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aidp-db2

Read or write IBM DB2 from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions DB2, IBM Db2, LUW, or `type=DB2`. Auth is host/port + database name + user/password.

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oracle-samples/oracle-aidp-samples
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SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
aidp-db2
description
Read or write IBM DB2 from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions DB2, IBM Db2, LUW, or `type=DB2`. Auth is host/port + database name + user/password.
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Read, Write, Edit, Bash
# `aidp-db2` — IBM DB2 via AIDP `aidataplatform` Use the built-in AIDP DB2 connector (`type=DB2`) for ingestion reads, writes, and SQL pushdown. External-catalog support is not included in the 4.1 release. ## When to use - Read from or write to an IBM DB2 database from an AIDP notebook. - Mentioned: "DB2", "Db2", "IBM Db2", or `type=DB2`. ## When NOT to use - For a database without a dedicated AIDP connector → [`aidp-jdbc-custom`](../aidp-jdbc-custom/SKILL.md). - For a DB2 external-catalog request; that capability is not available in 4.1. ## Ingestion read ```python import os from oracle_ai_data_platform_connectors.aidataplatform import ( AIDP_FORMAT, aidataplatform_options, ) opts = aidataplatform_options( type="DB2", host=os.environ["DB2_HOST"], port=int(os.environ.get("DB2_PORT", "50000")), database_name=os.environ["DB2_DATABASE"], user=os.environ["DB2_USER"], password=os.environ["DB2_PASSWORD"], schema=os.environ["DB2_SCHEMA"], table=os.environ["DB2_TABLE"], ) df = spark.read.format(AIDP_FORMAT).options(**opts).load() df.show(5) ``` ## Ingestion write `CREATE`, `APPEND`, `OVERWRITE`, and `MERGE` are supported. `write.merge.keys` is required for `MERGE`. ```python write_opts = aidataplatform_options( type="DB2", host=os.environ["DB2_HOST"], port=int(os.environ.get("DB2_PORT", "50000")), database_name=os.environ["DB2_DATABASE"], user=os.environ["DB2_USER"], password=os.environ["DB2_PASSWORD"], schema=os.environ["DB2_SCHEMA"], table=os.environ["DB2_TARGET_TABLE"], extra={"write.mode": "CREATE"}, ) df.write.format(AIDP_FORMAT).options(**write_opts).save() ``` ## Pushdown SQL ```python pushdown_df = (spark.read.format(AIDP_FORMAT) .options(**opts) .option("pushdown.sql", "SELECT * FROM <SCHEMA>.<TABLE_NAME> FETCH FIRST 10 ROWS ONLY") .load()) pushdown_df.show(5) ``` ## Gotchas - Use `DB2`, not the generic JDBC connector type. - `database.name` is required for DB2. - DB2 external-catalog access is not included in the 4.1 release. ## References - Official sample: [DB2 notebook](https://github.com/oracle-samples/oracle-aidp-samples/blob/main/data-engineering/ingestion/Read_Write_External_Ecosystem_Connectors/DB2.ipynb)
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