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add example scripts
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AstrakhantsevaAA committed Oct 26, 2023
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2 changes: 2 additions & 0 deletions docs/examples/chess_production/.dlt/secrets.toml
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[runtime]
slack_incoming_hook=""
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153 changes: 153 additions & 0 deletions docs/examples/chess_production/chess.py
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import threading
from typing import Any, Iterator

import dlt
from dlt.common import sleep
from dlt.common.runtime.slack import send_slack_message
from dlt.common.typing import StrAny, TDataItems
from dlt.sources.helpers.requests import client

@dlt.source
def chess(
chess_url: str = dlt.config.value,
title: str = "GM",
max_players: int = 2,
year: int = 2022,
month: int = 10,
) -> Any:
def _get_data_with_retry(path: str) -> StrAny:
r = client.get(f"{chess_url}{path}")
return r.json() # type: ignore

@dlt.resource(write_disposition="replace")
def players() -> Iterator[TDataItems]:
# return players one by one, you could also return a list
# that would be faster but we want to pass players item by item to the transformer
yield from _get_data_with_retry(f"titled/{title}")["players"][:max_players]

# this resource takes data from players and returns profiles
# it uses `defer` decorator to enable parallel run in thread pool.
# defer requires return at the end so we convert yield into return (we return one item anyway)
# you can still have yielding transformers, look for the test named `test_evolve_schema`
@dlt.transformer(data_from=players, write_disposition="replace")
@dlt.defer
def players_profiles(username: Any) -> TDataItems:
print(
f"getting {username} profile via thread {threading.current_thread().name}"
)
sleep(1) # add some latency to show parallel runs
return _get_data_with_retry(f"player/{username}")

# this resource takes data from players and returns games for the last month
# if not specified otherwise
@dlt.transformer(data_from=players, write_disposition="append")
def players_games(username: Any) -> Iterator[TDataItems]:
# https://api.chess.com/pub/player/{username}/games/{YYYY}/{MM}
path = f"player/{username}/games/{year:04d}/{month:02d}"
yield _get_data_with_retry(path)["games"]

return players(), players_profiles, players_games


from tenacity import retry, retry_if_exception, stop_after_attempt, wait_exponential

from dlt.pipeline.helpers import retry_load

@retry(
stop=stop_after_attempt(5),
wait=wait_exponential(multiplier=1.5, min=4, max=10),
retry=retry_if_exception(retry_load(("extract", "load"))),
reraise=True,
)
def load_data_with_retry(data):
return pipeline.run(data)


if __name__ == "__main__":
# create dlt pipeline
pipeline = dlt.pipeline(
pipeline_name="chess_pipeline",
destination="duckdb",
dataset_name="chess_data",
full_refresh=True,
)
max_players = 5
# get data for a few famous players
data = chess(chess_url="https://api.chess.com/pub/", max_players=max_players)
load_info = pipeline.run(data)
print(load_info)

# see when load was started
print(f"Pipeline was started: {load_info.started_at}")
# print the information on the first load package and all jobs inside
print(f"First load package info: {load_info.load_packages[0]}")
# print the information on the first completed job in first load package
print(
f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
)


# we reuse the pipeline instance below and load to the same dataset as data
pipeline.run([load_info], table_name="_load_info")
# save trace to destination, sensitive data will be removed
pipeline.run([pipeline.last_trace], table_name="_trace")

# print all the new tables/columns in
for package in load_info.load_packages:
for table_name, table in package.schema_update.items():
print(f"Table {table_name}: {table.get('description')}")
for column_name, column in table["columns"].items():
print(f"\tcolumn {column_name}: {column['data_type']}")

# save the new tables and column schemas to the destination:
table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
pipeline.run(table_updates, table_name="_new_tables")

# check for schema updates:
schema_updates = [p.schema_update for p in load_info.load_packages]
# send notifications if there are schema updates
if schema_updates:
# send notification
send_slack_message(
pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
)

from tenacity import (
Retrying,
retry_if_exception,
stop_after_attempt,
wait_exponential,
)

from dlt.common.runtime.slack import send_slack_message
from dlt.pipeline.helpers import retry_load

try:
for attempt in Retrying(
stop=stop_after_attempt(5),
wait=wait_exponential(multiplier=1.5, min=4, max=10),
retry=retry_if_exception(retry_load(())),
reraise=True,
):
with attempt:
pipeline.run(data)
except Exception:
# we get here after all the retries
raise

load_info_retry = load_data_with_retry(data)

with pipeline.sql_client() as client:
with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
count_client = cursor.fetchone()[0]
if count_client == 0:
print("Warning: No data in players table")
else:
print(f"Players table contains {count_client} rows")

normalize_info = pipeline.last_trace.last_normalize_info
count = normalize_info.row_counts.get("players", 0)
if count == 0:
print("Warning: No data in players table")
else:
print(f"Players table contains {count} rows")

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