Database

Iceberg

Apache Iceberg is a high performance open-source format for large analytic tables.

The Iceberg Wrapper allows you to read from and write to Apache Iceberg within your Postgres database.

Preparation

Before you can query Iceberg, you need to enable the Wrappers extension and store your credentials in Postgres.

Enable Wrappers

Make sure the wrappers extension is installed on your database:

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create extension if not exists wrappers with schema extensions;

Enable the Iceberg Wrapper

Enable the iceberg_wrapper FDW:

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create foreign data wrapper iceberg_wrapper  handler iceberg_fdw_handler  validator iceberg_fdw_validator;

Store your credentials (optional)

By default, Postgres stores FDW credentials inside pg_catalog.pg_foreign_server in plain text. Anyone with access to this table will be able to view these credentials. Wrappers is designed to work with Vault, which provides an additional level of security for storing credentials. We recommend using Vault to store your credentials.

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-- Save your AWS credentials in Vault and retrieve the created-- `aws_access_key_id` and `aws_secret_access_key`select vault.create_secret(  '<access key id>',  -- secret to be encrypted  'aws_access_key_id',  -- secret name  'AWS access key for Wrappers'  -- secret description);select vault.create_secret(  '<secret access key>'  'aws_secret_access_key',  'AWS secret access key for Wrappers');

Connecting to Iceberg

We need to provide Postgres with the credentials to connect to Iceberg. We can do this using the create server command.

For any server options need to be stored in Vault, you can add a prefix vault_ to its name and use the secret ID returned from the select vault.create_secret() statement as the option value.

Connecting to AWS S3 Tables

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create server iceberg_server  foreign data wrapper iceberg_wrapper  options (    -- The key id saved in Vault from above    vault_aws_access_key_id '<key_ID>',    -- The secret id saved in Vault from above    vault_aws_secret_access_key '<secret_key>',    -- AWS region    region_name 'us-east-1',    -- AWS S3 table bucket ARN    aws_s3table_bucket_arn 'arn:aws:s3tables:us-east-1:204203087419:bucket/my-table-bucket'  );

Connecting to Iceberg REST Catalog + AWS S3 (or compatible) storage

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create server iceberg_server  foreign data wrapper iceberg_wrapper  options (    -- The key id saved in Vault from above    vault_aws_access_key_id '<key_ID>',    -- The secret id saved in Vault from above    vault_aws_secret_access_key '<secret_key>',    -- AWS region    region_name 'us-east-1',    -- Iceberg REST Catalog URI    catalog_uri 'https://rest-catalog/ws',    -- Warehouse name    warehouse 'warehouse',    -- AWS S3 endpoint URL, optional    "s3.endpoint" 'https://alternative-s3-storage:8000'  );

Create a schema

We recommend creating a schema to hold all the foreign tables:

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create schema if not exists iceberg;

Options

The full list of foreign table options are below:

  • table - Fully qualified source table name with all namespaces in Iceberg, required.
  • rowid_column - The column to use as the row identifier for INSERT operations, required for data insertion.

Entities

We can use SQL import foreign schema to import foreign table definitions from Iceberg.

For example, using below SQL can automatically create foreign tables in the iceberg schema.

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-- create all the foreign tables from Iceberg "docs_example" namespaceimport foreign schema "docs_example"  from server iceberg_server into iceberg;-- or, only create "readme" and "guides" foreign tablesimport foreign schema "docs_example"  limit to ("readme", "guides")  from server iceberg_server into iceberg;-- or, create all foreign tables except "readme"import foreign schema "docs_example"  except ("readme")  from server iceberg_server into iceberg;

Iceberg Tables

This is an object representing Iceberg table.

Ref: Iceberg Table Spec

Operations

ObjectSelectInsertUpdateDeleteTruncate
table✅✅❌❌❌

Usage

You can manually create the foreign table like below if you did not use import foreign schema.

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create foreign table iceberg.guides (  id bigint,  title text,  content text,  created_at timestamp)  server iceberg_server  options (    table 'docs_example.guides',    rowid_column 'id'  );

Query Pushdown Support

This FDW supports where clause pushdown with below operators.

OperatorNote
=, >, >=, <, <=, <>, !=
is null, is not null
x, not x, x is true, x is not truecolumn x data type is boolean
x between a and bcolumn x data type can be datetime or numeric types
like 'abc%', not like 'abc%'only support starts with pattern
in (x, y, z), not in (x, y, z)

Supported Data Types

Postgres TypeIceberg Type
booleanboolean
realfloat
integerint
double precisiondouble
bigintlong
numericdecimal
textstring
datedate
timetime
timestamptimestamp, timestamp_ns
timestamptztimestamptz, timestamptz_ns
jsonbstruct, list, map
byteabinary
uuiduuid

Data Insertion

The Iceberg FDW supports inserting data into Iceberg tables using standard SQL INSERT statements.

Basic Insert

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-- insert a single rowinsert into iceberg.guides (id, title, content, created_at)values (1, 'Getting Started', 'Welcome to our guides', now());-- insert multiple rowsinsert into iceberg.guides (id, title, content, created_at)values  (2, 'Advanced Guide', 'Advanced topics', now()),  (3, 'Best Practices', 'Tips and tricks', now());

Insert from Select

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-- insert data from another tableinsert into iceberg.guides (id, title, content, created_at)select id, title, content, created_atfrom some_other_tablewhere condition = true;

Partition Considerations

When inserting data into partitioned Iceberg tables, the FDW automatically handles partitioning based on the table's partition spec. Data will be written to the appropriate partition directories.

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-- for a table partitioned by sale_date, data is automatically partitionedinsert into iceberg.sales (product_id, amount, sale_date)values (123, 99.99, '2025-01-15');

Performance Tips

  • Batch Inserts: Use multi-row inserts for better performance
  • Partition Awareness: When possible, insert data in partition order to optimize file organization
  • Transaction Size: Consider breaking very large inserts into smaller transactions

Limitations for Inserts

  • Schema evolution during insert is not supported
  • Only append operations are supported (no upserts)
  • Complex data types (nested structs, arrays, maps) have limited support

Limitations

This section describes important limitations and considerations when using this FDW:

  • Only supports specific data type mappings between Postgres and Iceberg
  • UPDATE, DELETE, and TRUNCATE operations are not supported
  • Apache Iceberg schema evolution is not supported
  • When using Iceberg REST catalog, only supports AWS S3 (or compatible) as the storage
  • Materialized views using these foreign tables may fail during logical backups

Examples

Basic Example

First, create a server for AWS S3 Tables:

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create server iceberg_server  foreign data wrapper iceberg_wrapper  options (    aws_access_key_id '<AWS_access_key_ID>',    aws_secret_access_key '<AWS_secret_access_key>',    region_name 'us-east-1',    aws_s3table_bucket_arn 'arn:aws:s3tables:us-east-1:204203087419:bucket/my-table-bucket'  );

Import the foreign table:

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-- Run below SQL to import all tables under namespace 'docs_example'import foreign schema "docs_example"  from server iceberg_server into iceberg;-- or, create the foreign table manuallycreate foreign table if not exists iceberg.guides (  id bigint,  title text,  content text,  created_at timestamp)  server iceberg_server  options (    table 'docs_example.guides',    rowid_column 'id'  );

Then query the foreign table:

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select * from iceberg.guides;

Read Cloudflare R2 Data Catalog

First, follow the steps in Getting Started Guide to create a R2 Catalog on Cloudflare. Once it is completed, create a server like below:

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create server iceberg_server  foreign data wrapper iceberg_wrapper  options (    aws_access_key_id '<R2_access_key_ID>',    aws_secret_access_key '<R2_secret_access_key>',    token '<R2 API token>',    warehouse 'xxx_r2-data-catalog-tutorial',    "s3.endpoint" 'https://xxx.r2.cloudflarestorage.com',    catalog_uri 'https://catalog.cloudflarestorage.com/xxx/r2-data-catalog-tutorial'  );

Then, import all the tables in default namespace and query it:

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import foreign schema "default" from server iceberg_server into iceberg;select * from iceberg.people;

Query Pushdown Examples

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-- the filter 'id = 42' will be pushed down to Icebergselect * from iceberg.guides where id = 42;-- the pushdown filter can also be on the partition column 'created_at',-- this can greatly reduce query costselect * from iceberg.guideswhere created_at >= timestamp '2025-05-16 12:34:56';-- multiple filters must use logical 'AND'select * from iceberg.guides where id > 42 and title like 'Supabase%';

Data Insertion Examples

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-- insert a single recordinsert into iceberg.guides (id, title, content, created_at)values (100, 'New Guide', 'This is a new guide', now());-- insert multiple records at onceinsert into iceberg.guides (id, title, content, created_at)values  (101, 'Guide A', 'Content for Guide A', now()),  (102, 'Guide B', 'Content for Guide B', now()),  (103, 'Guide C', 'Content for Guide C', now());-- insert data from a SELECT queryinsert into iceberg.guides (id, title, content, created_at)select  id + 1000,  'Migrated: ' || title,  content,  created_atfrom other_guideswhere id < 10;-- verify the inserted dataselect count(*) from iceberg.guides;select * from iceberg.guides where id >= 100 order by id;