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Apache Doris 4.0: Native Hybrid Search for AI Workloads
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Apache Doris 4.0: Native Hybrid Search for AI Workloads

Apache Doris 4.0 combines vector search, full-text search, and structured analytics into a single SQL engine, delivering a hybrid search and analytical processing infrastructure for AI workloads — no separate search stack needed.

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Doris at Xiaomi: How Xiaomi Built a Unified Data Platform with Apache Doris
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Doris at Xiaomi: How Xiaomi Built a Unified Data Platform with Apache Doris

Xiaomi consolidated its fragmented analytics infrastructure with Apache Doris, now running 40+ clusters managing petabytes of data and serving 50 million daily queries across the company.

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PostgreSQL + Apache Doris: Building an HTAP Architecture for Real-Time Analytics
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PostgreSQL + Apache Doris: Building an HTAP Architecture for Real-Time Analytics

Pairing PostgreSQL with Apache Doris creates an HTAP architecture that separates transactional and analytical workloads, enabling real-time data sync and fast analytics without straining your production database.

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How Inverted Index Works in Apache Doris: 59x Faster Full-Text Search, 14x Faster Point Lookups
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How Inverted Index Works in Apache Doris: 59x Faster Full-Text Search, 14x Faster Point Lookups

Apache Doris inverted indexes deliver 59x faster full-text search and 14x faster point lookups by mapping values directly to row locations — solving the sparse query problem that traditional OLAP systems struggle with.

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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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