Architecting a Solana Data Pipeline: Economics & System Design (Part 1)

If you have ever tried to track every single swap across Raydium, Orca, and Jupiter in real-time, you already know the painful truth: Solana is simply too fast for traditional Web2 architecture.

Standard REST API polling will get you rate-limited instantly. If you try to blindly open a WebSocket and dump the raw payload into a standard database, your server will run out of memory, drop packets, and corrupt your dataset. Drinking from the Solana firehose requires a paradigm shift in how we handle data ingestion.

This is Part 1 of a 3-part series where we will design a resilient, zero-data-loss swap indexer. We will architect the system, write a high-throughput ingestion service in Rust, and scale the analytics using ClickHouse.

Before we write a single line of code, we have to solve the hardest problem in Web3 infrastructure: Cloud Economics.

Cloud Economics & The RPC Math

The fastest way to kill a Web3 startup is uncontrolled RPC usage.

When you subscribe to real-time blockchain events, every message, parsed transaction, and account fetch burns RPC credits. If you scale your ingestion blindly, your infrastructure bill will scale exponentially right alongside it.

For high-throughput Solana pipelines, Helius is the industry standard, but you have to engineer your system to fit their pricing model.

Currently, the Helius Professional Plan offers 200 million monthly credits for $999/month. If you want to track all swap volume on Solana today, optimizing your WebSocket subscriptions and transaction fetching is mandatory. By filtering intelligently and only decoding the exact transaction signatures you need, tracking all swaps consumes roughly 5 to 8 million credits a day.

Helius Professional Plan — 200M credits / month · $999

Average daily swap volume — ~3M swaps / day

Credits per enhanced transaction — ~1–2 credits

Estimated daily credit burn — 5–8M credits / day

Peak monthly total — ≈ 240M credits — within plan ✓

This keeps your monthly consumption comfortably under the 200M credit ceiling, proving that high-throughput Web3 infrastructure doesn't have to bankrupt the startup — if engineered correctly.

The Core Architecture Flow

To handle this volume without dropping data, we must decouple the ingestion speed from the database write speed.

Helius (Enhanced WebSocket) → Rust Ingester (4-CPU · carbon crate) → NATS (Message Broker) → ClickHouse (Historical analytics) + WebSocket API (Live frontend feeds)

The Ingestion Layer (Rust)

We are processing thousands of messages a second. Traditional languages will choke on garbage collection pauses at this scale. By using Rust, we can safely manage memory and handle the intense workload of streaming and decoding raw transactions on a highly cost-effective 4-CPU server pod.

The Message Broker (NATS)

This is the MVP of the entire architecture. If ClickHouse goes down for a split-second or experiences a CPU spike, the Rust ingester doesn't care. It simply publishes the decoded payload to NATS. NATS handles the backpressure and guarantees message replayability, ensuring zero data loss.

Storage & Interface (ClickHouse & WebSockets)

ClickHouse independently consumes the NATS stream to build tables for heavy historical analytics. Simultaneously, a separate lightweight WebSocket service can listen to the exact same NATS subjects to push live swap events directly to frontend clients.

Network Topology: The Zero-Latency Requirement

Having the right hardware and software stack is useless if your network topology is flawed.

When processing real-time financial data, external internet routing is your biggest enemy. To prevent latency lag and bandwidth bottlenecks, your Rust Ingester, NATS broker, and ClickHouse database must be deployed within the same local network (Virtual Private Cloud).

Rule: Keep all internal service-to-service communication inside the VPC. Internal latency is measured in microseconds. External internet routing adds 5–50ms per hop — which at thousands of messages per second means a queue that grows faster than it is consumed.

By keeping all internal communication localized, you eliminate external SSL handshake overhead and internet routing hops. The only time data touches the public internet is when it arrives from Helius, and when it is served to your end user.

Conclusion & Next Steps

We now have the complete economic and architectural foundation. To summarise:

Helius Professional Plan with filtered WebSocket subscriptions keeps costs predictable and within budget even at peak Solana swap volume.

Rust is the only language that sustains this memory footprint safely at the required throughput with no GC pauses.

NATS decouples ingestion speed from write speed, absorbing backpressure and guaranteeing zero data loss during downstream hiccups.

VPC co-location reduces inter-service latency from milliseconds to microseconds and eliminates the public attack surface.

Now that our blueprint is locked in, we need to handle the incoming firehose. In Part 2: Ingesting the Solana Firehose with Rust, we will write the high-throughput service that decodes raw transactions using the carbon crate, handles reconnection logic, and publishes structured events to NATS with zero data loss.

Part 1: Architecture ✓ · Part 2: Ingestion ✓ · Part 3: Analytics ✓