ClickHouse v25.4

Rapidly evolving OLAP database with experimental Iceberg read support, time travel, REST catalogs, and comprehensive write capabilities planned for 2025

Key Features

75

REST + Experimental

Evolving Catalog Support

Path (Hadoop-style) since 24.3, REST catalog (Nessie, Polaris/Unity, Glue REST) in 24.12; HMS experimental & AWS Glue in testing; R2 catalog on roadmap

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70

Writes Q3 2025

Read-Only Analytics

ENGINE=Iceberg tables and icebergS3()/icebergCluster() functions; full SQL on Parquet files. Writes/compaction scheduled Q3 2025

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30

Delete File Reading Only

No DML Operations

Reading of position & equality deletes supported since 24.12; queries merge delete files on-the-fly (MoR). No DELETE/UPDATE/MERGE writers until write support lands

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60

CoW + MoR Read

Read-Only Storage Strategy

Copy-on-Write always readable; Merge-on-Read readable from 24.12 (non-materialized delete files)

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20

Polling + Kafka

No Streaming Support

No native streaming ingestion; users poll Iceberg or ingest with ClickHouse Kafka engine; roadmap includes ClickPipe Iceberg-CDC for near-real-time sync

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0

Q3 2025 Planned

No Format V3 Support

Not yet supported - engine rejects DV tables; v3 reader/writer planned post-spec-v2 completeness; DV/lineage scheduled Q3 2025

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100

Since 25.4

Time Travel Capabilities

Time-travel since 25.4 with SET iceberg_timestamp_ms=<epoch> or iceberg_snapshot_id; partition pruning via use_iceberg_partition_pruning=1

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60

Credential-Based

Basic Security Model

Relies on object-store credentials (AWS_ACCESS_KEY_ID, S3 V4 tokens) or catalog credential vending; ClickHouse RBAC controls database/table access; no column-masking yet

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70

Rapid Evolution

Experimental Engine Status

Engine still experimental; cold-start latency without distributed cache; complex joins benefit from data-shuffling behind stateless workers (prototype)

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90

Clear Timeline

Comprehensive 2025 Roadmap

2025-H1: full spec-v2 compliance; Q3: Iceberg write path, native compaction, spec-v3 enablement; Performance: distributed cache, stateless workers

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ClickHouse Iceberg Feature Matrix

Comprehensive breakdown of Iceberg capabilities in ClickHouse v25.4

Dimension	Support Level	Implementation Details	Since Version
Catalog Types	PartialREST + Experimental	Path-based (24.3), REST catalog (24.12), HMS experimental, AWS Glue testing, R2 roadmap	24.3+
SQL Analytics	PartialRead-Only	ENGINE=Iceberg, icebergS3(), icebergCluster() functions; full SQL reads; writes Q3 2025	24.3+
DML Operations	NoneDelete File Reading	Reads position/equality deletes (24.12), merges on-the-fly; no DELETE/UPDATE/MERGE writers	24.12
Storage Strategy	PartialRead CoW + MoR	CoW always readable; MoR readable from 24.12 (non-materialized delete files)	24.12
Streaming Support	LimitedPolling + Kafka	No native streaming; polling/Kafka engine patterns; ClickPipe Iceberg-CDC on roadmap	N/A
Format Support	Nonev1/v2 Only	Reads spec v1/v2; engine rejects DV tables; v3 DV/lineage scheduled Q3 2025	24.3+
Time Travel	FullSince 25.4	SET iceberg_timestamp_ms/iceberg_snapshot_id; partition pruning optimization	25.4
Schema Evolution	FullRead Support	Reads evolved schemas; manifest/metadata cache (25.4); DESCRIBE shows latest schema	24.8+
Security & Governance	PartialBasic RBAC	Object-store credentials, catalog vending, ClickHouse RBAC; no column-masking	24.3+
Performance Features	PartialCache + Roadmap	Metadata cache (25.4); distributed cache & stateless workers roadmap (H2 2025)	25.4
Engine Status	ExperimentalRapid Evolution	Experimental engine with rapid development; production readiness planned H2 2025	24.3+
2025 Roadmap	ComprehensiveDetailed Plan	H1: spec-v2 complete; Q3: write path, compaction, spec-v3; Performance optimizations	2025

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Live dataFor issues, click here (GitHub)

Use Cases

High-Performance Analytics

OLAP queries on large-scale Iceberg data lakes

• Real-time dashboards on batch-updated data
• Complex analytical queries with sub-second latency
• Large-scale data warehouse analytics
• Time-series analysis and aggregation

Data Lake Query Layer

Fast analytical layer over multi-engine data lakes

• Analytics on data written by Spark/Flink
• Cross-catalog federated queries
• Historical analysis with time travel
• Performance layer for BI tools

Experimental Early Adoption

Testing cutting-edge Iceberg features and performance

• Prototype development with latest features
• Performance benchmarking and testing
• Early feedback on roadmap features
• Migration planning for future production use

Future Production Workloads

Planning for comprehensive read-write capabilities

• Teams planning 2025 migration to ClickHouse + Iceberg
• Organizations requiring roadmap-based commitments
• ETL pipelines with planned write capabilities
• Data architectures evolving with ClickHouse roadmap

Resources & Documentation

Official Documentation

Complete API reference and guides

Getting Started Guide

Quick start tutorials and examples

Iceberg Engine Documentation

Documentation

ClickHouse Release 25.04

Documentation

ClickHouse Release 24.12

Documentation

Iceberg Roadmap (GitHub)

Documentation

Distributed Cache Blog

Documentation

Open House Conference Highlights

Documentation

S3 Table Function Docs

Documentation

S3Queue Engine Docs

Documentation