Merge pull request #3102 from ClickHouse/add_style_check

Author: gingerwizard

.github/workflows/check-build.yml

@@ -0,0 +1,40 @@
+---
+name: Style check
+
+env:
+  # Force the stdout and stderr streams to be unbuffered
+  PYTHONUNBUFFERED: 1
+
+on:
+  pull_request:
+    types:
+      - synchronize
+      - reopened
+      - opened
+jobs:
+  stylecheck:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Step 1: Check out the repository
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      # Step 2: Set up environment if required (e.g., installing Aspell)
+      - name: Install Aspell
+        run: sudo apt-get update && sudo apt-get install -y aspell aspell-en
+
+      # Step 3: Run the spellcheck script
+      - name: Run spellcheck
+        run: |
+          ./scripts/check-doc-aspell
+        continue-on-error: true
+        id: spellcheck
+
+      # Step 4: Fail the build if the script returns exit code 1
+      - name: Check exit code
+        run: |
+          if [ ${{ steps.spellcheck.outcome }} == 'failure' ]; then
+            echo "Spellcheck failed. See the logs for details."
+            exit 1
+          fi

.github/workflows/pull-request.yml

@@ -14,8 +14,6 @@ on:  # yamllint disable-line rule:truthy
       - synchronize
       - reopened
       - opened
-    branches-ignore:
-      - 'new-nav'
 
 # Cancel the previous wf run in PRs.
 concurrency:

docs/en/about-us/adopters.md

@@ -132,7 +132,7 @@ The following list of companies using ClickHouse and their success stories is as
 | [DNSMonster](https://dnsmonster.dev/) | Software & Technology | DNS Monitoring | — | — | [GitHub Repository](https://github.com/mosajjal/dnsmonster) |
 | [Darwinium](https://www.darwinium.com/) |  Software & Technology |  Security and Fraud Analytics | — | — | [Blog Post, July 2022](https://clickhouse.com/blog/fast-feature-rich-and-mutable-clickhouse-powers-darwiniums-security-and-fraud-analytics-use-cases) |
 | [Dash0](https://www.dash0.com/) | APM Platform | Main product | — | — | [Careers page](https://careers.dash0.com/senior-product-engineer-backend/en) |
-| [Dashdive](https://www.dashdive.com/) | Infrastructure management | Analytics | — | — | [Hackernews, 2024](https://news.ycombinator.com/item?id=39178753) |
+| [Dashdive](https://www.dashdive.com/) | Infrastructure management | Analytics | — | — | [Hacker News, 2024](https://news.ycombinator.com/item?id=39178753) |
 | [Dassana](https://lake.dassana.io/) | Cloud data platform | Main product | - | - | [Blog Post, Jan 2023](https://clickhouse.com/blog/clickhouse-powers-dassanas-security-data-lake) [Direct reference, April 2022](https://news.ycombinator.com/item?id=31111432) |
 | [Datafold](https://www.datafold.com/) | Data Reliability Platform | — | — | — | [Job advertisement, April 2022](https://www.datafold.com/careers) |
 | [Dataliance for China Telecom](https://www.chinatelecomglobal.com/) | Telecom | Analytics | — | — | [Slides in Chinese, January 2018](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup12/telecom.pdf) |
@@ -146,7 +146,7 @@ The following list of companies using ClickHouse and their success stories is as
 | [Didi](https://web.didiglobal.com/) | Transportation & Ride Sharing | Observability | 400+ logging, 40 tracing | PBs/day / 40GB/s write throughput, 15M queries/day, 200 QPS peak | [Blog, Apr 2024](https://clickhouse.com/blog/didi-migrates-from-elasticsearch-to-clickHouse-for-a-new-generation-log-storage-system) |
 | [DigiCert](https://www.digicert.com) | Network Security | DNS Platform | — | over 35 billion events per day | [Job posting, Aug 2022](https://www.indeed.com/viewjob?t=Senior+Principal+Software+Engineer+Architect&c=DigiCert&l=Lehi,+UT&jk=403c35f96c46cf37&rtk=1g9mnof7qk7dv800) |
 | [Disney+](https://www.disneyplus.com/) | Video Streaming | Analytics | — | 395 TiB | [Meetup Video, December 2022](https://www.youtube.com/watch?v=CVVp6N8Xeoc&list=PL0Z2YDlm0b3iNDUzpY1S3L_iV4nARda_U&index=8) [Slides, December 2022](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup67/Disney%20plus%20ClickHouse.pdf) |
-| [Dittofeed](https://dittofeed.com/) | Software & Technology | Open Source Customer Engagement | — | — | [Hackernews, June 2023](https://news.ycombinator.com/item?id=36061344) |
+| [Dittofeed](https://dittofeed.com/) | Software & Technology | Open Source Customer Engagement | — | — | [Hacker News, June 2023](https://news.ycombinator.com/item?id=36061344) |
 | [Diva-e](https://www.diva-e.com) | Digital consulting | Main Product | — | — | [Slides in English, September 2019](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup29/ClickHouse-MeetUp-Unusual-Applications-sd-2019-09-17.pdf) |
 | [Dolphin Emulator](https://dolphin-emu.org/) | Games | Analytics | — | — | [Twitter, September 2022](https://twitter.com/delroth_/status/1567300096160665601) |
 | [DoorDash](https://www.doordash.com/home) | E-commerce | Monitoring | — | — | [Meetup, December 2024](https://github.com/ClickHouse/clickhouse-presentations/blob/master/2024-meetup-san-francisco/Clickhouse%20Meetup%20Slides%20(1).pdf) |

docs/en/chdb/guides/jupysql.md

@@ -3,10 +3,10 @@ title: JupySQL and chDB
 sidebar_label: JupySQL
 slug: /en/chdb/guides/jupysql
 description: How to install chDB for Bun
-keywords: [chdb, jupysql]
+keywords: [chdb, JupySQL]
 ---
 
-[JupySQL](https://jupysql.ploomber.io/en/latest/quick-start.html) is a Python library that lets you run SQL in Jupyter notebooks and the iPython shell.
+[JupySQL](https://jupysql.ploomber.io/en/latest/quick-start.html) is a Python library that lets you run SQL in Jupyter notebooks and the IPython shell.
 In this guide, we're going to learn how to query data using chDB and JupySQL.
 
 <div class='vimeo-container'>
@@ -22,13 +22,13 @@ python -m venv .venv
 source .venv/bin/activate
 ```
 
-And then, we'll install JupySQL, iPython, and Jupyter Lab:
+And then, we'll install JupySQL, IPython, and Jupyter Lab:
 
 ```bash
 pip install jupysql ipython jupyterlab
 ```
 
-We can use JupySQL in iPython, which we can launch by running:
+We can use JupySQL in IPython, which we can launch by running:
 
 ```bash
 ipython
@@ -65,7 +65,7 @@ for file in files:
 
 ## Configuring chDB and JupySQL
 
-Next, let's import chDB's `dbapi` module:
+Next, let's import the `dbapi` module for chDB:
 
 ```python
 from chdb import dbapi
@@ -168,7 +168,7 @@ The default database doesn't persist data on disk, so we need to create another
 %sql CREATE DATABASE atp
 ```
 
-And now we're going to create a table called `rankings` whos schema will be derived from the structure of the data in the CSV files:
+And now we're going to create a table called `rankings` whose schema will be derived from the structure of the data in the CSV files:
 
 ```python
 %%sql

docs/en/chdb/guides/query-remote-clickhouse.md

@@ -41,7 +41,7 @@ You can also use the code in a Python script or in your favorite notebook.
 ## An intro to ClickPy
 
 The remote ClickHouse server that we're going to query is [ClickPy](https://clickpy.clickhouse.com).
-ClickPy keeps track of all the downloads of PyPi packages and lets you explore the stats of packages via a UI.
+ClickPy keeps track of all the downloads of PyPI packages and lets you explore the stats of packages via a UI.
 The underlying database is available to query using the `play` user.
 
 You can learn more about ClickPy in [its GitHub repository](https://github.com/ClickHouse/clickpy).
@@ -150,7 +150,7 @@ df.head(n=5)
 4  2018-03-02         5      23842
 ```
 
-We can then compute the ratio of Open AI downloads to scikit-learn downloads like this:
+We can then compute the ratio of Open AI downloads to `scikit-learn` downloads like this:
 
 ```python
 df['ratio'] = df['y_openai'] / df['y_sklearn']

docs/en/chdb/guides/querying-apache-arrow.md

@@ -3,7 +3,7 @@ title: How to query Apache Arrow with chDB
 sidebar_label: Querying Apache Arrow
 slug: /en/chdb/guides/apache-arrow
 description: In this guide, we'll learn how to query Apache Arrow tables with chDB
-keywords: [chdb, apache-arrow]
+keywords: [chdb, Apache Arrow]
 ---
 
 [Apache Arrow](https://arrow.apache.org/) is a standardized column-oriented memory format that's gained popularity in the data community.
@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install pyarrow, pandas, and ipython:
+And now we're going to install PyArrow, pandas, and ipython:
 
 ```bash
 pip install pyarrow pandas ipython
@@ -55,7 +55,7 @@ If you want to download more files, use `aws s3 ls` to get a list of all the fil
 
 
 
-Next, we'll import the Parquet module from the pyarrow package:
+Next, we'll import the Parquet module from the `pyarrow` package:
 
 ```python
 import pyarrow.parquet as pq

docs/en/chdb/guides/querying-parquet.md

@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install iPython:
+And now we're going to install IPython:
 
 ```bash
 pip install ipython

docs/en/chdb/guides/querying-s3-bucket.md

@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install iPython:
+And now we're going to install IPython:
 
 ```bash
 pip install ipython

docs/en/chdb/install/nodejs.md

@@ -3,7 +3,7 @@ title: Installing chDB for NodeJS
 sidebar_label: NodeJS
 slug: /en/chdb/install/nodejs
 description: How to install chDB for NodeJS
-keywords: [chdb, embedded, clickhouse-lite, nodejs, install]
+keywords: [chdb, embedded, clickhouse-lite, NodeJS, install]
 ---
 
 # Installing chDB for NodeJS

docs/en/chdb/install/python.md

@@ -67,7 +67,7 @@ res = chdb.query('select * from file("data.csv", CSV)', 'CSV');  print(res)
 print(f"SQL read {res.rows_read()} rows, {res.bytes_read()} bytes, elapsed {res.elapsed()} seconds")
 ```
 
-**Pandas dataframe output**
+**Pandas DataFrame output**
 ```python
 # See more in https://clickhouse.com/docs/en/interfaces/formats
 chdb.query('select * from file("data.parquet", Parquet)', 'Dataframe')
@@ -165,7 +165,7 @@ Some notes on the chDB Python UDF (User Defined Function) decorator.
         import json
         ...
     ```
-6. The Python interpertor used is the same as the one used to run the script. You can get it from `sys.executable`.
+6. The Python interpreter used is the same as the one used to run the script. You can get it from `sys.executable`.
 
 see also: [test_udf.py](https://github.com/chdb-io/chdb/blob/main/tests/test_udf.py).
 
@@ -207,7 +207,7 @@ chdb.query(
 
 1. You must inherit from chdb.PyReader class and implement the `read` method.
 2. The `read` method should:
-    1. return a list of lists, the first demension is the column, the second dimension is the row, the columns order should be the same as the first arg `col_names` of `read`.
+    1. return a list of lists, the first dimension is the column, the second dimension is the row, the columns order should be the same as the first arg `col_names` of `read`.
     1. return an empty list when there is no more data to read.
     1. be stateful, the cursor should be updated in the `read` method.
 3. An optional `get_schema` method can be implemented to return the schema of the table. The prototype is `def get_schema(self) -> List[Tuple[str, str]]:`, the return value is a list of tuples, each tuple contains the column name and the column type. The column type should be one of [the following](/en/sql-reference/data-types).
@@ -247,7 +247,7 @@ See also: [test_query_py.py](https://github.com/chdb-io/chdb/blob/main/tests/tes
 
 ## Limitations
 
-1. Column types supported: pandas.Series, pyarrow.array, chdb.PyReader
+1. Column types supported: `pandas.Series`, `pyarrow.array`,`chdb.PyReader`
 1. Data types supported: Int, UInt, Float, String, Date, DateTime, Decimal
 1. Python Object type will be converted to String
 1. Pandas DataFrame performance is all of the best, Arrow Table is better than PyReader