Apache Doris vs. the Others — Selection Guide

01When to Choose Apache Doris

Doris and ClickHouse are both leading real-time analytical databases. Doris pulls ahead on the workloads modern data teams run every day.

↑2–10×

Faster Joins

Distributed multi-table joins, no wide-table workarounds.

MPP architecture with a cost-based optimizer that automatically picks Broadcast, Shuffle, or Colocate joins. Doris completes the full TPC-DS suite; ClickHouse fails about 50% of the queries.

↓70%

Lower Cost

Storage-compute separation, open-source.

Elastic compute scales without rebalancing. The cost model ships with open-source 3.0+, with no commercial-cloud lock-in.

1000+

Concurrent Queries

Real-time UPSERTs without sacrificing query speed.

Merge-on-Write keeps reads stable under high-frequency updates. Full ACID, MySQL-compatible, with thousands of concurrent queries instead of fewer than 100.

02Featured Cases

Real production migrations at internet-scale companies running the same SQL workloads, with very different outcomes.

Case 01★ FEATURED

Kuaishou

Short-Form Video · 600M+ DAU

After replacing ClickHouse with Apache Doris, Kuaishou successfully upgraded to a unified lakehouse architecture, achieving unified storage and a simplified data pipeline.

Direct lakehouse queries: no ingestion, shorter pipeline

Materialized view rewriting for query acceleration across scenarios

Flexible data governance via automatic materialization

Case 02★ FEATURED

Tencent Music

Music Streaming · 800M MAU

After migrating the analytical engine from ClickHouse to Apache Doris, the platform effectively improved data timeliness, reduced operational costs, and resolved fragmented data management.

Strong multi-table and federated query performance

MySQL-protocol compatible: lower operational overhead

Partial-column updates for diverse data update patterns

Case 03★ FEATURED

Youzan

E-Commerce SaaS · 5M+ Merchants

Apache Doris has faster query response times than ClickHouse in the vast majority of scenarios, especially in complex join scenarios, where its performance is significantly superior.

Core business queries 2–3× faster

Complex join queries 2–10× faster

Runs all ClickHouse OOM queries successfully

03Core Differences

Side-by-side, every dimension that matters.

DIMENSION

Apache DorisRECOMMENDED

ClickHouse

01System Architecture

MPP distributed architecture
MySQL-protocol compatible, standard SQL
CBO automatic optimization

Scatter-Gather architecture
SQL-like syntax, non-standard
Requires manual tuning

02Join Query Performance

2–10× faster, cross-node distributed
CBO chooses Join strategy
Full TPC-DS suite passes
Efficient memory, avoids OOM

Subqueries + wide-table modeling
No CBO, manual tuning
~50% TPC-DS queries fail
Frequent OOM on large queries

03Real-time Updates

Merge-on-Write engine
Strong consistency, immediate visibility
High-throughput UPSERT, no degradation

ReplacingMergeTree, eventual consistency
FINAL causes 2–10× slowdown
High-frequency updates → merge overhead

04Transaction Support

Full ACID transactions
Atomic batch ingestion

No transaction support
Partial data may become visible

05Query Concurrency

Thousands of concurrent queries, 10×+
Efficient memory management

Usually below 100 concurrent
Memory-intensive workloads destabilize cluster

06Data API & Lakehouse

Arrow-Flight high-throughput protocol
Hive / Hudi / Iceberg / Parquet
Auto-scaling + multi-replica balancing

JDBC only
Limited lakehouse integration
Scaling requires manual rebalancing

07Storage-Compute Separation

Open-source 3.0+
Elastic compute, no rebalance on scale
Up to 70% cost reduction

Commercial Cloud edition only
Tightly coupled, scaling requires rebalance
Over-provisioning required for peaks

08Open-Source License

Apache Foundation, community-maintained

Controlled by a commercial company