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Cross-Cluster Replication for read-write separation: story of a grocery store brand
Best Practice

Cross-Cluster Replication for read-write separation: story of a grocery store brand

Cross-cluster replication (CCR) in Apache Doris is proven to be fast, stable, and easy to use. It secures a real-time data synchronization latency of 1 second.

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Apache Doris version 2.0.9 has been released
Release Notes

Apache Doris version 2.0.9 has been released

Thanks to our community users and developers, about 68 improvements and bug fixes have been made in Doris 2.0.9 version.

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Arrow Flight SQL in Apache Doris for 10X faster data transfer
Tech Sharing

Arrow Flight SQL in Apache Doris for 10X faster data transfer

Apache Doris 2.1 supports Arrow Flight SQL protocol for reading data from Doris. It delivers tens-fold speedups compared to PyMySQL and Pandas.

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Auto-increment columns in databases: a simple magic that makes a big difference
Tech Sharing

Auto-increment columns in databases: a simple magic that makes a big difference

Auto-increment columns in Apache Doris accelerates dictionary encoding and pagination without damaging data writing performance. This is an introduction to its usage, applicable scenarios, and implementation details.

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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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