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Total contributors
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Monthly active contributors
Apache Doris version 2.1.6 just released
Release Note

Apache Doris version 2.1.6 just released

This version brings continuous upgrades and improvements to the Lakehouse, Async Materialized Views, and Semi-Structured Data Management.

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Real-time data warehouse in TikTok based on Apache Doris
Best Practice

Real-time data warehouse in TikTok based on Apache Doris

We are excited to invite the data platform team of TikTok to talk about how they use Apache Doris in their real-time data architecture and how they benefit from it, which could serve as a model for effective real-time data warehousing.

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How AI unicorn MiniMax migrated from Loki and built a PB-scale logging system with Apache Doris
Top News

How AI unicorn MiniMax migrated from Loki and built a PB-scale logging system with Apache Doris

Serving a PB-scale data size with over 99.9% availability, Apache Doris is the vital signs monitor of MiniMax, a generative AI startup backed by Alibaba.

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Automatic and flexible data sharding: Auto Partition in Apache Doris
Tech Sharing

Automatic and flexible data sharding: Auto Partition in Apache Doris

Apache Doris 2.1.0 introduces Auto Partition. It supports partitioning data by RANGE or by LIST and further enhances flexibility on top of automatic partitioning.

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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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