_tables={
data_set: ('mondrian', data_set.id())
for data_set in mara_schema.config.data_sets()
},
personal_data=False,
description="Create flattened data set tables for various front-ends",
base_path=pathlib.Path(__file__).parent)
from .metabase import pipeline as metabase_pipeline
pipeline.add(metabase_pipeline)
from .mara_data_explorer import pipeline as mara_data_explorer_pipeline
pipeline.add(mara_data_explorer_pipeline)
from .mondrian import pipeline as mondrian_pipeline
pipeline.add(mondrian_pipeline)
pipeline.add_final(
Task(id='replace_schemas',
description='Replaces the frontend schemas with their next versions',
commands=[
ExecuteSQL(sql_file_name='switch_metabase_schema.sql',
db_alias='metabase-data-write'),
ExecuteSQL(
sql_statement=
f"SELECT util.replace_schema('data_sets', 'data_sets_next')",
db_alias='dwh'),
ExecuteSQL(
sql_statement=
f"SELECT util.replace_schema('mondrian', 'mondrian_next')",
db_alias='dwh')
]))
from mara_pipelines import config
pipeline = Pipeline(
id="load_marketing_data",
description=
"Jobs related with loading marketing leads data from the backend database",
max_number_of_parallel_tasks=5,
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "m_data"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the marketing data schema",
commands=[
ExecuteSQL(sql_file_name='../recreate_marketing_data_schema.sql',
file_dependencies=[
pathlib.Path(__file__).parent.parent /
'recreate_marketing_data_schema.sql'
])
]))
tables = ['closed_deal', 'marketing_qualified_lead']
for table in tables:
pipeline.add(
Task(id=f"load_{table}",
description=f'Loads the {table}s from the backend database',
commands=[
ExecuteSQL(sql_file_name=f'{table}/create_{table}_table.sql'),
Copy(sql_statement=f"""
SELECT *
import pathlib
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
pipeline = Pipeline(
id="marketing",
description="Builds the Leads cube based on marketing and e-commerce data",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "m_dim"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the schemas of the pipeline",
commands=[ExecuteSQL(sql_file_name="recreate_schemas.sql")]))
pipeline.add(
Task(id="preprocess_lead",
description="Preprocess the marketing leads",
commands=[ExecuteSQL(sql_file_name="preprocess_lead.sql")]))
pipeline.add(Task(
id="transform_smaller_dimensions",
description="Transform smaller marketing dimensions",
commands=[ExecuteSQL(sql_file_name="transform_smaller_dimensions.sql")]),
upstreams=["preprocess_lead"])
pipeline.add(Task(id="transform_lead",
description="Creates the lead dim table",
commands=[
ExecuteSQL(sql_file_name="transform_lead.sql",
"Creates data set tables for Metabase (completely flattened, without composed metrics, without personal data)",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": 'metabase'})
pipeline.add_initial(
Task(id="initialize_schema",
description="Recreates the metabase schema",
commands=[
ExecuteSQL(sql_statement=f"""
DROP SCHEMA IF EXISTS util CASCADE;
CREATE SCHEMA util;
DROP SCHEMA IF EXISTS metabase_next CASCADE;
CREATE SCHEMA metabase_next;
""",
echo_queries=False,
db_alias='metabase-data-write'),
ExecuteSQL(sql_file_name=str(
initialize_db.pipeline.nodes['initialize_utils'].base_path() /
'schema_switching.sql'),
db_alias='metabase-data-write'),
ExecuteSQL(sql_file_name=str(
initialize_db.pipeline.nodes['initialize_utils'].base_path() /
'cstore_fdw.sql'),
db_alias='metabase-data-write')
]))
for data_set in data_sets():
def query(data_set):
return data_set_sql_query(data_set=data_set,
from mara_pipelines.parallel_tasks.sql import ParallelExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
from etl_tools.create_attributes_table import CreateAttributesTable
pipeline = Pipeline(
id="pypi",
description=
"Builds a PyPI downloads cube using the public PyPi BigQuery data set",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "pypi_dim"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the schemas of the pipeline",
commands=[
ExecuteSQL(sql_file_name='recreate_schemas.sql'),
ExecuteSQL(sql_file_name="create_data_schema.sql",
file_dependencies=["create_data_schema.sql"])
]))
read_download_file_dependencies = [
"create_download_counts_data_table.sql", "create_data_schema.sql"
]
pipeline.add(
ParallelReadFile(
id="read_download_counts",
description="Loads PyPI downloads from pre_downloaded csv files",
file_pattern="*/*/*/pypi/downloads-v2.csv.gz",
read_mode=ReadMode.ONLY_NEW,
compression=Compression.GZIP,
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
from mara_schema.sql_generation import data_set_sql_query, database_identifier
from mara_schema.config import data_sets
pipeline = Pipeline(
id="flatten_data_sets_for_mondrian",
description="Creates data set tables for Mondrian (star schema, without composed metrics, without personal data)",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": 'mondrian'})
pipeline.add_initial(
Task(
id="initialize_schema",
description="Recreates the mondrian schema",
commands=[
ExecuteSQL(sql_statement=f"""
DROP SCHEMA IF EXISTS mondrian_next CASCADE;
CREATE SCHEMA mondrian_next;
""", echo_queries=False)]))
for data_set in data_sets():
pipeline.add(
Task(id=f"flatten_{data_set.id()}_for_mondrian",
description=f'Flattens the "{data_set.name}" data set for best use in Mondrian',
commands=[
ExecuteSQL(f"""
CREATE TABLE mondrian_next.{database_identifier(data_set.name)} AS
{data_set_sql_query(data_set=data_set, pre_computed_metrics=False, human_readable_columns=False, star_schema=True,
personal_data=False, high_cardinality_attributes=False)};
""",
echo_queries=False)]))
import pathlib
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
pipeline = Pipeline(
id="e_commerce",
description="Builds the e-commerce cubes and datasets",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "ec_dim"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the schemas of the pipeline",
commands=[
ExecuteSQL(sql_file_name="recreate_schemas.sql")
]))
pipeline.add(
Task(id="preprocess_customer",
description="Preprocess customers and consolidate the data to a single record per customer with "
"a unique ID",
commands=[
ExecuteSQL(sql_file_name="preprocess_customer.sql")
]))
pipeline.add(
Task(id="preprocess_order",
description="Preprocess orders to get correct unique customer ID",
commands=[
ExecuteSQL(sql_file_name="preprocess_order.sql")
from mara_pipelines.commands.bash import RunBash
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
from mara_singer.commands.singer import SingerTapDiscover
import mara_singer.config
pipeline = Pipeline(id='_singer',
description="Internal Singer.io management pipeline")
for tap_name in mara_singer.config.tap_names():
tap_pipeline = Pipeline(id=tap_name.replace('-', '_'),
description=f'Package {tap_name}')
tap_pipeline.add(
Task(id='discover',
description=f'Reload the {tap_name} catalog',
commands=[SingerTapDiscover(tap_name=tap_name)]))
pipeline.add(tap_pipeline)
def _internal_root_pipeline():
root_pipeline = Pipeline(id='mara_singer',
description='Root pipeline of module mara_singer')
root_pipeline.add(pipeline)
return root_pipeline
def add_parallel_tasks(self, sub_pipeline: Pipeline) -> None:
source_db = mara_db.dbs.db(self.source_db_alias)
target_db = mara_db.dbs.db(self.target_db_alias)
assert (isinstance(source_db, mara_db.dbs.PostgreSQLDB))
assert (isinstance(target_db, mara_db.dbs.PostgreSQLDB))
with mara_db.postgresql.postgres_cursor_context(
self.source_db_alias) as cursor:
pg_version = cursor.connection.server_version
ddl_task = Task(
id='create_tables_and_functions',
description=
'Re-creates the schema, tables structure and functions on the target db',
commands=[
# schema and table structure
bash.RunBash(
command="(echo 'DROP SCHEMA IF EXISTS " +
self.schema_name + " CASCADE;';\\\n" +
" pg_dump --username="******" --host=" +
source_db.host + " --schema=" + self.schema_name +
" --section=pre-data --no-owner --no-privileges " +
source_db.database + ") \\\n" + " | " +
mara_db.shell.query_command(
self.target_db_alias, echo_queries=False) + ' --quiet'
),
# function definitions
bash.RunBash(command=f'''echo "
SELECT CONCAT(pg_get_functiondef(pg_proc.oid),';') AS def
FROM (SELECT oid, prokind, pronamespace
FROM pg_proc p
WHERE {"p.prokind in ('p','f')" if pg_version >= 110000 else "NOT p.proisagg"}) pg_proc, pg_namespace
WHERE pg_proc.pronamespace = pg_namespace.oid
AND nspname = '{self.schema_name}'" \\\n''' + " | " +
mara_db.shell.copy_to_stdout_command(
self.source_db_alias) + ' \\\n' + " | " +
mara_db.shell.query_command(self.target_db_alias,
echo_queries=False))
])
sub_pipeline.add(ddl_task)
# copy content of tables
number_of_chunks = self.max_number_of_parallel_tasks * 3
table_copy_chunks = {i: [] for i in range(0, number_of_chunks)}
current_size_per_table_copy_chunk = [0] * number_of_chunks
table_types = {}
with mara_db.postgresql.postgres_cursor_context(
self.source_db_alias
) as cursor: # type: psycopg2.extensions.cursor
cursor.execute(
"""
SELECT
pg_class.relname AS table,
relkind,
CASE WHEN relkind = 'f'
THEN cstore_table_size(nspname || '.' || relname) * 10 -- cstore tables with similar size take longer to copy
ELSE pg_total_relation_size(pg_class.oid)
END / 1000000.0 AS size
FROM pg_class
JOIN pg_namespace ON pg_namespace.oid = pg_class.relnamespace
WHERE nspname = '""" + self.schema_name +
"""' AND relkind IN ('r', 'f') AND relhassubclass = 'f'
ORDER BY size DESC""")
for table_name, type, size in cursor.fetchall():
smallest_chunk_index = min(
range(len(current_size_per_table_copy_chunk)),
key=current_size_per_table_copy_chunk.__getitem__)
current_size_per_table_copy_chunk[smallest_chunk_index] += size
table_copy_chunks[smallest_chunk_index].append(table_name)
table_types[table_name] = type
copy_tasks = []
for i, tables in table_copy_chunks.items():
if tables:
task = Task(
id=f'copy_tables_{i}',
description='Copies table content to the frontend db',
commands=[
RunBash(
command=
f'echo {shlex.quote(f"COPY {self.schema_name}.{table_name} TO STDOUT")} \\\n'
+ ' | ' +
mara_db.shell.copy_to_stdout_command(
self.source_db_alias) + ' \\\n' + ' | ' +
mara_db.shell.copy_from_stdin_command(
self.target_db_alias,
target_table=
f'{self.schema_name}.{table_name}'))
for table_name in tables
])
copy_tasks.append(task)
sub_pipeline.add(task, upstreams=[ddl_task])
# create indexes
index_chunks = {i: [] for i in range(0, number_of_chunks)}
current_size_per_index_chunk = [0] * number_of_chunks
with mara_db.postgresql.postgres_cursor_context(
self.source_db_alias) as cursor:
cursor.execute("""
SELECT indexdef AS ddl, pg_total_relation_size(pg_class.oid) AS size
FROM pg_class
JOIN pg_namespace ON pg_namespace.oid = pg_class.relnamespace
JOIN pg_indexes ON pg_indexes.indexname = pg_class.relname AND pg_indexes.schemaname = nspname
WHERE nspname = '""" + self.schema_name + """' AND relkind = 'i'
ORDER BY size DESC;""")
for ddl, size in cursor.fetchall():
smallest_chunk_index = min(
range(len(current_size_per_index_chunk)),
key=current_size_per_index_chunk.__getitem__)
current_size_per_index_chunk[smallest_chunk_index] += size
index_chunks[smallest_chunk_index].append(ddl)
for i, index_statements in index_chunks.items():
if index_statements:
index_task = Task(
id=f'add_indexes_{i}',
description='Re-creates indexes on frontend db',
commands=[
ExecuteSQL(sql_statement=statement,
db_alias=self.target_db_alias)
for statement in index_statements
])
sub_pipeline.add(index_task, upstreams=copy_tasks)
from mara_pipelines.pipelines import Pipeline, Task
pipeline = Pipeline(
id="load_ecommerce_data",
description=
"Jobs related with loading e-commerce data from the backend database",
max_number_of_parallel_tasks=5,
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "ec_data"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the e-commerce data schema",
commands=[
ExecuteSQL(sql_file_name='../recreate_ecommerce_data_schema.sql',
file_dependencies=[
pathlib.Path(__file__).parent.parent /
'recreate_ecommerce_data_schema.sql'
])
]))
tables = [
'customer', 'order', 'order_item', 'product',
'product_category_name_translation', 'seller'
]
for table in tables:
pipeline.add(
Task(id=f"load_{table}",
description=f'Loads the {table}s from the backend database',
commands=[
import etl_tools.utils
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.parallel_tasks.sql import ParallelExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
from etl_tools.create_attributes_table import CreateAttributesTable
pipeline = Pipeline(
id="python_projects",
description=
"Combines PyPI downloads and github activities to a Python project activity cube",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "pp_dim"})
pipeline.add_initial(
Task(id="initialize_schemas",
description="Recreates the schemas of the pipeline",
commands=[ExecuteSQL(sql_file_name='recreate_schemas.sql')]))
pipeline.add(
Task(
id="extract_python_repo_activity",
description=
'Extracts activity metrics for github repos that have a corresponding pypi package (by name)',
commands=[
ExecuteSQL(sql_file_name="extract_python_repo_activity.sql")
]))
pipeline.add(ParallelExecuteSQL(
id="transform_python_project_activity",
description=
"Aggregates downloads at project level and combines them with github activity metrics",
from mara_pipelines.config import default_db_alias
pipeline = Pipeline(
id="facebook",
description="Processes the data downloaded from the FacebookAds API",
base_path=pathlib.Path(__file__).parent,
labels={"Schema": "fb_dim"})
pipeline.add_initial(
Task(
id="initialize_schemas",
description="Recreates the tmp and dim_next schemas",
commands=[
ExecuteSQL(
sql_statement=
"DROP SCHEMA IF EXISTS fb_dim_next CASCADE; CREATE SCHEMA fb_dim_next;"
),
ExecuteSQL(sql_file_name="create_data_schema.sql",
echo_queries=False,
file_dependencies=["create_data_schema.sql"]),
ExecuteSQL(sql_file_name="recreate_schemas.sql",
echo_queries=False)
]))
pipeline.add(
Task(id="read_campaign_structure",
description="Loads the adwords campaign structure",
commands=[
ExecuteSQL(
sql_file_name='create_campaign_structure_data_table.sql',
echo_queries=False),
ReadSQLite(
import pathlib
from mara_pipelines.commands.sql import ExecuteSQL
from mara_pipelines.pipelines import Pipeline, Task
from etl_tools import config
pipeline = Pipeline(id="create_time_dimensions",
description="Creates a day and a duration dimension table",
labels={"Schema": "time"},
base_path=pathlib.Path(__file__).parent)
pipeline.add(
Task(id="create_tables",
description="Re-creates the day and duration table and their schema",
commands=[
ExecuteSQL(sql_file_name='create_tables.sql',
echo_queries=False,
file_dependencies=['create_tables.sql'])
]))
pipeline.add(Task(
id="populate_time_dimensions",
description="fills the time dimensions for a configured time range",
commands=[
ExecuteSQL(
sql_statement=lambda: "SELECT time.populate_time_dimensions('" +
config.first_date_in_time_dimensions().isoformat() + "'::DATE, '" +
config.last_date_in_time_dimensions().isoformat() + "'::DATE);")
]),
upstreams=['create_tables'])
import pathlib
from etl_tools import initialize_utils, create_time_dimensions
from mara_pipelines.pipelines import Pipeline, Task
from mara_pipelines.commands.sql import ExecuteSQL
pipeline = Pipeline(
id="initialize_db",
description="Adds a number of utility functions & creates time dimensions",
base_path=pathlib.Path(__file__).parent)
pipeline.add(initialize_utils.utils_pipeline(with_cstore_fdw=True))
pipeline.add(create_time_dimensions.pipeline, upstreams=['initialize_utils'])
pipeline.add(
Task(id='create_read_only_user',
description='Creates a read-only user "dwh_read_only", useful for giving analysts direct access to DWH',
commands=[
ExecuteSQL(sql_file_name='create_read_only_user.sql')
]),
upstreams=['initialize_utils'])
def add_parallel_tasks(self, sub_pipeline: Pipeline) -> None:
attributes_table_name = f'{self.source_schema_name}.{self.source_table_name}{self.attributes_table_suffix}'
ddl = f'''
DROP TABLE IF EXISTS {attributes_table_name};
CREATE TABLE {attributes_table_name} (
attribute TEXT NOT NULL,
value TEXT NOT NULL,
row_count BIGINT NOT NULL
) PARTITION BY LIST (attribute);
'''
commands = []
with mara_db.postgresql.postgres_cursor_context(
self.db_alias) as cursor: # type: psycopg2.extensions.cursor
cursor.execute(
f'''
WITH enums AS (
SELECT DISTINCT
typname,
nspname
FROM pg_type
JOIN pg_enum ON pg_type.oid = pg_enum.enumtypid
JOIN pg_namespace ON pg_type.typnamespace = pg_namespace.oid
)
SELECT column_name
FROM information_schema.columns
LEFT JOIN enums ON udt_schema = enums.nspname AND udt_name = enums.typname
WHERE table_schema = {'%s'}
AND table_name = {'%s'}
AND (data_type IN ('text', 'varchar') OR enums.typname IS NOT NULL);
''', (self.source_schema_name, self.source_table_name))
i = 0
for column_name, in cursor.fetchall():
i += 1
ddl += f"""
CREATE TABLE {attributes_table_name}_{i} PARTITION OF {attributes_table_name} FOR VALUES IN ('{column_name}');
"""
commands.append(
ExecuteSQL(sql_statement=f'''
INSERT INTO {attributes_table_name}_{i}
SELECT '{column_name}', "{column_name}", count(*)
FROM {self.source_schema_name}.{self.source_table_name}
WHERE "{column_name}" IS NOT NULL
GROUP BY "{column_name}"
ORDER BY "{column_name}";
CREATE INDEX {self.source_table_name}_{self.attributes_table_suffix}_{i}__value
ON {attributes_table_name}_{i} USING GIN (value gin_trgm_ops);
''',
echo_queries=False))
sub_pipeline.add_initial(
Task(id='create_table',
description='Creates the attributes table',
commands=[ExecuteSQL(sql_statement=ddl, echo_queries=False)]))
chunk_size = math.ceil(
len(commands) /
(2 * mara_pipelines.config.max_number_of_parallel_tasks()))
for n, chunk in enumerate(more_itertools.chunked(commands,
chunk_size)):
task = Task(id=str(n),
description='Process a portion of the attributes')
task.add_commands(chunk)
sub_pipeline.add(task)
