| name | QuanuX-Annex Architecture |
| description | Operating procedures and architecture for the QuanuX-Annex Data Ingestion Engine and NATS JetStream mesh. |
QuanuX-Annex Architecture & Operations
Overview
QuanuX-Annex is the high-performance, zero-allocation C++ data ingestion engine layer inside the QuanuX cluster. It is designed for maximum throughput, hardware-enforced thread affinity, and strict High Availability (HA) failover. It hooks directly into the QUANUX.MARKET.TICK, QUANUX.EXECUTION.LOG, and QUANUX.SCHEMA.DRIFT NATS subjects.
Key Architectures
1. Zero-Allocation NATS JetStream C Client
The core ingestion loop avoids all Python/JVM garbage collection penalties by using the native nats.c library (<nats/nats.h>). It maps incoming byte streams directly into byte-aligned C++ structs defined in include/quanux/annex/TelemetryExhaust.hpp:
MarketTickExecutionLogSchemaDriftEvent
The client operates on an async callback model NatsSubscriber.cpp, preventing event loop blocking.
2. High Availability (HA) & Hardware Affinity
The QuanuX-Annex binary is deployed as a highly tuned systemd service (quanux-annex.service) to a dedicated edge compute node (e.g., DigitalOcean c-2 class). It requires the CLOUD_TARGET_DO CMake flag to enable DigitalOcean ZarrResolver_DO behaviors.
It dynamically binds to the 10.10.10.5 internal VPC interface. It explicitly prevents on-node builds during normal operations to preserve CPU cycles (though Phase 11 testing utilized on-node compilation, this is documented Technical Debt).
3. Sentinel Protocol
Telemetry for the edge nodes is handled by Telegraf and Promtail. Telegraf is deployed via direct .deb package injection to bypass broken APT repositories, and its collection interval is dynamically injected via quanuxctl habitat sentinel --telemetry-interval <seconds>.
4. Mock Injection (Testing)
A Python mock injector (tests/nats_injector.py) uses the Python struct module to pack simulated payload structs (<QIddIIB format) into the exact byte-alignment required by the C++ engine over NATS, validating the wire layout without live market data.
Phase 14: Hasura Federation & Streaming JSON Egress
The QuanuX-Annex Edge Node implements a cpp-httplib Remote Schema webhook listener for Hasura GraphQL.
- AWS SigV4 UNSIGNED-PAYLOAD: Bypasses heavy cryptographic SHA256 hashing during TLS
GET/PUT transfers to DO Spaces natively in C++, significantly multiplying ingestion throughput.
- Zero-Allocation Egress Streaming: Rather than building massive
nlohmann::json DOM trees representing gigabytes of ticks natively in RAM, ZarrResolver_DO.cpp directly intercepts libcurl streams, mapping them into raw memory chunks, and using cpp-httplib's chunked provider to asynchronously stream the snprintf JSON representations directly back to Hasura. CPU bottlenecking is eradicated.
Removed Technical Debt
- Python Path Hacking Eradicated:
quanuxctl/main.py is safely packaged and executed.
- NATS Decay Hazard Resolved: Dynamic Keyring injection of
NATS_URL is definitively hardened into permanent C++ object lifecycles, surviving all memory decaying scope drops.
- Synchronous Egress Resolved: The webhook thread-pool was aggressively scaled, and egress streaming prevents OOM kills on massive queries.
Commands
quanuxctl deploy -p annex_core -t quanux_annex_node --type cpp_binary
python tests/nats_injector.py --url nats://10.10.10.5:4222 --subject QUANUX.MARKET.TICK --type tick --count 1000
Tract 2: GCP Research Database Transpiler
The QuanuX-Annex includes the QuanuXDuckToBQTranspiler, critically bridging native DuckDB operator commands to BigQuery Standard SQL text for bounded remote execution.
Operator Workflows
The CLI quanuxctl infra gcp-sql exposes 3 deterministic states:
validate: Formally analyze AST tree restrictions strictly against the allowed matrix without cloud networking.transpile: Emits exact semantic BigQuery string transformations dynamically without native execution.execute: Dispatches transpiled AST requests enforcing rigorous payload constraints (--timeout, --max-rows). Machine-readable JSON structural guarantees flow efficiently to automated CI test grids.
Proven Bounded Matrix
- SQL Basics: Read-only
SELECT, FROM, WHERE, GROUP BY, ORDER BY, LIMIT, leveraging exactly matching COUNT/SUM/AVG/MIN/MAX statistical equivalencies over live dual-mapped BQ remote environments.
- Subqueries (Phase 3A): Shallow 1-level scalar depths isolating static equality loops. Resolves nested
IN (SELECT...) expressions securely to uncorrelated memory tables.
- Joins (Phase 3B): Unidirectionally permits exactly one static explicit equality constraint per logical tree exclusively mapped via
INNER JOIN.
Intentionally Unsupported (Fail-Closed)
To completely eradicate state mutability, internal DROP/UPDATE/INSERT commands invoke an immediate systemic execution halt. All unverified syntax vectors encompassing deep outer, cross, natural joins, chained correlated subqueries, raw recursive combinations, window functions, and proprietary database schemas emit fatal AST rejections forcing analysts natively onto GCP tooling bounds.
Known Bolts & Next Objectives
- Known Bolts (Later Tightening): Subquery parameters potentially adapt gracefully to layered complexity aggregations. Bounded subset joins tentatively mapped to nested grouping intersections to resolve larger mathematical domains securely over AWS SigV4.
- Next Architectural Objectives: Pivot explicitly back to Terraform/Ansible infrastructure orchestration, deep telemetry GraphQL/Hasura/superGraph/Aleph federations, and unified cold-data blob architectures.
