Author name: CanadianDataGuy

Delta Lake is a powerful open-source storage layer that brings ACID transactions, scalable metadata handling, and unified batch and streaming data processing to big data workloads. It’s designed to improve data reliability and enable complex data processing workflows. This technical blog will blend the key features of Delta Lake with resources for a deeper understanding of how these features are achieved. The resources in this guide, from essential whitepapers to insightful video tutorials, were key to my mastery of Delta Lake, offering a deep dive into its architecture and practical applications, and equipping me with the knowledge to effectively utilize its features in real-world data scenarios. Key Features of Delta Lake ACID Transactions Delta Lake provides serializable isolation levels, ensuring that readers always see consistent data, even in the presence of concurrent writes. This is achieved through a transaction log that records details about every change made to the data Scalable Metadata Handling With the help of Spark’s distributed processing power, Delta Lake can handle metadata for petabyte-scale tables, which may include billions of files and partitions. This scalability is crucial for managing large datasets efficiently Unified Batch and Streaming Data Processing Delta Lake tables serve as both batch tables and streaming sources/sinks, offering exactly-once semantics for data ingestion, backfill, and interactive queries. This unification simplifies the data pipeline and reduces the complexity of data processing Schema Evolution and Enforcement Delta Lake prevents the insertion of bad records during ingestion by enforcing schemas automatically. It also supports schema evolution, allowing for the addition of new columns to data tables without disrupting existing operations Time Travel (Data Versioning) Data versioning in Delta Lake enables rollbacks, full historical audit trails, and reproducible machine learning experiments. Users can access and revert to earlier versions of data for various purposes DML Operations Delta Lake supports merge, update, and delete operations, which are essential for use cases like change-data-capture (CDC) and slowly-changing-dimension (SCD) operations Deep Dive Resources To understand how Delta Lake achieves these features, the following resources provide in-depth technical knowledge: Lakehouse Storage Systems Whitepaper For a comprehensive technical understanding of Delta Lake’s internals, the Lakehouse Storage Systems Whitepaper is invaluable. It explains the architecture and mechanisms that enable Delta Lake’s features, such as ACID transactions and scalable metadata handling. Read the whitepaper here. Educational Videos Quick Overviews Real-World Use Cases To see Delta Lake in action, refer to The Delta Lake Series Complete Collection. This guide helps you understand various use cases and how Delta Lake addresses complex data challenges. Access it here. Conclusion Delta Lake is a sophisticated tool that addresses many of the challenges associated with big data processing and storage. By leveraging the resources provided, you can gain a deeper technical understanding of how Delta Lake ensures data reliability, consistency, and scalability. Whether you’re a data engineer, architect, or analyst, these insights will help you to effectively implement and utilize Delta Lake in your data solutions. Thank You for Reading! I hope you found this article helpful and informative. If you enjoyed this post, please consider giving it a clap 👏 and sharing it with your network. Your support is greatly appreciated! — CanadianDataGuy

Delta Lake is a powerful technology for bringing ACID transactions to your data lakes. It allows multiple operations to be performed on a dataset concurrently. However, dealing with concurrent operations can sometimes be tricky and may lead to issues such as ConcurrentAppendException, ConcurrentDeleteReadException, and ConcurrentDeleteDeleteException. In this blog post, we will explore why these issues occur and how to handle them effectively using a Python function, and how to avoid them with table design and using isolation levels and write conflicts. Why Do These Issues Happen? Understanding Isolation Levels: Serializable vs. WriteSerializable Isolation levels in a database control how much transactions are protected from each other’s changes. Delta Lake on Databricks offers two such levels: Serializable and WriteSerializable. 1. Serializable: — This is the highest level of isolation. — It ensures that all write and read operations are done in a specific order, just like how they appear in the table’s history. — This means operations are carried out one by one, maintaining the order and ensuring the final result is as expected. 2. WriteSerializable (Default): — This level is a bit more relaxed compared to Serializable. — It guarantees order only for write operations, not for reads. — Even though it’s more relaxed, it’s still more strict than the Snapshot isolation level. — This level is used by default as it offers a good mix of data consistency and availability for most operations. Solution 1: Setting the Isolation Level: Solution 2: Avoiding Conflicts Using Partitioning and Disjoint Command Conditions When working with tables, sometimes two operations can clash or conflict, especially if they are working on the same set of files. This can cause problems and errors. But, there’s a way to avoid this! You can organize or partition your table based on certain columns that are often used in operations. This way, different operations work on different parts of the table, preventing them from clashing. For example, imagine two commands — one is updating the table for dates after January 1, 2010, and another is deleting from the table for dates before January 1, 2010. These two can clash if the table is not organized by date, as both might try to change the same files. But if you partition the table by date, these operations won’t conflict, making things smooth and error-free. However, be careful while choosing the column for partitioning. If you choose a column that has a lot of unique values, it can create a large number of subdirectories. This can lead to other issues, affecting the performance of operations on the table. By using these strategies and understanding the insights from Databricks regarding isolation levels, row-level concurrency, and write conflicts, you can make your Delta operations more robust, reliable, and efficient. Solution 3: Code block with exponential retry The Python code below offers a robust solution to address this challenge. It is designed to manage concurrent write operations to a Delta table or path by intelligently retrying the operation in the event of specific concurrent exceptions. Streaming_write_with_concurrent_retry takes parameters such as the data stream, maximum attempts, and others to provide flexibility and control. It employs a while loop to attempt the write operation continuously and waits for its completion. In case of concurrent exceptions, it increments the attempt counter and calculates the sleep time using an exponential backoff strategy before retrying the operation. This approach ensures that the write operation is eventually successful, providing reliability and efficiency in handling concurrent operations on Delta tables. Explore the code below to understand its workings and integrate it into your projects to enhance concurrent operations handling. from datetime import datetime from time import sleep from delta.exceptions import ( ConcurrentAppendException, ConcurrentDeleteReadException, ConcurrentDeleteDeleteException, ) import math def streaming_write_with_concurrent_retry( stream, max_attempts=3, indefinite=False, table=None, path=None ): “”” Handles concurrent write operations to a Delta table or path by retrying the operation in case of specific concurrent exceptions. :param stream: The data stream to be written. :param max_attempts: The maximum number of retry attempts. Default is 3. :param indefinite: If True, will keep retrying indefinitely. Default is False. :param table: The Delta table to write to. :param path: The path to write to. :return: The result of writer.awaitTermination(). “”” attempt = 0 # Initialize attempt counter while True: try: # Choose the writer based on whether table or path is provided if table: writer = stream.table(table) elif path: writer = stream.start(path) else: writer = stream.start() # Attempt to write and wait for termination return writer.awaitTermination() # Handle concurrent exceptions except ( ConcurrentAppendException, ConcurrentDeleteReadException, ConcurrentDeleteDeleteException, ) as e: # Increment attempt counter attempt += 1 # If indefinite is False and attempts have reached max_attempts, raise the exception if not indefinite and attempt >= max_attempts: raise e from None # Calculate sleep time using exponential backoff strategy sleep_time = min(120, math.pow(2, attempt)) # Sleep for the calculated time before retrying sleep(sleep_time) Solution 4: Row-Level Concurrency (Advanced Feature)? Available only on Delta tables with deletion vectors enabled and on Photon-enabled compute running Databricks Runtime 14.0 and above. Reference Isolation levels and write conflicts on Databricks *Learn about the isolation levels and potential conflicts when performing concurrent transactions on tables on…*docs.databricks.com Thank You for Reading! I hope you found this article helpful and informative. If you enjoyed this post, please consider giving it a clap 👏 and sharing it with your network. Your support is greatly appreciated! — **CanadianDataGuy**