Analysis of Ethereum blockchairs into sqlite databases: effective approach

As the popularity of the cryptocurrency and blockchain continues to increase, the analysis of blockchairs in a relational database such as SQLITE3 is becoming increasingly important for different purposes, such as data analysis, research and development. In this article, we examine an effective method to analyze Ethereum blockchairs in the SQL database with open source software.

Why sqlite3?

SQLITE3 is an excellent choice for this task as a result:

• Relational Database Capacity : Easy to query, create, update and delete in the database.

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• Support for multiple databases

  

  : More databases can handle at the same time.

• Easy and fast : SQLITE3 is optimized for performance.

Ethereum Blockchain Data Structure

Before we get into the implementation details, understand how Ethereum blockchairs are structured:

• It consists of a list of blockchain blocks (such as Genesisblock,Blockchain1 ', etc.).

* The previous block hashja (iepartenthash)

* Number of transactions in the block (NumtransectionCount)

* List of transactions within the block (transactions’)

Blockchain analyst execution

We use Python as our programming language, together with SQLite3 for database operations. The ETH-Bocks directory is also used to retrieve the ETHEREUM blockchain data.

`Python

Import sqlite3

From DataTime Import Datetime

Class Blockparser:

Def __init __ (Self):

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

SELF.CURSOR = SELF.CONN.CURSOR ()

Def parse_blockchain (self, blockchain_url):

Roll first block from blockchain URL

block = eth_blocks.get (blockchain_url)

If the block is not:

restore

Create a table for the database

Self.create_table ()

Insert data into the database

Self.insert_data (block.timestamp, block.hash, block.parenthash, block.numtransectionCount, block.transections)

Return

Def Create_table (Self):

“” “Create a table with the columns you need.” “

sql = “” “”

Creating a table if there is no blockchain_data (

ID integer primary key auto installation,

The time stamp text is not zero,

Parent_hash text is not zero,

NUM_TRANSECTIONS whole number is not zero,

Transactions text

);

“” “

Self.curssor.execute (SQL)

Self.conn.comMit ()

Def Insert_Data (Self, Timestamp, Hash, Bracket, Numtransctions, Transactions):

“” “Insert data into the blockchain table.” “

sql = “” “”

Insert Blockchain_Data (time stamp, parent_hash, num_transctions, transactions)

Values ​​(?, ?,,, ?,,?);

“” “

Self.curssor.execute (SQL, (Timestamp, Hash, Numtransctions, Transactions))

Self.conn.comMit ()

An example of use

Analyst = Blockparser ()

URL = ‘

If the analysis.parse_blockchain (URL):

Print (“Blockchain successfully analyzed!”)

other:

Print (“Error Analysis of the Blockchain.”)

`

To optimize efficiency

Although the specified implementation is effective for most uses, there are some optimizations that can be done to further improve performance:

• Batching Transections : Instead of inserting each transaction separately, consider it connecting them and then placing them in items.

• Using a more efficient database scheme : If you need to store large amounts of data or make complex queries, consider using a more optimized database scheme, such as postgrescsql or MySQL.

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