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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OpenMLDB-main.zip
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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OpenMLDB-main.zip (2000个子文件)
api_server_test.cc 60KB
api_server_impl.cc 50KB
db_sdk.cc 27KB
sql_request_row.cc 17KB
node_adapter_test.cc 12KB
json_helper.cc 9KB
interface_provider.cc 8KB
log_exporter.cc 7KB
job_table_helper.cc 7KB
data_exporter.cc 6KB
tablemeta_reader.cc 5KB
parse_log.cc 3KB
result_set_sql_test.cc 2KB
.clang-format 3KB
simdjson.cpp 1.74MB
customdoxygen.css 31KB
customdoxygen.css 31KB
customdoxygen.css 31KB
bank_flattenRequest.csv 1.31MB
conn.go 7KB
go_sdk_test.go 2KB
driver.go 2KB
conn_test.go 2KB
driver_test.go 861B
simdjson.h 3.53MB
sql_node.h 104KB
physical_op.h 73KB
udf_registry.h 64KB
catalog_wrapper.h 39KB
name_server_impl.h 35KB
plan_node.h 31KB
runner.h 31KB
sql_cluster_router.h 27KB
tablet_impl.h 24KB
ir_base_builder_test.h 23KB
mem_catalog.h 23KB
task.h 22KB
display.h 21KB
node_manager.h 21KB
literal_traits.h 20KB
engine.h 20KB
engine_test_base.h 19KB
list_iterator_codec.h 18KB
catalog.h 17KB
list.h 17KB
generator.h 16KB
udf.h 16KB
transform.h 15KB
aggregator.h 14KB
sql_sdk_test.h 14KB
sql_router.h 14KB
tablet_client.h 14KB
skiplist.h 14KB
schema.h 14KB
containers.h 14KB
sql_case.h 13KB
sql_cmd.h 13KB
aggregator.h 13KB
skiplist.h 12KB
tablet_catalog.h 12KB
options_map_parser.h 12KB
ns_client.h 12KB
status_util.h 11KB
schemas_context.h 11KB
mini_cluster.h 11KB
file_util.h 11KB
result_set_sql.h 11KB
fe_row_codec.h 11KB
client_manager.h 10KB
type_codec.h 10KB
rpc_client.h 10KB
system_table.h 10KB
db_sdk.h 9KB
codec.h 9KB
toydb_engine_test_base.h 9KB
openmldb_api.h 8KB
node_enum.h 8KB
ast_node_converter.h 8KB
core_api.h 8KB
tablet_catalog.h 8KB
data_collector.h 8KB
fn_let_ir_builder_test.h 8KB
table.h 7KB
api_server_impl.h 7KB
base_struct.h 7KB
sql_sdk_base_test.h 7KB
sql_insert_row.h 7KB
group_and_sort_optimized.h 7KB
ddl_parser.h 7KB
type_node.h 7KB
split.h 7KB
eval.h 7KB
planner.h 7KB
schema_codec.h 7KB
mem_table_snapshot.h 7KB
result_set.h 7KB
query_response_time.h 7KB
distribute_iterator.h 6KB
fe_status.h 6KB
cluster_task.h 6KB
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