Swap of Analysis from unprocessed transaction data: a guide for best practices
As a developer that builds applications in Solana, the analysis of unprocessed transactions data is an essential step to understand the behavior of the network and the extraction of precious intuitions. In this article, we will immerse ourselves in the best practices for the SWAPS analysis from unprocessed transaction data.
What are the swaps?
In blockchain networks like Solana, swaps refer to transactions that provide the exchange of token or activities between the parties. This may be an exchange of a path (for example, send ether to a liquidity group) or an exchange in the place (for example, directly exchange two tokens). In our context, we are focusing on the first type.
Data of rough transactions
To analyze the data of unprocessed transactions, it is necessary to access the block explorer of the Solana network and the possibility of reading the binary data. The most convenient way to do so is through
Solana Cli or a web -based interface such as [Solana Explorer] (
The “Messagelogs” field in an unprocessed transaction represents the full message, including exchange information. However, the analysis of these records can be complex for the following reasons:
- Message size : exchange messages are generally longer than regular transactions.
- Structured data : Swaps often contain multiple fields, such as token amounts, liquidity suppliers and types of swaps.
The best practice: Analysis of unprocessed transaction data exchanges
To efficiently analyze the swaps of the approximate transaction data, follow these steps:
1. Identify the type of exchange
Before analyzing the exchange message, identify its type (for example, unilplpt). This will help you understand the relevant fields and their content.
2. Use a JSON analysis library
Use a library like [JSON-LANA] ( to analyze the exchange message like JSON. This library provides an efficient way of working with binary data and helps mitigate general analysis expenses.
`JavaScript
Const Jsonsolana = Requirement ('Json-Solana');
// assuming that "exchange" is an object of raw transaction
Const swapmessage = abveat jsonsolana.desriaizefruminarrybuffer (
// Binary dab that contains the exchange message
);
// Convert the Jon String Javascript for a simpler elaboration
Const swapdata = json.pars (json.strincify (swapmessage));
3. Extract significant fields
Carefully remove the relevant fields from the exchange data analyzed, such as:
- Amounts taken (for example,
quantity ',usages)
- Information about the liquidity supplier (for example,
Liquidyprolyprover ',Liquidarytken)
Use the data extracted to create the desired output format.
4. Manage unprocessed fields
Prepare to administer any unprocessed field in the unprocessed transaction message, such as error messages or unknown values. This may require greater elaboration or feedback strategies.
JavaScript
// Example: Management of an unknown field of amount of token
Const swapdata = json.pars (json.strincify (swapmessage));
If (! Swapdata.tkenamount) {
Console.error ("amount of unknown token");
// decide how to manage the problem (for example, ignore, launch error)
}
5. Find the result
Finally, issue the data analyzed and processed in a convenient format, such as JSON or a custom user interface.
JavaScript
Const swapdata = json.pars (json.strincify (swapmessage));
Outputswaps (swapdata);
EXAMPLE OF USE CASE: EXCHANGE OF RUGO TRANSACTION DATA ANALYSY
Suppose you are building an application based on the Solana liquidity group for trade. We have a record of unprocessed transactions that contains more exchange between users, each with different amounts of tokens and liquidity suppliers.
This is how you can analyze these swaps using the steps described above:
` JavaScript
Const Jsonsolana = Requirement (‘Json-Solana’);
Const swapmessage = jsonsolan wait.
