How to Read a Block Explorer and Understand Transactions, Traces, and Logs on Ethereum (EVM)

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Understanding blockchain data is essential for anyone diving into decentralized applications, DeFi protocols, or on-chain analytics. At the heart of this knowledge lies the ability to interpret transaction details using a block explorer—especially on Ethereum and other EVM-compatible chains. This guide breaks down the core components of transactions, logs, and traces in a clear, structured way to help you master on-chain data analysis.

Whether you're analyzing a simple token swap or reverse-engineering complex DeFi interactions, learning how to read and interpret block explorer data will empower your research, audits, and investment decisions.

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What’s Inside a Transaction?

When you execute a transaction on Ethereum—or any smart contract-enabled blockchain—you can view its details on platforms like Etherscan. What appears at first glance is just the surface of a much deeper data structure.

The Transaction Overview tab shows key fields such as:

But if you dive into the Logs or Traces (Internal Transactions) tabs, things get more complex—and more insightful.

While raw data may look like hexadecimal gibberish, every byte has meaning. Learning to decode it forms the foundation of advanced on-chain analysis.

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Breaking Down the Transaction Structure

All on-chain data is stored in hexadecimal format. However, tools like Etherscan decode parts of it for readability. The most important part of any transaction is the input data, which contains the function being called and its parameters.

Let’s take a sample 1inch aggregator swap transaction as an example.

Function Signature and ABI Encoding

The first 4 bytes (8 hex characters) of input data represent the function selector—a Keccak-256 hash of the function name and its parameter types. For instance:

0x12a7b914 → swap(address,uint256,uint256,bytes)

This encoding follows the Application Binary Interface (ABI) standard defined by Solidity. Think of the ABI as an API specification for smart contracts—it tells us how functions are structured and how to decode their inputs and outputs.

Developers often verify and publish their contract ABIs so explorers can decode calls automatically. However, some contracts—especially those involved in MEV (Maximal Extractable Value)—remain unverified to obscure logic.

In analytics platforms like Dune, verified ABIs allow automatic decoding of logs and traces into human-readable tables such as:

These tables let analysts query specific events or function calls across thousands of transactions without manually parsing hex data.

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Understanding Logs (Events)

Logs are records emitted by smart contracts during execution. They’re stored separately from transaction data but linked to it. Events are typically triggered at the end of a function, after state changes have been safely applied.

Each log entry includes:

For example, a Uniswap V3 Swap event emits data like:

{
  "sender": "0x...",
  "amount0In": "123",
  "amount1Out": "456"
}

To trace where this event originates, inspect the contract code on Etherscan and search for emit Swap(. This leads directly to the function that triggered it—in this case, line 786 of the pool contract.

Mastering navigation between events and their source code helps validate data accuracy and understand protocol behavior.

FAQ: Common Questions About Logs

Q: Can logs be modified after they’re emitted?
A: No. Logs are immutable once recorded on-chain and cannot be altered.

Q: Are all events decoded on block explorers?
A: Only if the contract ABI is verified. Unverified contracts show raw hex instead.

Q: Why are some parameters indexed and others not?
A: Indexed parameters appear in topics for efficient filtering via RPC calls; non-indexed ones go into data due to size or cost constraints.


Decoding Traces (Internal Transactions)

Traces reveal internal contract interactions—calls made from one contract to another during a single transaction. These are also known as internal transactions, though they don’t involve actual value transfers unless ETH is passed.

There are four main trace types:

Trace Address Hierarchy

Each trace has a trace_address array indicating call depth:

Visualizing this as a tree helps map complex interactions—like a router calling multiple DEXs in one swap.

While Etherscan’s trace view can be overwhelming, tools like Phalcon by Blocksec provide interactive, expandable trees that make navigation intuitive.

In Dune Analytics, decoded traces are mapped into tables using submitted ABIs. However, reconstructing exact execution order across traces isn't fully supported yet—this requires custom indexing solutions.

FAQ: Understanding Traces

Q: Is every internal transfer a trace?
A: Yes. Any interaction initiated by a contract (not an EOA) appears as a trace.

Q: Can I track token movements via traces?
A: Partially. Token transfers are usually recorded as ERC-20 Transfer events in logs—not traces—so always check both.

Q: Why do some traces show no value?
A: Most traces represent function calls without ETH transfer. Value only appears when ETH is sent between contracts.


Practical Tips for On-Chain Analysis

To become proficient in reading block explorers:

  1. Start with verified contracts to learn decoding patterns.
  2. Use Dune’s pre-decoded tables (contractName_chain.EventName) whenever possible.
  3. Cross-reference logs and traces with source code using keywords like emit and function.
  4. Combine multiple tools: Etherscan for basics, Phalcon for deep tracing, Dune for scalable queries.

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Final Thoughts

Mastering transaction, log, and trace interpretation unlocks powerful insights into user behavior, protocol mechanics, and market trends. As blockchain ecosystems grow more complex, the ability to navigate raw on-chain data becomes a critical skill for developers, analysts, and investors alike.

By understanding how input data encodes function calls, how logs capture state changes, and how traces map execution flow, you gain full visibility into what truly happens inside every Ethereum transaction.

With practice and the right tools, you’ll move beyond surface-level observation to perform deep forensic analysis—giving you an edge in the rapidly evolving world of Web3.