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Overview

BlockDB delivers the highest on-chain data granularity available, starting directly from decoded EVM logs and extending into multi-level analytical layers. Each layer preserves schema stability, row-level lineage, and deterministic hashes, allowing you to trace any record back to its originating transaction. You use these dataset levels to choose the exact resolution you need — from raw execution data to fully derived analytics optimized for modeling, research, and real-time applications.

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Granularity Levels

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Level 1 — Decoded Raw On-Chain Events

Raw EVM execution data decoded into structured, schema-stable tables.
  • Logs, transactions, calls, and function results
  • Full event arguments in decoded form
  • One row per event (no aggregation)
  • Lineage hash links each row to the source block and log index
Best for: building your own models, custom decoders, and audits requiring byte-level parity with RPC data.

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Level 2 — Protocol-Aware Entities

Enriched datasets derived from Level 1 with application-level context.
  • ERC-20 tokens
  • ERC-721 tokens
  • Liquidity pools (e.g., Uniswap v2/v3/v4)
  • Pool configuration, fee tiers, tick spacing, token metadata
Best for: joining protocol entities with execution data, building AMM analytics, and powering indexers or dashboards.

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Level 3 — State & Liquidity Snapshots

Structured snapshots of DeFi protocol states, computed per block or per event.
  • Liquidity pool reserves (token0/token1 amounts, active tick, square root price)
  • Real-time liquidity depth derived from tick state
  • Price ranges directly inferred from Uniswap v3 tick math
Example:
In Token-to-Token Prices L3, you receive the full price range implied by the active Uniswap v3 tick (upper and lower bounds). This gives you true microstructure-level visibility. Best for: price modeling, slippage estimation, liquidity defense strategies, arbitrage detection.

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Level 4 — Aggregated Analytics

Higher-order metrics derived from previous levels.
  • VWAP, LWAP
  • OHLC prices
  • Aggregated liquidity and volume metrics
  • Impact curves and depth-weighted prices
These layers maintain deterministic lineage so you can verify every aggregated record against the raw events that produced it. Best for: trading models, ML feature generation, backtesting, market microstructure research.

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Why Granularity Matters

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Traceability

Every record includes a lineage hash that lets you verify:
  • which block it came from
  • which log indices contributed
  • which derived rows depend on which raw rows

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Deterministic Modeling

Stable schemas and versioning ensure long-term reproducibility for:
  • backtests
  • ML pipelines
  • regulatory and audit requirements

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Microstructure Precision

High granularity allows you to:
  • compute exact price impact
  • reconstruct pool state at any block
  • analyze liquidity fragmentation
  • detect regime shifts earlier

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When to Choose Higher Granularity

Use higher granularity when you need:
  • raw execution accuracy
  • event-level resolution
  • precise pool state
  • deterministic replay of historical market conditions
Use aggregated or analytical levels when you want:
  • faster ingestion
  • ready-to-use price or liquidity metrics
  • simplified modeling inputs
If you want, I can add diagrams, Mintlify <Cards> or <Steps> components, or cross-link this to dataset pages such as Token-to-Token Prices L1-L3 or Liquidity Pool Reserves.