![]() ![]() 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. ![]()
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