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**English | [中文](README_cn.md)**
## Content
1. [Our Philosophy](#1-our-philosophy)
2. [A Feature Platform for ML Applications](#2-a-feature-platform-for-ml-applications)
3. [Highlights](#3-highlights)
4. [FAQ](#4-faq)
5. [Download and Install](#5-download-and-install)
6. [QuickStart](#6-quickstart)
7. [Use Cases](#7-use-cases)
8. [Documentation](#8-documentation)
9. [Roadmap](#9-roadmap)
10. [Contribution](#10-contribution)
11. [Community](#11-community)
12. [Publications](#12-publications)
13. [The User List](#13-the-user-list)
### OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
## 1. Our Philosophy
For the artificial intelligence (AI) engineering, 95% of the time and effort is consumed by data related workloads. In order to tackle this challenge, tech giants spend thousands of hours on building in-house data and feature platforms to address engineering issues such as data leakage, feature backfilling, and efficiency. The other small and medium-sized enterprises have to purchase expensive SaaS tools and data governance services.
OpenMLDB is an open-source machine learning database that is committed to solving the data and feature challenges. OpenMLDB has been deployed in hundreds of real-world enterprise applications. It prioritizes the capability of feature engineering using SQL for open-source, which offers a feature platform enabling consistent features for training and inference.
## 2. A Feature Platform for ML Applications
Real-time features are essential for many machine learning applications, such as real-time personalized recommendation and risk analytics. However, a feature engineering script developed by data scientists (Python scripts in most cases) cannot be directly deployed into production for online inference because it usually cannot meet the engineering requirements, such as low latency, high throughput and high availability. Therefore, a engineering team needs to be involved to refactor and optimize the source code using database or C++ to ensure its efficiency and robustness. As there are two teams and two toolchains involved for the development and deployment life cycle, the verification for consistency is essential, which usually costs a lot of time and human power.
OpenMLDB is particularly designed as a feature platform for ML applications to accomplish the mission of **Development as Deployment**, to significantly reduce the cost from the offline training to online inference. Based on OpenMLDB, there are three steps only for the entire life cycle:
- Step 1: Offline development of feature engineering script based on SQL
- Step 2: SQL online deployment using just one command
- Step 3: Online data source configuration to import real-time data
With those three steps done, the system is ready to serve real-time features, and highly optimized to achieve low latency and high throughput for production.
![workflow](docs/en/about/images/workflow.png)
In order to achieve the goal of Development as Deployment, OpenMLDB is designed to provide consistent features for training and inference. The figure above shows the high-level architecture of OpenMLDB, which consists of four key components: (1) SQL as the unified programming language; (2) The real-time SQL engine for for extra-low latency services; (3) The batch SQL engine based on [a tailored Spark distribution](https://github.com/4paradigm/spark); (4) The unified execution plan generator to bridge the batch and real-time SQL engines to guarantee the consistency.
## 3. Highlights
**Consistent Features for Training and Inference:** Based on the unified execution plan generator, correct and consistent features are produced for offline training and online inference, providing hassle-free time travel without data leakage.
**Real-Time Features with Ultra-Low Latency**: The real-time SQL engine is built from scratch and particularly optimized for time series data. It can achieve the response time of a few milliseconds only to produce real-time features, which significantly outperforms other commercial in-memory database systems (Figures 9 & 10, [the VLDB 2021 paper](http://vldb.org/pvldb/vol14/p799-chen.pdf)).
**Define Features as SQL**: SQL is used as the unified programming language to define and manage features. SQL is further enhanced for feature engineering, such as the extended syntax `LAST JOIN` and `WINDOW UNION`.
**Production-Ready for ML Applications**: Production features are seamlessly integrated to support enterprise-grade ML applications, including distributed storage and computing, fault recovery, high availability, seamless scale-out, smooth upgrade, monitoring, heterogeneous memory support, and so on.
## 4. FAQ
1. **What are use cases of OpenMLDB?**
At present, it is mainly positioned as a feature platform for ML applications, with the strength of low-latency real-time features. It provides the capability of Development as Deployment to significantly reduce the cost for machine learning applications. On the other hand, OpenMLDB contains an efficient and fully functional time-series database, which is used in finance, IoT and other fields.
2. **How does OpenMLDB evolve?**
OpenMLDB originated from the commercial product of [4Paradigm](https://www.4paradigm.com/) (a leading artificial intelligence service provider). In 2021, the core team has abstracted, enhanced and developed community-friendly features based on the commercial product; and then makes it publicly available as an open-source project to benefit more enterprises to achieve successful digital transformations at low cost. Before the open-source, it had been successfully deployed in hundreds of real-world ML applications together with 4Paradigm's other commercial products.
Irrespective of the name, it is unrelated to MLDB, a different open source project in development since 2015.
3. **Is OpenMLDB a feature store?**
OpenMLDB is more than a feature store to provide features for ML applications. OpenMLDB is capable of producing real-time features in a few milliseconds. Nowadays, most feature stores in the market serve online features by syncing features pre-computed at offline. But they are unable to produce low latency real-time features. By comparison, OpenMLDB is taking advantage of its optimized online SQL engine, to efficiently produce real-time features in a few milliseconds.
4. **Why does OpenMLDB choose SQL to define and manage features?**
SQL (wit
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OpenMLDB是一个开源机器学习数据库,面向机器学习应用提供正确、高效数据供给 (2000个子文件)
customdoxygen.css 31KB
customdoxygen.css 31KB
customdoxygen.css 31KB
conn.go 7KB
go_sdk_test.go 2KB
driver.go 2KB
conn_test.go 2KB
main.go 1KB
driver_test.go 861B
sql_node.h 112KB
physical_op.h 73KB
name_server_impl.h 36KB
plan_node.h 34KB
sql_cluster_router.h 28KB
tablet_impl.h 25KB
ir_base_builder_test.h 24KB
mem_catalog.h 23KB
task.h 22KB
display.h 21KB
engine.h 21KB
node_manager.h 20KB
list_iterator_codec.h 19KB
catalog.h 17KB
schema.h 15KB
tablet_client.h 15KB
sql_sdk_test.h 14KB
sql_case.h 14KB
mini_cluster.h 14KB
sql_router.h 14KB
skiplist.h 14KB
sql_cmd.h 13KB
aggregator.h 13KB
fe_row_codec.h 12KB
ns_client.h 12KB
options_map_parser.h 12KB
tablet_catalog.h 12KB
status_util.h 12KB
iot_segment.h 12KB
schemas_context.h 11KB
client_manager.h 11KB
file_util.h 11KB
result_set_sql.h 11KB
rpc_client.h 10KB
type_codec.h 10KB
db_sdk.h 10KB
codec.h 9KB
system_table.h 9KB
sql_insert_row.h 9KB
openmldb_api.h 8KB
node_enum.h 8KB
data_collector.h 8KB
fn_let_ir_builder_test.h 7KB
table.h 7KB
api_server_impl.h 7KB
base_struct.h 7KB
sql_sdk_base_test.h 7KB
group_and_sort_optimized.h 7KB
ddl_parser.h 7KB
type_node.h 7KB
split.h 7KB
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result_set.h 7KB
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segment.h 7KB
status.h 6KB
distribute_iterator.h 6KB
fe_status.h 6KB
sp_cache.h 6KB
disk_table.h 6KB
disk_table_iterator.h 6KB
context.h 6KB
interface_provider.h 6KB
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zk_client.h 6KB
strings.h 6KB
endianconv.h 6KB
batch_request_optimize.h 5KB
struct_ir_builder.h 5KB
sdk_catalog.h 5KB
ir_base_builder.h 5KB
fe_slice.h 5KB
engine_context.h 5KB
mem_table.h 5KB
combine_iterator.h 5KB
status.h 5KB
type.h 5KB
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json_helper.h 4KB
predicate_expr_ir_builder.h 4KB
expr_ir_builder.h 4KB
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glog_wrapper.h 4KB
fe_schema_codec.h 4KB
string_ref.h 4KB
sql_request_row.h 4KB
log_replicator.h 4KB
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