Building a Distributed Task Scheduler with etcd: A Hands-On Guide
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Jones Charles

Jones Charles @jones_charles_ad50858dbc0

About: go dev

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Dec 17, 2024

Building a Distributed Task Scheduler with etcd: A Hands-On Guide

Publish Date: Jul 4
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Task scheduling in today’s apps isn’t just about firing off cron jobs anymore. As systems scale and complexity spikes, we’re juggling multi-node setups, high availability, and state consistency—like ensuring database backups, order processing, or web crawlers run smoothly across a cluster. Think of a distributed task scheduler as an orchestra conductor: every node (musician) hits their cue perfectly, no duplicated efforts, no missed beats. So, how do we build one?

Enter etcd, the unsung hero of distributed coordination. It’s lightweight, consistent, and packed with features like real-time Watches, distributed locks, and Leases—perfect for wrangling tasks in a cluster. Unlike ZooKeeper’s hefty setup or Redis’s memory-hungry ways, etcd strikes a sweet spot: simple to deploy, easy to use, and a natural fit for Go developers (thanks to its native client library).

In this guide, I’ll walk you through designing and coding an etcd-based distributed task scheduler in Go. We’ll cover the architecture, implement core pieces, explore real-world use cases, and dodge common pitfalls—all from my own experience. Whether you’re a Go newbie with a year under your belt or a distributed systems enthusiast, this is for you. Let’s see why etcd is our scheduling MVP!

Who’s this for? Developers comfy with Go basics (goroutines, channels) and curious about distributed systems. New to etcd? No sweat—I’ll ease you in.

Why etcd Rocks for Task Scheduling

Before we sketch out a system, let’s unpack why etcd is a killer choice for distributed task scheduling—and what problems it solves.

Meet etcd: The Coordination Wizard

Born at CoreOS and now a Kubernetes staple, etcd is a distributed key-value store built on the Raft consensus protocol. It’s more than just storage—it’s a coordination powerhouse with:

  • Key-Value Storage: Stash task details like IDs, times, and states.
  • Watch Mechanism: Get real-time updates on changes (think task status flips).
  • Leases: Keep tabs on node liveness—zombie nodes, begone!
  • Distributed Locks: Ensure one node, one task, no chaos.

Compared to ZooKeeper (complex) or Redis (eventual consistency), etcd’s lightweight vibe and strong consistency make it a dream for small-to-medium teams.

The Distributed Scheduling Struggle

Picture 10 nodes running tasks. Without coordination, you’re in for:

  • Single Point of Failure: Scheduler dies, tasks stop.
  • Task Duplication: Two nodes grab the same job—oops.
  • State Sync Nightmares: Node crashes mid-task, now what?

These are ticking time bombs. etcd defuses them.

How etcd Saves the Day

  • High Availability: Raft keeps things humming even if nodes drop.
  • Real-Time Watches: Nodes spot task updates instantly.
  • Locks: Transactions guarantee exclusive task grabs.
  • Leases: Dead nodes lose their grip, tasks reassign.

It’s like etcd was made for this. Ready to build? Let’s design it.

Designing and Coding the Scheduler

Time to get hands-on. Our etcd-powered scheduler will be a relay race: nodes pass tasks smoothly, no tripping. Here’s the plan and code to make it happen.

Architecture at a Glance

Three key players:

  1. etcd (The Hub): Stores tasks, assigns them, tracks states.
  2. Go Workers (Nodes): Fetch and run tasks, report back.
  3. Task Flow: From acquisition to execution to updates. 
[etcd Hub]
├── /tasks: Task Metadata
├── /locks: Task Locks
└── /nodes: Node Heartbeats
    ↓
[Workers: Node 1, Node 2, ...]
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etcd’s the brain; Go workers are the muscle.

Core Modules in Go

Let’s code the essentials with etcd’s clientv3.

Task Storage

Tasks need a home. We’ll use a simple struct and JSON.

type Task struct {
    ID         string    `json:"id"`
    Name       string    `json:"name"`
    ScheduleAt time.Time `json:"schedule_at"`
    Status     string    `json:"status"`
}

func storeTask(cli *clientv3.Client, task Task) error {
    data, _ := json.Marshal(task)
    key := "/tasks/" + task.ID
    _, err := cli.Put(context.Background(), key, string(data))
    return err
}
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Task Assignment with Locks

No duplicate runs—locks to the rescue.

func grabTask(cli *clientv3.Client, taskID string) bool {
    lease, _ := cli.Grant(context.Background(), 10) // 10s lease
    lockKey := "/locks/" + taskID
    txn := cli.Txn(context.Background()).
        If(clientv3.Compare(clientv3.CreateRevision(lockKey), "=", 0)).
        Then(clientv3.OpPut(lockKey, "locked", clientv3.WithLease(lease.ID)))

    resp, _ := txn.Commit()
    return resp.Succeeded
}
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State Updates with Watch

Keep everyone in sync.

func watchTask(cli *clientv3.Client, taskID string) {
    key := "/tasks/" + taskID
    for resp := range cli.Watch(context.Background(), key) {
        for _, ev := range resp.Events {
            log.Printf("Task %s: %s", taskID, ev.Kv.Value)
        }
    }
}
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Node Liveness

Heartbeats via leases.

func heartbeat(cli *clientv3.Client, nodeID string) {
    lease, _ := cli.Grant(context.Background(), 15)
    key := "/nodes/" + nodeID
    cli.Put(context.Background(), key, "alive", clientv3.WithLease(lease.ID))
    for range cli.KeepAlive(context.Background(), lease.ID) {
        log.Printf("Node %s alive", nodeID)
    }
}
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Tech Tips

  • Concurrency: Spin up goroutines for locking, watching, and heartbeats.
  • Retries: Add backoff for etcd hiccups—keep it resilient.

Real-World Wins: Use Cases with Code

A scheduler’s true test is in the wild. Let’s tackle two classics—scheduled tasks and async queues—using our etcd setup, complete with runnable Go code.

Use Case 1: Scheduled Database Backups

Scenario: Back up a database daily at 2 a.m. across 10 nodes. Only one should run it; others wait or step in if it fails.

How It Works:

  • Store the task in etcd with a trigger time.
  • Nodes race for a lock when the clock hits—winner backs up.
  • Leases ensure failover if the winner crashes.

Code:

package main

import (
    "context"
    "log"
    "time"
    "go.etcd.io/etcd/clientv3"
)

type Task struct {
    ID         string    `json:"id"`
    ScheduleAt time.Time `json:"schedule_at"`
}

func runBackup(cli *clientv3.Client, task Task) {
    if wait := time.Until(task.ScheduleAt); wait > 0 {
        log.Printf("Waiting %v for backup", wait)
        time.Sleep(wait)
    }

    lease, _ := cli.Grant(context.Background(), 10)
    lockKey := "/locks/" + task.ID
    txn := cli.Txn(context.Background()).
        If(clientv3.Compare(clientv3.CreateRevision(lockKey), "=", 0)).
        Then(clientv3.OpPut(lockKey, "locked", clientv3.WithLease(lease.ID)))

    if resp, _ := txn.Commit(); resp.Succeeded {
        log.Printf("Backing up (Task %s)...", task.ID)
        time.Sleep(2 * time.Second) // Simulate backup
        cli.Delete(context.Background(), lockKey)
    } else {
        log.Printf("Task %s taken, skipping", task.ID)
    }
}

func main() {
    cli, _ := clientv3.New(clientv3.Config{Endpoints: []string{"localhost:2379"}})
    defer cli.Close()

    task := Task{ID: "backup-001", ScheduleAt: time.Now().Add(3 * time.Second)}
    go runBackup(cli, task) // Node 1
    go runBackup(cli, task) // Node 2
    time.Sleep(10 * time.Second)
}
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Output:

Waiting 3s for backup
Waiting 3s for backup
Backing up (Task backup-001)...
Task backup-001 taken, skipping
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One node wins, others back off—clean and reliable.

Use Case 2: Async Order Processing Queue

Scenario: E-commerce orders pile up—send emails, update stock. Nodes grab tasks dynamically, no overlap.

How It Works:

  • Tasks hit an etcd queue.
  • Workers Watch for new entries, lock, and process.
  • Status syncs in real-time.

Code:

type OrderTask struct {
    ID     string `json:"id"`
    Order  string `json:"order"`
    Status string `json:"status"`
}

func worker(cli *clientv3.Client, id string) {
    for resp := range cli.Watch(context.Background(), "/queue/", clientv3.WithPrefix()) {
        for _, ev := range resp.Events {
            if ev.Type != clientv3.EventTypePut {
                continue
            }
            var task OrderTask
            json.Unmarshal(ev.Kv.Value, &task)
            if task.Status != "pending" {
                continue
            }

            lease, _ := cli.Grant(context.Background(), 10)
            lockKey := "/locks/" + task.ID
            txn := cli.Txn(context.Background()).
                If(clientv3.Compare(clientv3.CreateRevision(lockKey), "=", 0)).
                Then(clientv3.OpPut(lockKey, id, clientv3.WithLease(lease.ID)))

            if resp, _ := txn.Commit(); resp.Succeeded {
                log.Printf("%s processing %s", id, task.Order)
                time.Sleep(1 * time.Second)
                task.Status = "done"
                data, _ := json.Marshal(task)
                cli.Put(context.Background(), "/queue/"+task.ID, string(data))
                cli.Delete(context.Background(), lockKey)
            }
        }
    }
}

func main() {
    cli, _ := clientv3.New(clientv3.Config{Endpoints: []string{"localhost:2379"}})
    defer cli.Close()

    go worker(cli, "w1")
    go worker(cli, "w2")

    tasks := []OrderTask{{ID: "t1", Order: "order-123", Status: "pending"}, {ID: "t2", Order: "order-456", Status: "pending"}}
    for _, t := range tasks {
        data, _ := json.Marshal(t)
        cli.Put(context.Background(), "/queue/"+t.ID, string(data))
        time.Sleep(500 * time.Millisecond)
    }
    time.Sleep(5 * time.Second)
}
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Output:

w1 processing order-123
w2 processing order-456
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Tasks split nicely—fast and fair.

Why It Shines

  • No Duplicates: Locks nail exclusivity.
  • Failover: Leases catch crashes.
  • Real-Time: Watches keep it snappy.

Pro Tips and Gotchas

Theory’s great, but production is where schedulers earn their stripes. Here’s what I’ve learned to keep things humming—and traps to avoid.

Best Practices

Tune Watch and Leases

Too many Watches clog etcd; short leases spam renewals.

  • Watch Smart: Start at a recent revision with WithRev.
  • Lease Right: Match duration to task length (e.g., 15s for slow jobs). 
watchChan := cli.Watch(context.Background(), "/tasks/", clientv3.WithRev(lastRev))
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Handle Failures Gracefully

Networks flake. Retry with backoff, rollback on flops.

func retry(cli *clientv3.Client, taskID string) {
    for i := 0; i < 3; i++ {
        if err := runTask(cli, taskID); err == nil {
            return
        }
        time.Sleep(time.Second << i)
    }
    log.Printf("Task %s gave up", taskID)
}
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Monitor Everything

Log states, track metrics—visibility is king.

  • Logs: Use zap for speed.
  • Metrics: Prometheus for task durations, failures.

Pitfalls I Hit (So You Don’t)

Timeout Troubles

Issue: Nodes dropped etcd connections in shaky networks.
Fix: Bump timeouts, add reconnection.

cli, _ := clientv3.New(clientv3.Config{
    Endpoints:   []string{"localhost:2379"},
    DialTimeout: 10 * time.Second,
})
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Duplicate Sneaks

Issue: Lock delays let tasks slip through.
Fix: Double-check state pre-run.

resp, _ := cli.Get(context.Background(), "/tasks/"+task.ID)
if string(resp.Kvs[0].Value) != `"pending"` {
    return
}
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Watch Overload

Issue: Thousands of tasks bogged down Watches.
Fix: Shard tasks by prefix, batch events.

watchChan := cli.Watch(context.Background(), "/tasks/shard1/", clientv3.WithPrefix())
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These tweaks turned chaos into calm.

Wrapping Up: What We’ve Built and What’s Next

We’ve journeyed from etcd’s basics to a fully functional distributed task scheduler in Go—pretty cool, right? Let’s recap and peek at the horizon.

What We’ve Achieved

Our etcd-powered scheduler is a lean, mean coordination machine:

  • Dead Simple: etcd’s lightweight setup got us rolling fast—no PhD in ops required.
  • Rock Solid: Raft and leases keep tasks flowing, even when nodes flake out.
  • Versatile: From nightly backups to order queues, it bends to fit.

Go’s concurrency magic—goroutines, channels—meshed perfectly with etcd’s client, making this a joy to code. Real-world tweaks (retries, sharding) polished it for battle. It’s not just theory—it’s a system you can deploy today.

What’s Next for Distributed Scheduling

The future’s bright, and etcd’s got room to grow:

  • Tool Team-Ups: Hook it to Kafka for massive task streams or Redis for quick caches.
  • Cloud-Native Vibes: Lean into Kubernetes—etcd’s already there. Imagine task CRDs managed by Operators.
  • Smart Scheduling: Machine learning could predict loads or optimize task splits—sci-fi stuff, but closer than you think.

This isn’t the end—it’s a launchpad.

Your Turn

Distributed systems sound scary, but they’re just puzzles waiting for you to solve. Start with a single task in etcd, add a worker, then scale it up. Break it, fix it, learn from it. My big takeaway? Hands-on beats head-scratching every time. Wrestling with Watch lags and lock races leveled up my skills more than any textbook.

So, grab this code, spin up etcd (Docker’s your friend), and build something. Got a cron job begging to go distributed? Start there. Share your wins—or epic fails—in the comments—I’d love to hear how it goes. Happy scheduling!

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