Iceberg Ingest

Apache Iceberg is a table format for large analytic datasets that stores data in immutable open-source file formats including Parquet. This table format, however, is not optimized for fast analytical queries or for serving business needs for real-time applications. SingleStore addresses these shortcomings through the addition of a speed layer to Iceberg tables. Iceberg tables can be directly ingested into SingleStore without the need for an external ETL tool and subsequently processed using SingleStore's high-performance database engine.

Remarks

The following are supported:

Ingest Iceberg Version 1 tables and Iceberg Version 2 tables without deletions with data files in Parquet format.
Ingest Iceberg tables stored in Amazon S3 with catalogs: GLUE, Snowflake, REST, and JDBC.
One-time ingest of Iceberg tables. When an Iceberg table is ingested, SingleStore will request Iceberg table metadata and load data from the latest snapshot available at that moment. Subsequent updates to the Iceberg table are not ingested.

Syntax

SQL

CREATE [OR REPLACE] PIPELINE <pipeline_name> AS
LOAD DATA S3 '[<warehouse_path>]'
CONFIG '{"catalog_type": "GLUE|SNOWFLAKE|REST|JDBC",
        "table_id": "<table_identifier>",
        <configuration_json>
        [, "catalog_name": "<your_catalog_name>" ]
        [, “catalog.<property>” : “property_value” [, …]]
}'
CREDENTIALS '<credentials_json>’
[REPLACE] INTO TABLE <table_name>
[ON DUPLICATE KEY UPDATE]      
<iceberg_subvalue_mapping>
FORMAT ICEBERG;

<iceberg_subvalue_mapping>:(
{<singlestore_col_name> | @<variable_name>}<- <iceberg_subvalue_path> [, ... ])

<iceberg_subvalue_path>: {ident [::ident ...]}

The warehouse_path specifies the path to the catalog warehouse. It is required for the JDBC catalog. The warehouse_path can be set to an empty string when not needed.

All the data shaping options for Parquet pipelines are supported for Iceberg pipelines. Refer to Data Shaping with Pipelines for more information.

Catalog Specification

The catalog_type and table_id are required for the catalog specification.
The table_id identifies the Iceberg table. The table identifier is catalog-specific, but is typically in the form: database_name.table_name.
The catalog_name is a name to associate with the catalog when reading table metadata and is used internally in SingleStore for logging and metrics purposes. The catalog_name is required for the JDBC catalog and is optional for other catalogs.
The catalog.property is a list of key-value pairs for configuring the catalog connection. The property and value are passed directly to the Iceberg SDK to establish the catalog connection.

S3 Specification

The configuration_json is a JSON string for S3 configuration parameters such as region, endpoint_url, and compatibility_mode. Refer to CREATE PIPELINE and CREATE LINK for more information.
The credentials_json is an AWS credential specification in JSON which includes fields such as aws_access_key_id and aws_secret_access_key. Refer to CREATE LINK for more information.

Subvalue Mappings

The iceberg_subvalue_mapping assigns fields from the Iceberg table to columns in the SingleStore table or to temporary variables. A ::-separated list of field names is used in iceberg_subvalue_path to look up fields in nested schemas. When the files in the Iceberg table are Parquet files, the ::-separated list of field names is used to look up fields in nested Parquet schemas. The following rules apply:

The last field in iceberg_subvalue_path must be a primitive type.
All iceberg_subvalue_path components containing whitespace or punctuation must be surrounded by backticks (`).
The iceberg_subvalue_path may not contain Parquet nested types (list or map types). Refer to Parquet - Nested Types for more information.

Extracting and Converting Values

Refer to CREATE PIPELINE for details on extracting and converting values from Parquet files.

Enable and Configure Iceberg Ingest

The following variables need to be set to for Iceberg ingest.

SQL

SET GLOBAL enable_iceberg_ingest = ON;
SET GLOBAL pipelines_extractor_get_offsets_timeout_ms = 90000;
SET GLOBAL java_pipelines_heap_size = 100;

The global engine variable enable_iceberg_ingest must be set to ON to use Iceberg ingest. This variable is set to OFF by default.

SingleStore recommends setting the global engine variable pipelines_extractor_get_offsets_timeout_ms to 90000 to prevent time outs and to provide an adequate amount of time to process the Iceberg catalog. Larger numbers of files in the Iceberg catalog require setting larger values for this variable. This variable should be adjusted higher if you see timeouts.

SingleStore recommends setting the global engine variable java_pipelines_heap_size to 100 to avoid out of memory errors. This variable should be adjusted higher if you experience out of memory errors.

Refer to Engine Variables for more information.

CREATE OR REPLACE - Data Refresh with Upserts

Iceberg pipelines can be used to upsert data from an Iceberg table into a SingleStore table. That is, after the initial one-time ingest from a snapshot in an Iceberg table is complete, CREATE OR REPLACE can be used to consume data that has been added to the Iceberg table.

CREATE OR REPLACE PIPELINE can be used with REPLACE INTO TABLE or ON DUPLICATE KEY UPDATE to perform upserts. An upsert updates a row in the table if there is a primary key match or inserts the row if there is no primary key match. Upserts can be performed only if the table to be updated has a PRIMARY KEY or a UNIQUE index.

When a pipeline with pipeline_name exists, CREATE OR REPLACE functions similarly to CREATE_PIPELINE, but preserves pipeline metadata, including loaded offsets and data files. When CREATE OR REPLACE is run on an existing pipeline, the Iceberg pipeline will retrieve a new snapshot, a new schema, and new data files and will insert data from the new data files to the destination table in SingleStore.

If there has been data compaction or if the Iceberg REPLACE operation was used, the pipeline might attempt to insert the same data so handling duplicates is important.

Refer to CREATE PIPELINE for syntax for CREATE OR REPLACE PIPELINE, REPLACE INTO TABLE, and ON DUPLICATE KEY UPDATE. Refer to Performing Upserts for more information on upserts.

Example - Glue Catalog on Amazon S3

An Iceberg table with data files in Parquet format that is stored in an AWS S3 bucket using AWS Glue can be loaded into a SingleStore table using a pipeline (CREATE PIPELINE. Refer to Apache Iceberg - Glue Catalog for information on using a GLUE catalog with Iceberg.

In this example, a table named books is created and data from an Iceberg table that meets this schema is loaded into the books table.

Create the table.

SQL

CREATE TABLE books(
  Id INT,
  Name TEXT,
  NumPages INT,
  Rating DOUBLE);

The following data is used for this example.

Shell

(1, 'Happy Place', 400, 4.9)
(2, 'Legends & Lattes', 304, 4.9)
(3, 'The Vanishing Half', 352, 4.9)
(4, 'The Wind Knows My Name', 304, 4.9)

The PIPELINE statement below will load data from an Iceberg table containing the data above into the books table. The column names on the left side of the <- are the column names from the SingleStore table into which the data will be loaded. The column names on the right side of the <- are the column names from the Iceberg table which is to be loaded into SingleStore.

SQL

CREATE PIPELINE books_pipe AS
LOAD DATA S3 ''
CONFIG '{"region":"us-west-2",
         "catalog_type": "GLUE", 
         "catalog_name": "s3_catalog", 
         "table_id": "db.books"
}' 
CREDENTIALS '{"aws_access_key_id": "<your_access_key_id>",
              "aws_secret_access_key": "<your_secrect_access_key>"
}'
INTO TABLE books
   (Id <- id,
   Name <- name,
   NumPages <- numPages,
   Rating <- rating)
FORMAT ICEBERG;

Test the pipeline.

SQL

TEST PIPELINE books_pipe;

+------+------------------------+----------+--------+
| Id   | Name                   | NumPages | Rating |
+------+------------------------+----------+--------+
|    4 | The Wind Knows My Name |      304 |    4.9 |
|    1 | Happy Place            |      400 |    4.9 |
|    2 | Legends & Lattes       |      304 |    4.9 |
|    3 | The Vanishing Half     |      352 |    4.9 |
+------+------------------------+----------+--------+

Refer to START PIPELINE for more information on starting pipelines.

Example - Use Subvalue Mappings

This example shows the use of subvalue mappings to load nested elements from an Iceberg schema into a SingleStore table.

Create a table.

SQL

CREATE TABLE addresses(
Id INT,
Name TEXT,
Street TEXT,
City TEXT,
Country TEXT);

The following data is used for this example.

Shell

(1, 'Mylo', struct('123 Main St', 'New York', 'USA'))
(2, 'Naya', struct('456 Elm St', 'San Francisco', 'USA'))

The PIPELINE statement below will load data from an Iceberg table containing the data above into the addresses table. The column names on the left side of the <- are the column names from the SingleStore table into which the data will be loaded. The column names on the right side of the <- are the column names from the Iceberg table which is to be loaded into SingleStore.

SQL

CREATE PIPELINE addresses_pipe AS
LOAD DATA S3 ''
CONFIG '{"region":"us-west-2",
         "catalog_type": "GLUE", 
         "catalog_name": "s3_catalog", 
         "table_id": "db2.addresses"
}' 
CREDENTIALS '{"aws_access_key_id": "<your_access_key_id>",
              "aws_secret_access_key": "<your_secrect_access_key>"
}'
INTO TABLE addresses
   (Id <- id,
   Name <- name,
   Street <- address::street,
   City <- address::city,
   Country <- address::country)
FORMAT ICEBERG;

Test the pipeline.

SQL

TEST PIPELINE addresses_pipe;

+------+------+-------------+---------------+---------+
| Id   | Name | Street      | City          | Country |
+------+------+-------------+---------------+---------+
|    1 | Mylo | 123 Main St | New York      | USA     |
|    2 | Naya | 456 Elm St  | San Francisco | USA     |
+------+------+-------------+---------------+---------+

Refer to START PIPELINE for more information on starting pipelines.

Example - Snowflake Catalog on Amazon S3

Ingest an Iceberg table stored in Amazon S3 with a Snowflake catalog.

Iceberg tables to be ingested in SingleStore must be created on an external volume. Refer to Tutorial: Create your first Iceberg table , Create an external volume , and Snowflake Iceberg Catalog SDK for more information.

SQL

CREATE PIPELINE addresses_pipe AS
LOAD DATA S3 ''
CONFIG '{"region" : "us-west-2",
        "catalog_type": "SNOWFLAKE",
        "table_id": "db_name.schema_name.table_name",
        "catalog.uri": "jdbc:snowflake://<acount_identifier>.snowflakecomputing.com",
        "catalog.jdbc.user":"<user_name>",
        "catalog.jdbc.password":"<password>",
        "catalog.jdbc.role":"<user role>"}'
CREDENTIALS '{"aws_access_key_id" : "<your_access_key_id>",
             "aws_secret_access_key": "<your_secretaccess_key>"
}'
INTO TABLE addresses
  (Id <- id,
  Name <- name,
  Street <- address::street,
  City <- address::city,
  Country <- address::country)
FORMAT ICEBERG;

For the Snowflake catalog, the table_id must consist of three parts - the database name, the schema name, and the table name. In addition, the catalog.uri, catalog.jdbc.user, catalog.jdbc.password, and catalog.jdbc.role are required.

Example - Upserts using REPLACE INTO

The SQL below shows how to use CREATE OR REPLACE and REPLACE INTO to do upserts with the Snowflake catalog.

Create a table into which data will be upserted. This table must have a primary key.

SQL

CREATE TABLE upsert_table(
  Id INT,
  Name TEXT,
  Val INT, 
  PRIMARY KEY(Id));

Create a pipeline to insert from the Iceberg table into the SingleStore table. Running this pipeline the first time will do the initial ingest of data from the Iceberg table into the SingleStore table.

SQL

CREATE PIPELINE upserts_pipe AS
LOAD DATA S3 ''
CONFIG '{"region" : "us-west-2",
      "catalog_type": "SNOWFLAKE",
      "table_id": "db_name.schema_name.table_name",
      "catalog.uri": "jdbc:snowflake://<acount_identifier>.snowflakecomputing.com",
      "catalog.jdbc.user":"<user_name>",
      "catalog.jdbc.password":"<password>",
      "catalog.jdbc.role":"<user role>"}'
CREDENTIALS '{"aws_access_key_id" : "<your_access_key_id>",
           "aws_secret_access_key": "<your_secretaccess_key>"
}'
REPLACE INTO TABLE upsert_table
(Id <- id,
 Name <- name,
 Val <- value)
FORMAT ICEBERG;

This command uses REPLACE INTO to indicate that if there are duplicates of the primary key in the Iceberg table, the last-processed value should be inserted into the SingleStore table. Refer to CREATE PIPELINE and LOAD DATA for more information.

After new data has been inserted into an Iceberg table, use the same CREATE OR REPLACE command to do an upsert from the (updated) Iceberg table into the SingleStore table.

Example - REST Catalog on Amazon S3

Ingest an Iceberg table stored in Amazon S3 with REST catalog. Refer to Decoupling using the REST Catalog for information on using a REST catalog with Iceberg.

SQL

CREATE PIPELINE addresses_pipe AS
LOAD DATA S3 ''
CONFIG '{"region" : "us-west-2",
        "catalog_type": "REST",
        "table_id": "db_name.table_name",
        "catalog.uri": "http://host.addresss:8181"}'
CREDENTIALS '{"aws_access_key_id" : "<your_access_key_id>",
             "aws_secret_access_key": "<your_secretaccess_key>"}'
INTO TABLE addresses
  (Id <- id,
  Name <- name,
  Street <- address::street,
  City <- address::city,
  Country <- address::country)
FORMAT ICEBERG;

Example - JDBC Catalog on Amazon S3

Ingest an Iceberg table stored in Amazon S3 with JDBC catalog. Refer to Iceberg JDBC Integration for more information on using Iceberg with JDBC catalog.

SingleStore supports Postgres, MySQL, and SQLite JDBC drivers by default.

The following example uses JDBC with SQLite.

SQL

CREATE PIPELINE addresses_pipe AS
LOAD DATA S3 's3://path_to_warehouse'
CONFIG '{"region" : "us-west-2",
        "catalog_type": "JDBC",
        "catalog_name": "catalog_name",
        "table_id": "db_name.table_name",
        "catalog.uri":"jdbc:sqlite_:file:/path_jdbc"}'
CREDENTIALS '{"aws_access_key_id" : "<your_access_key_id>",
             "aws_secret_access_key": "<your_secretaccess_key>"}'
INTO TABLE addresses
  (Id <- id,
  Name <- name,
  Street <- address::street,
  City <- address::city,
  Country <- address::country)
FORMAT ICEBERG;

The following example uses JDBC with MySQL.

SQL

CREATE PIPELINE addresses_pipe AS
LOAD DATA S3 's3://path_to_warehouse'
CONFIG '{"region" : "us-west-2",
        "catalog_type": "JDBC",
        "catalog_name": "catalog_name",
        "table_id": "db_name.table_name",
        "catalog.uri": "jdbc:mysql://host.address:3306/default",
        "catalog.jdbc.user": "<user_name>",
        "catalog.jdbc.password": "<password>"}'
CREDENTIALS '{"aws_access_key_id" : "<your_access_key_id>",
             "aws_secret_access_key": "<your_secretaccess_key>"}'
INTO TABLE addresses
  (Id <- id, 
   Name <- name, 
   Country <- address::country)
FORMAT ICEBERG;

The path_to_warehouse and catalog_name are required for JDBC catalogs.

Iceberg Ingest

On this page

Remarks

Syntax

Catalog Specification

S3 Specification

Subvalue Mappings

Extracting and Converting Values

Enable and Configure Iceberg Ingest

CREATE OR REPLACE - Data Refresh with Upserts

Example - Glue Catalog on Amazon S3

Example - Use Subvalue Mappings

Example - Snowflake Catalog on Amazon S3

Example - Upserts using REPLACE INTO

Example - REST Catalog on Amazon S3

Example - JDBC Catalog on Amazon S3

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Iceberg Ingest

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Remarks

Syntax

Catalog Specification

S3 Specification

Subvalue Mappings

Extracting and Converting Values

Enable and Configure Iceberg Ingest

CREATE OR REPLACE - Data Refresh with Upserts

Example - Glue Catalog on Amazon S3

Example - Use Subvalue Mappings

Example - Snowflake Catalog on Amazon S3

Example - Upserts using REPLACE INTO

Example - REST Catalog on Amazon S3

Example - JDBC Catalog on Amazon S3

Related Topics

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