Parsing Blockchains Ethereum in SQLite databases: an efficient approach

As the popularity of cryptocurrency and blockchain continues to grow, analyzing blockchains in a relational database such as SQLite3 has become increasingly important for various purposes, such as data analysis, research and development. In this article, we will exploit an efficient way to analyze Ethereum blocks in an SQL database using open source software.

Why SQLite3?

SQLite3 is an excellent option for this task due to your:

1.

• SQLếTиication : Supports SQL syntax, facilitating the recording of efficient consultations.

• Support for multiple databases : You can deal with multiple databases simultaneously.

• Light and fast : SQLite3 is optimized for performance.

Blockchain Ethereum Blockchain Data Structure **

Before diving into the details of the implementation, let’s understand how Ethereum blockchains are structured:

• A blockchain consists of a list of blocks (for example, geneseblock,blockchain1 etc.).

• Each block contains:

* Timestamp

* Hash of the previous block (ie, ‘parentheses’)

* Number of transactions in the block (numatransationcount)

* List of transactions within the block (transactions)

Implementing a blockchain analyzer

We will use Python as our programming language, along with SQLite3 for database operations. We will also use the Eth-Blocks' library to look for data from Blockchain Ethereum.

Python

Import sqlite3

DATETIME IMPORT DATETIME

Class blockparser:

def __init __ (self):

self.conn = sqlite3.connect (‘: memory:’)

self.cursor = self.conn.cursor ()

def parse_blockchain (self, blockchain_url):

Search for the first block from the blockchain URL

Block = eth_blocks.get (blockchain_url)

If the block is not:

return fake

Create a table for the database

self.create_table ()

Enter data into the database

SELF.INSERT_DATA (BLOCK.TIMESTAMP, BLOCK.HASH, BLOCK.PARENTHASH, BLOCK.NUMTRASACTIONCOUNT, BLOCK.TRASASATIONS)

return true

DEF CREATE_TABLE (SELF):

“” Create a table with the necessary columns. ” “”

SQL = “” “

Create the table if there is no blockchain_data (

Primary integer ID autoincrement, autoincrement,

Timestamp Non -Null Text,

Parent_hash text not null,

Num_Transactions non -null,

Transactions text

);

“” “

self.cursor.execute (SQL)

self.conn.commit ()

DEF insert_data (self, date and time record, hash, parentheses, numbers, transactions):

“” Enter data in the blockchain table. ” “”

SQL = “” “

Insert in blockchain_data (Timestamp, Parent_hash, Num_Transations, Transactions)

Values ​​(?,?,?);

“” “

self.cursor.execute (SQL, (Timestamp, Hash, Numansations, Transactions)))

self.conn.commit ()

Usage Example

Parser = blockparser ()

URL = ‘

If parser.pars_blockchain (URL):

Print (“Blockchain analyzed successfully!”)

other:

print (“Error analyzing blockchain”)))

`

Efficiency Optimization

Although the implementation provided is efficient for most use cases, there are some optimizations we can do to further improve performance:

• Lot transactions

  : Instead of inserting each transaction individually, consider lot from them and insert them into lots.

• Using a more efficient database scheme : If you need to store large amounts of data or run complex queries, consider using a more optimized database scheme like PostgreSQL or MySQL.

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