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Iceberg AWS Integrations🔗

Iceberg provides integration with different AWS services through the iceberg-aws module. This section describes how to use Iceberg with AWS.

Enabling AWS Integration🔗

The iceberg-aws module is bundled with Spark and Flink engine runtimes for all versions from 0.11.0 onwards. However, the AWS clients are not bundled so that you can use the same client version as your application. You will need to provide the AWS v2 SDK because that is what Iceberg depends on. You can choose to use the AWS SDK bundle, or individual AWS client packages (Glue, S3, DynamoDB, KMS, STS) if you would like to have a minimal dependency footprint.

All the default AWS clients use the Apache HTTP Client for HTTP connection management. This dependency is not part of the AWS SDK bundle and needs to be added separately. To choose a different HTTP client library such as URL Connection HTTP Client, see the section client customization for more details.

All the AWS module features can be loaded through custom catalog properties, you can go to the documentations of each engine to see how to load a custom catalog. Here are some examples.

Spark🔗

For example, to use AWS features with Spark 3.4 (with scala 2.12) and AWS clients (which is packaged in the iceberg-aws-bundle), you can start the Spark SQL shell with:

# start Spark SQL client shell
spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.4.3,org.apache.iceberg:iceberg-aws-bundle:1.4.3 \
    --conf spark.sql.defaultCatalog=my_catalog \
    --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket/my/key/prefix \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO

As you can see, In the shell command, we use --packages to specify the additional iceberg-aws-bundle that contains all relevant AWS dependencies.

To use AWS module with Flink, you can download the necessary dependencies and specify them when starting the Flink SQL client:

# download Iceberg dependency
ICEBERG_VERSION=1.4.3
MAVEN_URL=https://repo1.maven.org/maven2
ICEBERG_MAVEN_URL=$MAVEN_URL/org/apache/iceberg
wget $ICEBERG_MAVEN_URL/iceberg-flink-runtime/$ICEBERG_VERSION/iceberg-flink-runtime-$ICEBERG_VERSION.jar

wget $ICEBERG_MAVEN_URL/iceberg-aws-bundle/$ICEBERG_VERSION/iceberg-aws-bundle-$ICEBERG_VERSION.jar

# start Flink SQL client shell
/path/to/bin/sql-client.sh embedded \
    -j iceberg-flink-runtime-$ICEBERG_VERSION.jar \
    -j iceberg-aws-bundle-$ICEBERG_VERSION.jar \
    shell

With those dependencies, you can create a Flink catalog like the following:

CREATE CATALOG my_catalog WITH (
  'type'='iceberg',
  'warehouse'='s3://my-bucket/my/key/prefix',
  'catalog-impl'='org.apache.iceberg.aws.glue.GlueCatalog',
  'io-impl'='org.apache.iceberg.aws.s3.S3FileIO'
);

You can also specify the catalog configurations in sql-client-defaults.yaml to preload it:

catalogs: 
  - name: my_catalog
    type: iceberg
    warehouse: s3://my-bucket/my/key/prefix
    catalog-impl: org.apache.iceberg.aws.glue.GlueCatalog
    io-impl: org.apache.iceberg.aws.s3.S3FileIO

Hive🔗

To use AWS module with Hive, you can download the necessary dependencies similar to the Flink example, and then add them to the Hive classpath or add the jars at runtime in CLI:

add jar /my/path/to/iceberg-hive-runtime.jar;
add jar /my/path/to/aws/bundle.jar;

With those dependencies, you can register a Glue catalog and create external tables in Hive at runtime in CLI by:

SET iceberg.engine.hive.enabled=true;
SET hive.vectorized.execution.enabled=false;
SET iceberg.catalog.glue.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog;
SET iceberg.catalog.glue.warehouse=s3://my-bucket/my/key/prefix;

-- suppose you have an Iceberg table database_a.table_a created by GlueCatalog
CREATE EXTERNAL TABLE database_a.table_a
STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
TBLPROPERTIES ('iceberg.catalog'='glue');

You can also preload the catalog by setting the configurations above in hive-site.xml.

Catalogs🔗

There are multiple different options that users can choose to build an Iceberg catalog with AWS.

Glue Catalog🔗

Iceberg enables the use of AWS Glue as the Catalog implementation. When used, an Iceberg namespace is stored as a Glue Database, an Iceberg table is stored as a Glue Table, and every Iceberg table version is stored as a Glue TableVersion. You can start using Glue catalog by specifying the catalog-impl as org.apache.iceberg.aws.glue.GlueCatalog, just like what is shown in the enabling AWS integration section above.

Glue Catalog ID🔗

There is a unique Glue metastore in each AWS account and each AWS region. By default, GlueCatalog chooses the Glue metastore to use based on the user's default AWS client credential and region setup. You can specify the Glue catalog ID through glue.id catalog property to point to a Glue catalog in a different AWS account. The Glue catalog ID is your numeric AWS account ID. If the Glue catalog is in a different region, you should configure your AWS client to point to the correct region, see more details in AWS client customization.

Skip Archive🔗

AWS Glue has the ability to archive older table versions and a user can roll back the table to any historical version if needed. By default, the Iceberg Glue Catalog will skip the archival of older table versions. If a user wishes to archive older table versions, they can set glue.skip-archive to false. Do note for streaming ingestion into Iceberg tables, setting glue.skip-archive to false will quickly create a lot of Glue table versions. For more details, please read Glue Quotas and the UpdateTable API.

Skip Name Validation🔗

Allow user to skip name validation for table name and namespaces. It is recommended to stick to Glue best practices to make sure operations are Hive compatible. This is only added for users that have existing conventions using non-standard characters. When database name and table name validation are skipped, there is no guarantee that downstream systems would all support the names.

Optimistic Locking🔗

By default, Iceberg uses Glue's optimistic locking for concurrent updates to a table. With optimistic locking, each table has a version id. If users retrieve the table metadata, Iceberg records the version id of that table. Users can update the table as long as the version ID on the server side remains unchanged. Version mismatch occurs if someone else modified the table before you did, causing an update failure. Iceberg then refreshes metadata and checks if there is a conflict. If there is no commit conflict, the operation will be retried. Optimistic locking guarantees atomic transaction of Iceberg tables in Glue. It also prevents others from accidentally overwriting your changes.

Info

Please use AWS SDK version >= 2.17.131 to leverage Glue's Optimistic Locking. If the AWS SDK version is below 2.17.131, only in-memory lock is used. To ensure atomic transaction, you need to set up a DynamoDb Lock Manager.

Warehouse Location🔗

Similar to all other catalog implementations, warehouse is a required catalog property to determine the root path of the data warehouse in storage. By default, Glue only allows a warehouse location in S3 because of the use of S3FileIO. To store data in a different local or cloud store, Glue catalog can switch to use HadoopFileIO or any custom FileIO by setting the io-impl catalog property.

Table Location🔗

By default, the root location for a table my_table of namespace my_ns is at my-warehouse-location/my-ns.db/my-table. This default root location can be changed at both namespace and table level.

To use a different path prefix for all tables under a namespace, use AWS console or any AWS Glue client SDK you like to update the locationUri attribute of the corresponding Glue database. For example, you can update the locationUri of my_ns to s3://my-ns-bucket, then any newly created table will have a default root location under the new prefix. For instance, a new table my_table_2 will have its root location at s3://my-ns-bucket/my_table_2.

To use a completely different root path for a specific table, set the location table property to the desired root path value you want. For example, in Spark SQL you can do:

CREATE TABLE my_catalog.my_ns.my_table (
    id bigint,
    data string,
    category string)
USING iceberg
OPTIONS ('location'='s3://my-special-table-bucket')
PARTITIONED BY (category);

For engines like Spark that support the LOCATION keyword, the above SQL statement is equivalent to:

CREATE TABLE my_catalog.my_ns.my_table (
    id bigint,
    data string,
    category string)
USING iceberg
LOCATION 's3://my-special-table-bucket'
PARTITIONED BY (category);

DynamoDB Catalog🔗

Iceberg supports using a DynamoDB table to record and manage database and table information.

Configurations🔗

The DynamoDB catalog supports the following configurations:

Property Default Description
dynamodb.table-name iceberg name of the DynamoDB table used by DynamoDbCatalog

Internal Table Design🔗

The DynamoDB table is designed with the following columns:

Column Key Type Description
identifier partition key string table identifier such as db1.table1, or string NAMESPACE for namespaces
namespace sort key string namespace name. A global secondary index (GSI) is created with namespace as partition key, identifier as sort key, no other projected columns
v string row version, used for optimistic locking
updated_at number timestamp (millis) of the last update
created_at number timestamp (millis) of the table creation
p.<property_key> string Iceberg-defined table properties including table_type, metadata_location and previous_metadata_location or namespace properties

This design has the following benefits:

  1. it avoids potential hot partition issue if there are heavy write traffic to the tables within the same namespace because the partition key is at the table level
  2. namespace operations are clustered in a single partition to avoid affecting table commit operations
  3. a sort key to partition key reverse GSI is used for list table operation, and all other operations are single row ops or single partition query. No full table scan is needed for any operation in the catalog.
  4. a string UUID version field v is used instead of updated_at to avoid 2 processes committing at the same millisecond
  5. multi-row transaction is used for catalog.renameTable to ensure idempotency
  6. properties are flattened as top level columns so that user can add custom GSI on any property field to customize the catalog. For example, users can store owner information as table property owner, and search tables by owner by adding a GSI on the p.owner column.

RDS JDBC Catalog🔗

Iceberg also supports the JDBC catalog which uses a table in a relational database to manage Iceberg tables. You can configure to use the JDBC catalog with relational database services like AWS RDS. Read this AWS documentation for more details about configuring the JDBC catalog with IAM authentication.

Which catalog to choose?🔗

With all the available options, we offer the following guidelines when choosing the right catalog to use for your application:

  1. if your organization has an existing Glue metastore or plans to use the AWS analytics ecosystem including Glue, Athena, EMR, Redshift and LakeFormation, Glue catalog provides the easiest integration.
  2. if your application requires frequent updates to table or high read and write throughput (e.g. streaming write), Glue and DynamoDB catalog provides the best performance through optimistic locking.
  3. if you would like to enforce access control for tables in a catalog, Glue tables can be managed as an IAM resource, whereas DynamoDB catalog tables can only be managed through item-level permission which is much more complicated.
  4. if you would like to query tables based on table property information without the need to scan the entire catalog, DynamoDB catalog allows you to build secondary indexes for any arbitrary property field and provide efficient query performance.
  5. if you would like to have the benefit of DynamoDB catalog while also connect to Glue, you can enable DynamoDB stream with Lambda trigger to asynchronously update your Glue metastore with table information in the DynamoDB catalog.
  6. if your organization already maintains an existing relational database in RDS or uses serverless Aurora to manage tables, the JDBC catalog provides the easiest integration.

DynamoDb Lock Manager🔗

Amazon DynamoDB can be used by HadoopCatalog or HadoopTables so that for every commit, the catalog first obtains a lock using a helper DynamoDB table and then try to safely modify the Iceberg table. This is necessary for a file system-based catalog to ensure atomic transaction in storages like S3 that do not provide file write mutual exclusion.

This feature requires the following lock related catalog properties:

  1. Set lock-impl as org.apache.iceberg.aws.dynamodb.DynamoDbLockManager.
  2. Set lock.table as the DynamoDB table name you would like to use. If the lock table with the given name does not exist in DynamoDB, a new table is created with billing mode set as pay-per-request.

Other lock related catalog properties can also be used to adjust locking behaviors such as heartbeat interval.

S3 FileIO🔗

Iceberg allows users to write data to S3 through S3FileIO. GlueCatalog by default uses this FileIO, and other catalogs can load this FileIO using the io-impl catalog property.

Progressive Multipart Upload🔗

S3FileIO implements a customized progressive multipart upload algorithm to upload data. Data files are uploaded by parts in parallel as soon as each part is ready, and each file part is deleted as soon as its upload process completes. This provides maximized upload speed and minimized local disk usage during uploads. Here are the configurations that users can tune related to this feature:

Property Default Description
s3.multipart.num-threads the available number of processors in the system number of threads to use for uploading parts to S3 (shared across all output streams)
s3.multipart.part-size-bytes 32MB the size of a single part for multipart upload requests
s3.multipart.threshold 1.5 the threshold expressed as a factor times the multipart size at which to switch from uploading using a single put object request to uploading using multipart upload
s3.staging-dir java.io.tmpdir property value the directory to hold temporary files

S3 Server Side Encryption🔗

S3FileIO supports all 3 S3 server side encryption modes:

  • SSE-S3: When you use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3), each object is encrypted with a unique key. As an additional safeguard, it encrypts the key itself with a master key that it regularly rotates. Amazon S3 server-side encryption uses one of the strongest block ciphers available, 256-bit Advanced Encryption Standard (AES-256), to encrypt your data.
  • SSE-KMS: Server-Side Encryption with Customer Master Keys (CMKs) Stored in AWS Key Management Service (SSE-KMS) is similar to SSE-S3, but with some additional benefits and charges for using this service. There are separate permissions for the use of a CMK that provides added protection against unauthorized access of your objects in Amazon S3. SSE-KMS also provides you with an audit trail that shows when your CMK was used and by whom. Additionally, you can create and manage customer managed CMKs or use AWS managed CMKs that are unique to you, your service, and your Region.
  • SSE-C: With Server-Side Encryption with Customer-Provided Keys (SSE-C), you manage the encryption keys and Amazon S3 manages the encryption, as it writes to disks, and decryption when you access your objects.

To enable server side encryption, use the following configuration properties:

Property Default Description
s3.sse.type none none, s3, kms or custom
s3.sse.key aws/s3 for kms type, null otherwise A KMS Key ID or ARN for kms type, or a custom base-64 AES256 symmetric key for custom type.
s3.sse.md5 null If SSE type is custom, this value must be set as the base-64 MD5 digest of the symmetric key to ensure integrity.

S3 Access Control List🔗

S3FileIO supports S3 access control list (ACL) for detailed access control. User can choose the ACL level by setting the s3.acl property. For more details, please read S3 ACL Documentation.

Object Store File Layout🔗

S3 and many other cloud storage services throttle requests based on object prefix. Data stored in S3 with a traditional Hive storage layout can face S3 request throttling as objects are stored under the same file path prefix.

Iceberg by default uses the Hive storage layout but can be switched to use the ObjectStoreLocationProvider. With ObjectStoreLocationProvider, a deterministic hash is generated for each stored file, with the hash appended directly after the write.data.path. This ensures files written to s3 are equally distributed across multiple prefixes in the S3 bucket. Resulting in minimized throttling and maximized throughput for S3-related IO operations. When using ObjectStoreLocationProvider having a shared and short write.data.path across your Iceberg tables will improve performance.

For more information on how S3 scales API QPS, check out the 2018 re:Invent session on Best Practices for Amazon S3 and Amazon S3 Glacier. At 53:39 it covers how S3 scales/partitions & at 54:50 it discusses the 30-60 minute wait time before new partitions are created.

To use the ObjectStorageLocationProvider add 'write.object-storage.enabled'=true in the table's properties. Below is an example Spark SQL command to create a table using the ObjectStorageLocationProvider: 

CREATE TABLE my_catalog.my_ns.my_table (
    id bigint,
    data string,
    category string)
USING iceberg
OPTIONS (
    'write.object-storage.enabled'=true, 
    'write.data.path'='s3://my-table-data-bucket')
PARTITIONED BY (category);

We can then insert a single row into this new table 

INSERT INTO my_catalog.my_ns.my_table VALUES (1, "Pizza", "orders");

Which will write the data to S3 with a hash (2d3905f8) appended directly after the write.object-storage.path, ensuring reads to the table are spread evenly across S3 bucket prefixes, and improving performance. 

s3://my-table-data-bucket/2d3905f8/my_ns.db/my_table/category=orders/00000-0-5affc076-96a4-48f2-9cd2-d5efbc9f0c94-00001.parquet

Note, the path resolution logic for ObjectStoreLocationProvider is write.data.path then <tableLocation>/data. However, for the older versions up to 0.12.0, the logic is as follows: - before 0.12.0, write.object-storage.path must be set. - at 0.12.0, write.object-storage.path then write.folder-storage.path then <tableLocation>/data.

For more details, please refer to the LocationProvider Configuration section.

S3 Strong Consistency🔗

In November 2020, S3 announced strong consistency for all read operations, and Iceberg is updated to fully leverage this feature. There is no redundant consistency wait and check which might negatively impact performance during IO operations.

Hadoop S3A FileSystem🔗

Before S3FileIO was introduced, many Iceberg users choose to use HadoopFileIO to write data to S3 through the S3A FileSystem. As introduced in the previous sections, S3FileIO adopts the latest AWS clients and S3 features for optimized security and performance and is thus recommended for S3 use cases rather than the S3A FileSystem.

S3FileIO writes data with s3:// URI scheme, but it is also compatible with schemes written by the S3A FileSystem. This means for any table manifests containing s3a:// or s3n:// file paths, S3FileIO is still able to read them. This feature allows people to easily switch from S3A to S3FileIO.

If for any reason you have to use S3A, here are the instructions:

  1. To store data using S3A, specify the warehouse catalog property to be an S3A path, e.g. s3a://my-bucket/my-warehouse
  2. For HiveCatalog, to also store metadata using S3A, specify the Hadoop config property hive.metastore.warehouse.dir to be an S3A path.
  3. Add hadoop-aws as a runtime dependency of your compute engine.
  4. Configure AWS settings based on hadoop-aws documentation (make sure you check the version, S3A configuration varies a lot based on the version you use).

S3 Write Checksum Verification🔗

To ensure integrity of uploaded objects, checksum validations for S3 writes can be turned on by setting catalog property s3.checksum-enabled to true. This is turned off by default.

S3 Tags🔗

Custom tags can be added to S3 objects while writing and deleting. For example, to write S3 tags with Spark 3.3, you can start the Spark SQL shell with: 

spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket/my/key/prefix \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.write.tags.my_key1=my_val1 \
    --conf spark.sql.catalog.my_catalog.s3.write.tags.my_key2=my_val2
For the above example, the objects in S3 will be saved with tags: my_key1=my_val1 and my_key2=my_val2. Do note that the specified write tags will be saved only while object creation.

When the catalog property s3.delete-enabled is set to false, the objects are not hard-deleted from S3. This is expected to be used in combination with S3 delete tagging, so objects are tagged and removed using S3 lifecycle policy. The property is set to true by default.

With the s3.delete.tags config, objects are tagged with the configured key-value pairs before deletion. Users can configure tag-based object lifecycle policy at bucket level to transition objects to different tiers. For example, to add S3 delete tags with Spark 3.3, you can start the Spark SQL shell with: 

sh spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://iceberg-warehouse/s3-tagging \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.delete.tags.my_key3=my_val3 \
    --conf spark.sql.catalog.my_catalog.s3.delete-enabled=false

For the above example, the objects in S3 will be saved with tags: my_key3=my_val3 before deletion. Users can also use the catalog property s3.delete.num-threads to mention the number of threads to be used for adding delete tags to the S3 objects.

When the catalog property s3.write.table-tag-enabled and s3.write.namespace-tag-enabled is set to true then the objects in S3 will be saved with tags: iceberg.table=<table-name> and iceberg.namespace=<namespace-name>. Users can define access and data retention policy per namespace or table based on these tags. For example, to write table and namespace name as S3 tags with Spark 3.3, you can start the Spark SQL shell with: 

sh spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://iceberg-warehouse/s3-tagging \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.write.table-tag-enabled=true \
    --conf spark.sql.catalog.my_catalog.s3.write.namespace-tag-enabled=true
For more details on tag restrictions, please refer User-Defined Tag Restrictions.

S3 Access Points🔗

Access Points can be used to perform S3 operations by specifying a mapping of bucket to access points. This is useful for multi-region access, cross-region access, disaster recovery, etc.

For using cross-region access points, we need to additionally set use-arn-region-enabled catalog property to true to enable S3FileIO to make cross-region calls, it's not required for same / multi-region access points.

For example, to use S3 access-point with Spark 3.3, you can start the Spark SQL shell with: 

spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket2/my/key/prefix \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.use-arn-region-enabled=false \
    --conf spark.sql.catalog.test.s3.access-points.my-bucket1=arn:aws:s3::123456789012:accesspoint:mfzwi23gnjvgw.mrap \
    --conf spark.sql.catalog.test.s3.access-points.my-bucket2=arn:aws:s3::123456789012:accesspoint:mfzwi23gnjvgw.mrap
For the above example, the objects in S3 on my-bucket1 and my-bucket2 buckets will use arn:aws:s3::123456789012:accesspoint:mfzwi23gnjvgw.mrap access-point for all S3 operations.

For more details on using access-points, please refer Using access points with compatible Amazon S3 operations.

S3 Acceleration🔗

S3 Acceleration can be used to speed up transfers to and from Amazon S3 by as much as 50-500% for long-distance transfer of larger objects.

To use S3 Acceleration, we need to set s3.acceleration-enabled catalog property to true to enable S3FileIO to make accelerated S3 calls.

For example, to use S3 Acceleration with Spark 3.3, you can start the Spark SQL shell with: 

spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket2/my/key/prefix \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.acceleration-enabled=true

For more details on using S3 Acceleration, please refer to Configuring fast, secure file transfers using Amazon S3 Transfer Acceleration.

S3 Dual-stack🔗

S3 Dual-stack allows a client to access an S3 bucket through a dual-stack endpoint. When clients request a dual-stack endpoint, the bucket URL resolves to an IPv6 address if possible, otherwise fallback to IPv4.

To use S3 Dual-stack, we need to set s3.dualstack-enabled catalog property to true to enable S3FileIO to make dual-stack S3 calls.

For example, to use S3 Dual-stack with Spark 3.3, you can start the Spark SQL shell with: 

spark-sql --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket2/my/key/prefix \
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
    --conf spark.sql.catalog.my_catalog.s3.dualstack-enabled=true

For more details on using S3 Dual-stack, please refer Using dual-stack endpoints from the AWS CLI and the AWS SDKs

AWS Client Customization🔗

Many organizations have customized their way of configuring AWS clients with their own credential provider, access proxy, retry strategy, etc. Iceberg allows users to plug in their own implementation of org.apache.iceberg.aws.AwsClientFactory by setting the client.factory catalog property.

Cross-Account and Cross-Region Access🔗

It is a common use case for organizations to have a centralized AWS account for Glue metastore and S3 buckets, and use different AWS accounts and regions for different teams to access those resources. In this case, a cross-account IAM role is needed to access those centralized resources. Iceberg provides an AWS client factory AssumeRoleAwsClientFactory to support this common use case. This also serves as an example for users who would like to implement their own AWS client factory.

This client factory has the following configurable catalog properties:

Property Default Description
client.assume-role.arn null, requires user input ARN of the role to assume, e.g. arn:aws:iam::123456789:role/myRoleToAssume
client.assume-role.region null, requires user input All AWS clients except the STS client will use the given region instead of the default region chain
client.assume-role.external-id null An optional external ID
client.assume-role.timeout-sec 1 hour Timeout of each assume role session. At the end of the timeout, a new set of role session credentials will be fetched through an STS client.

By using this client factory, an STS client is initialized with the default credential and region to assume the specified role. The Glue, S3 and DynamoDB clients are then initialized with the assume-role credential and region to access resources. Here is an example to start Spark shell with this client factory:

spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.4.3,org.apache.iceberg:iceberg-aws-bundle:1.4.3 \
    --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=s3://my-bucket/my/key/prefix \    
    --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
    --conf spark.sql.catalog.my_catalog.client.factory=org.apache.iceberg.aws.AssumeRoleAwsClientFactory \
    --conf spark.sql.catalog.my_catalog.client.assume-role.arn=arn:aws:iam::123456789:role/myRoleToAssume \
    --conf spark.sql.catalog.my_catalog.client.assume-role.region=ap-northeast-1

HTTP Client Configurations🔗

AWS clients support two types of HTTP Client, URL Connection HTTP Client and Apache HTTP Client. By default, AWS clients use Apache HTTP Client to communicate with the service. This HTTP client supports various functionalities and customized settings, such as expect-continue handshake and TCP KeepAlive, at the cost of extra dependency and additional startup latency. In contrast, URL Connection HTTP Client optimizes for minimum dependencies and startup latency but supports less functionality than other implementations.

For more details of configuration, see sections URL Connection HTTP Client Configurations and Apache HTTP Client Configurations.

Configure the following property to set the type of HTTP client:

Property Default Description
http-client.type apache Types of HTTP Client.
urlconnection: URL Connection HTTP Client
apache: Apache HTTP Client

URL Connection HTTP Client Configurations🔗

URL Connection HTTP Client has the following configurable properties:

Property Default Description
http-client.urlconnection.socket-timeout-ms null An optional socket timeout in milliseconds
http-client.urlconnection.connection-timeout-ms null An optional connection timeout in milliseconds

Users can use catalog properties to override the defaults. For example, to configure the socket timeout for URL Connection HTTP Client when starting a spark shell, one can add: 

--conf spark.sql.catalog.my_catalog.http-client.urlconnection.socket-timeout-ms=80

Apache HTTP Client Configurations🔗

Apache HTTP Client has the following configurable properties:

Property Default Description
http-client.apache.socket-timeout-ms null An optional socket timeout in milliseconds
http-client.apache.connection-timeout-ms null An optional connection timeout in milliseconds
http-client.apache.connection-acquisition-timeout-ms null An optional connection acquisition timeout in milliseconds
http-client.apache.connection-max-idle-time-ms null An optional connection max idle timeout in milliseconds
http-client.apache.connection-time-to-live-ms null An optional connection time to live in milliseconds
http-client.apache.expect-continue-enabled null, disabled by default An optional true/false setting that controls whether expect continue is enabled
http-client.apache.max-connections null An optional max connections in integer
http-client.apache.tcp-keep-alive-enabled null, disabled by default An optional true/false setting that controls whether tcp keep alive is enabled
http-client.apache.use-idle-connection-reaper-enabled null, enabled by default An optional true/false setting that controls whether use idle connection reaper is used

Users can use catalog properties to override the defaults. For example, to configure the max connections for Apache HTTP Client when starting a spark shell, one can add: 

--conf spark.sql.catalog.my_catalog.http-client.apache.max-connections=5

Run Iceberg on AWS🔗

Amazon Athena🔗

Amazon Athena provides a serverless query engine that could be used to perform read, write, update and optimization tasks against Iceberg tables. More details could be found here.

Amazon EMR🔗

Amazon EMR can provision clusters with Spark (EMR 6 for Spark 3, EMR 5 for Spark 2), Hive, Flink, Trino that can run Iceberg.

Starting with EMR version 6.5.0, EMR clusters can be configured to have the necessary Apache Iceberg dependencies installed without requiring bootstrap actions. Please refer to the official documentation on how to create a cluster with Iceberg installed.

For versions before 6.5.0, you can use a bootstrap action similar to the following to pre-install all necessary dependencies: 

#!/bin/bash

ICEBERG_VERSION=1.4.3
MAVEN_URL=https://repo1.maven.org/maven2
ICEBERG_MAVEN_URL=$MAVEN_URL/org/apache/iceberg
# NOTE: this is just an example shared class path between Spark and Flink,
#  please choose a proper class path for production.
LIB_PATH=/usr/share/aws/aws-java-sdk/


ICEBERG_PACKAGES=(
  "iceberg-spark-runtime-3.3_2.12"
  "iceberg-flink-runtime"
  "iceberg-aws-bundle"
)

install_dependencies () {
  install_path=$1
  download_url=$2
  version=$3
  shift
  pkgs=("$@")
  for pkg in "${pkgs[@]}"; do
    sudo wget -P $install_path $download_url/$pkg/$version/$pkg-$version.jar
  done
}

install_dependencies $LIB_PATH $ICEBERG_MAVEN_URL $ICEBERG_VERSION "${ICEBERG_PACKAGES[@]}"

AWS Glue🔗

AWS Glue provides a serverless data integration service that could be used to perform read, write and update tasks against Iceberg tables. More details could be found here.

AWS EKS🔗

AWS Elastic Kubernetes Service (EKS) can be used to start any Spark, Flink, Hive, Presto or Trino clusters to work with Iceberg. Search the Iceberg blogs page for tutorials around running Iceberg with Docker and Kubernetes.

Amazon Kinesis🔗

Amazon Kinesis Data Analytics provides a platform to run fully managed Apache Flink applications. You can include Iceberg in your application Jar and run it in the platform.