Introduction to the data version control tool LakeFS, how to use it, and cases of combining it with other tools

Introduction to LakeFS

LakeFS is an open source data lake version control system that can help users manage and control data versions in data lakes. The following are some of the main uses and functions of LakeFS:

Data version control: LakeFS provides a version control function similar to Git, which can track and manage data versions in the data lake. Users can easily create, rollback, merge and branch data versions to ensure data consistency and traceability.
Database snapshot management: LakeFS supports the management of individual snapshots in the data lake, including data tables, files, and directories. Users can easily create and manage snapshots to facilitate data query and analysis in the data lake.
Multi-user collaboration: LakeFS allows multiple users to work on the data lake at the same time and provides strict permission control and access management. You can set read and write permissions for different users to ensure data security and isolation.
Data Lake Consistency: LakeFS provides consistency guarantees that ensure the consistency of the data lake even under concurrent writes and modifications. When multiple users perform write operations at the same time, LakeFS provides a mechanism to handle conflicts to avoid data inconsistency.
Data lake recovery: LakeFS provides data lake recovery capabilities that can easily roll back and restore data versions in the data lake. Users can roll back to previous versions as needed to correct errors or restore data.
Data Lake Analysis: LakeFS can be integrated with various data lake analysis tools and engines, such as Apache Spark, Presto, AWS Athena, etc. Through integration with these tools, users can more conveniently perform data analysis and query in the data lake.

LakeFS provides a wealth of functions and tools to help users effectively manage and control data versions in the data lake, improve the reliability, consistency, and availability of the data lake, and provide support for users' data analysis and business decisions.

Welcome to lakeFS | lakeFS Documentation

仓库： GitHub - 25280841/lakeFS: lakeFS - Data version control for your data lake | Git for data

LakeFS tool installation

LakeFS is an open source distributed object storage layer for managing and versioning big data collections in data lakes. Below is the complete process for installing and configuring LakeFS on different platforms:

Install and configure LakeFS on Windows:
- Download and install WSL2 (Windows Subsystem for Linux 2).
- Execute the following command to install LakeFS on WSL2:
```
curl https://github.com/treeverse/lakeFS/releases/latest/download/install.sh | bash
```
Install and configure LakeFS on Linux:
- Open a terminal and execute the following command to download and install LakeFS:
```
curl https://github.com/treeverse/lakeFS/releases/latest/download/install.sh | bash
```
Install and configure LakeFS using Docker:
- Install and configure Docker. Depending on the operating system, you can refer to Docker official documentation for installation.
- Execute the following commands to download and start the LakeFS container:
```
docker run -p 8000:8000 -v /path/to/repositories:/repositories treeverse/lakefs:latest

docker run --pull always \
		   --name lakefs \
		   -p 8000:8000 \
		   treeverse/lakefs:latest \
		   run --quickstart
```
  where /path/to/repositoriesis the local path where you want to create and store the LakeFS repository.

Regardless of the platform on which LakeFS is installed, once the installation is complete, you can begin configuring and starting LakeFS by following these steps:

Create a new LakeFS repository:
```
lakefs init my-repo
```
In this command, my-repoit is the name of the warehouse you want to create, which can be modified according to actual needs.
Start the LakeFS service:
```
lakefs server
```
Access the LakeFS Web UI:
- For local installations, http://localhost:8000the LakeFS web UI can be opened by visiting .
- For Docker installations, http://<docker-host-ip>:8000the LakeFS web UI can be opened by visiting . where <docker-host-ip>is the IP address of the host running Docker.

After configuration and startup are completed, you can perform operations such as warehouse management, version control, and data operations through the LakeFS Web UI.

Please note that the above steps are only a basic installation and configuration process. Before actually using LakeFS, please refer to the official documentation and examples for more detailed information.

Use of LakeFS

LakeFS is an open source version control tool for managing big data files in data lakes. The following are the detailed usage methods and steps of LakeFS:

Configure LakeFS:
- Use the command in the terminal lakefs initto initialize LakeFS.
- Follow the prompts to set the username and password of the administrator account, as well as the access credentials for the object storage service.
Create a warehouse:
- Use lakefs repo createthe command to create a warehouse for storing and managing files in the data lake.
- Specify the warehouse name and description, and select the type and configuration of the object storage service.
Clone the repository:
- Use lakefs clonethe command to clone the remote repository locally.
- Specify the URL, local path and credential information of the remote repository.
add files:
- Place the files to be added to the repository under the cloned local repository path.
- Use lakefs addthe command to add files to the local repository.
Submit changes:
- Commit the changes using lakefs committhe command and add a description to the commit.
- Changes after a commit generate a version number that identifies the commit.
Branching and merging:
- Use lakefs branchthe command to create a new branch.
- Make changes and commits on the branch.
- Use lakefs mergethe command to merge the branch onto the master branch.
Rollback code:
- Use lakefs diffthe command to view differences between the current code and a specific version.
- Use lakefs revertthe command to revert to a specific version of the code.
Data lake management:
- Use lakefs lsthe command to list the files in the repository.
- Use lakefs rmthe command to delete files.
- Use lakefs mvthe command to move or rename files.

The above are the basic usage methods and steps of LakeFS. I hope it will be helpful to you as a beginner. You can learn more about LakeFS's official documentation and examples for in-depth learning.

Combine with other tools

lakefs can be combined with many other tools to provide more comprehensive data lake management and version control capabilities. Here are some examples of common tools and platforms that lakefs may be used with:

Database: lakefs can be integrated with various databases such as PostgreSQL, MySQL or Amazon Redshift. By storing data in lakefs, you can track and manage database schema and data changes through version control and metadata management.
Data Lake Tools: Lakefs can be integrated with other data lake tools such as Apache Hadoop, Apache Spark, and Apache Flink for efficient data processing and analysis. By combining it with lakefs, you can ensure the consistency and traceability of your data during processing.
Data pipeline: lakefs can be integrated with common data pipeline tools, such as Apache Airflow or AWS Glue, to achieve automated data flow and processing. By using lakefs, you can ensure data version consistency and metadata management in your data pipeline.
Data integration platform: Lakefs can be integrated with data integration platforms such as Apache Kafka or AWS Kinesis to enable real-time data stream processing. By storing data in lakefs, you can track and manage data changes and rollback or restore when needed.
Machine learning platform: lakefs can be integrated with machine learning platforms, such as TensorFlow or PyTorch, to achieve data version control and model traceability. By storing data and models in lakefs, you can ensure consistency of data and model versions and track changes during model training.

Overall, lakefs can be flexibly integrated with various tools and platforms to provide comprehensive data lake management and version control capabilities. This integration makes lakefs a powerful data management tool suitable for various data workflows and application scenarios.

LakeFS combined with MinIO

To use lakeFS with MinIO to implement data version control and branch management functions, follow the steps below to configure and use:

Step 1: Install and configure MinIO

First, make sure MinIO is deployed and create an S3-compatible bucket for storing lakeFS-managed data. MinIO can be installed via Docker or directly on the server.

Install MinIO using Docker

docker run -p 9000:9000 \
  -e "MINIO_ACCESS_KEY=accesskey" \
  -e "MINIO_SECRET_KEY=secretkey" \
  minio/minio server /data --console-address ":9001"

This assumes that you set the access key and secret key and -e pass them to the container through parameters. At the same time, the directory within the container /data will be mounted as persistent storage.

Step 2: Install lakeFS

You can download the binary package of lakeFS or use Docker to deploy the lakeFS service.

Install lakeFS using Docker

docker run -d --name lakefs-server \
  -p 8000:8000 -p 8080:8080 \
  treeverse/lakefs:<version> server

Here :version should be replaced with the actual lakeFS version number.

Step 3: Initialize the lakeFS repository and connect with MinIO

lakectl Install the client tool on the local machine (obtain the client for the corresponding system according to the official documentation).

Then use lakectl to initialize a new lakeFS repository and point it to a bucket in MinIO.

# 下载并配置 lakectl 工具
curl -sfL https://raw.githubusercontent.com/treeverse/lakeFS/master/install.sh | bash

# 初始化 lakeFS 并关联到 MinIO 桶
export LAKEFS_API_URL=http://localhost:8000
export AWS_ACCESS_KEY_ID=accesskey
export AWS_SECRET_ACCESS_KEY=secretkey
lakectl init lakefs.example.com my-repo s3://my-lakefs-bucket

Step 4: Manipulate data in lakeFS

Now that lakeFS is connected to MinIO, version control and branch management operations can begin.

Create new branch:

lakectl branch create my-repo main
lakectl branch create my-repo experiment

Upload data to a specific branch:

lakectl cp local-file.txt lakefs://my-repo/experiment/path/to/file.txt

Switch branches and merge:

lakectl checkout my-repo experiment
# 在实验分支上工作...
lakectl merge my-repo experiment main

View history and rollback:

lakectl log my-repo path/to/object
lakectl reset my-repo <commit-id> --hard

Examples of combined use

For example, the development team can experiment process a new data set on a branch and then merge it into main the branch after completion, while Databend or other analysis tools can main read data from the branch for query analysis, thus achieving strict management and control of data versions.

lakeFS can be used in conjunction with machine learning platforms such as TensorFlow or PyTorch

Enhance ML workflows by providing data versioning and tracking capabilities. Here is a simplified example of how to integrate lakeFS into a TensorFlow or PyTorch-based project:

Prerequisites

The lakeFS service is installed and configured, and connected to MinIO or other S3-compatible storage.
Warehouses and branches for storing training data are created in lakeFS.

Configuration steps

Set the lakeFS storage path as the data source: In the code of the ML project, you need to access the data through the S3-compatible interface provided by lakeFS. In TensorFlow or PyTorch, it is usually possible boto3 to connect to lakeFS using the corresponding library (eg for Python).

Python

import boto3
from botocore.config import Config

# lakeFS API endpoint 和凭证
endpoint_url = "http://your-lakefs-server:8000"
access_key_id = "your-access-key-id"
secret_access_key = "your-secret-access-key"

# 创建一个指向 lakeFS 的 S3 客户端
s3_client = boto3.client(
    's3',
    endpoint_url=endpoint_url,
    aws_access_key_id=access_key_id,
    aws_secret_access_key=secret_access_key,
    config=Config(signature_version='s3v4'),
)

# 访问 lakeFS 中的数据，例如加载一个数据集
bucket_name = "my-lakefs-bucket"
data_prefix = "path/to/training/data"
response = s3_client.list_objects_v2(Bucket=bucket_name, Prefix=data_prefix)

Manage data versions on different lakeFS branches:
- When training a new model or conducting experiments, create a new lakeFS branch and upload a specific version of the dataset on this branch.
- Switch to different branches as needed to load different dataset versions for training.

# 切换 lakeFS 分支（此处为简化示意，实际需使用 lakectl 命令行工具）
lakectl checkout my-repo experiment-branch

# 然后从新分支加载数据
data_branch = "experiment-branch/path/to/data"
training_files = [obj["Key"] for obj in response["Contents"]]
for file_path in training_files:
    # 下载文件到本地或直接读取文件内容（取决于您的需求）
    data = s3_client.get_object(Bucket=bucket_name, Key=file_path)["Body"].read()
    # 进行数据预处理和训练...

Record the data version information during model training: Record the data set version information used in the training script, so that when viewing the model history, you can trace back to the corresponding lakeFS branch and data version based on this information.

# 记录正在使用的数据分支名
current_data_branch = "experiment-branch"
with open("model_metadata.txt", "a") as f:
    f.write(f"Data branch used for this model: {current_data_branch}\n")

Summarize

Through the above method, you can integrate lakeFS into the machine learning workflow of TensorFlow or PyTorch to ensure that each training has a clear data version record. In actual projects, the code may need to be further adjusted according to specific needs, such as realizing automatic data version switching, associating model versions with data versions, and other advanced functions. In the meantime, please follow best practices and security recommendations for lakeFS and related libraries

Using lakeFS and MinIO to implement data management of big data transmission interface in Node.js environment

First, you need to install the relevant SDK and write code to handle data upload, download, branch management and version control. Here's a simplified example of how this works together:

Install dependencies

npm install @treeverse/lakefs-sdk aws-sdk

Initialize SDKs

const LakeFSClient = require('@treeverse/lakefs-sdk').LakeFSClient;
const AWS = require('aws-sdk');

// lakeFS配置
const lakefsEndpoint = 'http://your-lakefs-server:8000';
const lakefsAccessKeyId = 'your-access-key-id';
const lakefsSecretAccessKey = 'your-secret-access-key';

// MinIO配置（假设用于实际存储）
const minioEndpoint = 'http://your-minio-server:9000';
const minioAccessKeyId = 'minio-access-key';
const minioSecretAccessKey = 'minio-secret-key';

// 创建lakeFS客户端
const lakefs = new LakeFSClient({
  endpoint: lakefsEndpoint,
  accessKeyId: lakefsAccessKeyId,
  secretAccessKey: lakefsSecretAccessKey,
});

// 创建MinIO客户端
AWS.config.update({
  accessKeyId: minioAccessKeyId,
  secretAccessKey: minioSecretAccessKey,
  endpoint: new AWS.Endpoint(minioEndpoint),
  s3ForcePathStyle: true, // 如果是非AWS S3兼容服务如MinIO，通常需要设置这个参数为true
});
const s3 = new AWS.S3();

// 假设已经初始化了一个仓库和分支
const repository = 'my-repo';
const branch = 'main';

Data management operation examples

1. Upload files to lakeFS

async function uploadFileToLakeFS(localFilePath, objectPath) {
  const fileStream = fs.createReadStream(localFilePath);
  await lakefs.uploadObject(repository, branch, objectPath, fileStream);
}

2. Switch branches or create new branches

async function createOrSwitchBranch(newBranchName, parentBranch = 'main') {
  await lakefs.createBranch(repository, newBranchName, parentBranch);
  // 或者切换到已存在的分支
  // await lakefs.checkoutBranch(repository, newBranchName);
}

3. Access objects in MinIO through the lakeFS interface

When you perform CRUD operations in lakeFS, lakeFS automatically interacts with the underlying MinIO.

async function downloadFileFromLakeFS(objectPath, localFilePath) {
  const { location } = await lakefs.getObject(repository, branch, objectPath);
  // location 包含了指向MinIO的实际URL
  // 使用S3 SDK下载对象
  const data = await s3.getObject({ Bucket: location.bucket, Key: location.key }).promise();
  fs.writeFileSync(localFilePath, data.Body);
}

The above code is only a conceptual example. In actual application, it needs to be adjusted according to specific business needs and specific API calls of the lakeFS SDK. Also, ensure that errors and exceptions are handled correctly, and that sensitive information is stored and processed securely.