Load from Postgres to Postgres faster

info

The source code for this example can be found in our repository at: https://github.com/dlt-hub/dlt/tree/devel/docs/examples/postgres_to_postgres

About this Example

info

Huge shout out to Simon Späti for this example!

This examples shows you how to export and import data from Postgres to Postgres in a fast way with ConnectorX and DuckDB since the default export will generate Insert_statement during the normalization phase, which is super slow for large tables.

As it's an initial load, we create a separate schema with timestamp initially and then replace the existing schema with the new one.

note

This approach is tested and works well for an initial load (--replace), however, the incremental load (--merge) might need some adjustments (loading of load-tables of dlt, setting up first run after an initial load, etc.).

We'll learn:

• How to get arrow tables from connector X and yield them in chunks.
• That merge and incremental loads work with arrow tables.
• How to use DuckDB for a speedy normalization.
• How to use argparse to turn your pipeline script into a CLI.
• How to work with ConnectionStringCredentials spec.

Be aware that you need to define the database credentials in .dlt/secrets.toml or dlt ENVs and adjust the tables names ("table_1" and "table_2").

Install dlt with duckdb as extra, also connectorx, Postgres adapter and progress bar tool:

pip install "dlt[duckdb]" connectorx pyarrow psycopg2-binary alive-progress

Run the example:

python postgres_to_postgres.py --replace

:::warn Attention: There were problems with data type TIME that includes nano seconds. More details in Slack

As well as with installing DuckDB extension (see issue here), that's why I manually installed the postgres_scanner.duckdb_extension in my Dockerfile to load the data into Postgres. :::

Full source code

import argparse
import os
from dlt.common import pendulum
from typing import List

import connectorx as cx
import duckdb
import psycopg2

import dlt
from dlt.sources.credentials import ConnectionStringCredentials

CHUNKSIZE = int(
    os.getenv("CHUNKSIZE", 1000000)
)  # 1 mio rows works well with 1GiB RAM memory (if no parallelism)


def read_sql_x_chunked(conn_str: str, query: str, chunk_size: int = CHUNKSIZE):
    offset = 0
    while True:
        chunk_query = f"{query} LIMIT {chunk_size} OFFSET {offset}"
        data_chunk = cx.read_sql(
            conn_str,
            chunk_query,
            return_type="arrow2",
            protocol="binary",
        )
        yield data_chunk
        if data_chunk.num_rows < chunk_size:
            break  # No more data to read
        offset += chunk_size


@dlt.source(max_table_nesting=0)
def pg_resource_chunked(
    table_name: str,
    primary_key: List[str],
    schema_name: str,
    order_date: str,
    load_type: str = "merge",
    columns: str = "*",
    credentials: ConnectionStringCredentials = None,
):
    print(
        f"dlt.resource write_disposition: `{load_type}` -- ",
        "connection string:"
        f" postgresql://{credentials.username}:*****@{credentials.host}:{credentials.host}/{credentials.database}",
    )

    query = (  # Needed to have an idempotent query
        f"SELECT {columns} FROM {schema_name}.{table_name} ORDER BY {order_date}"
    )

    source = dlt.resource(  # type: ignore
        name=table_name,
        table_name=table_name,
        write_disposition=load_type,  # use `replace` for initial load, `merge` for incremental
        primary_key=primary_key,
        standalone=True,
        parallelized=True,
    )(read_sql_x_chunked)(
        credentials.to_native_representation(),  # Pass the connection string directly
        query,
    )

    if load_type == "merge":
        # Retrieve the last value processed for incremental loading
        source.apply_hints(incremental=dlt.sources.incremental(order_date))

    return source


def table_desc(table_name, pk, schema_name, order_date, columns="*"):
    return {
        "table_name": table_name,
        "pk": pk,
        "schema_name": schema_name,
        "order_date": order_date,
        "columns": columns,
    }


if __name__ == "__main__":
    # Input Handling
    parser = argparse.ArgumentParser(description="Run specific functions in the script.")
    parser.add_argument("--replace", action="store_true", help="Run initial load")
    parser.add_argument("--merge", action="store_true", help="Run delta load")
    args = parser.parse_args()

    source_schema_name = "example_data_1"
    target_schema_name = "example_data_2"
    pipeline_name = "loading_postgres_to_postgres"

    tables = [
        table_desc("table_1", ["pk"], source_schema_name, "updated_at"),
        table_desc("table_2", ["pk"], source_schema_name, "updated_at"),
    ]

    # default is initial loading (replace)
    load_type = "merge" if args.merge else "replace"
    print(f"LOAD-TYPE: {load_type}")

    resources = []
    for table in tables:
        resources.append(
            pg_resource_chunked(
                table["table_name"],
                table["pk"],
                table["schema_name"],
                table["order_date"],
                load_type=load_type,
                columns=table["columns"],
                credentials=dlt.secrets["sources.postgres.credentials"],
            )
        )

    if load_type == "replace":
        pipeline = dlt.pipeline(
            pipeline_name=pipeline_name,
            destination="duckdb",
            dataset_name=target_schema_name,
            dev_mode=True,
            progress="alive_progress",
        )
    else:
        pipeline = dlt.pipeline(
            pipeline_name=pipeline_name,
            destination="postgres",
            dataset_name=target_schema_name,
            dev_mode=False,
        )  # dev_mode=False

    # start timer
    startTime = pendulum.now()

    # 1. extract
    print("##################################### START EXTRACT ########")
    pipeline.extract(resources)
    print(f"--Time elapsed: {pendulum.now() - startTime}")

    # 2. normalize
    print("##################################### START NORMALIZATION ########")
    if load_type == "replace":
        info = pipeline.normalize(
            workers=2, loader_file_format="parquet"
        )  # https://dlthub.com/docs/blog/dlt-arrow-loading
    else:
        info = pipeline.normalize()

    print(info)
    print(pipeline.last_trace.last_normalize_info)
    print(f"--Time elapsed: {pendulum.now() - startTime}")

    # 3. load
    print("##################################### START LOAD ########")
    load_info = pipeline.load()
    print(load_info)
    print(f"--Time elapsed: {pendulum.now() - startTime}")

    # check that stuff was loaded
    row_counts = pipeline.last_trace.last_normalize_info.row_counts
    assert row_counts["table_1"] == 9
    assert row_counts["table_2"] == 9

    if load_type == "replace":
        # 4. Load DuckDB local database into Postgres
        print("##################################### START DUCKDB LOAD ########")
        # connect to local duckdb dump
        conn = duckdb.connect(f"{load_info.destination_displayable_credentials}".split(":///")[1])
        conn.sql("INSTALL postgres;")
        conn.sql("LOAD postgres;")
        # select generated timestamp schema
        timestamped_schema = conn.sql(
            f"""select distinct table_schema from information_schema.tables
                     where table_schema like '{target_schema_name}%'
                     and table_schema NOT LIKE '%_staging'
                     order by table_schema desc"""
        ).fetchone()[0]
        print(f"timestamped_schema: {timestamped_schema}")

        target_credentials = ConnectionStringCredentials(
            dlt.secrets["destination.postgres.credentials"]
        )
        # connect to destination (timestamped schema)
        conn.sql(
            "ATTACH"
            f" 'dbname={target_credentials.database} user={target_credentials.username} password={target_credentials.password} host={target_credentials.host} port={target_credentials.port}'"
            " AS pg_db (TYPE postgres);"
        )
        conn.sql(f"CREATE SCHEMA IF NOT EXISTS pg_db.{timestamped_schema};")

        for table in tables:
            print(
                f"LOAD DuckDB -> Postgres: table: {timestamped_schema}.{table['table_name']} TO"
                f" Postgres {timestamped_schema}.{table['table_name']}"
            )

            conn.sql(
                f"CREATE OR REPLACE TABLE pg_db.{timestamped_schema}.{table['table_name']} AS"
                f" SELECT * FROM {timestamped_schema}.{table['table_name']};"
            )
            conn.sql(
                f"SELECT count(*) as count FROM pg_db.{timestamped_schema}.{table['table_name']};"
            ).show()

        print(f"--Time elapsed: {pendulum.now() - startTime}")
        print("##################################### FINISHED ########")

        # check that stuff was loaded
        rows = conn.sql(
            f"SELECT count(*) as count FROM pg_db.{timestamped_schema}.{table['table_name']};"
        ).fetchone()[0]
        assert int(rows) == 9

        # 5. Cleanup and rename Schema
        print("##################################### RENAME Schema and CLEANUP ########")
        try:
            con_hd = psycopg2.connect(
                dbname=target_credentials.database,
                user=target_credentials.username,
                password=target_credentials.password,
                host=target_credentials.host,
                port=target_credentials.port,
            )
            con_hd.autocommit = True
            print(
                "Connected to HD-DB: "
                + target_credentials.host
                + ", DB: "
                + target_credentials.username
            )
        except Exception as e:
            print(f"Unable to connect to HD-database! The reason: {e}")

        with con_hd.cursor() as cur:
            # Drop existing target_schema_name
            print(f"Drop existing {target_schema_name}")
            cur.execute(f"DROP SCHEMA IF EXISTS {target_schema_name} CASCADE;")
            # Rename timestamped-target_schema_name to target_schema_name
            print(f"Going to rename schema {timestamped_schema} to {target_schema_name}")
            cur.execute(f"ALTER SCHEMA {timestamped_schema} RENAME TO {target_schema_name};")

        con_hd.close()