Get 30x speedups when reading databases with ConnectorX + Arrow + dlt
Marcin Rudolf,
Co-Founder & CTO
If rust + arrow + duckb is a new data engineering stack, now you can get a feel of it with dlt. We recently added native arrow tables (and panda frames) loading. What it means? You can pass an Arrow table to dlt pipeline.run or pipeline.extract methods, have it normalized, saved to parquet and loaded to your destination.
Here we achieved ~30x speedups when loading data from (local) postgres database using ConnectorX + Arrow compared to SqlAlchemy + json. (both use dlt as an engine, read disclaimer at the end!)
Load postgres table with Arrow
We’ll start with ConnectorX library that creates Arrow tables from SQL queries on most of the popular database engines.
pip install connectorxLib has Rust inside, zero copy extraction and is amazingly fast. We’ll extract and normalize 10 000 000 test rows from local postgresql. The table chat_message looks like Slack messages dump. Messages have unique autoincrement id which we use to load in chunks:
import connectorx as cx
import dlt
from dlt.sources.credentials import ConnectionStringCredentials
def read_sql_x(
conn_str: str
):
# load in chunks by one million
for _id in range(1, 10_000_001, 1_000_000):
table = cx.read_sql(conn_str,
"SELECT * FROM arrow_test_2.chat_message WHERE id BETWEEN %i AND %i" % (_id, _id + 1000000 - 1),
return_type="arrow2",
protocol="binary"
)
yield table
chat_messages = dlt.resource(
read_sql_x,
name="chat_messages"
)("postgresql://loader:loader@localhost:5432/dlt_data")In this demo I just extract and normalize data and skip the loading step.
pipeline = dlt.pipeline(destination="duckdb", full_refresh=True)
# extract first
pipeline.extract(chat_messages)
info = pipeline.normalize()
# print count of items normalized
print(info)
# print the execution trace
print(pipeline.last_trace)Let’s run it:
$ PROGRESS=enlighten python connector_x_speed.py
Items 10000001 [00:00, 241940483.70/s]
Normalized data for the following tables:
- _dlt_pipeline_state: 1 row(s)
- chat_messages: 10000000 row(s)
Run started at 2023-10-23T19:06:55.527176+00:00 and COMPLETED in 16.17 seconds with 2 steps.
Step extract COMPLETED in 16.09 seconds.
Step normalize COMPLETED in 0.08 seconds.Load postgres table with SqlAlchemy
Here’s corresponding code working with SqlAlchemy. We process 10 000 000 rows, yielding in 100k rows packs and normalize to parquet in 3 parallel processes.
from itertools import islice
import dlt
from sqlalchemy import create_engine
CHUNK_SIZE=100000
def read_sql_a(conn_str: str):
engine = create_engine(conn_str)
with engine.connect() as conn:
rows = conn.execution_options(yield_per=CHUNK_SIZE).exec_driver_sql("SELECT * FROM arrow_test_2.chat_message")
while rows_slice := list(islice(map(lambda row: dict(row._mapping), rows), CHUNK_SIZE)):
yield rows_slice
chat_messages = dlt.resource(
read_sql_a,
name="chat_messages",
write_disposition="append",
)("postgresql://loader:loader@localhost:5432/dlt_data")
pipeline = dlt.pipeline(destination="duckdb", full_refresh=True)
# extract first
pipeline.extract(chat_messages)
info = pipeline.normalize(workers=3, loader_file_format="parquet")
print(info)
print(pipeline.last_trace)Let’s run it:
$ PROGRESS=enlighten python sql_alchemy_speed.py
Normalized data for the following tables:
- _dlt_pipeline_state: 1 row(s)
- chat_messages: 10000000 row(s)
Run started at 2023-10-23T19:13:55.898598+00:00 and COMPLETED in 8 minutes and 12.97 seconds with 2 steps.
Step extract COMPLETED in 3 minutes and 32.75 seconds.
Step normalize COMPLETED in 3 minutes and 40.22 seconds.
Normalized data for the following tables:
- _dlt_pipeline_state: 1 row(s)
- chat_messages: 10000000 row(s)Results
So we can see ~30x overall speedup on extract and normalize steps (~16 seconds vs ~8 minutes). The extract step is ~13x faster, while normalize is few thousand times faster. Arrow normalizer is just checking the schemas and moves parquet files around. JSON normalizer is inspecting every row to first infer the schema and then to validate the data.
As the output in both of methods is the same (parquet files) - the actual load step takes the same time in both cases and is not compared. I could easily push the load packages (parquet files) to any of supported destinations
What’s next:
- Reads our docs on Arrow
- Add merge and incremental loads to code above
- I'm on dltHub Slack all the time.
Disclaimers
- Playing field is not level. classical (sql alchemy)
dltrun is processing data row by row, inferring and validating schema. that’s why it so slow. The Arrow version benefits from the fact, that data is already structured in the source. - We load from local database. That means that network roundtrip during extraction is not included. That isolates Arrow speedups well. In case of remote database engine, the speedups will be smaller.
You could optimize extract (both classical and arrow) by reading data from postgres in parallel or use partitions in ConnectorX