Analyze the exchanges of raw transaction data: a guide to best practices
As a developer, creating applications on Solana, the analysis of raw transaction data is an essential step to understand the behavior of the network and extract precious information. In this article, we will dive into best practices to analyze the exchanges of raw transaction data.
What are the exchanges?
In blockchain networks like Solana, Swaps refer to transactions that involve the exchange of tokens or assets between the parties. This can be a one -way exchange (for example, sending the ether in a liquidity pool) or a swap in place (for example, directly exchanging two tokens). In our context, we focus on the old type.
Raw transaction data
To analyze the raw transaction data, you need to access the Solana network’s block explorer and the possibility of reading binary data. The most practical way to do so is via
Solana Cli or a web interface like [Solana Explorer] (
The Messagelogs’ field in a gross transaction represents the entire message, including Swap information. However, the analysis of these newspapers can be complex for the following reasons:
- message size : exchange messages are generally longer than regular transactions.
- Structured data : exchanges often contain several fields, such as quantities of token, liquidity suppliers and types of exchanges.
Best Practice: Analyze the exchanges of raw transaction data
To effectively analyze exchanges from raw transaction data, follow these steps:
1. Identify the type of exchange
Before analyzing the exchange message, identify your type (for example, unilp or ‘lpt). This will help you understand the relevant fields and their content.
2. Use a JSON analysis library
Use a library like [JSON-SOLANA] ( to analyze the exchange message as JSON. This library provides an effective way to work with binary data and helps to mitigate the general analysis costs.
Javascript
Const Jsonsolana = require ("JSON-SOLANA");
// Suppose that "swap" is a gross transaction object
Const swapmessage = wait jsonana.desirializefrombinarybuffer (
// binary stamp containing the exchange message
));
// Convert the JSON channel into a JavaScript object for easier treatment
Const swapdata = JSON.PARSE (JSON.STRINGIFY (swapmessage));
'
3. Extract relevant fields
Carefully extract the relevant fields from the exchange data analyzed, such as:
- Token amounts (for example,
amount '',uses ")
- Information on the liquidity supplier (for example, "liquidityprovider", "liquiditytoken")
Use the data extracted to build the desired output format.
4. Manage unrealized fields
Be ready to manage all unprocessed fields in the gross transaction message, such as error messages or unknown values. This may require additional treatment or additional rescue strategies.
Javascript
// Example: Manage an unknown token field
Const swapdata = JSON.PARSE (JSON.STRINGIFY (swapmessage));
if (! swapdata.tokenamount) {
Console.error ("amount of unknown token");
// Decides how to manage the problem (for example, ignore, launch an error)
}
'
5. Release the result
Finally, publish your data analyzed and processed in a practical format, such as JSON or a personalized user interface.
Javascript
Const swapdata = JSON.PARSE (JSON.STRINGIFY (swapmessage));
Openwaps (swapdata);
'
Example of use cases: analysis of gross transaction data exchanges
Suppose you create an application based on Solana's liquidity pool for trading. You have a gross transaction journal containing several swaps between users, each with quantities of token and different liquidity suppliers.
Here is how you could analyze these exchanges using the steps described above:
` Javascript
Const Jsonsolana = require (“JSON-SOLANA”);
Const swapmessage = wait jsonsolana.