mkpipe-loader-snowflake 0.5.0

Snowflake loader for mkpipe.

mkpipe-loader-snowflake

Snowflake loader plugin for MkPipe. Writes Spark DataFrames into Snowflake tables using the native spark-snowflake connector, which stages data via internal cloud storage (S3/Azure/GCS) — significantly faster than JDBC for large datasets.

Documentation

For more detailed documentation, please visit the GitHub repository.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

---

Connection Configuration

connections:
  snowflake_target:
    variant: snowflake
    host: myaccount.snowflakecomputing.com
    port: 443
    database: MY_DATABASE
    schema: MY_SCHEMA
    user: myuser
    password: mypassword
    warehouse: MY_WAREHOUSE

With RSA key pair authentication:

connections:
  snowflake_target:
    variant: snowflake
    host: myaccount.snowflakecomputing.com
    port: 443
    database: MY_DATABASE
    schema: MY_SCHEMA
    user: myuser
    warehouse: MY_WAREHOUSE
    private_key_file: /path/to/rsa_key.p8
    private_key_file_pwd: mypassphrase

---

Table Configuration

pipelines:
  - name: pg_to_snowflake
    source: pg_source
    destination: snowflake_target
    tables:
      - name: public.events
        target_name: STG_EVENTS
        replication_method: full
        batchsize: 50000

---

Write Parallelism & Throughput

Snowflake loader inherits from JdbcLoader. Two parameters control write performance:

      - name: public.events
        target_name: STG_EVENTS
        replication_method: full
        batchsize: 50000        # rows per JDBC batch insert (default: 10000)
        write_partitions: 4     # coalesce DataFrame to N partitions before writing

How they work

• batchsize: number of rows buffered before sending a single INSERT to Snowflake. Larger batches reduce round-trips and staging overhead.
• write_partitions: calls coalesce(N) on the DataFrame before writing, reducing the number of concurrent JDBC connections to Snowflake.

Performance Notes

• Snowflake Warehouse size is the primary write performance lever. A larger warehouse processes inserts faster regardless of partition count.
• JDBC writes to Snowflake stage data internally before committing. Large batchsize (50,000+) reduces staging overhead.
• For very large loads, consider using Snowflake's native COPY INTO via an external stage (S3/GCS) instead of JDBC — that is significantly faster but requires additional infrastructure.
• write_partitions: 4–8 is a good default to balance throughput and connection count.

---

All Table Parameters

Parameter	Type	Default	Description
name	string	required	Source table name
target_name	string	required	Snowflake destination table name
replication_method	full / incremental	full	Replication strategy
batchsize	int	10000	Rows per JDBC batch insert
write_partitions	int	—	Coalesce DataFrame to N partitions before writing
dedup_columns	list	—	Columns used for mkpipe_id hash deduplication
tags	list	[]	Tags for selective pipeline execution
pass_on_error	bool	false	Skip table on error instead of failing