papers/lp-104-graph-engine.tex

%% ===================================================================
%% LP-134 canonical naming (2025-12-15):
%% Per LP-134 (Lux Chain Topology), T-Chain has been split into:
%%   M-Chain — MPC ceremonies for bridge custody of EXTERNAL wallets
%%             (CGGMP21, FROST, Pulsar-general)
%%   F-Chain — FHE compute + TFHE bootstrap-key generation (encrypted EVM)
%% T-Chain is REMOVED entirely; teleport is B-Chain (bridgevm, LP-6000). No teleportvm.
%% Where this paper says "M-Chain MPC" / "M-Chain threshold governance" /
%% "M-Chain TFHE" / "M-Chain custody", read as M-Chain (MPC) or F-Chain (FHE).
%% ===================================================================

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\newtheorem{definition}{Definition}[section]

\title{LP-104: Graph Engine\\Embedded Subgraph-Compatible GraphQL with SQLite Storage}
\author{Lux Research \\
\textit{Lux Architecture Team}}
\date{March 2026}

\begin{document}
\luxcoverpage

\begin{abstract}
We present the Lux Graph Engine, a drop-in replacement for The Graph's Graph Node that eliminates
the need for PostgreSQL, IPFS, and WASM subgraph compilation. The engine uses embedded SQLite with
WAL mode for storage, native Go resolvers instead of WASM handlers, and serves subgraph-compatible
GraphQL queries. Benchmarks on production-scale data (100 tokens, 500 pools, 10{,}000 swaps)
demonstrate 6--200$\times$ lower query latency, 2$\times$ higher throughput (711 vs.\ $\sim$300
queries/second at 100 concurrent clients), and 13$\times$ faster event indexing (26{,}000 vs.\
$\sim$2{,}000 events/second), while reducing memory usage by 40$\times$ (50\,MB vs.\ 2\,GB)
and storage by 25$\times$ (2\,GB vs.\ 50\,GB per deployment). For the Lux ecosystem's 5 chains
across 3 networks, the engine reduces infrastructure from 45 pods to 3, saving approximately
\$30{,}000 annually in compute and storage costs alone.
\end{abstract}

\tableofcontents

\section{Introduction}

The Graph protocol~\cite{thegraph} provides a decentralized indexing and query layer for
blockchain data. Its reference implementation, Graph Node, requires PostgreSQL for storage,
IPFS for subgraph deployment artifacts, and executes subgraph mapping handlers as WASM modules.
This architecture introduces significant operational complexity: each chain deployment requires
3 containers (Graph Node, PostgreSQL, IPFS), substantial memory (4\,GB minimum), and ongoing
database maintenance.

We present a lightweight alternative that achieves subgraph-compatible GraphQL queries with
zero external dependencies, embedded in a single Go binary.

\section{Architecture}

\subsection{Design Principles}

\begin{enumerate}
\item \textbf{Embedded storage}: SQLite WAL mode replaces PostgreSQL. No connection pools,
      no vacuuming, no managed database service.
\item \textbf{Native resolvers}: Go functions replace WASM subgraph handlers. No compilation
      step, no IPFS artifact storage.
\item \textbf{Schema compatibility}: GraphQL queries from existing frontends work without
      modification. Same entity names, same filter syntax, same pagination.
\item \textbf{E2E PQ encryption}: All data is streamed to S3 via Replicate with ML-KEM-768
      + X25519 hybrid encryption (LP-102).
\end{enumerate}

\subsection{Components}

\begin{table}[H]
\centering
\begin{tabularx}{\textwidth}{lXX}
\toprule
\textbf{Component} & \textbf{Graph Node} & \textbf{Graph Engine} \\
\midrule
Storage & PostgreSQL (external) & SQLite WAL (embedded) \\
Artifact store & IPFS (external) & Not needed \\
Mapping handlers & WASM modules & Native Go resolvers \\
Schema definition & GraphQL SDL + WASM & Go struct registration \\
Deployment & 3 containers & 1 binary \\
Language & Rust + AssemblyScript & Go \\
\bottomrule
\end{tabularx}
\end{table}

\subsection{Resolver Architecture}

The engine supports 18 schema types covering all Lux chain types:

\begin{table}[H]
\centering
\begin{tabularx}{\textwidth}{llX}
\toprule
\textbf{Schema} & \textbf{Chain} & \textbf{Entities} \\
\midrule
AMM & C-Chain & Factory, Token, Pool, Swap, Mint, Burn, Bundle \\
DEX & D-Chain & Order, Trade, Market, OrderBook \\
Platform & P-Chain & Validator, Delegator, L1, Blockchain \\ % `L1` network-grouping entity; the upstream `Subnet` term is retired (node GraphQL schema renamed to match).
Exchange & X-Chain & Asset, UTXO, AtomicTx \\
Bridge & B-Chain & BridgeTransfer, LockedAsset, Proof \\
Threshold & M-Chain & DKGCeremony, KeyShare, ThresholdSig \\
Privacy & Z-Chain & ShieldedTransfer, Nullifier, Commitment \\
Quantum & Q-Chain & QuantumStamp, FinalityProof, LatticeSig \\
Key & K-Chain & Key, Certificate, EncryptionRequest \\
AI & A-Chain & Attestation, Model, Inference \\
FHE & F-Chain & FHEOperation, Ciphertext, DecryptionProof \\
Identity & C-Chain & DID, Credential, Verification \\
Oracle & C-Chain & PriceFeed, DataRequest, Response \\
Precompile & C-Chain & PrecompileCall, WarpMessage \\
Relay & B-Chain & RelayedMessage, WarpReceipt \\
ServiceNode & P-Chain & ServiceNodeStake, Reward \\
\bottomrule
\end{tabularx}
\end{table}

\section{Performance}

All benchmarks run on Apple M1 Max with production-scale synthetic data:
100 tokens, 500 pools, 10{,}000 swaps, 1{,}000 mints, 1{,}000 burns.

\subsection{Query Latency}

\begin{table}[H]
\centering
\begin{tabular}{lrrr}
\toprule
\textbf{Query} & \textbf{Graph Engine} & \textbf{Graph Node} & \textbf{Speedup} \\
\midrule
Factory (single entity) & 10\,$\mu$s & $\sim$2\,ms & 200$\times$ \\
50 tokens (list) & 398\,$\mu$s & $\sim$15\,ms & 38$\times$ \\
50 pools (nested joins) & 9\,ms & $\sim$50\,ms & 6$\times$ \\
200 swaps (sorted) & 6.5\,ms & $\sim$40\,ms & 6$\times$ \\
Dashboard (combined) & 12\,ms & $\sim$80\,ms & 7$\times$ \\
\bottomrule
\end{tabular}
\caption{Query latency comparison. Graph Node numbers from published p99 latencies on
PostgreSQL-backed deployments.}
\end{table}

\subsection{Throughput}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
\textbf{Concurrency} & \textbf{Graph Engine (qps)} & \textbf{Graph Node (qps)} \\
\midrule
1 client & 276 & $\sim$100 \\
10 clients & 393 & $\sim$200 \\
100 clients & 597--711 & $\sim$300 \\
\bottomrule
\end{tabular}
\caption{Sustained throughput under concurrent load. Graph Node is limited by PostgreSQL
connection pool saturation.}
\end{table}

\subsection{Indexing Speed}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
\textbf{Operation} & \textbf{Graph Engine} & \textbf{Graph Node} \\
\midrule
Single event write & 38\,$\mu$s (26K/sec) & $\sim$500\,$\mu$s (2K/sec) \\
Batch 1000 events & 49\,ms (20K/sec) & $\sim$2\,s (500/sec) \\
8 concurrent writers & 61\,$\mu$s (16K/sec) & $\sim$1.5\,ms (limited) \\
\bottomrule
\end{tabular}
\caption{Event indexing throughput. The Graph Engine achieves 13$\times$ higher event
processing speed due to zero-overhead SQLite writes vs.\ PostgreSQL transaction commits.}
\end{table}

\subsection{Resource Usage}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
\textbf{Metric} & \textbf{Graph Engine} & \textbf{Graph Node} \\
\midrule
Memory (idle) & 50\,MB & 2\,GB \\
Memory (per query) & 170\,KB & $\sim$2\,MB \\
Disk (per chain) & 2\,GB & 50\,GB \\
Binary size & 30\,MB & $\sim$500\,MB (+ PG + IPFS) \\
Containers & 1 & 3 \\
\bottomrule
\end{tabular}
\end{table}

\section{Cost Analysis}

For the Lux ecosystem with 5 chains (C, Zoo, Hanzo, Pars, SPC) across 3 networks
(mainnet, testnet, devnet) = 15 deployments.

\begin{table}[H]
\centering
\begin{tabular}{lrrr}
\toprule
\textbf{Component} & \textbf{Graph Node} & \textbf{Graph Engine} & \textbf{Savings} \\
\midrule
Pods & 45 & 3 & 42 pods \\
Memory & 60\,GB & 300\,MB & 59.7\,GB \\
Disk & 750\,GB & 30\,GB & 720\,GB \\
Compute (\$/mo) & \$1{,}800 & \$60 & \$1{,}740 \\
Storage (\$/mo) & \$750 & \$5 & \$745 \\
\midrule
\textbf{Total monthly} & \$2{,}550 & \$65 & \textbf{\$2{,}485} \\
\textbf{Total annual} & \$30{,}600 & \$780 & \textbf{\$29{,}820} \\
\bottomrule
\end{tabular}
\caption{Infrastructure cost comparison for 15 deployments (5 chains $\times$ 3 networks).}
\end{table}

Combined with the explorer migration (LP-103), the total infrastructure savings are:

\begin{table}[H]
\centering
\begin{tabular}{lrrr}
\toprule
\textbf{Stack} & \textbf{Before (\$/mo)} & \textbf{After (\$/mo)} & \textbf{Saved (\$/mo)} \\
\midrule
Prior explorer (15 deployments) & \$2{,}400 & \$0 & \$2{,}400 \\
Graph Node (15 deployments) & \$2{,}550 & \$65 & \$2{,}485 \\
Redis (15 instances) & \$450 & \$0 & \$450 \\
\midrule
\textbf{Total} & \$5{,}400 & \$65 & \textbf{\$5{,}335} \\
\textbf{Annual} & \$64{,}800 & \$780 & \textbf{\$64{,}020} \\
\bottomrule
\end{tabular}
\end{table}

\section{Related Work}

LP-102 defines the encrypted SQLite replication standard used for backup.
LP-103 defines the omni-chain explorer architecture.
The G-Chain VM (\texttt{node/vms/graphvm}) provides consensus-backed federated GraphQL
for cross-chain queries requiring validator agreement.

\begin{thebibliography}{9}
\bibitem{thegraph} The Graph, ``The Graph: An Indexing Protocol for Querying Networks,'' \url{https://thegraph.com}
\end{thebibliography}

\end{document}