Principle:Apache Paimon Target Table Preparation
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Data_Ingestion |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for creating and configuring target Paimon tables with schemas that match incoming data for ingestion.
Description
Target table preparation ensures that a Paimon table exists with the correct schema to receive incoming data. This involves creating a Schema from a PyArrow schema definition (including partition keys, primary keys, and table options like bucket count), then creating the table in the catalog.
The preparation process consists of several steps:
- Define the PyArrow schema: Declare column names and types using pyarrow.schema(), matching the structure of the incoming data.
- Create a Paimon Schema: Use Schema.from_pyarrow_schema() to convert the PyArrow schema into a Paimon Schema object, specifying additional metadata such as partition keys, primary keys, and table options.
- Create the table in the catalog: Call catalog.create_table() with the schema to register the table. Use ignore_if_exists=True for idempotent pipeline runs.
- Obtain a table reference: Retrieve the table handle via catalog.get_table() for subsequent read and write operations.
The schema must be compatible with the incoming Ray Dataset's schema to avoid type mismatches during write. Key schema properties include:
- Primary keys: Enable upsert semantics and deduplication during merge-on-read or compaction.
- Partition keys: Control physical data layout for partition pruning during reads.
- Table options: Configure storage behavior such as bucket count ({'bucket': '4'}), compaction settings, and file format.
Usage
Use when setting up a new target table before a Ray-based data ingestion pipeline. This step must be completed before calling write_ray() on a table, as the table must exist with a compatible schema.
Theoretical Basis
This principle follows the schema-on-write paradigm where the target table's structure is defined before data arrives, ensuring type safety and enabling optimized storage layout (partitioning, bucketing).
Key theoretical properties:
- Type safety: The pre-defined schema acts as a contract. Data that does not conform to the schema will be rejected at write time, preventing silent data corruption.
- Storage optimization: Declaring partition keys and bucket counts at table creation time enables the storage engine to physically organize data for efficient query patterns. Partitioning enables partition pruning, while bucketing enables hash-based data distribution across files.
- Idempotent creation: The ignore_if_exists flag supports idempotent pipeline execution, a key requirement for fault-tolerant ETL workflows that may be retried.