Analyze swaps from rough transaction data: a better guide -pratic

As a developer who creates applications in solar, the analysis of the data on row transactions is an essential step to understand the behavior of the network and extract precious knowledge. In this article we will immerse ourselves with the best practices to analyze swaps from raw transaction data.

What are the swaps?

In blockchain networks such as Solana, the swaps refer to transactions that include the exchange of tokens or activities between the parties. This can be a disposable exchange (e.g. sending an ether to a pool of liquidity) or an exchange on the spot (e.g. exchange two token). In our context we focus on the former boy.

Rough transaction data

To analyze the raw transaction data, it is necessary to access the blockade of the Solana network blocks and the possibility of reading the binary data. The most convenient way to do it is the
Solana CLI or an interface based on the web such as [Solana Explorer] (

The “Messagelogs” field in a raw transaction represents the entire message, including swap information. However, the analysis of these protocols can be complex for the following reasons:

• Message size : Swap messages are generally longer than normal transactions.

• Structured data : swaps often contain several fields such as tokens, liquidity suppliers and types of exchange.

Best Practice: analyze swaps from raw transaction data

Follow the following steps to efficiently analyze swaps from rough transaction data:

1. Identify the type of swap

Before the analysis of the exchange message, identify the type (eg “UNILP” or “LPT”). This will help you understand the relevant fields and your content.

2. Use a Json -Parsing library

Use a library like [Json-lana] ( to analyze the message of swap like Json. This library offers an efficient way to work with the binary data and helps to mitigate the analysis suit.

`Javascript

Const Jonsolana = churches (‘Json-Solana’);

// assumed that “swap” is a raw transaction object

Const Swapmessage = Wait Jsonsolana.

// Binary buffer containing the Swap message

);

// Convert Json -String to a Javascript object to simplify processing

Const SwapData = json.pars (json.strincify (swapmessage));

`

3 .. Extract Relevant Fields

Carefully extract the fields relevant from the analyzed swap data, such as: b.:

• Taken amounts (e.g. "Amount", "Usages")

• Information from liquidity suppliers (for exampleliquidtyprovideris,liquiditytken)

Use the extracted data to create the desired output format.

4. Act not transformed fields

Prepare to elaborate not transformed fields in the raw transaction message, e.g. B. Error messages or unknown values. This may require further Fallback elaboration or strategies.

`Javascript

// Example: treat with a field of amount of the unknown token

Const SwapData = Json.pars (json.strincify (swapmessage));

If (! swapdata.tickenamount) {

console.error (“amount of unknown token”);

// decide how to treat the problem (for example ignoring, launching errors).

}

`

5. Provide the output of the result

If you finally exceed the data analyzed and processed in a comfortable format such as Json or a personalized user interface.

Javascript

Const SwapData = Json.pars (json.strincify (swapmessage));

Outputswaps (SwapData);

`

Example of use: swap from raw transaction data analyze **

Suppose to present an application based on Solanas liquidity pool for trade. You have a raw transaction protocol with different swaps between users with different tokens and liquidity suppliers.

So you can analyze these swaps with the steps described above:

Javascript

Const Jonsolana = churches (‘Json-Solana’);

Const Swapmessage = Wait Jsonsolana.