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How an Autonomous Driving Company Unified Multimodal Search on a Single Analytics Engine
Best Practice

How an Autonomous Driving Company Unified Multimodal Search on a Single Analytics Engine

An autonomous driving company consolidated fragmented data platforms by adopting Apache Doris as a unified analytics engine, enabling seamless search across text, vectors, labels, and metadata while reducing query times from minutes to seconds.

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Querying Apache Doris Data as a Graph with PuppyGraph
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Querying Apache Doris Data as a Graph with PuppyGraph

PuppyGraph enables graph analytics on Apache Doris data without requiring separate graph databases or ETL pipelines, supporting relationship traversal queries through Cypher and Gremlin.

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Set Up a Lakehouse with PostgreSQL, Apache Iceberg, and Apache Doris in 15 Minutes
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Set Up a Lakehouse with PostgreSQL, Apache Iceberg, and Apache Doris in 15 Minutes

A step-by-step guide to building a lakehouse with PostgreSQL, Apache Iceberg, and Apache Doris in 15 minutes, covering CDC setup, data ingestion, and analytical queries on a fully open-source stack.

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From ClickHouse + Elasticsearch to Apache Doris: How Kwai Unified Trillion-Scale Ad Analytics
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From ClickHouse + Elasticsearch to Apache Doris: How Kwai Unified Trillion-Scale Ad Analytics

Kwai, a short-video platform with over 400 million daily active users, migrated its advertising analytics from ClickHouse and Elasticsearch to Apache Doris, achieving up to 90% latency reduction and 3x write throughput.

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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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