Go Concurrency Patterns for High-Throughput Node Indexers

The Syncing Bottleneck

Synchronizing a blockchain from the genesis block using a single-threaded script can take weeks. An indexing engine must process historical blocks as fast as the database can accept them. Golang's native concurrency primitives make it the undisputed king of high-throughput network tasks.

The Worker Pool Pattern

Spawning a new Goroutine for every single block (e.g., 800,000 Goroutines) will exhaust memory and overwhelm the RPC node, triggering rate limits. The solution is a Worker Pool.

You create a fixed number of workers (e.g., 50) and feed them block heights via a Go Channel.

func worker(id int, jobs <-chan int, results chan<- BlockData) {
    for height := range jobs {
        // Fetch block from RPC
        data := fetchBlock(height)
        results <- data
    }
}

func main() {
    jobs := make(chan int, 100)
    results := make(chan BlockData, 100)
    
    // Start 50 workers
    for w := 1; w <= 50; w++ {
        go worker(w, jobs, results)
    }
    
    // Send jobs
    go func() {
        for i := 1; i <= 100000; i++ {
            jobs <- i
        }
        close(jobs)
    }()
}
      

Handling Order and State

The difficulty with concurrent block fetching is that blocks return out of order. Worker 10 might fetch Block 500 before Worker 1 fetches Block 490. If you are calculating UTXO state, order is strictly required.

The Solution: The workers fetch and parse the JSON data concurrently (the slowest part due to network I/O). They send the parsed structures to a results channel. A single, dedicated database writer Goroutine reads from this channel, buffers the blocks into a map, and only writes to PostgreSQL sequentially when the next expected block arrives in the buffer.

Conclusion

By separating the I/O bound tasks (fetching and parsing) across a worker pool, and keeping the state-bound tasks (database writing) sequential, you achieve the absolute maximum theoretical syncing speed for a custom block explorer.