search
HomeBackend DevelopmentPython TutorialLDAP (Lightweight Directory Access Protocol

LDAP (Lightweight Directory Access Protocol

LDAP is the standard TCP/IP stack protocol that is used to store and query information from a hierarchical directory. It is an alternative to X.500 Directory service protocol which is more resource-intensive. LDAP is often used for SSO authentication and storage. By standard LDAP uses TCP port 389 for unencrypted communication and TLS port 636 over an encrypted channel.

How Does LDAP work?

  1. Client starts an LDAP session through the dedicated TCP port.
  2. (Optional) Read and modify the session option values.
  3. Establish a connection to the LDAP server or explicitly bind to the server with a privileged authenticated client using one of the binding functions.
  4. Submit a query to an email server or establish a connection to a printer. The server receives the query and returns the corresponding information to the user.
  5. After completion, close the connection to the LDAP server.

LDAP unlike most modern http-based protocols, uses persistent connections which can live for days when communication with a directory server.

Advantages of Using LDAP

  1. It is a mature protocol which keeps on evolving. It is a critical component of most large enterprises therefore the need to maintain revisions and update standards of the protocol.
  2. LDAP is a lightweight version of the X.500 protocol but is also very lightweight compared to other modern day protocols.
  3. LDAP is secure and is often used to store usernames, passwords and other sensitive information. But its security is subject to its implementation. It is important to follow best practices when adopting this protocol such as:
    • establishing an access control policy.
    • maintaining multiple copies of the directory data.
    • encrypting sensitive information such as passwords.

Components of LDAP

Attribute: the data in the LDAP system is stored in key-value pairs known as attributes. You can set an attributes value by separating the name and the value using a colon and a space. e.g.

mail: johndoe@gmail.com

Use an equal's sign to refer to an attribute and its data without setting it. e.g.

mail=johndoe@gmail.com

The most commonly used attributes include:
- ou: Organizational Unit
- _ dn_: distinguished name
- cn: common name
- description
- dc: domain component
- givenName: first name
- mail: e-mail address
- sn: surname

Entries: an entry is a collection of attributes which are associated to or describe something. An entry could be a user in your system. Think of it as a row in a relational database. Each entry consists of:
- a distinguished name (uniquely identifies a specific entry in the DIT hierarchy
- a collection of attributes (they hold the data for the entry)
- a collection of object classes (they indicate what kind of object an entry represents e.g. information about a device or person)

dn: ou=Users,dc=example,dc=com,uid=jd001
objectClass: EntUsers
cn: Jane Doe
sn: Doe
mail: jdoe@example.com
uid: jd001

Search Filters: used to define the criteria for identifying entries that contain certain kinds of information.
LDAP URLS: this URL contains different pieces of information that can reference a directory server or a search criteria.

Primary Operators of LDAP

  1. Add: Insert a new entry into the directory.
  2. Modify: change existing directory entries.
  3. Bind: authenticate and connect the LDAP client to the server.
  4. Delete: Remove directory entries.

LDAP is used by Microsoft's Active Directory and other directory servers such as OpenLDAP and Red Hat Directory Server. To setup LDAP within an enterprise, you need a directory server, users with different permissions, directory data that can be queried and an LDAP client application.
-

The above is the detailed content of LDAP (Lightweight Directory Access Protocol. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How to Use Python to Find the Zipf Distribution of a Text FileHow to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML?How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Image Filtering in PythonImage Filtering in PythonMar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

How to Perform Deep Learning with TensorFlow or PyTorch?How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Introduction to Parallel and Concurrent Programming in PythonIntroduction to Parallel and Concurrent Programming in PythonMar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

How to Implement Your Own Data Structure in PythonHow to Implement Your Own Data Structure in PythonMar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and Deserialization of Python Objects: Part 1Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: StatisticsMathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
4 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks agoBy尊渡假赌尊渡假赌尊渡假赌

Hot Tools

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)