Real-time Updates: Subgraph Streaming vs API Polling

Push-based, event-driven architecture: Data is streamed to your application via WebSockets or SSE the moment it's indexed. This matters for high-frequency dashboards, live notifications, and trading interfaces where sub-second latency is critical. Eliminates the waste of empty polling cycles.

Pull-based, request-response architecture: Your application periodically queries a REST or RPC endpoint. This matters for batch processing, infrequent updates, or when integrating with legacy systems. Offers simplicity and direct control over request timing but can miss events between polls.

Managed indexing with vendor lock-in: You rely on The Graph's decentralized network or a hosted service (Subgraph Studio). This reduces DevOps burden but requires defining a subgraph schema and dealing with syncing times and potential indexing errors. Best for teams wanting to avoid building and maintaining an indexer.

Pay-per-request model with predictable scaling: Costs scale linearly with request volume from providers like Alchemy. You have full control over polling intervals and failure retries. This matters for budget-conscious projects with predictable traffic patterns or those needing to avoid the subgraph development lifecycle.

Push-based, event-driven architecture: Indexers push new data to your client via WebSocket the moment it's indexed. This matters for high-frequency dashboards (e.g., NFT mint trackers, DEX price feeds) where sub-second latency is critical. Eliminates wasteful, empty polling calls.

Increased complexity & cost: Requires managing persistent WebSocket connections and handling reconnection logic. Vendor lock-in: Tied to The Graph's indexing speed and reliability; if the subgraph falls behind, so do your updates. This matters for mission-critical, self-hosted data pipelines where you need absolute control over the data ingestion layer.

Simplicity and control: Simple HTTP GET requests at a fixed interval. Easy to implement, debug, and scale horizontally. This matters for batch processing jobs, cron-based analytics, or applications where data freshness of 30+ seconds is acceptable (e.g., portfolio overviews, historical charts).

Inefficient and high-latency: Most polls return no new data, wasting bandwidth and compute. Poor user experience: Updates are delayed until the next poll cycle. This matters for real-time applications like live auctions or gaming leaderboards, where the delay is noticeable and costly in terms of RPC calls and user retention.

Real-time event-driven updates: Data is pushed to your client via WebSocket the moment it's indexed (e.g., a new Uniswap swap). Latency from block to client can be < 1 second. This is critical for high-frequency dashboards, live notifications, or trading bots where speed is paramount.

Simple, universal compatibility: Works with any REST or GraphQL endpoint. No need for specialized streaming infrastructure. Ideal for batch processing, cron jobs, or applications where updates every 5-60 seconds are acceptable. The dominant model for services like Etherscan and Covalent.

Higher initial complexity & cost: Requires managing WebSocket connections, handling reconnections, and parsing real-time event streams. Indexer costs for high-volume streams can be significant. Avoid this for simple, low-traffic applications where the overhead isn't justified.

Inefficient & High-Latency: Constant polling wastes bandwidth and compute on empty responses. For a dApp with 10k users, this can mean millions of redundant RPC calls per day, increasing costs and missing critical events between polls. Not suitable for live feeds.