Advanced task management platform featuring AI-powered automation, real-time collaboration, and enterprise workflow optimization. Built with Go microservices, gRPC communication, and distributed architecture for high-performance team productivity at scale.
High-performance Go microservices with intelligent automation
High-performance Go microservices with gRPC communication, providing exceptional concurrency, memory efficiency, and sub-millisecond response times.
Machine learning algorithms for intelligent task prioritization, deadline prediction, workload balancing, and automated workflow optimization.
WebSocket-based real-time updates with conflict resolution, live editing, instant notifications, and seamless team synchronization.
Comprehensive productivity analytics with Elasticsearch indexing, custom dashboards, performance insights, and predictive modeling.
Sophisticated workflow engine with custom triggers, conditional logic, integration capabilities, and enterprise process optimization.
Cloud-native architecture with Kubernetes orchestration, auto-scaling, load balancing, and distributed caching for enterprise performance.
Advanced Go patterns and distributed systems architecture
package main
import (
"context"
"fmt"
"log"
"time"
"github.com/go-redis/redis/v8"
"google.golang.org/grpc"
"gorm.io/gorm"
)
type TaskService struct {
pb.UnimplementedTaskServiceServer
db *gorm.DB
redis *redis.Client
aiService *AIService
kafkaClient *kafka.Writer
}
// CreateTask handles task creation with AI-powered optimization
func (s *TaskService) CreateTask(ctx context.Context, req *pb.CreateTaskRequest) (*pb.Task, error) {
// Validate request
if err := s.validateTaskRequest(req); err != nil {
return nil, status.Errorf(codes.InvalidArgument, "validation failed: %v", err)
}
// Create task entity
task := &Task{
ID: generateUUID(),
Title: req.Title,
Description: req.Description,
ProjectID: req.ProjectId,
AssigneeID: req.AssigneeId,
Priority: req.Priority,
Status: TaskStatusPending,
CreatedAt: time.Now(),
UpdatedAt: time.Now(),
}
// AI-powered task optimization
aiInsights, err := s.aiService.AnalyzeTask(ctx, task)
if err != nil {
log.Printf("AI analysis failed: %v", err)
} else {
task.EstimatedHours = aiInsights.EstimatedHours
task.SuggestedPriority = aiInsights.SuggestedPriority
task.OptimalAssignee = aiInsights.OptimalAssignee
}
// Database transaction
tx := s.db.Begin()
defer func() {
if r := recover(); r != nil {
tx.Rollback()
}
}()
if err := tx.Create(task).Error; err != nil {
tx.Rollback()
return nil, status.Errorf(codes.Internal, "failed to create task: %v", err)
}
// Cache the task
taskJSON, _ := json.Marshal(task)
s.redis.Set(ctx, fmt.Sprintf("task:%s", task.ID), taskJSON, time.Hour*24)
// Publish event to Kafka
event := &TaskCreatedEvent{
TaskID: task.ID,
ProjectID: task.ProjectID,
AssigneeID: task.AssigneeID,
Timestamp: time.Now(),
}
if err := s.publishEvent(ctx, "task.created", event); err != nil {
log.Printf("Failed to publish event: %v", err)
}
tx.Commit()
return task.ToProto(), nil
}
// UpdateTaskStatus with real-time notifications
func (s *TaskService) UpdateTaskStatus(ctx context.Context, req *pb.UpdateTaskStatusRequest) (*pb.Task, error) {
// Get task from cache first
taskKey := fmt.Sprintf("task:%s", req.TaskId)
cached, err := s.redis.Get(ctx, taskKey).Result()
var task *Task
if err == redis.Nil {
// Not in cache, fetch from database
if err := s.db.First(&task, "id = ?", req.TaskId).Error; err != nil {
return nil, status.Errorf(codes.NotFound, "task not found")
}
} else {
json.Unmarshal([]byte(cached), &task)
}
// Update status with validation
oldStatus := task.Status
task.Status = req.NewStatus
task.UpdatedAt = time.Now()
// AI-powered status change validation
if valid, reason := s.aiService.ValidateStatusChange(ctx, oldStatus, req.NewStatus, task); !valid {
return nil, status.Errorf(codes.FailedPrecondition, "invalid status change: %s", reason)
}
// Update in database
if err := s.db.Save(task).Error; err != nil {
return nil, status.Errorf(codes.Internal, "failed to update task: %v", err)
}
// Update cache
taskJSON, _ := json.Marshal(task)
s.redis.Set(ctx, taskKey, taskJSON, time.Hour*24)
// Publish real-time update
statusEvent := &TaskStatusChangedEvent{
TaskID: task.ID,
OldStatus: oldStatus,
NewStatus: req.NewStatus,
UpdatedBy: getUserFromContext(ctx),
Timestamp: time.Now(),
}
s.publishEvent(ctx, "task.status.changed", statusEvent)
// Trigger automation workflows
go s.triggerAutomationWorkflows(task, oldStatus, req.NewStatus)
return task.ToProto(), nil
}
// AI-powered task assignment optimization
func (s *TaskService) OptimizeTaskAssignment(ctx context.Context, req *pb.OptimizeAssignmentRequest) (*pb.AssignmentRecommendations, error) {
// Get team workload data
workloadData, err := s.getTeamWorkloadData(ctx, req.TeamId)
if err != nil {
return nil, status.Errorf(codes.Internal, "failed to get workload data: %v", err)
}
// Get task complexity analysis
tasks, err := s.getUnassignedTasks(ctx, req.ProjectId)
if err != nil {
return nil, status.Errorf(codes.Internal, "failed to get tasks: %v", err)
}
// AI optimization
recommendations, err := s.aiService.OptimizeAssignments(ctx, &AIOptimizationRequest{
Tasks: tasks,
TeamMembers: workloadData.Members,
Constraints: req.Constraints,
Objectives: req.Objectives,
})
if err != nil {
return nil, status.Errorf(codes.Internal, "AI optimization failed: %v", err)
}
return &pb.AssignmentRecommendations{
Recommendations: recommendations.ToProto(),
ConfidenceScore: recommendations.ConfidenceScore,
ExpectedImprovement: recommendations.ExpectedImprovement,
}, nil
}
type AIService struct {
mlClient *MLClient
modelCache *ModelCache
}
func (ai *AIService) AnalyzeTask(ctx context.Context, task *Task) (*AITaskInsights, error) {
// Feature extraction
features := ai.extractTaskFeatures(task)
// Load trained models
estimationModel, err := ai.modelCache.GetModel("task_estimation")
if err != nil {
return nil, err
}
priorityModel, err := ai.modelCache.GetModel("priority_prediction")
if err != nil {
return nil, err
}
// Run predictions
estimatedHours := estimationModel.Predict(features.EstimationFeatures)
suggestedPriority := priorityModel.Predict(features.PriorityFeatures)
// Optimal assignee prediction
optimalAssignee, err := ai.predictOptimalAssignee(ctx, task, features)
if err != nil {
log.Printf("Assignee prediction failed: %v", err)
}
return &AITaskInsights{
EstimatedHours: estimatedHours,
SuggestedPriority: suggestedPriority,
OptimalAssignee: optimalAssignee,
ConfidenceScore: ai.calculateConfidence(features),
}, nil
}
func (ai *AIService) OptimizeAssignments(ctx context.Context, req *AIOptimizationRequest) (*OptimizationResult, error) {
// Multi-objective optimization using genetic algorithm
optimizer := &GeneticOptimizer{
PopulationSize: 100,
Generations: 50,
MutationRate: 0.1,
CrossoverRate: 0.8,
}
// Define fitness function
fitnessFunc := func(assignment []int) float64 {
return ai.calculateAssignmentFitness(assignment, req)
}
// Run optimization
bestAssignment, score := optimizer.Optimize(fitnessFunc, len(req.Tasks), len(req.TeamMembers))
// Convert to recommendations
recommendations := make([]*AssignmentRecommendation, len(req.Tasks))
for i, taskIdx := range bestAssignment {
recommendations[i] = &AssignmentRecommendation{
TaskID: req.Tasks[taskIdx].ID,
RecommendedAssignee: req.TeamMembers[i].ID,
ConfidenceScore: score,
Reasoning: ai.generateReasoningText(req.Tasks[taskIdx], req.TeamMembers[i]),
}
}
return &OptimizationResult{
Recommendations: recommendations,
ConfidenceScore: score,
ExpectedImprovement: ai.calculateExpectedImprovement(req, recommendations),
}, nil
}
package websocket
import (
"encoding/json"
"log"
"net/http"
"sync"
"github.com/gorilla/websocket"
)
type Hub struct {
clients map[*Client]bool
broadcast chan []byte
register chan *Client
unregister chan *Client
rooms map[string]map[*Client]bool
roomsMutex sync.RWMutex
}
type Client struct {
hub *Hub
conn *websocket.Conn
send chan []byte
userID string
projectID string
}
type Message struct {
Type string `json:"type"`
ProjectID string `json:"project_id"`
UserID string `json:"user_id"`
Data interface{} `json:"data"`
Timestamp int64 `json:"timestamp"`
}
func NewHub() *Hub {
return &Hub{
clients: make(map[*Client]bool),
broadcast: make(chan []byte),
register: make(chan *Client),
unregister: make(chan *Client),
rooms: make(map[string]map[*Client]bool),
}
}
func (h *Hub) Run() {
for {
select {
case client := <-h.register:
h.registerClient(client)
case client := <-h.unregister:
h.unregisterClient(client)
case message := <-h.broadcast:
h.broadcastMessage(message)
}
}
}
func (h *Hub) registerClient(client *Client) {
h.clients[client] = true
// Add to project room
h.roomsMutex.Lock()
if h.rooms[client.projectID] == nil {
h.rooms[client.projectID] = make(map[*Client]bool)
}
h.rooms[client.projectID][client] = true
h.roomsMutex.Unlock()
// Send welcome message
welcomeMsg := &Message{
Type: "user_joined",
ProjectID: client.projectID,
UserID: client.userID,
Data: map[string]interface{}{
"message": "User joined the project",
},
Timestamp: time.Now().Unix(),
}
h.broadcastToRoom(client.projectID, welcomeMsg)
log.Printf("Client %s joined project %s", client.userID, client.projectID)
}
func (h *Hub) broadcastToRoom(projectID string, message *Message) {
h.roomsMutex.RLock()
room := h.rooms[projectID]
h.roomsMutex.RUnlock()
if room == nil {
return
}
messageBytes, err := json.Marshal(message)
if err != nil {
log.Printf("Error marshaling message: %v", err)
return
}
for client := range room {
select {
case client.send <- messageBytes:
default:
// Client buffer full, remove client
h.unregisterClient(client)
}
}
}
// Handle real-time task updates
func (h *Hub) HandleTaskUpdate(taskUpdate *TaskUpdateEvent) {
message := &Message{
Type: "task_updated",
ProjectID: taskUpdate.ProjectID,
UserID: taskUpdate.UpdatedBy,
Data: map[string]interface{}{
"task_id": taskUpdate.TaskID,
"field": taskUpdate.Field,
"old_value": taskUpdate.OldValue,
"new_value": taskUpdate.NewValue,
"timestamp": taskUpdate.Timestamp,
},
Timestamp: time.Now().Unix(),
}
h.broadcastToRoom(taskUpdate.ProjectID, message)
}
// Handle collaborative editing
func (h *Hub) HandleCollaborativeEdit(edit *CollaborativeEditEvent) {
message := &Message{
Type: "collaborative_edit",
ProjectID: edit.ProjectID,
UserID: edit.UserID,
Data: map[string]interface{}{
"task_id": edit.TaskID,
"field": edit.Field,
"operation": edit.Operation,
"position": edit.Position,
"content": edit.Content,
"version": edit.Version,
},
Timestamp: time.Now().Unix(),
}
// Apply operational transformation for conflict resolution
resolvedEdit := h.applyOperationalTransform(edit)
if resolvedEdit != nil {
message.Data = resolvedEdit
}
h.broadcastToRoom(edit.ProjectID, message)
}
func (h *Hub) applyOperationalTransform(edit *CollaborativeEditEvent) map[string]interface{} {
// Implement operational transformation algorithm
// for real-time collaborative editing without conflicts
// This is a simplified version - production would need
// more sophisticated conflict resolution
return map[string]interface{}{
"task_id": edit.TaskID,
"field": edit.Field,
"operation": edit.Operation,
"position": edit.Position,
"content": edit.Content,
"version": edit.Version + 1, // Increment version
"resolved": true,
}
}
Machine learning capabilities driving productivity optimization
Measurable improvements in team efficiency and collaboration
Interested in developing scalable Go microservices with AI integration? Let's discuss how these patterns can revolutionize your enterprise productivity infrastructure.