How to Coordinate Mempool Sharing Policies

A mempool (memory pool) is a node's temporary holding area for unconfirmed transactions. When a user submits a transaction, it is broadcast to the network and stored in the mempools of listening nodes. These nodes then compete to include it in the next block. However, nodes do not automatically share their entire mempool contents with all peers. Mempool coordination refers to the policies and protocols that govern how these pending transactions are shared, gossiped, and prioritized across a peer-to-peer network. Efficient coordination is critical for timely transaction inclusion, fair fee estimation, and preventing network partitions.

The default behavior in networks like Bitcoin and Ethereum involves a gossip protocol. When a node receives a valid transaction, it forwards it to a subset of its peers. Key policies include transaction replacement (RBF), fee filtering, and orphan transaction handling. For example, a node might set a minimum fee rate (minfeefilter) and only relay transactions meeting that threshold. Developers can customize these policies by modifying client software like Bitcoin Core or Geth, or by running specialized middleware to intercept and manage transaction flow.

Why Customize Mempool Policies?

Custom coordination is essential for specific use cases. MEV searchers need to see transactions as fast as possible to identify arbitrage opportunities, often operating private mempools or using services like Flashbots Protect. Wallet providers and dApp frontends may run gateway nodes with relaxed policies to ensure user transactions are accepted. Exchange operators require robust monitoring to detect double-spend attempts. Without coordination, nodes risk having a stale or incomplete view of the network state, leading to failed transactions or economic loss.

Implementing a custom policy often involves interfacing with the node's RPC (Remote Procedure Call) API. For instance, using the bitcoind JSON-RPC interface, you can programmatically manage the mempool. The command getmempoolinfo returns current size and usage, while getrawmempool lists all transaction IDs. To control propagation, you can use prioritisetransaction to increase the effective fee of a specific TXID locally, or connect to peers with specific -whitelist permissions. In Geth, you can use the txpool_content and txpool_inspect methods to analyze pending and queued transactions.

Advanced coordination requires a network of dedicated nodes. Services like BloXroute, Blockdaemon, and Alchemy operate global, optimized node networks that provide low-latency transaction broadcasting via their own peer-to-peer meshes. They use proprietary protocols to share transactions faster than the public gossip network. For developers, integrating with these services via their APIs can significantly improve transaction reliability. The core challenge remains balancing efficiency with decentralization—overly centralized coordination can create single points of failure or censorship.

In practice, effective mempool coordination means understanding your node's configuration flags, monitoring network health, and potentially running multiple nodes in different geographic regions. Tools like Ethereum's Erigon or Bitcoin's Core with -mempoolfullrbf enable more granular control. The goal is not to hoard transactions, but to ensure they propagate through the network efficiently and resiliently, which ultimately strengthens the entire blockchain ecosystem.

Effective mempool coordination requires a foundational grasp of the mempool itself. In blockchain networks like Ethereum or Bitcoin, the mempool is a temporary, unconfirmed transaction pool where pending transactions wait to be included in a block by a miner or validator. Understanding its role in transaction lifecycle, fee estimation, and network congestion is critical. You should be familiar with concepts like gas prices, priority fees, and how different transaction types (e.g., simple transfers vs. complex smart contract interactions) behave in the queue.

A core prerequisite is knowledge of transaction propagation protocols. Nodes use protocols like Ethereum's eth/65 or eth/66 to gossip transactions across the peer-to-peer network. You must understand how transactions are validated, relayed, and potentially withheld. This includes the mechanics of transaction pools, their size limits, and eviction policies. For coordinated sharing, you'll need to interact with a node's RPC endpoints, such as eth_sendRawTransaction or the txpool namespace methods available on clients like Geth and Erigon.

Security considerations are paramount. Sharing mempool data introduces risks like frontrunning, sandwich attacks, and transaction censorship. You must understand these attack vectors to design policies that mitigate them. Furthermore, you need to be comfortable with private key management and transaction signing off-chain, as sharing often involves broadcasting signed transactions to multiple endpoints. Tools like Ethers.js, Web3.py, or direct JSON-RPC calls will be necessary for implementation.

Finally, you should have a configured blockchain node or reliable access to node provider APIs (e.g., Infura, Alchemy, a local Geth instance). Coordinated sharing policies are often implemented as scripts or services that query multiple mempool sources. A basic understanding of network latency and the impact of mempool fragmentation across different nodes or geographic regions will help you design more robust and efficient sharing logic for your specific use case.

A mempool sharing policy is a set of rules that determines which transactions a node will accept from peers and which it will forward. Coordination is essential to prevent network fragmentation and ensure consistent transaction visibility. Key policy parameters include minimum fee rates, maximum transaction size, and allowed transaction types (e.g., excluding certain smart contract interactions). Without coordination, nodes with mismatched policies can create isolated pools of transactions, harming the reliability of fee estimation and consensus.

To coordinate policies, node operators must first define their local requirements. This involves configuring parameters in the client software, such as Bitcoin Core's minrelaytxfee or Geth's txpool.pricelimit. The next step is peer discovery and handshake negotiation. Protocols like Bitcoin's version message or Ethereum's Status message can be extended to communicate policy summaries. For example, a node can advertise its minimum acceptable gas price or its willingness to relay pre-confirmation transactions, allowing peers to self-select based on compatibility.

For more dynamic coordination, especially in private or consortium networks, a shared configuration layer is effective. This can be a simple configuration file distributed to all participants or a smart contract on a governance chain that nodes query on startup. Tools like HashiCorp Consul or Apache ZooKeeper can manage these distributed configurations. The policy itself might be expressed in a structured format like JSON, specifying fields for maxTxSize, requiredFeePerGas, and bannedContracts.

Implementing policy enforcement requires hooks in the node's transaction validation logic. When a transaction is received via the P2P network, the node must check it against both consensus rules and the active sharing policy before adding it to the local mempool and relaying it. Here is a simplified pseudocode example of this check:

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def validate_for_sharing(tx, policy):
    if tx.fee_rate < policy.min_fee_rate:
        return False, "insufficient fee"
    if tx.size > policy.max_size:
        return False, "transaction too large"
    if tx.to_address in policy.banned_contracts:
        return False, "interaction with banned contract"
    return True, ""

Monitoring and adapting policies is crucial. Operators should track metrics like policy rejection rates, peer connection churn, and mempool composition divergence from the network average. Significant divergence may indicate an overly restrictive policy that could lead to isolation. In decentralized settings, coordination often occurs off-chain through community channels (Discord, forums) or via improvement proposals (BIPs, EIPs). The goal is to balance individual node resource constraints with the collective need for a robust, shared transaction pool.

The mempool (or transaction pool) is a node's local cache of pending transactions. By default, Geth broadcasts all valid transactions it receives to its peers, propagating them across the network. However, you may want to restrict this behavior for privacy reasons—such as running a MEV searcher—or to reduce bandwidth. Geth's transaction pool configuration is managed via command-line flags or a configuration file (geth.toml), primarily using the --txpool flags. The key parameters for sharing are globalslots, globalqueue, and the pricebump for replacement, but the broadcast logic itself is controlled by other settings.

To disable transaction broadcasting entirely, use the --nodiscover flag in conjunction with --maxpeers 0. This creates a isolated node that does not share its mempool. For more granular control, you can use the --txpool.locals flag. This flag accepts a list of Ethereum addresses (e.g., --txpool.locals "0xYourAddress1,0xYourAddress2"). Transactions from these "local" addresses will be prioritized in the pool and, crucially, will not be broadcast to the network. This is essential for keeping private transactions, like those from a builder, from leaking before they are included in a block.

Another critical setting is --txpool.pricelimit. This defines the minimum gas price (in wei) for a transaction to enter the pool. Setting this to a high value (e.g., --txpool.pricelimit 1000000000 for 1 Gwei) filters out low-fee spam, reducing the mempool size and broadcast load. Remember, a transaction must still meet the pricelimit to be considered for inclusion in your node's pool; it does not affect the validation of incoming blocks. For a complete list of tunable parameters, refer to the official Geth Command Line Options.

Here is an example command to start a Geth node with a restricted mempool for MEV operations:

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geth --syncmode snap \
     --txpool.locals "0xabc...123" \
     --txpool.pricelimit 2000000000 \
     --txpool.globalslots 512 \
     --txpool.globalqueue 1024 \
     --maxpeers 25

This configuration prioritizes and hides transactions from the local address 0xabc...123, ignores transactions with gas price below 2 Gwei, and sets the in-memory pool size. The maxpeers setting is kept at a moderate level to maintain block and state sync while limiting propagation channels.

After configuring and starting Geth, you can verify the transaction pool's status using the JSON-RPC API. The txpool_content and txpool_status methods are particularly useful. Call txpool_status to see the number of pending and queued transactions currently in your node's pool. Use txpool_content to inspect the actual transactions. Monitoring these endpoints helps you confirm that your local transactions are being held as expected and that the pricelimit is filtering transactions correctly, ensuring your node's behavior matches your operational requirements.

The Solana validator's mempool is a critical component for transaction propagation, holding unconfirmed transactions before they are included in a block. Configuring its sharing policies directly impacts network latency, censorship resistance, and validator performance. The primary configuration is managed via the --gossip-pull-crds-filter and related RPC flags, which determine which transactions are broadcast to peers. A restrictive policy can lead to network fragmentation, while a permissive one may increase bandwidth usage and vulnerability to spam.

Key parameters are set in the validator's command-line arguments or configuration file. The --gossip-pull-crds-filter <value> flag controls the filter for pulling CRDS data, which includes transactions. Setting it to 0.0 disables filtering, sharing all mempool entries, while higher values (e.g., 0.5) increase selectivity. For RPC, the --rpc-pubsub-enable-block-subscription and --rpc-send-transaction-tpu-disable flags influence how transactions are received and forwarded. It's essential to balance these settings with your node's resources and desired role in the network.

To implement a basic configuration for a staked validator prioritizing network health, you might start with: solana-validator --gossip-pull-crds-filter 0.1 --rpc-pubsub-enable-block-subscription --dynamic-port-range 8000-8020. The 0.1 filter value is moderately selective. For a sentry node or a node serving public RPC requests, you may need a more open policy: --gossip-pull-crds-filter 0.0 and enabling the TPU for transaction forwarding via RPC. Always monitor your node's gossip and tpu metrics in logs or using tools like Solana's metrics dashboard to tune these values.

Coordination with other validators is crucial. If multiple validators in a stake pool or coordinated group adopt overly restrictive filters, they can create isolated mempool islands, harming transaction propagation. Best practice involves agreeing on a baseline filter setting (e.g., 0.05 to 0.2) and similar RPC configurations. This ensures transactions are reliably shared within the group and with the broader network. Documentation for the current recommended defaults is found in the Solana operational guides.

Advanced tuning involves the --gossip-pull-crds-timeout and stake-weighted propagation. Validators with higher stake should generally adopt more permissive sharing policies to support network stability. The timeout setting (default 2000ms) determines how long to wait for a pull response; increasing it can improve data completeness but also latency. These policies must be evaluated against real-world network conditions and updated with client releases, as the Solana Labs team frequently optimizes the gossip protocol.

You should now have a functional understanding of how to design and implement a mempool sharing policy. The key components are: a policy engine to evaluate and filter transactions, a secure relay network for peer-to-peer data transmission, and a consensus mechanism among participating nodes to agree on policy rules. Tools like libp2p for networking and a custom gossipsub protocol for efficient broadcast are foundational. The primary goal is to create a system that balances censorship resistance with network efficiency, preventing spam and malicious transactions without becoming a centralized gatekeeper.

For practical implementation, start by defining your policy's core logic in a language like Go or Rust. A basic filter might check for minimum gas prices, whitelisted smart contract interactions, or transaction nonce sequences. Integrate this logic into your node's transaction pool handling, using hooks provided by clients like Geth or Erigon. Next, establish a private mempool network among trusted validators or searchers using authenticated peer connections. This network should use a gossip protocol to propagate only transactions that pass the shared policy, ensuring all participants see the same filtered view of pending activity.

Looking ahead, advanced coordination involves moving beyond static rules. Consider implementing dynamic policy updates based on real-time network metrics like gas price volatility or MEV activity. Research frameworks like the Ethereum Builder API (v1.0) and PBS (Proposer-Builder Separation) to understand how block builders coordinate. The future may involve cryptoeconomic incentives for policy adherence or zero-knowledge proofs to validate that a transaction meets policy criteria without revealing its full content, enhancing privacy. Staying current with Ethereum Improvement Proposals (EIPs) related to transaction pools, such as discussions around EIP-1559's fee market effects, is crucial for long-term relevance.