feat: merge main into v0.9

.github/workflows/hybridsql-docker.yml

@@ -93,6 +93,6 @@ jobs:
         with:
           context: docker
           push: ${{ github.event_name == 'push' }}
-          platforms: linux/amd64,linux/arm64
+          platforms: linux/amd64
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}

.github/workflows/udf-doc.yml

@@ -50,11 +50,10 @@ jobs:
           make -C hybridse/tools/documentation/udf_doxygen sync
 
       - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v4
+        uses: peter-evans/create-pull-request@v6
         if: github.event_name != 'pull_request'
         with:
           add-paths: |
-            docs/en/reference/sql/udfs_8h.md
             docs/zh/openmldb_sql/udfs_8h.md
           labels: |
             udf

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+## [0.9.2] - 2024-07-26
+
+### Bug Fixes
+- Fix upgrade openmldb sdk version in self host (#3962 @aceforeverd)
+- Fix select from JOB_INFO should always in online mode (#3963 @aceforeverd)
+- Fix update create-pull-request action to v6 in udf-doc-gen workflow & rm deprecated file sync (#3964 @Jayaprakash0511)
+- Fix build in centos7 EOL (#3965 @aceforeverd)
+- Fix numpy version lock (#3966 @aceforeverd)
+
 ## [0.9.1] - 2024-07-17
 
 ### Features

CMakeLists.txt

@@ -41,7 +41,7 @@ message (STATUS "CMAKE_PREFIX_PATH: ${CMAKE_PREFIX_PATH}")
 message (STATUS "CMAKE_BUILD_TYPE: ${CMAKE_BUILD_TYPE}")
 set(OPENMLDB_VERSION_MAJOR 0)
 set(OPENMLDB_VERSION_MINOR 9)
-set(OPENMLDB_VERSION_BUG 0)
+set(OPENMLDB_VERSION_BUG 1)
 
 function(get_commitid CODE_DIR COMMIT_ID)
     find_package(Git REQUIRED)

demo/Dockerfile

@@ -16,7 +16,7 @@ RUN  apt-get update \
      && rm -rf /var/lib/apt/lists/*
 
 RUN if [ -f "/additions/pypi.txt" ] ; then pip config set global.index-url $(cat /additions/pypi.txt) ; fi
-RUN pip install --no-cache-dir py4j==0.10.9 numpy lightgbm==3 tornado requests pandas==1.5 xgboost==1.4.2
+RUN pip install --no-cache-dir py4j==0.10.9 lightgbm==3 tornado requests pandas==1.5 xgboost==1.4.2 numpy==1.26.4
 
 COPY init.sh /work/
 COPY predict-taxi-trip-duration/script /work/taxi-trip/

demo/java_quickstart/demo/pom.xml

@@ -29,7 +29,7 @@
     <dependency>
       <groupId>com.4paradigm.openmldb</groupId>
       <artifactId>openmldb-jdbc</artifactId>
-      <version>0.9.0</version>
+      <version>0.9.2</version>
     </dependency>
     <dependency>
       <groupId>org.testng</groupId>

demo/predict-taxi-trip-duration/README.md

@@ -28,7 +28,7 @@ w2 as (PARTITION BY passenger_count ORDER BY pickup_datetime ROWS_RANGE BETWEEN
 
 **Start docker**
 ```
-docker run -it 4pdosc/openmldb:0.9.0 bash
+docker run -it 4pdosc/openmldb:0.9.2 bash
 ```
 **Initialize environment**
 ```bash
@@ -138,7 +138,7 @@ python3 predict.py
 **Start docker**
 
 ```bash
-docker run -it 4pdosc/openmldb:0.9.0 bash
+docker run -it 4pdosc/openmldb:0.9.2 bash
 ```
 **Initialize environment**
 

demo/talkingdata-adtracking-fraud-detection/README.md

@@ -15,7 +15,7 @@ We recommend you to use docker to run the demo. OpenMLDB and dependencies have b
 **Start docker**
 
 ```
-docker run -it 4pdosc/openmldb:0.9.0 bash
+docker run -it 4pdosc/openmldb:0.9.2 bash
 ```
 
 #### Run locally

docker/Dockerfile

@@ -21,9 +21,13 @@ ARG TARGETARCH
 
 LABEL org.opencontainers.image.source https://github.com/4paradigm/OpenMLDB
 
-COPY setup_deps.sh /
+COPY ./*.sh /
 # hadolint ignore=DL3031,DL3033
-RUN yum update -y && yum install -y centos-release-scl epel-release && \
+RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo && \
+    sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo && \
+    sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo && \
+    yum update -y && yum install -y centos-release-scl epel-release && \
+    /patch_yum_repo.sh && \
     yum install -y devtoolset-8 rh-git227 devtoolset-8-libasan-devel flex doxygen java-1.8.0-openjdk-devel rh-python38-python-devel rh-python38-python-wheel rh-python38-python-requests rh-python38-python-pip && \
     curl -Lo lcov-1.15-1.noarch.rpm https://github.com/linux-test-project/lcov/releases/download/v1.15/lcov-1.15-1.noarch.rpm && \
     yum localinstall -y lcov-1.15-1.noarch.rpm && \
@@ -33,7 +37,7 @@ RUN yum update -y && yum install -y centos-release-scl epel-release && \
     tar xzf zookeeper.tar.gz -C /deps/src && \
     rm -v ./*.tar.gz && \
     /setup_deps.sh -a "$TARGETARCH" -z "$ZETASQL_VERSION" -t "$THIRDPARTY_VERSION" && \
-    rm -v /setup_deps.sh
+    rm -v /*.sh
 
 ENV THIRD_PARTY_DIR=/deps/usr
 ENV THIRD_PARTY_SRC_DIR=/deps/src

docker/patch_yum_repo.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+set -e
+
+sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
+sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
+sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
+
+if [[ "$ARCH" = "aarch64" ]]; then
+    sed -i s/vault.centos.org\\/centos/vault.centos.org\\/altarch/g /etc/yum.repos.d/*.repo
+fi

docs/en/blog_post/20240402_OpenmldbVsRedis.md

@@ -44,7 +44,7 @@ We plan to test with 1 million (referred to as 1M) keys, each corresponding to 1
 Deployment can be done through containerization or directly on physical machines using software packages. There is no significant difference between the two methods. Below is an example of using containerization for deployment:
 
 - OpenMLDB
-    - Docker image: `docker pull 4pdosc/openmldb:0.9.0`
+    - Docker image: `docker pull 4pdosc/openmldb:0.9.2`
     - Documentation: [https://openmldb.ai/docs/zh/main/quickstart/openmldb_quickstart.html](https://openmldb.ai/docs/zh/main/quickstart/openmldb_quickstart.html)
 
 - Redis:

docs/en/blog_post/20240503_OpenmldbRelease.md

@@ -0,0 +1,56 @@
+# OpenMLDB v0.9.0 Release: Major Upgrade in SQL Capabilities Covering the Entire Feature Servicing Process
+
+OpenMLDB has just released a new version v0.9.0, including SQL syntax extensions, MySQL protocol compatibility, TiDB storage support, online feature computation, feature signatures, and more. Among these, the most noteworthy features are the MySQL protocol and ANSI SQL compatibility, along with the extended SQL syntax capabilities.
+
+Firstly, MySQL protocol compatibility allows OpenMLDB users to access OpenMLDB clusters using any MySQL client, not limited to GUI applications like NaviCat or Sequal Ace but also Java JDBC MySQL Driver, Python SQLAlchemy, Go MySQL Driver, and various programming language SDKs. For more information, you can refer to "[**Ultra High-Performance Database OpenM(ysq)LDB: Seamless Compatibility with MySQL Protocol and Multi-Language MySQL Client**](20240322_Openmysqldb.md)".
+
+Secondly, the new version significantly expands SQL capabilities, especially implementing OpenMLDB’s unique request mode and stored procedure execution within standard SQL syntax. Compared to traditional SQL databases, OpenMLDB covers the entire machine learning process, including offline and online modes. In online mode, users can input sample data, and get feature results through SQL feature extraction. On the contrary, in the past, we needed to deploy SQL as a stored procedure through the `Deploy` command and then perform online feature computation through SDKs or HTTP interfaces. The new version adds `SELECT CONFIG` and `CALL` statements, allowing users to directly specify request mode and sample data in SQL to compute feature results, as shown below:
+
+```
+-- Execute online request mode query for action (10, "foo", timestamp(4000))
+SELECT id, count(val) over (partition by id order by ts rows between 10 preceding and current row)
+FROM t1
+CONFIG (execute_mode = 'online', values = (10, "foo", timestamp(4000)))
+```
+You can also use the ANSI SQL `CALL` statement to invoke stored procedures with sample rows as parameters, as shown below:
+
+```
+-- Execute online request mode query for action (10, "foo", timestamp(4000))
+DEPLOY window_features SELECT id, count(val) over (partition by id order by ts rows between 10 preceding and current row)
+FROM t1;
+
+CALL window_features(10, "foo", timestamp(4000))
+```
+For detailed release notes, please refer to: [https://github.com/4paradigm/OpenMLDB/releases/tag/v0.9.0](https://github.com/4paradigm/OpenMLDB/releases/tag/v0.9.0)
+
+Please feel free to download and explore the latest release. Your feedback is highly valued and appreciated. We encourage you to share your thoughts and suggestions to help us improve and enhance the platform. Thank you for your support!
+
+## Release Date
+
+April 25, 2024
+
+## Release Note
+
+[https://github.com/4paradigm/OpenMLDB/releases/tag/v0.9.0](https://github.com/4paradigm/OpenMLDB/releases/tag/v0.9.0)
+
+## Highlighted Features
+
+* Added support for the latest version of SQLAlchemy 2, seamlessly integrating with popular Python frameworks such as Pandas and Numpy.
+
+* Expanded support for more data backends, integrating TiDB’s distributed file storage capability with OpenMLDB’s high-performance in-memory feature computation capability.
+
+* Enhanced ANSI SQL support, fixed `first_value` semantics, supported `MAP` type and feature signatures, and added offline mode support for `INSERT` statements.
+
+* Added support for MySQL protocol, allowing access to OpenMLDB clusters using MySQL clients like NaviCat, Sequal Ace, and various MySQL SDKs for programming languages.
+
+* Extended SQL syntax support, enabling online feature computation directly through `SELECT CONFIG` or `CALL` statements.
+
+--------------------------------------------------------------------------------------------------------------
+
+**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/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:0.9.2 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 +11,9 @@ 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>
+
+    OpenMLDB v0.9.0 Release: Major Upgrade in SQL Capabilities Covering the Entire Feature Servicing Process <20240503_OpenmldbRelease.md>
+
+    Introducing OpenMLDB’s New Feature: Feature Signatures — Enabling Complete Feature Engineering with SQL <20240523_OpenmldbFeatureSignatures.md>
+