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Reading and Writing Iceberg in Bodo

SQL

BodoSQL can be used to read, create, or insert into an Iceberg table. Iceberg Tables are automatically detected by existing catalogs and are used during read:

To query an Iceberg table, use the standard SELECT syntax. To learn more about supported SELECT syntax, see the SELECT API reference.

SELECT ... FROM <... namespace_path ...>.<table_name> ...

Write Support

The CREATE TABLE syntax can be used to create Iceberg tables:

CREATE [OR REPLACE] [TRANSIENT | TEMPORARY] TABLE <...namespace_path...>.<table_name>

Inserting into existing Iceberg tables is supported via the INSERT INTO syntax:

INSERT INTO <...namespace_path...>.<table_name>
Snowflake Iceberg Write Support

To create Iceberg tables in Snowflake, a Snowflake External Volume is required. The volume to use must be specified via the exvol argument to the SnowflakeCatalog:

catalog = bodosql.SnowflakeCatalog(
    ...
    exvol='<... Snowflake Volume ...>'
)

bc = bodosql.BodoSQLContext(catalog=catalog)

Warning

  • Inserting into Snowflake Managed Iceberg Tables is not supported.
  • When the exvol parameter is specified, all tables constructed via CREATE TABLE will be Snowflake Iceberg tables.

Python

Bodo supports reading and writing to Iceberg tables from multiple catalogs and object stores (local, S3, and HDFS).

  • Iceberg Reads are supported through the pandas.read_sql_table API.
  • Iceberg Writes are supported through the pandas.DataFrame.to_sql API.

Connection String Syntax

To specify the Iceberg catalog in the Pandas APIs, the conn parameter must contain a connection string in one of the following formats.

Iceberg connection strings vary by catalog, but in general are of the form iceberg<+conn>://<path><?params> where - <conn>://<path> is the location of the catalog or Iceberg warehouse - params is a list of properties to pass to the catalog. Each parameter must be of the form <key>=<value> and separated with &, similar to HTTP URLs.

The following parameters are supported: - type: Type of catalog. The supported values are listed below. When the connection string is ambiguous, this parameter is used to determine the type of catalog implementation. - warehouse: Location of the warehouse. Required when creating a new table using a Glue or Hive catalog.

The following catalogs are supported:

  • Hadoop Catalog on Local Filesystem:

    • Used when type=hadoop is specified or when <conn> is file or empty
    • <path> is the absolute path to the warehouse (directory containing the database schema)
    • Parameter warehouse will be ignored if specified
    • E.g. iceberg://<ABSOLUTE PATH TO ICEBERG WAREHOUSE> or iceberg+file://<ABSOLUTE PATH TO ICEBERG WAREHOUSE>

  • Hadoop Catalog on S3

    • Used when type=hadoop-s3 is specified or when <conn> is s3.
    • <conn>://<path> is the S3 path to the warehouse (directory or bucket containing the database schema).
    • Parameter warehouse will be ignored if specified.
    • E.g. iceberg+s3://<S3 PATH TO ICEBERG WAREHOUSE>

  • AWS Glue Catalog

    • Connection string must be of the form iceberg+glue?<params>.
    • Parameter type will be ignored if specified.
    • Parameter warehouse is required to create a table.
    • E.g. iceberg+glue or iceberg+glue?warehouse=s3://<ICEBERG-BUCKET>

  • Hive / Thrift Catalog

    • Used when type=hive is specified or when <conn> is thrift.
    • <conn>://<path> is the URL to the Thrift catalog, i.e. thrift://localhost:9083.
    • Parameter warehouse is required to create the table.
    • E.g. iceberg+thrift://<THRIFT URL>

Pandas APIs

Example code for reading:

@bodo.jit
def example_read_iceberg() -> pd.DataFrame:
    return pd.read_sql_table(
        table_name="<... Name of the Iceberg Table ...>",
        con="<... Connection String. See previous section ...>",
        schema="<... Namespace Path to Iceberg Table ...>"
    )

Note

  • The schema argument is required for reading Iceberg tables.

  • The Iceberg table to read should be located at <warehouse-location>/<schema>/<table_name>, where schema and table_name are the arguments to pd.read_sql_table, and warehouse-location is inferred from the connection string based on the description provided above.

An example for writing to Iceberg via pandas.DataFrame.to_sql:

@bodo.jit(distributed=["df"])
def write_iceberg_table(df: pandas.DataFrame):
    df.to_sql(
        name="<... Name of the Iceberg Table ...>",
        con="<... Connection String. See previous section ...>",
        schema="<... Namespace Path to Iceberg Table ..>",
        if_exists="replace"
    )

Note

  • schema argument is required for writing Iceberg tables.
  • Writing a Pandas Dataframe index to an Iceberg table is not supported. If index and index_label are provided, they will be ignored.
  • chunksize, dtype and method arguments are not supported and will be ignored if provided.