add blog post (#3936)

docs/en/blog_post/20240523_OpenmldbFeatureSignatures.md

@@ -0,0 +1,108 @@
+# Introducing OpenMLDB’s New Feature: Feature Signatures — Enabling Complete Feature Engineering with SQL
+
+## Background
+
+Rewinding to 2020, the Feature Engine team of Fourth Paradigm submitted and passed an invention patent titled “[Data Processing Method, Device, Electronic Equipment, and Storage Medium Based on SQL](https://patents.google.com/patent/CN111752967A)”. This patent innovatively combines the SQL data processing language with machine learning feature signatures, greatly expanding the functional boundaries of SQL statements.
+
+![Screenshot of Patent in Cinese](https://cdn-images-1.medium.com/max/2560/1*V5fQ3koN8HFikmZWJPtykA.png)
+
+At that time, no SQL database or OLAP engine on the market supported this syntax, and even on Fourth Paradigm’s machine learning platform, the feature signature function could only be implemented using a custom DSL (Domain-Specific Language).
+
+Finally, in version v0.9.0, OpenMLDB introduced the feature signature function, supporting sample output in formats such as CSV and LIBSVM. This allows direct integration with machine learning training or prediction while ensuring consistency between offline and online environments.
+
+## Feature Signatures and Label Signatures
+
+The feature signature function in OpenMLDB is implemented based on a series of OpenMLDB-customized UDFs (User-Defined Functions) on top of standard SQL. Currently, OpenMLDB supports the following signature functions:
+
+* `continuous(column)`: Indicates that the column is a continuous feature; the column can be of any numerical type.
+
+* `discrete(column[, bucket_size])`: Indicates that the column is a discrete feature; the column can be of boolean type, integer type, or date and time type. The optional parameter `bucket_size` sets the number of buckets. If `bucket_size` is not specified, the range of values is the entire range of the int64 type.
+
+* `binary_label(column)`: Indicates that the column is a binary classification label; the column must be of boolean type.
+
+* `multiclass_label(column)`: Indicates that the column is a multiclass classification label; the column can be of boolean type or integer type.
+
+* `regression_label(column)`: Indicates that the column is a regression label; the column can be of any numerical type.
+
+These functions must be used in conjunction with the sample format functions `csv` or `libsvm` and cannot be used independently. `csv` and `libsvm` can accept any number of parameters, and each parameter needs to be specified using functions like `continuous` to determine how to sign it. OpenMLDB handles null and erroneous data appropriately, retaining the maximum amount of sample information.
+
+## Usage Example
+
+First, follow the [quick start](https://openmldb.ai/docs/en/main/tutorial/standalone_use.html) guide to get the image and start the OpenMLDB server and client.
+```bash
+docker run -it 4pdosc/openmldb:0.9.0 bash
+/work/init.sh
+/work/openmldb/sbin/openmldb-cli.sh
+```
+
+Create a database and import data in the OpenMLDB client.
+```sql
+--OpenMLDB CLI
+CREATE DATABASE demo_db;
+USE demo_db;
+CREATE TABLE t1(id string, vendor_id int, pickup_datetime timestamp, dropoff_datetime timestamp, passenger_count int, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, store_and_fwd_flag string, trip_duration int);
+SET @@execute_mode='offline';
+LOAD DATA INFILE '/work/taxi-trip/data/taxi_tour_table_train_simple.snappy.parquet' INTO TABLE t1 options(format='parquet', header=true, mode='append');
+```
+
+Use the `SHOW JOBS` command to check the task running status. After the task is successfully executed, perform feature engineering and export the training data in CSV format.
+
+Currently, OpenMLDB does not support overly long column names, so specifying the column name of the sample as `instance` using `SELECT csv(...)` AS instance is necessary.
+
+```sql
+--OpenMLDB CLI
+USE demo_db;
+SET @@execute_mode='offline';
+WITH t1 as (SELECT trip_duration,
+ passenger_count,
+ sum(pickup_latitude) OVER w AS vendor_sum_pl,
+ count(vendor_id) OVER w AS vendor_cnt,
+ FROM t1
+ WINDOW w AS (PARTITION BY vendor_id ORDER BY pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW))
+SELECT csv(
+ regression_label(trip_duration),
+ continuous(passenger_count),
+ continuous(vendor_sum_pl),
+ continuous(vendor_cnt),
+ discrete(vendor_cnt DIV 10)) AS instance
+FROM t1 INTO OUTFILE '/tmp/feature_data_csv' OPTIONS(format='csv', header=false, quote='');
+```
+
+If LIBSVM format training data is needed, simply change `SELECT csv(...)` to `SELECT libsvm(...)`. Note that the `OPTIONS` should still use the CSV format because the exported data only has one column, which already contains the complete LIBSVM format sample.
+
+Moreover, the `libsvm` function will start numbering continuous features and discrete features with a known number of buckets from 1. Therefore, specifying the number of buckets ensures that the feature encoding ranges of different columns do not conflict. If the number of buckets for discrete features is not specified, there is a small probability of feature signature conflict in some samples.
+
+```sql
+--OpenMLDB CLI
+USE demo_db;
+SET @@execute_mode='offline';
+WITH t1 as (SELECT trip_duration,
+ passenger_count,
+ sum(pickup_latitude) OVER w AS vendor_sum_pl,
+ count(vendor_id) OVER w AS vendor_cnt,
+ FROM t1
+ WINDOW w AS (PARTITION BY vendor_id ORDER BY pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW))
+SELECT libsvm(
+ regression_label(trip_duration),
+ continuous(passenger_count),
+ continuous(vendor_sum_pl),
+ continuous(vendor_cnt),
+ discrete(vendor_cnt DIV 10, 100)) AS instance
+FROM t1 INTO OUTFILE '/tmp/feature_data_libsvm' OPTIONS(format='csv', header=false, quote='');
+```
+
+## Summary
+
+By combining SQL with machine learning, feature signatures simplify the data processing workflow, making feature engineering more efficient and consistent. This innovation extends the functional boundaries of SQL, supporting the output of various formats of data samples, directly connecting to machine learning training and prediction, improving data processing flexibility and accuracy, and having significant implications for data science and engineering practices.
+
+OpenMLDB introduces signature functions to further bridge the gap between feature engineering and machine learning frameworks. By uniformly signing samples with OpenMLDB, offline and online consistency can be improved throughout the entire process, reducing maintenance and change costs. In the future, OpenMLDB will add more signature functions, including one-hot encoding and feature crossing, to make the information in sample feature data more easily utilized by machine learning frameworks.
+
+--------------------------------------------------------------------------------------------------------------
+
+**For more information on OpenMLDB:**
+* Official website: [https://openmldb.ai/](https://openmldb.ai/)
+* GitHub: [https://github.com/4paradigm/OpenMLDB](https://github.com/4paradigm/OpenMLDB)
+* Documentation: [https://openmldb.ai/docs/en/](https://openmldb.ai/docs/en/)
+* Join us on [**Slack**](https://join.slack.com/t/openmldb/shared_invite/zt-ozu3llie-K~hn9Ss1GZcFW2~K_L5sMg)!
+
+> _This post is a re-post from [OpenMLDB Blogs](https://openmldb.medium.com/)._

docs/en/blog_post/index.rst

@@ -11,4 +11,6 @@ OpenMLDB Blogs
 
  Ultra High-Performance Database OpenM(ysq)LDB: Seamless Compatibility with MySQL Protocol and Multi-Language MySQL Client <20240322_Openmysqldb.md>
 
- Comparative Analysis of Memory Consumption: OpenMLDB vs Redis Test Report <20240402_OpenmldbVsRedis.md>
+ Comparative Analysis of Memory Consumption: OpenMLDB vs Redis Test Report <20240402_OpenmldbVsRedis.md>
+
+ Introducing OpenMLDB’s New Feature: Feature Signatures — Enabling Complete Feature Engineering with SQL <20240523_OpenmldbFeatureSignatures.md>

docs/zh/blog_post/20240523_OpenmldbFeatureSignatures.md

@@ -0,0 +1,95 @@
+# OpenMLDB 新功能介绍：特征签名，让 SQL 完成特征工程全流程
+
+## 背景
+
+时间回溯到2020年，第四范式的特征引擎团队提交并通过了一项发明专利[《基于SQL的数据处理方法、装置、电子设备和 存储介质》](https://patents.google.com/patent/CN111752967A/zh)，这项专利创新性地把 SQL 数据处理语言和机器学习的特征签名结合起来，极大拓展了 SQL 语句的功能边界。
+
+![patent.png](./images/20240523-patent.png)
+
+当时市面上还没有任何一种 SQL 数据库或 OLAP 引擎支持这种语法，而第四范式的机器学习平台上也只能用自定义的 DSL 领域描述语言来实现特征签名功能。
+
+终于在 v0.9.0 版本迭代后， OpenMLDB 新增了特征签名功能，支持输出为 CSV、LIBSVM 等格式的样本，可以直接对接机器学习的训练或预估，同时保障了离线和在线的一致性。
+
+## 特征签名和标签签名
+
+OpenMLDB 的特征签名功能是在标准 SQL 的基础上，基于一系列 OpenMLDB 定制的 UDF 实现的，目前OpenMLDB支持以下几种签名函数：
+
+- `continuous(column)` 表示 column 是一个连续特征，column 可以是任意数值类型。
+- `discrete(column[, bucket_size])` 表示 column 是一个离散特征，column 可以是 bool 类型，整数类型，日期与时间类型。 `bucket_size` 是可选参数，用于设置分桶数量，在没有指定 `bucket_size` 时，值域是 int64 类型的全部取值范围。
+- `binary_label(column)` 表示 column 是一个二分类标签， column 必须是 bool 类型。
+- `multiclass_label(column)` 表示 column 是多分类标签， column 可以是 bool 类型或整数类型。
+- `regression_label(column)` 表示 column 是回归标签， column 可以是任意数值类型。
+
+这些函数必须配合样本格式函数 csv 或 libsvm 使用，而不能单独使用。csv 和 libsvm可以接收任意数量的参数，每个参数都需要经过 continuous 等函数来确定如何签名。OpenMLDB 会合理处理空数据和错误数据，保留最大的样本信息量。
+
+## 使用示例
+首先参照[快速入门](https://openmldb.ai/docs/zh/main/tutorial/standalone_use.html)获取镜像并启动 OpenMLDB 服务端和客户端。
+
+```bash
+docker run -it 4pdosc/openmldb:0.9.0 bash
+/work/init.sh
+/work/openmldb/sbin/openmldb-cli.sh
+```
+
+在 OpenMLDB 客户端中创建数据库并导入数据。
+
+```sql
+--OpenMLDB CLI
+CREATE DATABASE demo_db;
+USE demo_db;
+CREATE TABLE t1(id string, vendor_id int, pickup_datetime timestamp, dropoff_datetime timestamp, passenger_count int, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, store_and_fwd_flag string, trip_duration int);
+SET @@execute_mode='offline';
+LOAD DATA INFILE '/work/taxi-trip/data/taxi_tour_table_train_simple.snappy.parquet' INTO TABLE t1 options(format='parquet', header=true, mode='append');
+```
+
+使用命令 `SHOW JOBS` 查看任务运行状态，等待任务运行成功后，进行特征工程并导出 CSV 格式的训练数据。
+
+当前版本的 OpenMLDB 不支持过长的列名，所以通过 `SELECT csv(...) AS instance` 指定样本的列名是必要的。
+
+```sql
+--OpenMLDB CLI
+USE demo_db;
+SET @@execute_mode='offline';
+WITH t1 as (SELECT trip_duration,
+ passenger_count,
+ sum(pickup_latitude) OVER w AS vendor_sum_pl,
+ count(vendor_id) OVER w AS vendor_cnt,
+ FROM t1
+ WINDOW w AS (PARTITION BY vendor_id ORDER BY pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW))
+SELECT csv(
+ regression_label(trip_duration),
+ continuous(passenger_count),
+ continuous(vendor_sum_pl),
+ continuous(vendor_cnt),
+ discrete(vendor_cnt DIV 10)) AS instance
+FROM t1 INTO OUTFILE '/tmp/feature_data_csv' OPTIONS(format='csv', header=false, quote='');
+```
+
+如果需要 LIBSVM 格式的训练数据，仅需要将 `SELECT csv(...)` 改为 `SELECT libsvm(...)` 函数，需要注意的是 OPTIONS 中仍然使用 csv 格式，因为导出的数据实际上只有一列，而这一列已经包含了完整的 libsvm 格式的样本。
+
+此外 libsvm 函数会从 1 开始对连续特征和已知分桶数量的离散特征进行编号，因此在指定分桶数量后，可以保证不同列对应的特征编码范围没有冲突。如果不指定离散特征的分桶数量，一些样本的特征签名会有小概率发生冲突。
+
+```sql
+--OpenMLDB CLI
+USE demo_db;
+SET @@execute_mode='offline';
+WITH t1 as (SELECT trip_duration,
+ passenger_count,
+ sum(pickup_latitude) OVER w AS vendor_sum_pl,
+ count(vendor_id) OVER w AS vendor_cnt,
+ FROM t1
+ WINDOW w AS (PARTITION BY vendor_id ORDER BY pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW))
+SELECT libsvm(
+ regression_label(trip_duration),
+ continuous(passenger_count),
+ continuous(vendor_sum_pl),
+ continuous(vendor_cnt),
+ discrete(vendor_cnt DIV 10, 100)) AS instance
+FROM t1 INTO OUTFILE '/tmp/feature_data_libsvm' OPTIONS(format='csv', header=false, quote='');
+```
+
+## 总结
+特征签名通过将 SQL 与机器学习相结合，简化了数据处理流程，使得特征工程更加高效和一致。这一创新扩展了 SQL 的功能边界，支持输出多种格式的数据样本，直接对接机器学习训练和预测，提高了数据处理的灵活性和精度，对数据科学和工程实践具有重要意义。
+
+OpenMLDB 引入签名功能进一步缩小了特征工程和机器学习框架的距离，通过 OpenMLDB 统一签名样本，可以进一步提高全流程的离线在线一致性，降低维护变更成本。后续 OpenMLDB 将添加更多的签名函数，包括 onehot 编码以及特征交叉等，使样本特征数据中的信息更容易被机器学习框架充分利用。
+

docs/zh/blog_post/index.rst

@@ -12,4 +12,6 @@
 
  超高性能数据库 OpenM(ysq)LDB：无缝兼容 MySQL 协议 和多语言 MySQL 客户端 <20240322_Openmysqldb.md>
 
- OpenMLDB vs Redis 内存占用量测试报告 <20240402_OpenmldbVsRedis.md>
+ OpenMLDB vs Redis 内存占用量测试报告 <20240402_OpenmldbVsRedis.md>
+
+ OpenMLDB 新功能介绍：特征签名，让 SQL 完成特征工程全流程 <20240523_OpenmldbFeatureSignatures.md>