In this blog, we are going to study the usability of DAG called Directed Acyclic Graph in the Crypto world and how it can help to build the right consensus.
The Difference Between DAG and Blockchain Transactions. While blockchain transactions are always sequential, DAG (directed acyclic graph) transactions can be executed asynchronously.
This means that multiple transactions can occur at once, rather than just one per block. So while one person’s transaction is being validated, another’s could be carried out successfully as well.
This makes sense when you think about it: even though they’re separate processes, there’s only one network connection between them.
What is Directed Acyclic Graph?
A directed acyclic graph or DAG (also sometimes called a dag, for short) is a data modeling or structuring tool typically used in cryptocurrencies.
Unlike a blockchain, a directed acyclic graph (DAG) is a tool for creating data structures. Unlike a blockchain, DAGs have vertices and edges where crypto transactions are recorded as vertices.
The benefit of using a DAG over a blockchain is that it can handle more transactions at once without slowing down.
In addition, it’s less energy-intensive than blockchains because miners don’t need to solve complex puzzles to add new blocks to their chains.
As mentioned above, Bitcoin’s Lightning Network relies on dag technology to increase transaction speed and reduce costs by processing payments off-chain instead of on-chain.
What is DAG in Blockchain?
DAGs are essential to blockchain technology, but people often get confused about what it is. Simply put, DAGs are blockchains without blocks.
Sounds confusing? It can be. Let’s break it down into three steps:
1) What Is a Blockchain?,
2) What is a Block?, and
3) How Does Ethereum Use DAGs?
These three concepts will help you understand how directed acyclic graphs work with cryptocurrency and specifically with Ethereum.
If you don’t know anything about blockchain or cryptocurrencies, we recommend reading our guide on what is blockchain? first.
This knowledge should provide some context for why DAGs are useful for decentralized applications (apps).
And if you want to dig deeper, check out our comprehensive guide on understanding blockchain technology.
Related Article: How Blockchain Works? : Introduction To Blockchain Technology
How does a DAG work?
In cryptocurrencies, there are no blocks, just records of transactions called vertexes. These vertexes are connected to form a graph of edges.
To add to its speed and efficiency, these edges run through multiple vertexes at once.
The result is known as a directed acyclic graph (DAG), which leaves out hashes and block times and gets rid of blockchain’s biggest flaws.
Most people think that DAGs have advantages over blockchain technology, but they also have disadvantages too.
One such disadvantage is that it takes longer for transaction data to be verified on a DAG-based system than it does on a traditional blockchain.
For example, bitcoin miners can verify about 1MB worth of transaction data per 10 minutes; on IOTA’s tangle network it takes about 2 minutes to verify 1MB worth of transaction data.
What Crypto uses Directed Acyclic Graph?
DAGs are currently the most widely used in cryptocurrencies to store transactions. They function as an alternative to traditional blockchain structures and can be optimized for scalability.
A directed acyclic graph (DAG) is a type of data structure made up of vertices with edges that connect them.
They are usually displayed as abstract illustrations or diagrams but can also be represented digitally using computers.
DAGs have many applications, including in computer science, mathematics, and quantum physics.
In cryptocurrencies, DAGs are used to store transactions on blockchains. To understand how DAGs work in crypto, it’s important to first understand what they are and how they differ from blockchain structures like Bitcoin’s.
A blockchain is a continuously growing list of records called blocks linked together through cryptography and protected by some form of incentive scheme.
Related Article: What is a Smart Contract for Cryptocurrency?
How does a Directed Acyclic Graph apply to the Crypto Space?
Crypto transactions are recorded as vertices. With each transaction, there are two parties involved, a sender and a receiver.
One party gives another party something of value, which may be a fiat currency or cryptocurrency. In return, they receive something else of value.
In both cases, a DAG can help to track ownership of that asset over time. For example, let’s say you want to sell your car for Bitcoin (BTC).
You post an ad on Craigslist and someone responds who wants to buy it with BTC. They agree to send you their payment first before sending their driver to pick up your car.
Once they pay, you mark them as paid in your DAG, signifying that they now own part of your car.
Then, when their driver arrives at your house and picks up the keys, he marks himself paid in his version of the DAG – meaning he now owns part of your car.
Related Article: Cryptocurrency: Is it the Future of Money?
What Problems can DAG solve?
In a nutshell, DAG-based blockchains promise to solve many of blockchain’s scalability issues, which will let them handle much more data.
Currently, cryptocurrencies use blocks for transactions on their blockchains.
Each block can contain only a finite amount of information; typically one transaction per block and up to 1 MB (1 million bytes) per block.
This means that as each new transaction occurs, it has to wait in line behind all previous transactions until it gets its turn on the network. This results in long wait times for transactions and slow networks.
By contrast, DAG-based blockchains do not have blocks—instead, they have vertices (or nodes).
This means that each new transaction doesn’t have to wait its turn before being added to a chain; instead, it simply adds itself as an edge or connection between two existing vertices.
Applications of Directed Acyclic Graph in Crypto
Applications of DAG (Directed Acyclic Graph) are very much in demand. In cryptocurrencies, DAGs are used to handle transactions with higher efficiency. Here are some of its applications
A cryptocurrency transaction can be recorded as a vertex and each transaction has an edge connecting it to another transaction.
This way, every new transaction references one or more previous transactions as inputs and creates one or more new outputs that can be referenced by future transactions.
The main advantage of using directed acyclic graphs is that they allow for parallel processing which means that multiple transactions can be confirmed at once instead of having to wait for them all to finish before confirming any of them.
As a result, confirmation times are reduced significantly. Another benefit of using DAGs is that they allow for scalability.
Since there is no limit on how many transactions can be confirmed simultaneously, there is no limit on how many transactions per second (fps) a blockchain can process either.
Top Directed Acyclic Graph (DAG) Based Crypto
Bitcoin, Ripple, and Ethereum are just some of today’s most popular cryptocurrencies. All three utilize a distributed ledger that incorporates a directed acyclic graph (DAG).
Here are some examples of cryptocurrencies that use DAGs
This cryptocurrency utilizes an alternative consensus mechanism called proof-of-correctness where it relies on correct solutions rather than computational power or time spent working out solutions.
This currency also employs a form of proof-of-correctness as well as Masternodes, which have been described as decentralized autonomous servers (DAS).
The IOTA cryptocurrency also uses DAG technology but doesn’t have miners or blocks.
Obyte is a new digital currency platform that has no transaction fees and allows users to send money around the world instantly. Conclusion As you can see, there are many differences between each type of cryptocurrency; however, all rely on DAG technology to function.
Like other cryptocurrencies, Nano utilizes DAG technology to process transactions. It differs from other cryptocurrencies because it uses block-lattice architecture instead of a blockchain.
The block-lattice architecture consists of individual chains for each user that makes up their database instead of having one single chain like other cryptos such as Bitcoin.
Why Directed Acyclic Graph is Good for Crypto?
The blockchain, which has been applied to crypto since bitcoin’s inception, requires transactions to be recorded as blocks.
The problem with this type of structure is that, if one block re-visits information from another block, it creates an acyclic graph (i.e., blockchains are acyclic).
This makes implementing scalability solutions like sharding and state channels impossible.
To overcome these limitations, cryptocurrencies have adopted directed acyclic graphs (DAGs) instead of blockchains. Here’s what you need to know about DAGs and how tDAGspply to cryptocurrency.
The first use case for DAGs was IOTA: When IOTA released its white paper in 2015, it described its platform as a blockless blockchain based on a directed acyclic graph called Tangle.
What makes Tangle unique is that each transaction confirms two previous transactions so each new transaction validates two older ones thereby eliminating miners.
This paper presents a formal definition of DAGs, followed by an analysis of existing cryptocurrency protocols.
The history and evolution of DAG-based blockchain designs, together with their relative strengths and weaknesses, are discussed.
The final section concludes with future directions for research into DAG-based distributed ledgers.
While there has been significant interest in using DAGs to solve some of Bitcoin’s scalability issues, we believe that such proposals will have limited success due to fundamental design flaws.
For instance, most prior work assumes that transaction blocks must be solved sequentially; but if they can be mined out of order then they may not even form a directed acyclic graph at all!
Nitin is a professional data Engineer, Who has a Post Graduation in Data Science and Analytics and working in the healthcare sector. Experts in Data analysis, Machine learning, AI, blockchain, Data related tools, and technologies. He is the Co-founder and editor of analyticslearn.com