Unlock the Power of IPLD with IPFS Fetcher

Table of Contents

1. Introduction
2. The IPLD Prime Library
3. The Universal Node Concept
4. The Go Merkle DAG Library
5. Introducing the IPFS Fetcher
6. Fetching Blocks
7. Fetching Blocks with Selectors
8. Fetching an Entire Graph
9. Specialized Node Types
10. Future Developments

Introduction

In this article, we will explore the exciting world of IPLD, specifically focusing on IPLD Prime and its integration into Go IPFS. We will begin by understanding the IPLD Prime library and how it differs from the old IPLD. Next, we will dive into the concept of the universal node and its significance in both libraries. Moving on, we will explore the Go Merkle DAG library, which serves as a gateway to working with IPLD in IPFS. Finally, we will introduce the IPFS Fetcher and its various functions for retrieving IPLD data. So, let's get started and discover the power of IPLD Prime!

The IPLD Prime Library

Before diving into the technical details, let's briefly understand what IPLD Prime is and how it differs from the old IPLD library. IPLD Prime is an advanced library that encompasses all aspects of the IPLD specification, including selectors and schemas. It serves as a robust data model for various applications within the IPLD ecosystem. On the other HAND, the old IPLD library is limited in its capabilities and does not fully comply with the IPLD specification. By integrating IPLD Prime into Go IPFS, we unlock a plethora of new data operations and enable seamless connectivity across the entire IPFS infrastructure.

The Universal Node Concept

Both IPLD Prime and the old IPLD library share a common Notion known as the node, but they have different meanings and implementations. To bridge the gap between the two libraries, a concept called the universal node was introduced. The universal node serves as the basic unit in both libraries, allowing for compatibility and smooth transition. With the universal node in place, we can leverage the power of IPLD Prime while still maintaining backward compatibility with existing codebases. This gradual approach ensures a seamless migration process without disrupting the overall system.

The Go Merkle DAG Library

In order to interact with IPLD data within the IPFS ecosystem, we need a reliable entry point. This is where the Go Merkle DAG library comes into play. The Go Merkle DAG library acts as a service that facilitates various operations on IPLD data. It provides a transparent layer for reading, writing, and deleting IPLD data. Additionally, it incorporates the old IPLD's dag protobuf implementation, ensuring compatibility with legacy systems. However, the current implementation of the Go Merkle DAG library uses IPLD Prime under the hood to unleash the true potential of IPLD.

Introducing the IPFS Fetcher

Now, let's take a closer look at the IPFS Fetcher, which serves as the interface for requesting IPLD data. Whether it's from a local store or the IPFS network, the IPFS Fetcher provides seamless access to IPLD data. One advantage of the IPFS Fetcher is its ability to fetch blocks individually, allowing for selective retrieval and traversal of IPLD dags. This enables efficient handling of large datasets by only loading the required subset of the dag. Moreover, the IPFS Fetcher supports fetching an entire graph at once, simplifying data retrieval operations.

Fetching Blocks

Fetching individual blocks is a fundamental operation when working with IPLD data. The IPFS Fetcher offers functions specifically designed for this purpose. By utilizing the fetch block function, developers can easily retrieve individual blocks from a local store or through the IPFS network. This granular level of access ensures efficient data retrieval without the need to load the entire dag.

Fetching Blocks with Selectors

In addition to fetching individual blocks, the IPFS Fetcher provides the ability to fetch blocks based on selectors. Selectors define a set of criteria that must be met by the blocks in order to be fetched. This functionality enables developers to perform traversals on IPLD dags, focusing only on the blocks that meet their specific requirements. By utilizing selectors, developers can efficiently navigate through complex data structures and retrieve the desired information.

Fetching an Entire Graph

Sometimes, it may be necessary to fetch an entire graph at once instead of retrieving individual blocks. The IPFS Fetcher offers interfaces that allow developers to fetch a complete graph from either a local store or the IPFS network. This simplifies the process of working with comprehensive datasets and enables faster data retrieval for analysis or processing purposes.

Specialized Node Types

IPLD Prime introduces a powerful feature called specialized node types. This feature allows developers to define custom types for nodes, enabling precise control over how data structures should be represented. By incorporating specialized node types, developers can ensure that their IPLD data conforms to specific standards and expectations. This enhances data integrity and enables efficient handling of structured data within the IPLD ecosystem.

Future Developments

As IPLD Prime continues to evolve, so does the IPFS ecosystem. Future developments aim to enhance the IPFS Fetcher and further integrate it with other components such as XFS and Graph Sync. By utilizing these advancements, developers can expect even greater efficiency and flexibility when working with IPLD data. The future of IPLD looks promising, with continuous improvements and exciting possibilities on the horizon.

Highlights

• IPLD Prime is an advanced library that provides a comprehensive data model for IPLD.
• The universal node concept bridges the gap between IPLD Prime and the old IPLD library.
• The Go Merkle DAG library acts as a gateway for working with IPLD in IPFS.
• The IPFS Fetcher provides seamless access to IPLD data, enabling selective retrieval and traversal.
• Fetching blocks and fetching blocks with selectors offer flexibility in data retrieval.
• Fetching an entire graph simplifies working with comprehensive datasets.
• Specialized node types allow for precise control over IPLD data structures.
• Future developments aim to enhance the IPFS Fetcher and integrate it with other components, such as XFS and Graph Sync.

FAQ

Q: What is IPLD? A: IPLD (InterPlanetary Linked Data) is a protocol-agnostic data model that enables linking and traversing data across various decentralized systems.

Q: What is IPLD Prime? A: IPLD Prime is an advanced library that encompasses the complete IPLD specification, including selectors and schemas, providing a robust data model for IPLD.

Q: How does IPLD Prime differ from the old IPLD library? A: IPLD Prime offers enhanced capabilities and better compliance with the IPLD specification compared to the old IPLD library. It unlocks new data operations and provides a more efficient data model.

Q: What is the Go Merkle DAG library? A: The Go Merkle DAG library is a service in Go IPFS that facilitates operations on IPLD data, serving as an entry point for reading, writing, and deleting IPLD data.

Q: What is the IPFS Fetcher? A: The IPFS Fetcher is an interface that allows for seamless retrieval of IPLD data, whether from local storage or the IPFS network.

Q: Can I fetch individual blocks with the IPFS Fetcher? A: Yes, the IPFS Fetcher provides functions specifically designed for fetching individual blocks from a local store or through the IPFS network.

Q: How can I fetch an entire graph with the IPFS Fetcher? A: The IPFS Fetcher offers interfaces that allow you to fetch an entire IPLD graph at once, simplifying the process of working with comprehensive datasets.

Q: What are specialized node types in IPLD Prime? A: Specialized node types allow developers to define custom types for nodes, enabling precise control over how data structures are represented in IPLD.

Q: What's next for IPLD and IPFS? A: The future of IPLD and IPFS involves further enhancements to the IPFS Fetcher and integration with other components like XFS and Graph Sync to provide even greater efficiency and flexibility in working with IPLD data.