Files

Jing Shen 4a474420c8 [feature](function) Add ntile function (#9867 )

Add ntile function.
For non-vectorized-engine, I just implemented like Impala, rewrite ntile to row_number and count.
But for vectorized-engine, I implemented WindowFunctionNTile.

2022-06-10 10:32:40 +08:00

community

[doc][fix]English document title case (#9935 )

2022-06-02 20:52:58 +08:00

developer

[doc](website)Add Doris new official website code and documents (#9977 )

2022-06-08 17:45:12 +08:00

docs

[feature](function) Add ntile function (#9867 )

2022-06-10 10:32:40 +08:00

downloads

[doc][fix]English document title case (#9935 )

2022-06-02 20:52:58 +08:00

sql-manual/sql-functions/array-functions

[feature-wip](array-type) Add array functions size and cardinality (#9921 )

2022-06-09 15:03:03 +08:00

userCase

[doc](website)Add Doris new official website code and documents (#9977 )

2022-06-08 17:45:12 +08:00

README.md

[website][doc]fix docs bugs with sidebar can not display and some style problems (#10039 )

2022-06-10 08:49:37 +08:00

README.md

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home

Welcome to

Apache Doris

A fast MPP database for all modern analytics on big data

QUICK START GITHUB

A modern, high-performance and real-time analysical database based on MPP

It is well known for its high-performance and easy-to-use. It can return query results under massive data within only sub-seconds.It can support not only high concurrent point query scenarios, but also complex analysis scenarios with high throughput.

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Data Import

Apache Doris provides rich data synchronization methods, supports fast loading of data from localhost, Hadoop, Flink, Spark, Kafka and other systems.

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Data Access

Apache Doris can directly access data in MySQL, PostgreSQL, Oracle, S3, Hive, Iceberg, Elasticsearch and other systems without data replication. At the same time, the data stored in Doris can also be read by Spark and Flink, and can be output to the downstream data application for display and analysis.

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Data Application

Apache Doris supports outputting data to downstream applications through JDBC standard protocol, and also supports various BI / Client tools to connect Doris through MySQL protocol。Based on this, Apache Doris can be well applied in many business fields, such as multi-dimensional reporting, user portrait, ad-hoc query, real-time dashboard and so on.

Learn More

Why Apache Doris

Extreme
Performance

Efficient column storage engine and modern MPP architecture,combined with intelligent materialized view,vectorized execution and various index acceleration, achieve the ultimate query performance.
Easy
to Use

Fully compatible with MySQL protocol and standard SQL, user-friendly. Support online table structure change and pre aggregation rollup, and easily integrate with the existing system framework.
Hybrid
Batch-Stream

Support efficient import of offline batch data and real-time streaming data, and ensure second level real-time performance. Multi version polar mechanism combined with import transaction support solves read-write conflicts and implements exactly once
Simple
Maintenance

Highly integrated, without any dependence on external components, and the cluster scale is elastically scalable online.The system is highly available, node failure, automatic replica switching, data fragmentation and automatic load balancing.
Rich
Ecology

Support loading and accessing multiple heterogeneous data sources, has extensive big data ecological compatibility, and adapts with mainstream BI tools to realize the ecological closed loop from data processing to data analysis.
Ultra-high
Concurency

Without concurrent bottleneck, it can support tens of thousands of users to use at the same time in thereal production environment. Through flexible resource allocations trategy, it can meet high concurrent point queries and high through put large queries at the same time.

Core Features

As a mature analytical database project, Apache Doris has many widely recognized core features and achieves the ultimate query performance in a variety of ways.

Vectorization execution engine

Give full play to the parallel planning ability of modern CPU through
vectorization execution engine.
Intelligent materialized view

Users can create intelligent materialized views to accelerate pre aggregation queries, and the query will automatically match the optimal materialized views.
Column storage engine

Apache Doris is equipped with an efficient column storage engine, which not only reduces the amount of data scanning, but also realizes ultra-high data compression ratio.
Rich index structure

Doris provides a rich index structure to speed up data reading and filtering. Using the partition and bucket cutting function, Doris can support ultra-high concurrency of online service business, and a single node can support up to thousands of QPS.

Resource

Downloads Document

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Copyright © 2022 The Apache Software Foundation. Licensed under the Apache License, Version 2.0. Apache
Doris(Incubating), Apache Incubator, Apache, the Apache feather logo, the Apache Doris(Incubating) logo and the
Apache Incubator project logo are trademarks of The Apache Software Foundation.

Apache Doris(incubating) is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored
by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that
the infrastructure, communications, and decision making process have stabilized in a manner consistent with other
successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of
the code, it does indicate that the project has yet to be fully endorsed by the ASF.