eth_getTransactionByBlockNumberAndIndex | Hyperliquid EVM

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Parameters

1. Block number - The number of the block containing the transaction
2. Transaction index - The index position of the transaction within the block

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Parameter details

• blockNumber (string, required) — Block identifier: "latest", "earliest", "pending", or a specific block number in hexadecimal
• transactionIndex (string, required) — The index position of the transaction in the block as a hexadecimal string (0-based)

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Response

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Response structure

• hash — The 32-byte transaction hash
• nonce — Transaction nonce (number of transactions sent by sender)
• blockHash — Hash of the block containing the transaction
• blockNumber — Number of the block containing the transaction
• transactionIndex — Index of the transaction in the block
• from — Address of the transaction sender
• to — Address of the transaction receiver (null for contract creation)
• value — Value transferred in wei as a hexadecimal string
• gas — Gas limit provided by the sender
• gasPrice — Gas price provided by the sender in wei
• input — Data sent along with the transaction
• v, r, s — ECDSA signature components
• type — Transaction type (0x0 for legacy, 0x1 for EIP-2930, 0x2 for EIP-1559)

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Block number formats

• "latest" — Most recent block’s transactions
• "earliest" — Genesis block transactions (usually none)
• "pending" — Pending block transactions

• "0x0" — Genesis block
• "0x9d0c37" — Block 10,291,255 (decimal)

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Sequential processing

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Range-based analysis

• Process transactions across multiple consecutive blocks
• Build comprehensive transaction datasets systematically
• Analyze transaction patterns over time periods
• Generate time-series transaction analytics

• Process transactions in chronological order
• Track transaction evolution and patterns over time
• Build historical transaction databases
• Analyze network activity trends and cycles

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Efficient iteration

• Iterate through blocks sequentially using block numbers
• Process all transactions within each block systematically
• Maintain transaction order and block context
• Build complete blockchain transaction datasets

• Process multiple blocks concurrently for efficiency
• Distribute transaction processing across workers
• Optimize throughput for large-scale analysis
• Balance load across processing resources

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Development patterns

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Systematic data collection

• Process all transactions from genesis to latest block
• Build comprehensive transaction databases
• Generate complete network activity statistics
• Create historical blockchain datasets

• Process new blocks as they are mined
• Maintain up-to-date transaction datasets
• Implement efficient incremental updates
• Handle real-time transaction processing

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Transaction indexing

• Build transaction indexes based on block position
• Enable efficient transaction lookup by position
• Maintain transaction ordering within blocks
• Optimize for sequential access patterns

• Process multiple transactions from the same block
• Optimize API usage and network efficiency
• Implement efficient batch processing workflows
• Reduce overhead for bulk operations

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Integration strategies

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Analytics platforms

• Analyze transaction patterns over time using block numbers
• Build time-based transaction metrics and KPIs
• Generate historical transaction reports
• Create transaction trend analysis systems

• Monitor transaction activity in real-time using latest blocks
• Track network utilization and capacity metrics
• Generate network health indicators
• Build transaction-based alerting systems

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Block explorers

• Enable navigation through blocks using block numbers
• Display transactions in chronological order
• Provide block-based transaction browsing
• Show transaction context within block sequences

• Browse historical transactions using block numbers
• Provide time-based transaction search capabilities
• Enable exploration of transaction history
• Show transaction evolution over time

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Example request

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"eth_getTransactionByBlockNumberAndIndex","params":["0x9d0c37","0x0"],"id":1}' \
  https://hyperliquid-mainnet.core.chainstack.com/4f8d8f4040bdacd1577bff8058438274/evm

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Use cases

• Sequential processing: Process transactions across block ranges systematically
• Time-series analysis: Analyze transaction patterns over time using block numbers
• Blockchain synchronization: Build and maintain synchronized blockchain datasets
• Analytics platforms: Collect comprehensive transaction data for analysis
• Block explorers: Display transactions in chronological block order
• Historical analysis: Study transaction evolution and network growth patterns
• Data archival: Archive transaction data with proper temporal context
• Network monitoring: Track transaction activity and network utilization over time
• Research platforms: Study blockchain behavior and transaction patterns
• Audit systems: Verify transaction sequences and blockchain integrity
• Development tools: Debug and test applications with historical transaction data
• Compliance monitoring: Track transactions for regulatory compliance over time
• Performance analysis: Analyze network performance and transaction throughput
• Integration services: Provide chronological transaction data to external systems
• Educational platforms: Demonstrate blockchain concepts using real transaction data
• Forensic analysis: Investigate transaction patterns and blockchain activities
• Risk management: Analyze transaction risks and patterns over time periods
• Portfolio tracking: Track user transactions across multiple blocks chronologically
• DeFi analytics: Analyze DeFi protocol activity and usage patterns over time
• Market analysis: Study trading activity and market behavior using transaction data
• MEV research: Analyze maximal extractable value patterns across blocks
• Network economics: Study transaction fees and network economics over time
• Chain analysis: Analyze blockchain structure and transaction relationships
• Security monitoring: Monitor suspicious activities across block ranges
• Data visualization: Create time-based charts and graphs of transaction activity