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. table_name [ WHERE predicate] To optimize query times, all predicates are pushed down to where the data lives. It unifies the live job and the backfill job source to Iceberg. Apache Iceberg is an open-source table format for data stored in data lakes. In that case, we have to query the table with the snapshot-id corresponding to the deleted row. you can go to the documentations of each engine to see how to load a custom catalog. If this is the first time that youre using Athena to run queries, create another globally unique S3 bucket to hold your Athena query output. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. SparkSQL Spark-Shell PySpark S3 Dual-stack allows a client to access an S3 bucket through a dual-stack endpoint. In the next few steps, lets focus on a record in the table with review ID RZDVOUQG1GBG7. It may take up to 15 minutes for the commands to complete. While inserting the data, we partition the data by review_date as per the table definition. Apache Iceberg is an open table format designed for huge, petabyte-scale tables. Sep 28, 2022 9 The new generation data lake table formats ( Apache Hudi, Apache Iceberg, and Delta Lake) are getting more traction every day with their superior capabilities. Second, it was reducing commit errors due to parallel overwrites of a version-hint.txt file. ismailsimsek/iceberg-examples: Apache iceberg Spark s3 examples - GitHub I want to understand if Apache Iceberg is a good fit to provide indexing of my S3 files. Jared Keating is a Senior Cloud Consultant with AWS Professional Services. Apache Iceberg is an open table format for huge analytic datasets. In order to improve the query performance, its recommended to compact small data files to larger data files. Queries follow the Apache Iceberg format v2 spec and perform merge-on-read of both position and equality deletes. For more details, please refer to Lock catalog properties. He holds 12 AWS certifications and is passionate about helping customers implement cloud enterprise strategies for digital transformation. With ObjectStoreLocationProvider, a deterministic hash is generated for each stored file, with the hash appended Convert data to Iceberg table format and move data to the curated zone. Apache Iceberg is dramatically cost-effective compared to Apache Hive. To use the Tez engine on Hive 3.1.2 or later, Tez needs to be upgraded to >= 0.10.1 which contains a necessary fix TEZ-4248.. To use the Tez engine on Hive 2.3.x, you will need to manually build Tez from the branch-0.9 branch due to a backwards incompatibility issue with Tez 0.10.1. There are two types of actions: Amazon S3 uses object tagging to categorize storage where each tag is a key-value pair. Amazon S3 is designed for 99.999999999% (11 9s) of durability, S3 Standard is designed for 99.99% availability, and Standard IA is designed for 99.9% availability. With ObjectStoreLocationProvider, a deterministic hash is generated for each stored file and a subfolder is appended right after the S3 folder specified using the parameter write.data.path (write.object-storage-path for Iceberg version 0.12 and below). With optimistic locking, each table has a version id. This dramatically increases the I/O operation and slows down the queries. 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. During a planned or unplanned regional traffic disruption, failover controls let you control failover between buckets in different Regions and accounts within minutes. He is an Apache Iceberg Committer and PMC member. In this example, we use a Hive catalog, but we can change to the Data Catalog with the following configuration: Before you run this step, create a S3 bucket and an iceberg folder in your AWS account with the naming convention /iceberg/. Youre redirected to the cluster detail page, where you wait for the EMR cluster to transition from Starting to Waiting. We walk you through how query scan planning and partitioning work in Iceberg and how we use them to improve query performance. Mohit Mehta is a Principal Architect at AWS with expertise in AI/ML and data analytics. What Is Apache Iceberg? Features & Benefits | Dremio Drop the AWS Glue tables and database from Athena or run the following code in your notebook: Select the Workspace you created and choose. The following diagram illustrates our solution architecture. The manifest file tracks data files as well as additional details about each file, such as the file format. Navigate to the Athena console and choose Query editor. The examples are run on a Jupyter Notebook environment attached to the EMR cluster. The DynamoDB catalog supports the following configurations: The DynamoDB table is designed with the following columns: Iceberg also supports the JDBC catalog which uses a table in a relational database to manage Iceberg tables. Iceberg allows users to write data to S3 through S3FileIO. More details about loading the catalog can be found in individual engine pages, such as Spark and Flink. After you complete the test, clean up your resources to avoid any recurring costs: As companies continue to build newer transactional data lake use cases using Apache Iceberg open table format on very large datasets on S3 data lakes, there will be an increased focus on optimizing those petabyte-scale production environments to reduce cost, improve efficiency, and implement high availability. Delete the S3 bucket and any other resources that you created as part of the prerequisites for this post. Solution overview In this post, we walk you through a solution to build a high-performing Apache Iceberg data lake on Amazon S3; process incremental data with insert, update, and delete SQL statements; and tune the Iceberg table to improve read and write performance. Iceberg - Amazon EMR You can write the data files at any time, but only commit the change explicitly, which creates a new version of the snapshot and metadata files. She helps enterprise customers create data analytics strategies and build solutions to accelerate their businesses outcomes. The iceberg-aws module is bundled with Spark and Flink engine runtimes for all versions from 0.11.0 onwards. Timeout of each assume role session. Still, to make your data lake workloads highly available in an unlikely outage situation, you can replicate your S3 data to another AWS Region as a backup. Getting Started - The Apache Software Foundation This means for any table manifests containing s3a:// or s3n:// file paths, S3FileIO is still able to read them. There is an increased need for data lakes to support database like features such as ACID transactions, record-level updates and deletes, time travel, and rollback. Iceberg. SELECT * FROM [ db_name .] Leave other settings at their default and choose, Leave the remaining settings unchanged and choose. As introduced in the previous sections, S3FileIO adopts the latest AWS clients and S3 features for optimized security and performance Here is an example to start Spark shell with this client factory: AWS clients support two types of HTTP Client, URL Connection HTTP Client http-client.urlconnection.socket-timeout-ms, http-client.urlconnection.connection-timeout-ms, http-client.apache.connection-acquisition-timeout-ms, http-client.apache.connection-max-idle-time-ms, http-client.apache.connection-time-to-live-ms, http-client.apache.expect-continue-enabled, http-client.apache.tcp-keep-alive-enabled, http-client.apache.use-idle-connection-reaper-enabled, namespace operations are clustered in a single partition to avoid affecting table commit operations. S3 and many other cloud storage services throttle requests based on object prefix. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Iceberg is an open table format from the Apache Software Foundation that supports huge analytic datasets. Features & Benefits The Apache Iceberg Open Table Format Apache Iceberg is a new table format that solves the challenges with traditional catalogs and is rapidly becoming an industry standard for managing data in data lakes. The metadata file location can be fetched from the metadata log entries metatable as illustrated earlier. Reading from a branch or tag can be done as usual via the Table Scan API, by passing in a branch or tag in the API. In your notebook, run the following code: This sets the following Spark session configurations: In our Spark session, run the following commands to load data: Iceberg format v2 is needed to support row-level updates and deletes. 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= and iceberg.namespace=. A tag already exists with the provided branch name. Iceberg helps data engineers tackle complex challenges in data lakes such as managing continuously evolving datasets while maintaining query performance. Most cloud blob storage like S3 don't charge cross-AZ network . At the top of the hierarchy is the metadata file, which stores information about the tables schema, partition information, and snapshots. For more details on using access points, refer to Using access points with compatible Amazon S3 operations. All rights reserved. Planning in an Iceberg table is very efficient, because Icebergs rich metadata can be used to prune metadata files that arent needed, in addition to filtering data files that dont contain matching data. Examples are including Apache iceberg with Spark SQL and using Apache iceberg api with java. Apache Iceberg is an open table format for large datasets in Amazon Simple Storage Service (Amazon S3) and provides fast query performance over large tables, atomic commits, concurrent writes, and SQL-compatible table evolution. Please check the full article Apache Hudi vs. Delta Lake vs. Apache Iceberg for fantastic and detailed feature comparison, including illustrations of table services and supported platforms and ecosystems. If you're not familiar with EMR, it's a simple way to get a Spark cluster running in about ten minutes. He focuses on helping customers develop, adopt, and implement cloud services and strategy. Finally, we show you how to performance tune the process to improve read and write performance. To use the console to create a cluster with Iceberg installed, follow the steps in Build an Apache Iceberg data lake using Amazon Athena, Amazon EMR, and AWS Glue. Use the following code to alter the table format: To make a query run fast, the less data read the better. and Apache HTTP Client. If there is no commit conflict, the operation will be retried. Configure the Spark session for Apache Iceberg. Update your-iceberg-storage-blog in the following configuration with the bucket that you created to test this example. You can adjust your retry strategy by increasing the maximum retry limit for the default exponential backoff retry strategy or enabling and configuring the additive-increase/multiplicative-decrease (AIMD) retry strategy. access-point for all S3 operations. If these columns are used in the query condition, it allows query engines to further skip data files, thereby enabling even faster queries. For cross-Region access points, we need to additionally set the use-arn-region-enabled catalog property to true to enable S3FileIO to make cross-Region calls. In 2022, Amazon Athena announced support of Iceberg and Amazon EMR added support of Iceberg starting with version 6.5.0. ADD COLUMNS (points string); Step 3: Delete column field example on AWS Athena using Iceberg. More data files lead to more metadata. The table book_reviews is available for querying. It is recommended to stick to Glue best practice in Read the JDBC integration page for guides and examples about using the JDBC catalog. Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. 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. With S3 data residing in multiple Regions, you can use an S3 multi-Region access point as a solution to access the data from the backup Region. Spark is currently the most feature-rich compute engine for Iceberg operations. Using Apache Iceberg tables - Amazon Athena This location provider has been recently open sourced by Amazon EMR via Core: Improve bit density in object storage layout and should be available starting from Iceberg 1.3.0. What is Iceberg? You can improve the read and write performance on Iceberg tables by adjusting the table properties. For this demo, we use an EMR notebook to run Spark commands. For more details on using access-points, please refer Using access points with compatible Amazon S3 operations. Daniel Li is a Sr. He is an active contributor in open source projects like Apache Spark and Apache Iceberg. When updating and deleting records in Iceberg table, if the read-on-merge approach is used, you might end up with many small deletes or new data files. Click here to return to Amazon Web Services homepage, Apache Iceberg integration with the AWS Glue Data Catalog, Amazon Athena announced support of Iceberg, Amazon EMR added support of Iceberg starting with version 6.5.0, a variety of other open-source compute engines, Maintain transactional consistency where files can be added, removed, or modified atomically with full read isolation and multiple concurrent writes, Implement full schema evolution to process safe table schema updates as the table data evolves, Organize tables into flexible partition layouts with partition evolution, enabling updates to partition schemes as queries and data volume changes without relying on physical directories, Perform row-level update and delete operations to satisfy new regulatory requirements such as the General Data Protection Regulation (GDPR), Provide versioned tables and support time travel queries to query historical data and verify changes between updates, Roll back tables to prior versions to return tables to a known good state in case of any issues. You can view the existing metadata files from the metadata log entries metatable after the expiration of snapshots: The snapshots that have expired show the latest snapshot ID as null. Note the configuration parameters s3.write.tags.write-tag-name and s3.delete.tags.delete-tag-name, which will tag the new S3 objects and deleted objects with corresponding tag values. With the s3.delete.tags config, objects are tagged with the configured key-value pairs before deletion. In the following sections, we provide examples for these use cases. This client factory has the following configurable catalog properties: By using this client factory, an STS client is initialized with the default credential and region to assume the specified role. For more information, refer to Retry Amazon S3 requests with EMRFS. Amazon S3 supports a request rate of 3,500 PUT/COPY/POST/DELETE or 5,500 GET/HEAD requests per second per prefix in a bucket. A comprehensive overview of Data Lake Table Formats Services by Onehouse.ai (reduced to rows with differences only). Details about this feature can be found in the custom FileIO section. If for any reason you have to use S3A, here are the instructions: To ensure integrity of uploaded objects, checksum validations for S3 writes can be turned on by setting catalog property s3.checksum-enabled to true. to run fully managed Apache Flink applications. For example, in Spark SQL you can do: For engines like Spark that support the LOCATION keyword, the above SQL statement is equivalent to: Iceberg supports using a DynamoDB table to record and manage database and table information. Jack Ye is a software engineer of the Athena Data Lake and Storage team. In Athena, you can use the following syntax to travel to a time that is after when the first version was committed: One of the most important features of a data lake is for different systems to seamlessly work together through the Iceberg open-source protocol. For more details, please read S3 ACL Documentation. If the AWS SDK version is below 2.17.131, only in-memory lock is used. Iceberg contains a built-in implementation that uses Hive metastore to work with s3 reliably (lock/unlock). In order to use the column-level stats effectively, you want to further sort your records based on the query patterns. Iceberg: a fast table format for S3 - SlideShare After you restore the objects back in S3 Standard class, you can register the metadata and data as an archival table for query purposes. You can see the database name, the location (S3 path) of the Iceberg table, and the metadata location. Spark and Iceberg Quickstart - The Apache Software Foundation 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. Amazon Kinesis Data Analytics provides a platform If this is your first time using the Athena query editor, you need to configure to use the S3 bucket you created earlier to store the query results. More and more customers are building data lakes, with structured and unstructured data, to support many users, applications, and analytics tools. If users retrieve the table metadata, Iceberg records the version id of that table. Amazon EMR can provision clusters with Spark (EMR 6 for Spark 3, EMR 5 for Spark 2), After all the operations are performed in Athena, lets go back to Amazon EMR and confirm that Amazon EMR Spark can consume the updated data. The following examples are also available in the sample notebook in the aws-samples GitHub repo for quick experimentation. Iceberg also let you configure a tag-based object lifecycle policy at the bucket level to transition objects to different Amazon S3 tiers. From an Apache Iceberg perspective, it supports custom Amazon S3 object tags that can be added to S3 objects while writing and deleting into the table. Using Iceberg's S3FileIO Implementation to Store Your Data in MinIO When using ObjectStoreLocationProvider having a shared and short write.data.path across your Iceberg tables will improve performance. This eliminates the need to reconcile them during reads. Let's do an example that uses S3 as our data source and Iceberg catalog. Then we walk through a solution to build a high-performance and evolving Iceberg data lake on Amazon Simple Storage Service (Amazon S3) and process incremental data by running insert, update, and delete SQL statements. Choose the same VPC and subnet as those for the EMR cluster, and the default security group. 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 its complete, you should be able to see the table on the AWS Glue console, under the reviews database, with the table_type property shown as ICEBERG. Open source: Apache Iceberg is an open source project, which means that it is free to use and can be customized to meet your specific needs. We delete the new single record that we inserted with the current review_date: We can now check that a new snapshot was created with the operation flagged as delete: This is useful if we want to time travel and check the deleted row in the future. If your data file size is small, you might end up with thousands or millions of files in an Iceberg table. properties are flattened as top level columns so that user can add custom GSI on any property field to customize the catalog. User can choose the ACL level by setting the s3.acl property. Hive, Flink, To set up and test this solution, we complete the following high-level steps: To follow along with this walkthrough, you must have the following: To create an S3 bucket that holds your Iceberg data, complete the following steps: Because S3 bucket names are globally unique, choose a different name when you create your bucket. Example Iceberg catalog configuration To use AWS module with Flink, you can download the necessary dependencies and specify them when starting the Flink SQL client: With those dependencies, you can create a Flink catalog like the following: You can also specify the catalog configurations in sql-client-defaults.yaml to preload it: To use AWS module with Hive, you can download the necessary dependencies similar to the Flink example,

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