Writings

Suppose we have a table similar to this:

CREATE TABLE test1(
    id integer,
    content varchar
);

and the application issues many queries of the form:

SELECT content FROM test1 WHERE id = constant;

With no advance preparation, the system would have to scan entire test1 table, row by row, to find all matching entries. If there are many rows in test1 and only a few rows (perhaps zero or one) that would be returned by such a query, this is clearly an inefficient method. But if the system has been instructed to maintain an index on the id column, it can use a more efficient method for locating matching rows. For instance, it might only have to walk a few levels deep into a search tree.

To understand Proposer-Builder Separation, we must first explore how we arrived at this concept. As with most complex topics, understanding the complete history and thinking process requires delving into the early days of Ethereum itself.

In this article, we emabark on a journey through intricacies of the Proposer-Builder Separation (PBS), a grounbreaking approach altering roles within Ethereum's network. We delve into the complex world of Maximal Extractable Value and its pivotal role in the economic strategies of builders and propsers particularly focusing on how profits are generated and distributed under this model. This exploration further extends to the dynamics of profit distribution, shedding light on both the efficiency gains and the challenges it poses to equitable distribution. Beyond the immediate financial implications, we also scrutinize what these transformative changes mean for Ethereum's commitment to decentralization and its brpader blockchain community. Concluding with an overview and a forward-looking perspective, this article provies a comprehensive understanding of how the Proposer-Builder Separation is charting a new course in the world of blockchain economics.

The state of Ethereum (the totality of all accounts, balances, and smart contracts), is encoded into a special version of data structure known generally in computer science as Merkle Tree. This structure is useful for many applications in cryptography because it creates a relationship between all individual pieces of data entangled in the tree, resulting in a single root value that can be used to prove things about the data.

Ethereum's data structure is a 'modified Merkle-Patricia Trie', named so because it borrows some features of PATRICA ( the Practical Algorithm to Retrieve Information Coded in Alphanumeric), and because it is designed for efficient data retrieval of items that comprise the Ethereum state.

A Merkle-Patricia trie is deterministic and cryptographically verifiable: The only way to generate a state root is by computing it from each individual piece of state, and two states that are identical can be easily proven by comparing the root hash and the hashes that led to it (a Merkle proof). Conversely, there is no way to create two different states with the same root hash, and any attempt to modify state with different values will result in different state root hash. Theoretically, this structure provides 'holy grail' of $O\log(n)$ efficiency for inserts, lookups, and deletes.

A Merkle tree is nothing but a hash-based data structure that is a generalization of hash list. It is a tree structure in which leaf node is a hash of a block of data, and each non-leaf node is a hash of its children. Typically Merkle trees have a branching factor of 2, meaning that each node has up to two children.01

I don't know about you but for me understanding hypothesis testing had always been difficult. The certain concepts of hypothesis testing like p-value, alternative hypothesis always confused me. This is my try to make you all understand hypothesis testing in the most simplest manner.