Envisioning LakeDB: The Next Evolution of the Lakehouse Architecture
A Conceptual Framework for Next-Generation Data Platforms
The world of data management is undergoing a rapid transformation. The rise of cloud storage, coupled with the increasing demand for real-time analytics, has led to the emergence of the Data Lakehouse. This paradigm combines the flexibility of data lakes with the performance and reliability of data warehouses. Apache Iceberg, Apache Hudi, and Delta Lake have been at the forefront of this revolution, bringing essential capabilities like schema evolution, ACID transactions, and efficient updates to the Lakehouse architecture. However, as described in Google's internal data management system, Google’s Napa presents a compelling vision that suggests the potential for a next-generation architecture.
We call this class next-generation Lakehouse LakeDB and hope the Lakehouse community collectively takes the logical next step toward it.
This article delves into Napa's core concepts, compares it with Iceberg, Hudi, and Delta Lake, and argues that Napa's design principles, combined with innovations from systems like Apache Pinot, pave the way for LakeDB, a more integrated, performant, and flexible approach to data management.
The Current Lakehouse Landscape: Iceberg, Hudi, and Delta Lake
Apache Iceberg, Apache Hudi, and Delta Lake have significantly advanced the Lakehouse concept.
Apache Iceberg provides a robust table format that enables schema evolution, time travel, and efficient query planning through detailed metadata management. It excels at managing large analytical datasets and ensuring data consistency.
Apache Hudi focuses on bringing database-like capabilities to the data lake, such as upserts, deletes, and change data capture (CDC). It leverages log-structured storage and indexing to facilitate efficient data mutations.
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It provides schema enforcement, time travel (data versioning), and unified batch and streaming processing. Delta Lake's Delta Live Tables feature offers native support for declarative pipeline development and materialized views.
All three have become essential components in modern data architectures. Still, they primarily address specific aspects of the Lakehouse: Iceberg focuses on the table format and metadata management, Hudi focuses on data mutability and incremental processing, and Delta Lake focuses on enabling ACID properties and unified processing.
Google's Napa: A Holistic Approach
Google's Napa presents a different perspective. It's not just a table format or a data ingestion framework; it's a comprehensive analytical data management system designed for massive scale, low-latency queries and continuous data ingestion. Napa's key features include:
Log-Structured Merge-Tree (LSM) based Ingestion: Napa uses an LSM-tree approach, optimized for high write throughput, to handle continuous data ingestion. It efficiently merges incoming data with existing data through a series of compactions.
Materialized Views for Query Acceleration: Napa heavily relies on materialized views, which are automatically maintained and updated to deliver exceptional query performance. These views are indexed and optimized for fast lookups and range scans.
Queryable Timestamp (QT): Napa's QT mechanism provides a global view of data consistency and allows users to balance data freshness with query performance. Unlike traditional time-travel features, QT offers a more granular and consistent view of data across the system.
F1 Query Integration: Napa leverages Google's F1 Query engine for query optimization and execution, enabling efficient handling of complex analytical queries. While F1 Query is a key component, Napa's architecture is flexible and could integrate with other query engines.
Configurability: Napa offers extensive configuration options, allowing users to tune the system to meet their needs regarding data freshness, query performance, and cost.
Comparing Napa, Iceberg, Hudi, and Delta Lake
The following table summarizes the key differences between Napa, Iceberg, Hudi, and Delta Lake:
The Case for LakeDB: Inspired by Napa and Learning from Others
Napa's design philosophy, combined with the strengths of existing Lakehouse technologies and innovations from systems like Apache Pinot, suggests a paradigm shift – a more integrated and powerful approach we call LakeDB.
What is LakeDB?
LakeDB envisions a unified data management system that empowers users to define their desired trade-offs for freshness, cost, correctness, and indexes while the system handles these requirements seamlessly. It is characterized by:
1. Integrated Storage, Ingestion, Metadata Management, and Query Processing
A single, cohesive system that seamlessly integrates all critical data management functions: storage, ingestion, metadata management, and query processing. Users can specify their needs for data freshness and correctness, and LakeDB will automatically optimize the underlying processes to meet these requirements.
Gap Analysis: Current Lakehouses often involve a fragmented collection of tools, leading to complexity, performance bottlenecks, and metadata inconsistencies. For example:
Apache Iceberg excels at metadata management but requires separate tools for ingestion (e.g., Apache Spark) and query processing (e.g., Trino or Spark).
Delta Lake integrates well with Spark but lacks native real-time ingestion and advanced query optimization support.
Apache Hudi provides real-time ingestion but relies on external systems for query execution and metadata management. This fragmentation forces users to combine multiple tools manually, increasing operational overhead and the risk of inconsistencies.
2. Intelligent Optimization of Materialized Views and Data Layout
Dynamically chooses between Copy-on-Write (CoW) and Merge-on-Read (MoR) strategies based on workload characteristics and user-defined preferences for cost and performance. LakeDB automatically manages snapshots for optimal query performance and intelligently creates, maintains, and optimizes materialized views to meet user-defined freshness and correctness requirements.
Gap Analysis: Existing systems often require manual selection of CoW/MoR and lack automated, adaptive optimization of materialized views and snapshots based on access patterns. For example:
Apache Hudi requires users to choose between CoW and MoR at table creation, which can lead to suboptimal performance if workload patterns change.
Delta Lake supports materialized views through Delta Live Tables but lacks automated optimization for dynamic workloads.
Apache Iceberg does not natively support materialized views, requiring users to implement and manage them manually. These limitations result in higher operational complexity and suboptimal resource utilization.
3. Granular Control Over Data Freshness
Incorporates a mechanism like Napa's Queryable Timestamp (QT) to provide fine-grained control over data freshness. Users can specify their desired freshness levels, and LakeDB will balance these requirements with performance and cost considerations, ensuring that queries return results within the defined freshness constraints.
Gap Analysis: Current Lakehouses offer limited and often coarse-grained control over data freshness, making it difficult to optimize for different workloads. For example:
Apache Iceberg and Delta Lake rely on periodic snapshots, which can delay data availability for real-time use cases.
Apache Hudi supports near real-time ingestion but lacks a mechanism like QT to balance freshness with query performance.
Most systems require users to manually manage data ingestion pipelines to achieve desired freshness levels, which can be error-prone and resource-intensive.
4. Advanced Indexing and Partitioning, Inspired by Apache Pinot
It supports various index types, similar to Apache Pinot, where you can select the index types you want for each column. Users can define their indexing needs, and LakeDB will automatically manage and optimize these indexes to meet query performance requirements while minimizing storage costs.
Gap Analysis: Current Lakehouse formats lack comprehensive indexing support, particularly for advanced index types like Star-Tree indexes, leading to performance limitations on analytical workloads, especially those involving high-cardinality dimensions. For example:
Apache Iceberg and Delta Lake rely on basic partitioning and data skipping, which are insufficient for high-cardinality dimensions or complex queries.
Apache Hudi supports indexing but lacks advanced indexing techniques like Apache Pinot, which are critical for real-time analytics.
5. Embraces Configurability
This system offers extensive configuration options on the same table by multiple consumers, allowing users to tune it to their specific needs regarding performance, cost, data freshness, and storage optimization. Users can define their desired trade-offs, and LakeDB will automatically configure itself to meet these requirements, reducing the need for manual intervention.
Gap Analysis: Current Lakehouse components often offer limited configurability, hindering optimization for specific use cases and workloads. For example:
Delta Lake provides some configurability through Spark settings but lacks fine-grained control over data freshness and cost.
Apache Iceberg allows the configuration of metadata and table layout but does not provide options for dynamic workload optimization.
Apache Hudi offers configurability for table types (CoW/MoR) but requires manual tuning to balance performance and cost—this lack of configurability forces users to compromise or invest significant effort in manual tuning.
6. Supports ACID Properties and Schema Evolution
LakeDB Vision: Provides strong consistency guarantees (ACID properties) and robust support for schema evolution, building on the foundations of Iceberg and Delta Lake. Users can define their correctness requirements, and LakeDB will ensure that these requirements are met, even as schemas evolve.
Gap Analysis: While existing Lakehouse formats offer varying degrees of ACID support and schema evolution capabilities, challenges remain in ensuring data consistency and handling schema changes gracefully. For example:
Delta Lake and Apache Iceberg support ACID transactions but can struggle with concurrent schema changes and data consistency in distributed environments.
Apache Hudi provides ACID guarantees but requires careful table versions and metadata management.
Users must often manually handle schema evolution and data consistency, which can be complex and error-prone.
7. Open and Extensible
Built on open standards, it supports open data formats, interoperates with various query engines, and allows custom extensions. Users can define their desired freshness, cost, correctness, and index trade-offs. LakeDB will handle these requirements out of the box while providing flexibility to extend the system as needed.
Gap Analysis: Some Lakehouse solutions can lead to vendor lock-in and limited interoperability, hindering innovation and flexibility. For example:
Delta Lake is tightly integrated with Databricks, which can limit interoperability with other platforms.
Apache Iceberg and Apache Hudi are more open but require significant effort to integrate with different query engines and tools.
Users often face challenges extending these systems to meet specific use cases, limiting their innovation ability.
Why LakeDB is the Next Evolution
Performance: By tightly integrating all data management functions, leveraging advanced indexing and materialized views, and dynamically optimizing data layout, LakeDB can achieve significantly better query performance than current Lakehouse architectures, approaching the speed of specialized OLAP systems. Users can define their performance requirements, and LakeDB will automatically optimize to meet these needs.
Simplicity: A unified system is inherently simpler to manage and operate than a collection of disparate tools. Users can define their desired trade-offs, and LakeDB will handle the rest, reducing the need for manual intervention and configuration.
Efficiency: LakeDB's integrated approach and intelligent automation can optimize resource utilization and reduce operational overhead. Users can define their cost requirements, and LakeDB will automatically optimize resource usage to meet these constraints.
Flexibility: Granular control over data freshness and extensive configuration options empower users to adapt the system to diverse workloads. Users can define their desired trade-offs, and LakeDB will automatically adjust to meet these requirements.
Real-Time Capabilities: Advanced indexing, particularly techniques from Apache Pinot, enables LakeDB to support real-time analytical workloads with low latency and high throughput. Users can define their real-time requirements, and LakeDB will automatically optimize to meet these needs.
Conclusion
Apache Iceberg, Apache Hudi, and Delta Lake have been instrumental in shaping the data Lakehouse paradigm. However, Google's Napa presents a compelling vision for the future – a vision further enhanced by incorporating ideas from systems like Apache Pinot. This vision culminates in LakeDB, a more integrated, performant, flexible, and intelligent data management system.
References:
1. Google's Napa and Related Research
Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google
Summary: This paper details the design and implementation of Napa, Google's analytical data management system. The system emphasizes materialized views, robust query performance, and flexibility in balancing freshness, cost, and correctness.
F1 Query: Declarative Querying at Scale
Link: F1 Query Paper
Summary: This paper describes F1 Query, Google's federated query processing platform integrated with Napa for query optimization and execution.
Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
Link: Mesa Paper
Summary: Mesa is Google's distributed data warehousing system. It is similar to Napa in terms of scalability and real-time data ingestion.
2. Apache Pinot and Advanced Indexing
StarTree Indexes in Apache Pinot
Summary: This blog series explains how StarTree indexes in Apache Pinot provide intelligent materialized views, significantly improving query performance for high-cardinality data.
Change Data Capture with Apache Pinot
Link: CDC with Apache Pinot
Summary: This article discusses how Apache Pinot handles Change Data Capture (CDC) and integrates it with real-time data streams, enabling efficient updates and querying.
3. Data Lakehouse Technologies (Hudi, Iceberg, Delta Lake)
Apache Hudi vs. Delta Lake vs. Apache Iceberg
Summary: This article compares the three leading data Lakehouse formats, highlighting their strengths and use cases.
Understanding Apache Hudi’s consistency model
Link: Hudi consistency Model
Summary: This article discusses the Hudi consistency model. The blog contains consistency models for all table formats.
Delta Lake: Unpacking the Transaction Log
Summary: This resource explains how Delta Lake's transaction log works, enabling ACID transactions and time travel.
Apache Iceberg: High-Performance Table Format
Link: Iceberg Documentation
Summary: This is the official documentation for Apache Iceberg, detailing its features, such as schema evolution, partitioning, and integration with query engines.
4. Materialized Views and Query Optimization
Optimizing Queries Using Materialized Views: A Practical, Scalable Solution
Link: Materialized Views Paper
Summary: This foundational paper discusses algorithms for query optimization using materialized views, which are central to Napa's design.
Calcite: Materialized Views
Summary: This documentation explains how Apache Calcite implements materialized views and query rewriting, which is relevant to LakeDB's vision of intelligent optimization.
5. Additional Resources
Debezium: Change Data Capture
Link: Debezium Documentation
Summary: Debezium is a CDC tool that integrates with systems like Apache Pinot, enabling real-time data ingestion and updates.
Apache Pinot: Real-Time OLAP
Summary: The official documentation for Apache Pinot covers its architecture, indexing, and real-time analytics capabilities.
6. Academic and Industry Papers
Progressive Partitioning for Parallelized Query Execution in Google's Napa
Summary: This paper discusses Napa's partitioning strategies for efficient query execution.
Krypton: Real-Time Serving and Analytical SQL Engine at ByteDance
Link: Krypton Paper
Summary: Krypton is a cloud-native HSAP system that offers real-time analytics, similar to Napa and LakeDB.
7. Practical Guides and Tutorials
Delta Lake Example: Setup and Initialization
Link: Delta Lake Example
Summary: A practical guide to setting up and using Delta Lake for ACID transactions and data versioning.
Apache Iceberg Example: Setup and Initialization
Link: Iceberg Example
Summary: A step-by-step guide to creating and querying Iceberg tables.
Apache Hudi Example: Setup and Initialization
Link: Hudi Example
Summary: A tutorial on setting up Apache Hudi for upserts, deletes, and incremental processing.
8. Blogs and Community Resources
Calcite Materialized Views Explained
Summary: A detailed blog post explaining Calcite's materialized view implementation and query rewriting algorithms.
StarTree Indexes in Apache Pinot: Part 1
Link: StarTree Indexes Blog
Summary: A comprehensive blog series on StarTree indexes and their impact on query performance.