### Install chDB

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Install the base chDB package.

```bash
pip install "chdb>=4.0"
```

--------------------------------

### Install optional dependencies

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Install chDB with optional dependencies for pandas, PyArrow, or all.

```bash
# For pandas DataFrame support
pip install "chdb[pandas]>=4.0"

# For PyArrow support
pip install "chdb[arrow]>=4.0"

# All optional dependencies
pip install "chdb[all]>=4.0"
```

--------------------------------

### Run ClickStack Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/use-cases/observability/clickstack/example-datasets/_snippets/_instrument_application.md

Execute this command to start the ClickStack example after stopping any previous runs.

```bash
# Ctrl-C the previous run, then:
./run.sh
```

--------------------------------

### Verify Installation

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Check the installed chDB version and confirm DataStore readiness.

```python
import chdb
print(chdb.__version__)  # Should print 4.x.x or higher

from chdb import datastore as pd
print("DataStore ready!")
```

--------------------------------

### Install and Run ClickHouse CLI

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/deployment-modes.md

Installs the ClickHouse CLI, sets the latest version as default, starts a local server, and opens the client.

```bash
# Install the CLI
curl https://clickhouse.com/cli | sh

# Install the latest ClickHouse, set it as your default, and symlink the clickhouse binary onto your PATH
clickhousectl local use latest
clickhousectl local server start
clickhousectl local client
```

--------------------------------

### Create the database and table

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/operations_/backup_restore/_snippets/_example_setup.md

SQL commands to create a test database named `test_db` and a `test_table` with various columns for backup and restoration examples.

```sql
CREATE DATABASE test_db;

CREATE TABLE test_db.test_table (
    id UUID,
    name String,
    email String,
    age UInt8,
    salary UInt32,
    created_at DateTime,
    is_active UInt8,
    department String,
    score Float32,
    country String
) ENGINE = MergeTree()
ORDER BY id;
```

--------------------------------

### WSL Installation Success Output

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/getting-started/install/_snippets/_windows_install.md

Example output after successful WSL installation and initial setup.

```text
Welcome to Ubuntu 24.04.1 LTS (GNU/Linux 5.15.133.1-microsoft-WSL2 x86_64)
```

--------------------------------

### Install Pandas and Apache Arrow

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/getting-started.md

Command to install Pandas and PyArrow libraries.

```bash
pip install pandas pyarrow
```

--------------------------------

### Create sample tables

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/cloud/managed-postgres/quickstart.md

Create two sample tables, 'events' and 'users', for demonstration.

```sql
CREATE TABLE events (
   event_id SERIAL PRIMARY KEY,
   event_name VARCHAR(255) NOT NULL,
   event_type VARCHAR(100),
   event_timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
   event_data JSONB,
   user_id INT,
   user_ip INET,
   is_active BOOLEAN DEFAULT TRUE,
   created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
   updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE users (
   user_id SERIAL PRIMARY KEY,
   name VARCHAR(100),
   country VARCHAR(50),
   platform VARCHAR(50)
);
```

--------------------------------

### Old distributions method for installing the deb-packages

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/getting-started/install/_snippets/_deb_install.md

Commands for installing ClickHouse on older Debian/Ubuntu distributions, including prerequisites, GPG key, repository setup, package installation, and starting services.

```bash
# Install prerequisite packages
sudo apt-get install apt-transport-https ca-certificates dirmngr

# Add the ClickHouse GPG key to authenticate packages
sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 8919F6BD2B48D754

# Add the ClickHouse repository to apt sources
echo "deb https://packages.clickhouse.com/deb stable main" | sudo tee \
    /etc/apt/sources.list.d/clickhouse.list
    
# Update apt package lists
sudo apt-get update

# Install ClickHouse server and client packages
sudo apt-get install -y clickhouse-server clickhouse-client

# Start the ClickHouse server service
sudo service clickhouse-server start

# Launch the ClickHouse command line client
clickhouse-client # or "clickhouse-client --password" if you set up a password.
```

--------------------------------

### Clone and Run the Application

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/use-cases/observability/clickstack/example-datasets/_snippets/_instrument_application.md

Clone the repository, install dependencies, and start the application. This sets up the basic environment before instrumentation.

```bash
git clone https://github.com/ClickHouse/hn-news-analyzer.git
cd hn-news-analyzer
npm install
cp .env.example .env
```

```bash
./run.sh
```

--------------------------------

### Creating a DataStore

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Examples of creating a DataStore from dictionaries, pandas DataFrames, CSV files, and Parquet files.

```python
from chdb import datastore as pd

# From a dictionary
ds = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'city': ['NYC', 'LA', 'NYC']
})

# From a pandas DataFrame
import pandas
pdf = pandas.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
ds = pd.DataFrame(pdf)

# From a CSV file
ds = pd.read_csv("data.csv")

# From a Parquet file (recommended for large datasets)
ds = pd.read_parquet("data.parquet")
```

--------------------------------

### Installing npm and yarn - Linux (deb) - nvm example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/contribute/contrib-writing-guide.md

Example commands to use nvm for managing Node.js versions.

```bash
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.0/install.sh | bash
nvm use
```

--------------------------------

### pg_hba.conf entry for ClickPipes user

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/data-ingestion/clickpipes/postgres/source/generic.md

Example configuration line for pg_hba.conf to allow connections for the ClickPipes user.

```config
host    all   clickpipes_user     0.0.0.0/0          scram-sha-256
```

--------------------------------

### Databases

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Examples of connecting to MySQL and PostgreSQL databases, including using a URI.

```python
from chdb.datastore import DataStore

# MySQL
ds = DataStore.from_mysql(
    host="localhost",
    database="mydb",
    table="users",
    user="root",
    password="pass"
)

# PostgreSQL
ds = DataStore.from_postgresql(
    host="localhost",
    database="mydb",
    table="users",
    user="postgres",
    password="pass"
)

# Using URI
ds = DataStore.uri("mysql://user:pass@localhost:3306/mydb/users")
```

--------------------------------

### Quick Start

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/debugging/profiling.md

Example demonstrating how to enable profiling, run DataStore operations, and view the performance report.

```python
from chdb import datastore as pd
from chdb.datastore.config import config, get_profiler

# Enable profiling
config.enable_profiling()

# Run your operations
ds = pd.read_csv("large_data.csv")
result = (ds
    .filter(ds['amount'] > 100)
    .groupby('category')
    .agg({'amount': 'sum'})
    .sort('sum', ascending=False)
    .head(10)
    .to_df()
)

# View report
profiler = get_profiler()
print(profiler.report())
```

--------------------------------

### Correct way to get Tokyo timezone

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/language-clients/java/date_time_guide.md

Example demonstrating the preferred method for obtaining a timezone by ID using `java.time.ZoneId` to avoid silent fallbacks.

```Java
TimeZone.getTimeZone(ZoneId.of("Asia/Tokyo"))
```

--------------------------------

### Example Application Setup and Run

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/language-clients/go/index.md

Steps to create a new Go application, add the ClickHouse client, and run a simple connection test.

```bash
mkdir my-clickhouse-app && cd my-clickhouse-app

cat > go.mod <<-END
  module my-clickhouse-app

  go 1.21

  require github.com/ClickHouse/clickhouse-go/v2 main
END

cat > main.go <<-END
  package main

  import (
    "fmt"
    "github.com/ClickHouse/clickhouse-go/v2"
  )

  func main() {
   conn, _ := clickhouse.Open(&clickhouse.Options{Addr: []string{"127.0.0.1:9000"}})
    v, _ := conn.ServerVersion()
    fmt.Println(v.String())
  }
END

go mod tidy
go run main.go
```

--------------------------------

### MySQL 5.7 Binary Log Settings in Config File

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/data-ingestion/clickpipes/mysql/source/generic.md

Example configuration for MySQL 5.7 binary log settings in the `my.cnf` file.

```ini
[mysqld]
server_id = 1
log_bin = ON
binlog_format = ROW
binlog_row_image = FULL
expire_logs_days = 1
```

--------------------------------

### Build the docs

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/contribute/contrib-writing-guide.md

Step-by-step commands to clone the documentation repository, prepare the ClickHouse repo docs, install dependencies, and start the Docusaurus development server.

```bash
git clone https://github.com/ClickHouse/clickhouse-docs.git
cd clickhouse-docs # local docs repo


# Below you can choose only ***ONE*** of the two prep commands

# yarn copy-clickhouse-repo-docs
# This command will clone master ClickHouse/Clickhouse branch to a temp folder and 
# from there it will copy over the relevant docs folders to this folder

yarn copy-clickhouse-repo-docs

# OR

# yarn prep-from-local
# This command will use a locally available folder containing ClickHouse/Clickhouse
# and from there it will copy over the relevant docs folders to this folder

# running this without using the local full path as an argument will lead to error

yarn prep-from-local /full/path/to/your/local/Clickhouse/Clickhouse 


# Once you have run prep command (ONE of the two) the below command installs the Docusaurus packages and prerequisites in a subdirectory of `clickhouse-docs` named `node_modules`

yarn install

# This command will start Docusaurus in development mode, which means that as you edit source (for example, .md files)
# files the changes will be rendered into HTML files and served by the Docusaurus development server.

# Edit your files. Remember that if you are editing files in the ClickHouse/ClickHouse repo then you should edit them in
# that repo and then copy the edited file into the ClickHouse/clickhouse-docs/ directory structure so that they are updated
# in your develoment environment.

yarn start

# 'yarn start' probably opened a browser for you when you ran it; if not, open a browser to http://localhost:3000/docs/intro
# and navigate to the documentation that you are changing. If you have already made the changes, you can verify them here; if
# not, make them, and you will see the page update as you save the changes.
```

--------------------------------

### Quick Start

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/debugging/logging.md

Example demonstrating how to enable debug logging and perform operations.

```python
from pathlib import Path
Path("data.csv").write_text("""\
name,age,city,salary,department
Alice,25,NYC,55000,Engineering
Bob,30,LA,65000,Product
Charlie,35,NYC,80000,Engineering
Diana,28,SF,70000,Design
Eve,42,NYC,95000,Product
""")

from chdb import datastore as pd
from chdb.datastore.config import config

# Enable debug logging
config.enable_debug()

# Now all operations will log details
ds = pd.read_csv("data.csv")
result = ds.filter(ds['age'] > 25).to_df()
```

--------------------------------

### Start ClickHouse server

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/getting-started/install/_snippets/_nixos_install.md

Command to start the ClickHouse server after installation.

```bash
clickhouse-server
```

--------------------------------

### Grant schema-level, read-only access

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/data-ingestion/clickpipes/postgres/source/azure-flexible-server-postgres.md

Commands to grant usage on a schema and select permissions on all tables within that schema to the ClickPipes user. This example is for the 'public' schema.

```sql
GRANT USAGE ON SCHEMA "public" TO clickpipes_user;
GRANT SELECT ON ALL TABLES IN SCHEMA "public" TO clickpipes_user;
ALTER DEFAULT PRIVILEGES IN SCHEMA "public" GRANT SELECT ON TABLES TO clickpipes_user;
```

--------------------------------

### Create Sample Database

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/knowledgebase/projection_example.mdx

Initializes a new database for testing projection usage.

```sql
CREATE database db1;
```

--------------------------------

### Grant schema permissions

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/data-ingestion/clickpipes/mysql/source/azure-flexible-server-mysql.md

Grant schema permissions. The following example shows permissions for the `mysql` database. Repeat these commands for each database and host you want to replicate.

```sql
GRANT SELECT ON `mysql`.* TO 'clickpipes_user'@'%';
```

--------------------------------

### Selecting Columns

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Examples of selecting columns from a DataStore using both pandas-style and SQL-style methods.

```python
# Pandas style
subset = ds[['name', 'age']]

# SQL style
subset = ds.select('name', 'age')
```

--------------------------------

### Setup for Partitioned Table Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/operations_/backup_restore/01_local_disk.md

Creates a partitioned table, inserts data into it, and verifies the partition counts before performing backup/restore operations.

```sql
CREATE IF NOT EXISTS test_db;
       
-- Create a partitioend table
CREATE TABLE test_db.partitioned (
    id UInt32,
    data String,
    partition_key UInt8
) ENGINE = MergeTree()
PARTITION BY partition_key
ORDER BY id;

INSERT INTO test_db.partitioned VALUES
(1, 'data1', 1),
(2, 'data2', 2),
(3, 'data3', 3),
(4, 'data4', 4);

SELECT count() FROM test_db.partitioned;

SELECT partition_key, count() 
FROM test_db.partitioned
GROUP BY partition_key
ORDER BY partition_key;
```

--------------------------------

### Query Endpoint v2 GET Response Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/cloud/guides/SQL_console/query-endpoints.md

Example of a response in application/x-ndjson format from a version 2 query endpoint GET request.

```application/x-ndjson
{"database":"INFORMATION_SCHEMA","num_tables":"COLUMNS"}
{"database":"INFORMATION_SCHEMA","num_tables":"KEY_COLUMN_USAGE"}
{"database":"INFORMATION_SCHEMA","num_tables":"REFERENTIAL_CONSTRAINTS"}
```

--------------------------------

### Pandas to DataStore Migration Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Demonstrates migrating a pandas workflow to DataStore by only changing the import statement, showing identical results with faster execution.

```python
from pathlib import Path
Path("employees.csv").write_text("""\
name,age,city,salary,department,dept_id,status,email
Alice,28,NYC,75000,Engineering,1,active,alice@company.com
Bob,35,LA,85000,Engineering,1,active,bob@company.com
Charlie,52,NYC,95000,Product,2,active,charlie@company.com
Diana,32,SF,70000,Design,3,active,diana@company.com
Eve,23,LA,48000,Product,2,inactive,eve@company.com
""")

# Original pandas code
import pandas as pd

df = pd.read_csv("employees.csv")
result = (df[df['salary'] > 50000]
          .groupby('department')['salary']
          .agg(['mean', 'count'])
          .sort_values('mean', ascending=False))
print(result)

# DataStore version - just change the import!
from chdb import datastore as pd

df = pd.read_csv("employees.csv")
result = (df[df['salary'] > 50000]
          .groupby('department')['salary']
          .agg(['mean', 'count'])
          .sort_values('mean', ascending=False))
print(result)  # Same result, faster execution!
```

--------------------------------

### Tab Structure for Operating System Specific Content

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/contribute/contrib-writing-guide.md

Example of using Docusaurus Tabs and TabItem components to provide operating system-specific installation instructions while keeping common introductory and next steps content.

```md
---
slug: /integrations/sql-clients/clickhouse-client-local
sidebar_label: clickhouse-client
title: clickhouse-client and clickhouse-local
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# clickhouse-client and clickhouse-local

Common information

:::tip
If you have already installed ClickHouse server locally you may have **clickhouse-client** and **clickhouse local** installed.  Check by running **clickhouse client** and **clickhouse local** at the commandline.  Otherwise follow the instructions for your operating system.
:::

## Install clickhouse-client and clickhouse-local

<Tabs groupId="os">
<TabItem value="linux" label="Linux" default>

#### Install the clickhouse-client package:

more details for Linux

</TabItem>
<TabItem value="mac" label="macOS">

#### Download ClickHouse:

We do not provide an installer for macOS.  Download the binary build for your architecture (x86_64 or Apple Silicon).

more details for macOS

</TabItem>
<TabItem value="wsl" label="Microsoft Windows with WSL 2">

In Microsoft Windows 10 or 11 with the Windows Subsystem for Linux (WSL) version 2 (WSL 2) you can run Ubuntu Linux, and then install `clickhouse-client` and `clickhouse-local` by following the Debian install instructions.

more details for Windows WSL

</TabItem>
</Tabs>

## Next Steps

Common content
```

--------------------------------

### Start ClickHouse server

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/getting-started/install/_snippets/_deb_install.md

Command to start the ClickHouse server service.

```bash
sudo service clickhouse-server start
```

--------------------------------

### Insert random data

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/operations_/backup_restore/_snippets/_example_setup.md

SQL command to insert one thousand rows of randomly generated data into the `test_table` for demonstration purposes.

```sql
INSERT INTO test_table (id, name, email, age, salary, created_at, is_active, department, score, country)
SELECT
    generateUUIDv4() as id,
    concat('User_', toString(rand() % 10000)) as name,
    concat('user', toString(rand() % 10000), '@example.com') as email,
    18 + (rand() % 65) as age,
    30000 + (rand() % 100000) as salary,
    now() - toIntervalSecond(rand() % 31536000) as created_at,
    rand() % 2 as is_active,
    arrayElement(['Engineering', 'Marketing', 'Sales', 'HR', 'Finance', 'Operations'], (rand() % 6) + 1) as department,
    rand() / 4294967295.0 * 100 as score,
    arrayElement(['USA', 'UK', 'Germany', 'France', 'Canada', 'Australia', 'Japan', 'Brazil'], (rand() % 8) + 1) as country
FROM numbers(1000);
```

--------------------------------

### Cloud Storage

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Examples of connecting to data stored in S3 (anonymously and with credentials) and via HTTP/HTTPS.

```python
from chdb.datastore import DataStore

# S3 (anonymous)
ds = DataStore.uri("s3://bucket/data.parquet?nosign=true")

# S3 (with credentials)
ds = DataStore.from_s3(
    "s3://bucket/data.parquet",
    access_key_id="KEY",
    secret_access_key="SECRET"
)

# HTTP/HTTPS
ds = DataStore.uri("https://example.com/data.csv")
```

--------------------------------

### Quick start

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/language-clients/csharp.md

Example demonstrating how to create a ClickHouse client, execute a query, and print the result.

```csharp
using ClickHouse.Driver;

// Create a client (typically as a singleton)
using var client = new ClickHouseClient("Host=my.clickhouse;Protocol=https;Port=8443;Username=user");

// Execute a query
var version = await client.ExecuteScalarAsync("SELECT version()");
Console.WriteLine(version);
```

--------------------------------

### Delete example models directory

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/integrations/data-ingestion/etl-tools/dbt/guides.md

Command to remove the default example models directory.

```bash
clickhouse-user@clickhouse:~/imdb$ rm -rf models/example
```

--------------------------------

### Joining DataStores

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/quickstart.md

Examples of performing inner and left joins between DataStore objects, including using the pandas-style merge function.

```python
from pathlib import Path
Path("departments.csv").write_text("""\
dept_id,department_name
1,Engineering
2,Product
3,Design
""")

from chdb import datastore as pd

employees = pd.read_csv("employees.csv")
departments = pd.read_csv("departments.csv")

# Inner join
result = employees.join(departments, on='dept_id', how='inner')

# Left join
result = employees.join(departments, on='dept_id', how='left')

# Using merge (pandas style)
result = pd.merge(employees, departments, on='dept_id')
```

--------------------------------

### Install IPython

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/guides/clickhouse-local.md

Installs the IPython interactive shell, which is used for running commands throughout the guide.

```bash
pip install ipython
```

--------------------------------

### Create Table Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/cloud/reference/01_changelog/02_release_notes/25_06.md

Example of creating a MergeTree table with primary key and partitioning.

```sql
CREATE TABLE t0 (
  key Int32,
  value Int32
) ENGINE=MergeTree()
PRIMARY KEY key
PARTITION BY key % 2;
```

--------------------------------

### Extract and install server package with configuration

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/getting-started/install/_snippets/_linux_tar_install.md

Commands to extract and install the clickhouse-server package and start the server.

```bash
# Extract and install server package with configuration
tar -xzvf "clickhouse-server-$LATEST_VERSION-${ARCH}.tgz" \
  || tar -xzvf "clickhouse-server-$LATEST_VERSION.tgz"
sudo "clickhouse-server-$LATEST_VERSION/install/doinst.sh" configure
sudo /etc/init.d/clickhouse-server start  # Start the server
```

--------------------------------

### Create a table

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/_snippets/_users-and-roles-common.md

Create a sample table for examples.

```sql
CREATE TABLE db1.table1 (
   id UInt64,
   column1 String,
   column2 String
)
ENGINE MergeTree
ORDER BY id;
```

--------------------------------

### Install libraries

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/use-cases/AI_ML/MCP/ai_agent_libraries/streamlit.md

Install the required libraries by running the following commands:

```bash
pip install streamlit agno ipywidgets
```

--------------------------------

### Example `get /keeper/config` output

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/guides/sre/keeper/index.md

An example of the output when querying the `/keeper/config` virtual node.

```sql
:) get /keeper/config
server.1=zoo1:9234;participant;1
server.2=zoo2:9234;participant;1
server.3=zoo3:9234;participant;1
```

--------------------------------

### String Length Example

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/chdb/datastore/accessors.md

Example of getting string length in bytes using `.str.len()`.

```python
ds['name_len'] = ds['name'].str.len()
```

--------------------------------

### Start PostgreSQL + ClickHouse Stack

Source: https://github.com/clickhouse/clickhouse-docs/blob/main/docs/cloud/managed-postgres/local-development.md

Clone the repository and start the local development environment with PostgreSQL, ClickHouse, and PeerDB preconfigured. This command initiates all necessary services for local testing.

```bash
git clone https://github.com/ClickHouse/postgres-clickhouse-stack.git
cd postgres-clickhouse-stack

./run.sh start
```