Choose the Right Catalog for Data Lakehouse Metadata - OpenLake

This topic compares the metadata management solutions of DLF and self-managed file systems.

FileSystem catalog: Lightweight to start, but struggles with production workloads

The FileSystem catalog organizes table metadata using a directory structure, such as warehouse/dbName.db/tableName. It requires no external services, works out of the box, and is the default catalog type.

However, it has the following key limitations:

Unsafe concurrent writes: It relies on the rename operation in Object Storage to simulate commits. This operation is not atomic, which risks data loss during concurrent writes.
Inability to decouple compaction: Lacking a centralized metadata service for coordination, compaction must be embedded within write jobs. This consumes write resources and impacts stability.
Inefficient table lifecycle actions: Creating, deleting, or renaming tables requires traversing a large number of files, a slow and error-prone process.
Poor metadata retrieval performance: All metadata retrieval depends on list operations in Object Storage. This process causes high latency and high costs for large tables.
No interface or governance capabilities: It lacks production-grade features such as monitoring, storage overview, access control, and hot/cold data management.

DLF REST catalog: The fully managed metadata engine for enterprise data lakehouses

The DLF REST catalog, created by the original Apache Paimon team, centrally manages metadata through a standalone REST service. This design decouples compute from storage and is built for large-scale, high-reliability production environments.

Its core advantages over FileSystem catalogs are:

Comparison item	FileSystem catalog (self-managed)	DLF REST catalog (fully managed)
Safe, high-concurrency writes	Object Storage lacks atomic commit capabilities, risking data loss during concurrent writes. Compaction must be embedded, which impacts write stability.	Metadata is the source of truth, and REST-based commits ensure concurrency safety. Compaction is fully managed and decoupled from writes.
Fully automated storage optimization	Compaction and clustering are inlined with write jobs, requiring over-provisioned resources and fixed bucketing that hinders performance and small file management.	Adaptive bucketing and merging with automatic compaction, clustering, and expiration cleanup. Resources are scheduled adaptively.
Decoupled metadata and paths	`DROP` and `RENAME` operations require traversing and modifying all files, which is slow and prone to error.	Metadata is managed independently. `DROP` and `RENAME` operations complete in milliseconds, making them lightweight and reliable.
Standard REST protocol	Metadata relies on Object Storage `list` operations, resulting in high latency, high costs, and limited scalability.	Provides an open, standard REST API with support for Java and Python SDKs, enabling simple and efficient multi-language integration.
Visualization and observability	No UI. Metrics must be obtained by manually parsing the file system, making real-time monitoring impossible.	The console displays real-time metrics such as number of rows, number of files, and storage size. It automatically generates a comprehensive storage overview for quick issue identification.
Enterprise-grade access control	Only supports file system ACLs, which cannot provide table- or column-level permissions. This makes it difficult to meet compliance requirements.	Supports fine-grained, table- and column-level access control and secure table sharing across projects, meeting enterprise governance and audit needs.
Hot/cold data tiering	Relies on file modification times for data tiering. This approach does not align with business logic and can lead to operational errors.	Supports hot/cold policy configuration at the table or partition level. This precisely matches business semantics to balance performance and cost.

Secure, high-concurrency writes

FileSystem catalog
Lacks atomic, concurrent commit support. Simultaneous write jobs on the same table can lead to file renaming conflicts and data loss.
This necessitates embedding compaction within write jobs, impacting write stability and resource planning.
DLF REST catalog
All writes are submitted via the REST interface. Metadata serves as the single source of truth, ensuring concurrency safety.
Compaction and other maintenance tasks are automatically handled by the fully managed DLF backend, decoupled from writes by default for stability and efficiency.

Fully automated storage optimization

FileSystem catalog
Storage optimization (compaction, clustering) is tightly coupled with write jobs.
- Any policy adjustment is disruptive to write stability
- Frequent failures often necessitate resource over-provisioning, leading to waste.
- Fixed bucketing offers limited flexibility: too few buckets create write bottlenecks, while too many result in numerous small files, hindering the balance between performance and cost.
DLF REST catalog
Fully automated storage optimization, decoupled from write jobs.
- It automatically handles compaction, clustering, partition expiration, and snapshot expiration.
- Adaptive bucketing and merging eliminate the need for manual tuning or resource allocation.
- Background merging utilizes multi-mode scheduling and native technology for accelerated performance.

Decoupled metadata and paths

FileSystem catalog
Tightly couples table paths and metadata. Operations like DROP TABLE or RENAME are slow and failure-prone for large tables as they require moving or deleting data files individually.
DLF REST catalog
Decouples metadata from physical paths. DROP TABLE and RENAME operations are milliseconds-fast, lightweight, and reliable, as they only update metadata. This prevents residual files from impacting new table structures.

Standard REST protocol

FileSystem catalog
Metadata stored in file system directories requires slow, costly List operations for retrieval, leading to high latency and poor extensibility due to strong underlying storage dependencies.
DLF REST catalog
Offers lightweight, fast metadata reads/writes via a standard, open REST protocol. Providing Java and Python SDKs, it simplifies multi-language integration and reduces business complexity.

Visualization and observability

FileSystem catalog
Lacks a graphical interface; manual file system traversal is required for table information (row count, file count, size), resulting in cumbersome processes, high latency, and difficulty obtaining real-time table status.
DLF REST catalog
Offers a unified console with real-time core metrics for tables and partitions (row count, file count, total size) and automatically generates a complete storage overview of all table versions. This facilitates quick identification of issues (e.g., small files, redundant snapshots) and efficient optimization decisions.

Enterprise-grade access control

FileSystem catalog
Permissions are limited to directory/file-level read/write access via underlying ACLs, preventing table- or column-level access control and failing to meet enterprise data security and compliance needs.
DLF REST catalog
Offers granular, metadata-based access control at table and column levels. It enables secure table sharing across projects and teams, fulfilling enterprise-grade governance requirements.

Hot and cold data management

FileSystem catalog
File-level policies based on modification times lack business alignment, leading to incorrect data classification and potential loss of critical data.
DLF REST catalog
Supports table- and partition-level tiering for business-aligned data lifecycle control. This ensures high performance for hot data and low cost for cold data, optimizing efficiency and security.