Full History Storage excels at providing complete, unaltered data access because it maintains every transaction and state change from genesis. For example, a protocol like Ethereum running a full archive node requires over 12TB of storage, enabling deep forensic analysis, unlimited historical queries, and seamless support for services like The Graph's subgraphs that may need to re-index from block zero. This is non-negotiable for audit firms, complex DeFi analytics platforms, and protocols requiring verifiable, complete history.
LABS
Comparisons
Indexer Data Pruning vs Full History Storage: The Graph vs Custom Indexers
A technical comparison for engineering leaders on the trade-offs between The Graph's complete historical archive and custom-built indexers with data pruning strategies. Focus on long-term cost, query performance, and architectural control.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Billion-Block Dilemma
A foundational choice between data accessibility and operational efficiency defines modern blockchain infrastructure.
Indexer Data Pruning takes a different approach by strategically discarding older, non-essential state data while preserving block headers and recent history. This results in a dramatic reduction in operational overhead—a pruned Bitcoin Core node uses ~500GB versus 500GB+ for a full node—but at the cost of losing the ability to answer arbitrary historical queries. This trade-off is ideal for validators, RPC providers, and applications focused on real-time chain state and recent transaction history.
The key trade-off: If your priority is unfettered data access for compliance, analytics, or re-indexing, choose a Full History solution. If you prioritize cost-effective scalability, faster node synchronization, and lower infrastructure overhead for live applications, choose a Pruned Indexer architecture. The decision hinges on whether your application's core value is derived from the entire historical ledger or from the current, actionable state of the chain.
tldr-summary
Indexer Data Pruning vs Full History Storage
TL;DR: Core Differentiators
Key strengths and trade-offs at a glance for infrastructure architects.
01
Indexer Pruning: Cost & Performance
Specific advantage: Reduces storage requirements by 90%+ for high-throughput chains like Solana (3k+ TPS). This matters for cost-sensitive deployments where operational overhead from petabyte-scale storage is prohibitive. Enables faster sync times and lower hardware costs for nodes.
02
Indexer Pruning: Operational Simplicity
Specific advantage: Simplifies state management by focusing on recent, actionable data. This matters for real-time applications like DeFi dashboards (e.g., Uniswap analytics) and NFT marketplaces that primarily query the last 30-90 days of activity, not ancient history.
03
Full History: Unmatched Data Integrity
Specific advantage: Provides complete, verifiable audit trails from genesis block. This matters for compliance-heavy protocols (e.g., institutional DeFi, on-chain treasuries) and long-tail analytics that require forensic analysis of all historical events for security or research.
04
Full History: Future-Proof Flexibility
Specific advantage: Enables novel queries and historical analysis not anticipated at deployment. This matters for protocols building long-lived infrastructure (e.g., The Graph's subgraphs, Etherscan explorers) where unknown future use cases depend on immutable, complete data availability.
HEAD-TO-HEAD COMPARISON
Indexer Data Pruning vs. Full History Storage
Direct comparison of operational and performance metrics for blockchain data management strategies.
| Metric | Data Pruning (e.g., The Graph, Subsquid) | Full History (e.g., Archive Node, QuickNode) |
|---|---|---|
Storage Cost per Month (1TB) | $50-100 | $300-500 |
Historical Data Access | Limited to indexed events | All blocks & transactions |
Query Latency (p95) | < 100ms | ~500ms - 2s |
Initial Sync Time (Mainnet) | Hours to days | Weeks |
Required Disk Space (ETH Mainnet) | ~500 GB | ~15 TB |
Real-time Data Freshness | < 1 block | < 1 block |
Supports Arbitrary Historical Queries |
INDEXER DATA PRUNING VS. FULL HISTORY STORAGE
Cost Analysis: Storage & Query Economics
Direct comparison of operational costs and performance for different blockchain data strategies.
| Metric | Indexer Data Pruning | Full History Storage |
|---|---|---|
Storage Cost per 1M Transactions | $5-15 | $150-500 |
Historical Query Latency (1 year ago) | 100-300 ms | 2-5 seconds |
Archive Node Dependency | ||
Initial Sync Time | 2-4 hours | 5-14 days |
Data Retention Policy | Configurable (e.g., 90 days) | Permanent |
Infrastructure Complexity | Medium | High |
pros-cons-a
INDEXER DATA PRUNING VS FULL HISTORY STORAGE
The Graph (Full History): Pros & Cons
Key strengths and trade-offs for indexers managing historical blockchain data.
01
Indexer Data Pruning: Pros
Operational Efficiency: Reduces storage costs by up to 80% for mature subgraphs. This matters for indexers serving high-volume, recent data queries where full history is not required.
Faster Sync Times: New subgraph deployments index faster by ignoring older blocks. This is critical for protocols like Uniswap v3 where the most recent liquidity positions are the primary query target.
02
Indexer Data Pruning: Cons
Limited Historical Queries: Cannot serve requests for data beyond the pruned window (e.g., "total DAI traded in 2021"). This is a deal-breaker for analytics dashboards, tax reporting tools, or on-chain reputation systems that require full lifecycle data.
Inflexible for New Use Cases: If a developer later needs historical data, the subgraph must be re-deployed and re-indexed from genesis, causing significant downtime and cost.
03
Full History Storage: Pros
Complete Data Fidelity: Provides access to every event from genesis block. This is non-negotiable for comprehensive chain analysis, forensic tools like Chainalysis, and protocols like MakerDAO that need the complete history of vaults for risk assessment.
Future-Proof API: Developers can build any historical query without constraint. Serves as a single source of truth for applications like NFT provenance trackers (e.g., checking an Art Blocks NFT's full mint and transfer history).
04
Full History Storage: Cons
Exponential Storage Costs: Storing all Ethereum data can require 10TB+ and growing, leading to high operational overhead. This impacts indexer profitability and can centralize service to only well-funded nodes.
Slower Initial Sync: Indexing a subgraph from scratch (e.g., for Aave's entire lending history) can take days or weeks, delaying time-to-market for developers compared to a pruned index.
pros-cons-b
INDEXER DATA PRUNING VS FULL HISTORY STORAGE
Custom Indexer with Pruning: Pros & Cons
Key strengths and trade-offs for infrastructure architects choosing between a pruned custom indexer and full archival node storage.
01
Pruned Indexer: Cost Efficiency
Specific advantage: Reduces storage costs by 70-95% by discarding historical state not required for the application logic. This matters for high-throughput dApps like DeFi aggregators (e.g., 1inch) or gaming protocols that only need recent 30-90 days of data, enabling leaner, more scalable infrastructure on AWS S3 or GCP.
02
Pruned Indexer: Performance at Scale
Specific advantage: Smaller datasets enable faster query response times (<100ms) and simpler sharding strategies. This matters for real-time applications like order book DEXs (e.g., dYdX) or social feeds that require sub-second latency for user experience, avoiding the bloat of a full node's multi-terabyte chain history.
03
Full History Storage: Data Completeness
Specific advantage: Provides an immutable, verifiable record of all transactions and state changes since genesis. This matters for compliance & audit-heavy protocols like institutional DeFi (e.g., Maple Finance) or on-chain analytics platforms (e.g., Dune, Nansen) that require forensic analysis and proving historical ownership or events.
04
Full History Storage: Protocol Agnosticism
Specific advantage: Serves as a single source of truth for any query, future or past, without dependency on custom logic. This matters for infrastructure providers (e.g., Alchemy, QuickNode) and general-purpose explorers (e.g., Etherscan) that must support arbitrary, ad-hoc queries from developers and users across the entire ecosystem.
CHOOSE YOUR PRIORITY
Decision Framework: When to Choose Which
Full History Storage for DApp Developers
Verdict: The default for most applications requiring robust analytics or historical verification. Strengths: Enables complex historical queries (e.g., user transaction history, protocol fee accrual over time). Essential for on-chain analytics platforms like Dune Analytics or Nansen. Supports data integrity proofs for audits and compliance. Works seamlessly with tools like The Graph's subgraphs that query the entire chain state. Trade-offs: Requires significant infrastructure (e.g., archival nodes, high-performance databases like TimescaleDB). Query latency can increase as dataset grows.
Indexer Data Pruning for DApp Developers
Verdict: Optimal for high-performance, state-focused applications where only recent data matters. Strengths: Drastically reduces database size and improves query performance for real-time state (e.g., current liquidity pool TVL, live NFT floor prices). Lowers operational costs for indexers serving high-throughput chains like Solana or Avalanche. Ideal for applications using specialized indexers like Helius on Solana. Trade-offs: Impossible to answer historical questions beyond the pruned window. Relies on external data lakes (e.g., Google BigQuery public datasets) for historical analysis.
verdict
THE ANALYSIS
Final Verdict & Strategic Recommendation
Choosing between data pruning and full history storage is a foundational decision that dictates your protocol's scalability, cost, and long-term data integrity.
Indexer Data Pruning excels at operational efficiency and cost control by discarding historical data beyond a defined window. This results in significantly lower storage overhead—often reducing node storage requirements from tens of terabytes to a few hundred gigabytes—and faster sync times. For example, protocols like Solana's validators and many Layer 2 rollup sequencers employ aggressive pruning to maintain high TPS without being crippled by state bloat, enabling them to run on commodity hardware.
Full History Storage takes a different approach by preserving the complete, immutable ledger. This strategy is non-negotiable for protocols where ultimate data verifiability and censorship resistance are paramount, such as Bitcoin and Ethereum's archive nodes. The trade-off is substantial: operating a full archive node requires petabytes of storage, specialized infrastructure, and higher operational costs, but it provides the gold standard for auditability and enables complex historical data queries for analytics platforms like Dune Analytics or The Graph.
The key trade-off: If your priority is scalability, lower operational cost, and developer agility for a high-throughput dApp (e.g., a high-frequency DeFi protocol or gaming NFT mint), choose Pruning. If you prioritize maximum security, regulatory compliance, deep historical analysis, or building a foundational layer where data permanence is critical, choose Full History Storage. Your choice ultimately defines your protocol's trust model and long-term operational footprint.
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall