New Course: Building and Running Indexers on Solana

[L0STE]

Create a comprehensive course titled "Run Your Indexer on Solana" that teaches developers how to build, deploy, and maintain high-performance indexers for the Solana blockchain.

Module 1 - indexer-101: Understand the core concepts and importance of blockchain indexing

• Indexing Basics: Data streaming concepts, Storage architectures, Data deserialization patterns
• Why Indexing Matters: RPC endpoint limitations and bottlenecks, Validator load distribution issues, Requirements for external data persistence
• Solana-Specific Challenges: Managing high transaction throughput (thousands of transactions per second), Handling chain reorganizations effectively, Maintaining data consistency across network update
• Geyser Plugin Architecture: Understanding Geyser's role in Solana's ecosystem, How plugins redirect account, block, slot, and transaction data, Integration patterns with external data stores

Module 2 - streaming-data: Master techniques for streaming blockchain data reliably

• gRPC Fundamentals: Connection lifecycle management, Advanced filtering strategies, Graceful handling of disconnections and reconnections
• Streaming Solutions: LaserStream (Helius), Fumarole (Triton)

Module 3 - storing-data: Implement scalable and reliable data storage solutions

• Message Queuing Systems: Apache Kafka for high-throughput scenarios, RabbitMQ for reliable message delivery, Queue configuration and monitoring
• Storage Architecture Design: PostgreSQL for relational data patterns, Time-series databases for performance metrics, Hybrid storage strategies
• Production Reliability: Handling missing slot notifications, Implementing retroactive finalization marking, Comprehensive monitoring and alerting systems

Module 4 - deserializing-data: Choose and implement optimal data parsing approaches

• Framework-Based Approach (Carbon)
• Managed Parsing Solutions (Vixen)

[Nagaprasadvr]

Yellowstone-Vixen Overview

🔗 Yellowstone-Vixen GitHub

Vixen is a high-performance Solana indexer built on top of DragonMouthGrpc, leveraging Tokio for async, multi-threaded execution. Its design enables efficient event streaming, parsing, and handling at scale.

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Core Components

Runtime

• Serves as the entrypoint for configuring the indexer.
• Allows registration of Account / Instruction parsers, handlers, and filters (for accounts, instructions, and slots).
• Can subscribe to events across Solana commitment levels: processed, confirmed, and finalized.
• Includes built-in Prometheus metrics for performance monitoring.
• Supports multiple event sources, such as RPC, geyser-grpc, and yellowstone-fumarole.
• Consumers can register custom handlers to process parsed events (e.g., store in a DB, log, or trigger network requests).

Parsers / Handlers

• Supports Account and Instruction parsers.

• Parsers (protobuf included) can be generated with Codama.

  • Example: Codama Vixen Parser

• Vixen also supports protobuf-based gRPC streaming, allowing clients to subscribe to parsed event streams.

Config

• Configurable via a TOML file: Example Config
• Requires an RPC URL with geyser-grpc support for event streaming.(Triton , Helius provides geyser-grpc)

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Why Vixen?

• Minimal setup: can be used as a complete indexer stack with just a few lines of code.
• High throughput and scalability from async multi-threaded design.
• Flexible integration points via parsers, filters, and handlers.
• Observability baked in via Prometheus metrics.

[0xIchigo]

LaserStream

LaserStream is Helius' high-performance gRPC-based data streaming service for Solana, designed for real-time delivery of blocks, transactions, and account updates. It enables developers to build efficient indexers by providing low-latency streams that greatly surpass traditional RPC polling

Key Capabilities

• Historical Replay: replay up to 3,000 slots (~20 minutes) via fromSlot to recover cleanly after any restarts or disconnects
• Multi-region Endpoints: Developers can pick the region closest to them for the best possible latency
• Auto-reconnects: LaserStream's clients track your last slot and resume automatically, removing the need for any custom reconnection code
• Throughput Headroom: Notably, the TS client uses native Rust bindings to achieve 1.3 GB/s throughput. This is over 40x faster than the Yellowstone gRPC JavaScript client, which maxes out at 30 MB/s and sees progressive delays at high bandwidth

Why LaserStream for Indexers?

• Low Latency: Indexers can act on data within milliseconds of it being produced. This allows for real-time dashboards, trading systems, and alerts
• Throughput at Scale: Solana has a lot of data. LaserStream's clients can handle gigabytes per second, ensuring you won't bottleneck at ingestion any time soon
• Resilient Against Disruptions: Built-in auto-reconnects and historical replay allow for indexers to recover gracefully without any data gaps
• Operational Simplicity: Running LaserStream is a lot easier than running multiple dedicated nodes in various regions around the world with custom retry logic
• Future-Proofing: With support for multiple languages and deployment regions, LaserStream can easily adapt to your architecture, whether that be a simple analytics indexer or a global, multi-region prod system

Reliability, Sizing, and Ops

A module covering LaserStream should outline the following to learners:

• Co-locate with the nearest chosen region to measure and achieve low latency
• Probe bandwidth before going live. As a rule of thumb, plan for 50 TB/mo to stream transactions, 100 TB/mo to stream blocks, and 150 TB/mo for all accounts. Ideally, keep ~2x headroom
• A typical backpressure pattern would be: LaserStream → queue (Kafka/RabbitMQ) → workers → DB
  • The queueing system is there to absorb spikes while the workers process ingress at a sustainable rate
  • Workers should be designed with idempotent writes using keys like signature, pubkey+slot, or slot to avoid duplication
  • Normalize entries (i.e., transactions, accounts, slots), but keep JSON blobs for flexible queries to balance performance with adaptability
  • Test locally to validate the pipeline works as intended under load before rolling out to prod

It also might be nice to go over how to track metrics such as slot lag, dropped messages, queue depth, etc., using something like Grafana/Prometheus

Integration Notes

LaserStream is a drop-in replacement for existing gRPC setups. All a developer needs to do to leverage LaserStream's capabilities is change their endpoint and API token

There are also clients in TS, Go, and Rust, making the integration process very straightforward

Quickstart (minimal TypeScript example to stream all confirmed blocks)

import { subscribe, CommitmentLevel, LaserstreamConfig, SubscribeRequest } from 'helius-laserstream';

async function streamBlocks() {
  const config: LaserstreamConfig = {
    apiKey: 'YOUR_API_KEY',
    endpoint: 'https://laserstream-mainnet-ewr.helius-rpc.com',
  };

  const request: SubscribeRequest = {
    transactions: {},
    commitment: CommitmentLevel.CONFIRMED,
    accounts: {},
    slots: {},
    transactionsStatus: {},
    blocks: {
      client: {
        includeTransactions: true,
        includeAccounts: false,
        includeEntries: false,
        accountRequired: [],
      },    
    },
    blocksMeta: {},
    entry: {},
    accountsDataSlice: []
  };

  // The SDK handles reconnection and replay automatically
  await subscribe(config, request, 
    async (data) => {
      console.log('New block:', data);
    }, 
    async (error) => {
      console.error('Error:', error);
    }
  );
}

streamBlocks().catch(console.error);

Relevant Links

🔗 LaserStream Documentation
🔗 Introducing LaserStream: Next-Gen Solana Data Streaming
🔗 Winning the Millisecond Game: Shreds, LaserStream, and the Edge of Solana
🔗 How DFlow Uses LaserStream to Quote Solana's Best Prices