Apache iceberg example12/13/2023 ![]() ![]() ![]() Schema and partition changes on an existing table can be performed with ease, as these changes are tracked as separate components in snapshots on the metadata layer.This enables faster query processing, as the query provided by users pulls data at the file level rather than the partition level. Any update or delete to the data layer creates a new snapshot in the metadata layer from the previous latest snapshot and parallelly chains up the snapshot. ![]() Concurrent commits on the same datasets ensure atomicity of transaction with optimistic concurrency control.The metadata layer manages the snapshot list, and Iceberg supports integration with multiple query engines. Each commit at any timeline is stored as an event on the data layer when data is added. It uses a file structure (metadata and manifest files) that is managed in the metadata layer.The design structure of Iceberg is different from Apache Hive, where the metadata layer and data layer are managed and maintained on object storage like Hadoop or Amazon Simple Storage Service (Amazon S3).The key difference is in how Iceberg stores records in object storage.įigure 1 – Apache Iceberg table architecture. For example, if your raw zone was \raw\year\month\day and you wanted to change it to \raw\year\month\day\hour then you would need to rebuild your entire raw zone partition structure.Īpache Iceberg is designed to overcome the drawbacks faced when using Apache Hive. Updates to existing partitions in Apache Hive needs a recreation of existing table mapping to a new location, as partitions are defined at the creation of table and cannot be modified as the tables grow.Users even need to keep track of the physical layout of tables while writing queries. If multiple partitions are present, this adds an additional layer of overheads to querying datasets. Querying of data from Apache Hive takes a long time as the datasets grow over a period due to its directory structure to store partitions.In Apache Hive, the files in a partition are scanned at runtime, while in Iceberg there is a manifest file which improves performance. Fetching the entire directory list from a partition level takes a long time for large tables.There’s a possibility of data loss as the last write operation wins and querying during these concurrent writes provides different results. Concurrent writes on the same dataset are not a safe operation in Apache Hive.This is inefficient for large partitions since the complete partitions need to be rewritten to a new location frequently, for each update or delete.When changes are made to the existing data, like when updates or deletes are performed, the changes cannot be handled at a file level in Apache Hive. Data changes for large datasets are inefficient.Some of the key challenges faced by Apache Hive are: When used at scale with large datasets, there are many issues due to its design. Iceberg is a new table format design which addresses the issues faced by Apache Hive. Consistent and concurrent writes in parallel.Using data lakes at scale (petabyte-scalable tables).The key problems Iceberg tries to address are: It was open sourced in 2018 as an Apache Incubator project and graduated from the incubator in 2020. Why Use Apache Iceberg?Īpache Iceberg is an open table format, originally designed at Netflix in order to overcome the challenges faced when using existing data lake formats like Apache Hive. Rackspace is an AWS Premier Tier Services Partner and Managed Cloud Services Provider (MSP) that helps businesses tap the power of Amazon Web Services (AWS) from a trusted partner with a track record of managing business-critical applications. Additionally, I will review design differences between Apache Hive and Iceberg.Īs a Professional Services Big Data Engineer at Rackspace Technology, I have architected enterprise-level solutions which includes developing data lakes, designing data warehouses, and implementing event-driven architectures. In this post, I will discuss the drawbacks of existing data lake architecture, what Apache Iceberg is, and how it overcomes the shortcomings of the current state of data lakes. Apache Hive is a standard for data lakes, but while Apache Hive can solve some of the issues with the processing of data, it falls short at a few other objectives for next-generation data processing. To fuel this transformation, data lakes have evolved over the last decade. With this transformation, there has been a rapid adoption of data lakes across the industry. By Chaitanya Varma Mudundi, Professional Services Big Data Engineer – Rackspaceĭata-driven decision making is accelerating and defining the way organizations work. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |