Data Lake Formation:Benefits of DLF

FileSystem catalog: lightweight to start, limited in production

The FileSystem catalog organizes table metadata using a directory structure, such as warehouse/dbName.db/tableName. It requires no external services and works out of the box, making it a convenient starting point.

In production, however, it runs into fundamental constraints:

• Unsafe concurrent writes: It relies on Object Storage rename operations to simulate commits. Because these operations are not atomic, concurrent writes on the same table can cause file renaming conflicts and data loss.

• Compaction tied to write jobs: Without a centralized metadata service, compaction must run inside write jobs. This consumes write resources, complicates resource planning, and reduces stability.

• Slow table lifecycle operations: Creating, deleting, or renaming a table requires traversing a large number of files—a slow and error-prone process that worsens at scale.

• High-latency metadata reads: All metadata retrieval depends on list operations in Object Storage, resulting in high latency and high costs for large tables.

• No visibility or governance: It lacks production-grade capabilities such as monitoring, storage overviews, access control, and hot/cold data management.

DLF REST catalog: built for enterprise data lakehouses

The DLF REST catalog, built by the original Apache Paimon team, manages metadata through a standalone REST service. All writes go through the REST interface, where metadata serves as the single source of truth—eliminating the concurrency risks and operational complexity of file-based catalog management.

The table below summarizes how the two catalog types compare across key production requirements.

Safe, high-concurrency writes

FileSystem catalog: Object Storage does not support atomic commits. When multiple write jobs run concurrently on the same table, they can produce file renaming conflicts that result in data loss. Compaction must be embedded in write jobs, which consumes write resources and complicates resource planning.

DLF REST catalog: All writes are submitted through the REST interface. Metadata acts as the single source of truth, ensuring concurrency safety regardless of how many jobs write simultaneously. Compaction and all other maintenance tasks run in the fully managed DLF backend, decoupled from write jobs by default.

Automated storage optimization

FileSystem catalog: Storage optimization (compaction, clustering) is tightly coupled with write jobs. Any policy adjustment risks write instability. Fixed bucketing creates a difficult trade-off: too few buckets bottleneck writes, while too many produce excessive small files. Frequent failures often force resource over-provisioning.

DLF REST catalog: Storage optimization runs fully in the background, with no impact on write jobs:

• Compaction: Runs automatically alongside clustering, partition expiration, and snapshot expiration.

• Postponed bucketing and merging: Eliminates manual tuning and resource allocation by adjusting bucket count dynamically.

• Multi-mode scheduling: Accelerates background merging using native technology.

Fast metadata operations

FileSystem catalog: Table paths and metadata are tightly coupled. DROP TABLE and RENAME operations must move or delete data files individually, making them slow and failure-prone for large tables.

DLF REST catalog: Metadata is managed independently from physical storage paths. DROP TABLE and RENAME complete in milliseconds by updating only metadata. Residual files cannot affect new table structures.

Standard REST protocol

FileSystem catalog: Metadata stored in file system directories requires list operations for retrieval. These are slow, costly, and create strong dependencies on the underlying storage, limiting extensibility.

DLF REST catalog: Provides lightweight, fast metadata reads and writes via an open, standard REST API. Java and Python SDKs reduce integration complexity across multi-language environments.

Visibility and observability

FileSystem catalog: No graphical interface. Getting table information—row count, file count, size—requires manually traversing the file system. The process is slow and does not provide real-time table status.

DLF REST catalog: The console provides real-time metrics for tables and partitions, including row count, file count, and total storage size. It automatically generates a complete storage overview across all table versions, making it straightforward to identify issues such as small files and redundant snapshots and take action.

• The overview contains all physical storage data for all versions of the current table.

• This helps you quickly identify potential issues, such as small files and redundant snapshots, and make efficient optimization decisions.

Enterprise-grade access control

FileSystem catalog: Access control is limited to directory- and file-level read/write permissions through underlying Access Control Lists (ACLs). Table- or column-level permissions are not supported, which makes it difficult to meet enterprise data security and compliance requirements.

DLF REST catalog: Provides fine-grained, metadata-based access control at the table and column level. Secure table sharing across projects and teams is built in, supporting enterprise governance and compliance audit needs.

Hot/cold data tiering

FileSystem catalog: Data tiering is based on file modification times. This approach does not reflect business logic and can result in incorrect data classification, including potential loss of critical data.

DLF REST catalog: Tiering policies can be configured at the table or partition level, directly aligned to business semantics. Hot data stays in high-performance storage; cold data moves to lower-cost tiers.