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Apache Doris Up To 40x Faster Than ClickHouse | OLAP Showdown Part 2
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Apache Doris Up To 40x Faster Than ClickHouse | OLAP Showdown Part 2

In every benchmark tested: CoffeeBench, TPC-H, and TPC-DS, Apache Doris consistently pulled ahead, establishing clear dominance over both ClickHouse v25.8 on-premises and ClickHouse Cloud.

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The Ultimate OLAP Showdown: Apache Doris vs. ClickHouse vs. Snowflake (Part 1)
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The Ultimate OLAP Showdown: Apache Doris vs. ClickHouse vs. Snowflake (Part 1)

Apache Doris consistently delivers significantly faster performance in large-scale benchmarks spanning both straightforward JOINs and production-grade TPC-H/TPC-DS workloads. On top of that, Apache Doris requires just 10%-20% of the cost of Snowflake or ClickHouse for OLAP workloads.

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Apache Doris Tops RTABench, 6x Faster Than ClickHouse, 30x Faster Than PostgreSQL
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Apache Doris Tops RTABench, 6x Faster Than ClickHouse, 30x Faster Than PostgreSQL

Apache Doris, a popular real-time data warehouse, ranked first in the latest RTABench results, setting a new benchmark for real-time analytics performance. In standardized tests, Doris delivered up to 6 times the performance of ClickHouse, 30 times that of PostgreSQL, and 100 times that of MongoDB.

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Deep Dive: Data Pruning in Apache Doris
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Deep Dive: Data Pruning in Apache Doris

At Apache Doris, we have implemented multiple strategies to make the system more intelligent, enabling it to skip unnecessary data processing. In this article, we will discuss all the data pruning techniques used in Apache Doris.

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What is Apache Doris

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Core capabilities

Real-time ingestion and storage
Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation.
Lightning-fast query
Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine.
Federated querying
Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL.
Semi-structured data
Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches.
Elastic architecture
Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.
Open ecosystem
Compatible with MySQL protocol and ANSI SQL, easily integrated with BI tools. Provide open data API to be accessible for external compute engines like Spark, Flink and ML/AI.

Unified data warehouse

for various analytics use cases

From traditional batch reporting to real-time reporting and dashboards. From internal-facing analytics like traditional BI to customer-facing analytics. From decision support analytics to algorithm-driven real-time decision-making.
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