Taming the Live Stream: Wrangling Limitations of Delta Live Tables

Delta Live Tables, a powerful feature within Databricks, offer a compelling solution for near real-time data pipelines. However, no technology is without its limitations. Understanding these limitations is crucial for making informed decisions when designing and implementing Delta Live Tables in your Databricks workloads. This blog dives into the key limitations you should be aware of, guiding you towards a successful implementation.

1. Data Volume Constraints:

While Delta Live Tables can handle significant data volumes, there are practical considerations. Excessive data volume can strain your Databricks cluster’s resources, leading to:

• Performance Degradation: High data ingestion rates might overwhelm your cluster, causing processing delays and impacting downstream applications.
• Increased Costs: Scaling your cluster to handle a larger data volume translates to higher compute costs.
• Cluster Resources: Continuous data ingestion can lead to higher resource utilization, impacting your cluster costs.

Strategies:

• Data Partitioning: Partitioning your source data based on ingestion time or other relevant criteria can improve performance and scalability.
• Enabling Change Data Feed: Enabling Change Data Feed on the source tables might help to track row-level changes between versions of a Delta table. It is beneficial for streaming data reads as instead of reading the entire parquet file, it reads only a single change in the row
• Micro-Batching: Instead of large, infrequent batches, consider dividing data into smaller, more manageable micro-batches. This can improve processing efficiency. Although it can improve performance, frequent micro-batching can increase processing overhead and potentially raise costs.
• Resource Optimization: Ensure your cluster configuration aligns with your expected data volume. Scaling resources like worker nodes and memory can enhance processing capabilities.
• Right-size Your Cluster: Analyze your expected data volume and processing requirements to configure an appropriately sized cluster for your Delta Live Tables workload.

• Optimize Batch Size: Balance performance and cost by finding the optimal batch size that minimizes unnecessary processing overhead without compromising real-time needs.
• Cost Monitoring: Regularly monitor your Databricks costs and adjust configurations or cluster resources as needed to optimize cost efficiency

2. Pipelining and Schema Evolution Challenges:

Delta Live Tables support schema evolution, but it’s not frictionless. Modifications, particularly adding columns, can introduce complexity:

• Retroactive Data Updates: Adding new columns won’t automatically populate existing data with null values. You might need to backfill data or perform data transformations to handle missing values.
• Downstream Compatibility: Schema changes can cause compatibility issues with existing downstream consumers of the data. Carefully consider the impact on dependent processes.
• Identity Columns: Identity columns are typically managed by the database internally, and DLT might struggle to handle conflicting updates or recomputations involving these columns within the APPLY CHANGES INTO or materialized view context.
• You cannot have a single pipeline write to multiple target tables with different schemas within the same pipeline definition. This is a limitation of the current DLT functionality using Hive Metastore.
• DLT doesn’t inherently support writing data from multiple live tables (created by separate pipelines) into a single target Delta table. This means you cannot directly configure two or more DLT pipelines to update the same table simultaneously due to “Checkpointing”.

Strategies:

• Plan for Schema Evolution: Design your schema with potential future changes in mind.
• Use Nested Data Structures: For anticipated future additions, consider using nested data structures like arrays or structs to accommodate new information within existing columns.
• Data Versioning: If frequent schema changes are expected, leverage Delta Tables’ time travel capabilities to access previous versions of the data that reflect older schema definitions.
• Use Identity Columns with Streaming Tables: DLT recommends using identity columns primarily with streaming tables. Since streaming tables are continuously appended with new data, the auto-incrementing nature of identity columns works well in this scenario.
• Generate Unique IDs Externally: If you need unique identifiers for tables used with APPLY CHANGES INTO or materialized views, consider generating these IDs outside of DLT using Spark functions like monotonically_increasing_id() or uuid(). You can then add these generated IDs as a separate column during data transformations before ingestion into the Delta table.
• Separate Sequence Table: Explore using a separate table to manage a sequence of unique IDs. This sequence table can be updated with DLT, and you can then reference it during data transformations to assign unique IDs to records before inserting them into the target table.

3. Slowly Changing Data (SCD):

Delta Live Tables offer a compelling solution for near real-time data pipelines, but they have limitations when dealing with Slowly Changing Data (SCD). SCD poses challenges due to its nature of updating existing data instead of simply adding new records. Let’s delve into these limitations and strategies for addressing them:

• Limited Native Support for SCD Types: Delta Live Tables natively support Type 1 SCD (overwrite existing values) but not Type 2 SCD (add a new record with updated values) or Type 3 SCD (inactivate the old record and create a new one). Implementing these types requires additional processing steps.
• Data Lineage and Auditability Concerns: When handling SCD, understanding the history of changes and maintaining data lineage can become more complex. Delta Live Tables don’t natively capture the complete change history for SCD Type 2 and 3 scenarios.
• Performance Implications for Frequent Updates: Depending on the volume and frequency of updates, Delta Live Tables might experience performance overhead when handling SCD Type 2 or 3. Merging updates with existing data can be resource-intensive.

Strategies:

• External Transformation for SCD Types 2 and 3: Implement the logic for SCD Type 2 (new record with updates) and Type 3 (inactivate old, create new) outside of Delta Live Tables. Utilize Spark SQL or other tools to perform these transformations before data ingestion.
• Leverage Delta Table Versioning for Auditability: While Delta Live Tables don’t natively capture complete change history for SCD Types 2 and 3, you can leverage Delta Tables’ time travel capabilities to access previous versions of the data. This approach provides a point-in-time view of the data at different stages, aiding in auditing updated values.
• Optimize Update Logic and Batch Sizes: When handling SCD updates within Delta Live Tables (for Type 1), optimize the update logic to minimize unnecessary operations. Consider batching updates to improve efficiency, but balance this with the need for near real-time updates.
• Utilize dlt.apply_changes() function: Databricks provide dlt.apply_changes() function to implement SCD on Streaming LIVE Tables. But apply_changes() creates a permanent view over a delta table. These views can not be used as a streaming source for further downstream jobs.

4. Limited Support for Complex Transformations:

Delta Live Tables are optimized for simple data ingestion and updates. Complex transformations might require additional processing steps outside the Live Tables functionality:

• UDFs (User-Defined Functions): Complex data manipulations might necessitate implementing UDFs, which can introduce overhead compared to native Delta Live Tables operations.
• External Data Processing: In some cases, complex transformations might be better handled using external tools like Spark SQL queries before data is ingested into Delta Live Tables.

Strategies:

• Identify Complexity Early: Analyze your transformation needs and distinguish between simple and complex ones.
• Leverage External Processing: Utilize Spark SQL or other tools for complex data transformations before ingesting the data into Delta Live Tables.
• Break Down Complex Transformations: Consider breaking down complex transformations into smaller, more manageable steps that can be executed within Delta Live Tables.

5. Security Considerations:

While Delta Live Tables offer robust security features, specific limitations require attention:

• Dynamic Access Control (DAC): Delta Live Tables currently don’t support DAC, which dynamically grants access based on data characteristics. Authorization might require additional configuration through external tools.
• Row-Level Security (RLS): Implementing RLS at the point of ingestion might not be straightforward. You might need to use external tools or define access control lists (ACLs) on the underlying Delta tables.

Strategies:

• Leverage Databricks ACLs: Utilize Databricks workspace and cluster ACLs to manage access to data and resources.
• External Authorization: Consider implementing authorization checks in external processing steps before data is ingested into Delta Live Tables.
• Monitor Data Access: Regularly monitor data access patterns and adjust security configurations as needed.

6. Debugging and Monitoring Challenges:

Debugging and monitoring live data pipelines can be more complex:

• Real-Time Behavior: Due to the real-time nature, errors might not be immediately apparent. Understanding and troubleshooting issues can require additional effort.
• Logging and Metrics: Carefully configure logging and metrics collection to capture detailed information about data flow and identify potential problems.

Strategies:

• Thorough Testing: Rigorously test your Delta Live Tables pipeline in a development environment before deployment to production.
• Detailed Logging: Implement comprehensive logging throughout your pipeline to capture data flow details and error messages.
• Utilize Databricks Monitoring: Leverage Databricks monitoring tools to track job execution and identify performance issues.

7. Integration with External Systems:

Streaming Data Sources: Delta Live Tables are primarily designed for batch data ingestion. Integrating with real-time streaming data sources might require additional infrastructure like Apache Kafka or custom development.

Strategies:

• Leverage Streaming Workflows: Utilize Databricks Structured Streaming capabilities to ingest real-time data into a separate stream and then periodically batch the data for ingestion into Delta Live Tables.
• Choose Compatible Data Formats: Opt for data formats that Delta Live Tables natively support, minimizing the need for data conversion.

8. Version Control and Rollbacks:

While Delta Tables offer time travel capabilities, rolling back a Delta Live Table might be more challenging:

• State Management: Rollbacks might require managing the state of the live tables, including metadata and configuration changes during the timeframe you want to revert to.
• Partial Rollbacks: Depending on the nature of your live tables, complete rollbacks might not be feasible. You might need to consider partial rollbacks or data corrections.

Strategies:

• Thorough Testing: Rigorously test configuration changes and data transformations before applying them to production Delta Live Tables.
• Version Control Practices: Implement version control practices for your Delta Live Tables configuration and code to track changes and facilitate potential rollbacks.
• Utilize Monitoring: Actively monitor your Delta Live Tables for any anomalies or unexpected behavior that might necessitate rollbacks.

9. Testing and Maintenance Considerations:

Testing and maintaining Delta Live Tables have their own nuances:

• Realistic Test Data: Generating realistic test data that reflects the volume and characteristics of your live data flow can be challenging.
• Integration Testing: Testing the interaction between Delta Live Tables and other components of your data pipeline might require additional effort compared to simple batch jobs.

Strategies:

• Utilize Mocks and Stubs: Employ mocks and stubs to simulate external systems and data sources during testing.
• Focus on End-to-End Testing: Integrate testing throughout your pipeline, encompassing data ingestion, transformations, and consumption.
• Modular Design: Design your pipeline with modular components to facilitate easier testing and maintenance.

10. Limited Community Support:

As a relatively new technology, Delta Live Tables might have a less extensive community of users compared to other Databricks features. This can impact the availability of resources and support:

• Limited Documentation and Examples: You might encounter fewer readily available documentation resources and examples for complex use cases.
• Troubleshooting Assistance: Finding help with troubleshooting specific Delta Live Tables challenges might take more effort due to a smaller community base.

Strategies:

• Leverage Databricks Documentation: Utilize the official Databricks documentation and resources as the primary source of information.
• Engage with the Databricks Community: Participate in Databricks community forums and discussions to connect with other users and share experiences.
• Build Internal Expertise: Encourage knowledge sharing and best practices within your organization for effectively using Delta Live Tables.

By understanding these limitations and employing mitigation strategies, you can make informed decisions about using Delta Live Tables and maximize their effectiveness within your Databricks workloads. Remember, continuous monitoring, efficient resource management, and a strategic approach are key to optimizing your Delta Live Tables implementation.

This post was created on 17th March 2024. All the limitations and strategies discussed are as of 1st February 2024 present in GA and public preview state. Experimental/Under-Development/Private preview features might not be correct and are not recommended solutions. Any important developments will be disccussed in future posts.

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Ankit Adak
Analyst