Real-time analytics has become essential for businesses seeking to extract immediate insights from streaming data. In this guide, we'll walk through how to build a scalable, high-performance real-time analytics system using Go, with a special focus on AI applications.
By the end of this guide, you'll understand how to:
- Set up efficient data streaming pipelines in Go
- Implement key stream processing patterns
- Connect your system to time-series databases
- Apply memory-efficient algorithms for online learning
- Optimize your system for maximum performance
Prerequisites
Before getting started, make sure you have:
- Go 1.16+ installed on your system
- Basic familiarity with Go programming
- A development environment set up (VS Code, GoLand, or your preferred IDE)
- Docker (optional, for running databases locally)
1. Setting Up Your Go Environment for Real-Time Streaming
1.1 Create Your Project Structure
Start by creating a new Go module:
mkdir go-streaming-analytics
cd go-streaming-analytics
go mod init github.com/yourusername/go-streaming-analytics
1.2 Adding Essential Dependencies
go get -u github.com/influxdata/influxdb-client-go/v2
go get -u github.com/gorilla/websocket
go get -u github.com/prometheus/client_golang/prometheus
1.3 Creating a Basic Stream Processor
Create a file named main.go with this skeleton:
package main
import (
"context"
"log"
"os"
"os/signal"
"syscall"
"time"
)
func main() {
// Setup signal handling for graceful shutdown
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
signalChan := make(chan os.Signal, 1)
signal.Notify(signalChan, syscall.SIGINT, syscall.SIGTERM)
// Start your streaming components here
log.Println("Stream processor started. Press Ctrl+C to exit.")
// Wait for termination signal
<-signalChan
log.Println("Shutdown signal received, initiating graceful shutdown...")
// Add a timeout for shutdown
shutdownCtx, shutdownCancel := context.WithTimeout(context.Background(), 30*time.Second)
defer shutdownCancel()
// Perform cleanup
log.Println("Shutdown complete")
}
2. Implementing Concurrent Data Streams with Go
Go's concurrency model makes it ideal for handling multiple data streams. Let's implement a basic streaming pipeline.
2.1 Defining Your Data Models
Create a file called models.go:
package main
import "time"
// DataPoint represents a single measurement from a data source
type DataPoint struct {
Timestamp time.Time
SourceID string
Value float64
Metadata map[string]string
}
// ProcessedResult represents an analyzed data point
type ProcessedResult struct {
OriginalPoint DataPoint
AnomalyScore float64
Prediction float64
ProcessedAt time.Time
}
2.2 Building a Basic Pipeline
Now let's implement a simple processing pipeline in pipeline.go:
package main
import (
"context"
"log"
"sync"
"time"
)
// Pipeline stage function types
type SourceFunc func(ctx context.Context, out chan<- DataPoint)
type ProcessFunc func(ctx context.Context, in <-chan DataPoint, out chan<- ProcessedResult)
type SinkFunc func(ctx context.Context, in <-chan ProcessedResult)
// Pipeline represents a complete data processing pipeline
type Pipeline struct {
source SourceFunc
processors []ProcessFunc
sink SinkFunc
bufferSize int
concurrency int
}
// NewPipeline creates a new processing pipeline
func NewPipeline(source SourceFunc, sink SinkFunc, bufferSize, concurrency int) *Pipeline {
return &Pipeline{
source: source,
sink: sink,
bufferSize: bufferSize,
concurrency: concurrency,
processors: make([]ProcessFunc, 0),
}
}
// AddProcessor adds a processing stage to the pipeline
func (p *Pipeline) AddProcessor(processor ProcessFunc) {
p.processors = append(p.processors, processor)
}
// Run starts the pipeline and blocks until the context is canceled
func (p *Pipeline) Run(ctx context.Context) error {
// Create channels
sourceChannel := make(chan DataPoint, p.bufferSize)
// Create intermediate channels for each processor
channels := make([]chan ProcessedResult, len(p.processors)+1)
for i := range channels {
channels[i] = make(chan ProcessedResult, p.bufferSize)
}
// WaitGroup to track all goroutines
var wg sync.WaitGroup
// Start source
wg.Add(1)
go func() {
defer wg.Done()
defer close(sourceChannel)
p.source(ctx, sourceChannel)
}()
// Start processors (with fan-out for each stage)
for i, processor := range p.processors {
// Input is either source channel (for first processor) or previous processor's output
var input chan DataPoint
if i == 0 {
input = sourceChannel
} else {
// We'd need to convert ProcessedResult back to DataPoint in a real implementation
// This is simplified for the example
}
output := channels[i]
// Fan out each processor to multiple goroutines
for j := 0; j < p.concurrency; j++ {
wg.Add(1)
go func(proc ProcessFunc, in <-chan DataPoint, out chan<- ProcessedResult) {
defer wg.Done()
proc(ctx, in, out)
}(processor, input, output)
}
}
// Start sink
wg.Add(1)
go func() {
defer wg.Done()
p.sink(ctx, channels[len(channels)-1])
}()
// Wait for completion or cancellation
go func() {
wg.Wait()
// Close the final channel when all processing is done
close(channels[len(channels)-1])
}()
// Wait for context cancellation
<-ctx.Done()
log.Println("Pipeline shutdown initiated")
return ctx.Err()
}
2.3 Creating a Sample Data Source
Let's implement a simple data generator in source.go:
package main
import (
"context"
"math/rand"
"time"
)
// RandomDataSource generates random data points at a specified interval
func RandomDataSource(interval time.Duration, sourceID string) SourceFunc {
return func(ctx context.Context, out chan<- DataPoint) {
ticker := time.NewTicker(interval)
defer ticker.Stop()
for {
select {
case <-ctx.Done():
return
case t := <-ticker.C:
out <- DataPoint{
Timestamp: t,
SourceID: sourceID,
Value: rand.Float64() * 100.0, // Random value between 0-100
Metadata: map[string]string{
"type": "random",
"unit": "percentage",
},
}
}
}
}
}
2.4 Implementing a Processor
Now, let's create a simple anomaly detector in processor.go:
package main
import (
"context"
"math"
"time"
)
// SimpleAnomalyDetector detects values that deviate significantly from recent history
func SimpleAnomalyDetector(windowSize int, threshold float64) ProcessFunc {
return func(ctx context.Context, in <-chan DataPoint, out chan<- ProcessedResult) {
// Store recent values in a circular buffer
buffer := make([]float64, windowSize)
bufferIndex := 0
bufferFilled := 0
for {
select {
case <-ctx.Done():
return
case dataPoint, ok := <-in:
if !ok {
// Channel closed
return
}
// Add value to buffer
buffer[bufferIndex] = dataPoint.Value
bufferIndex = (bufferIndex + 1) % windowSize
if bufferFilled < windowSize {
bufferFilled++
}
// Calculate average and standard deviation
var sum, sumSquared float64
for i := 0; i < bufferFilled; i++ {
sum += buffer[i]
sumSquared += buffer[i] * buffer[i]
}
mean := sum / float64(bufferFilled)
variance := (sumSquared / float64(bufferFilled)) - (mean * mean)
stdDev := math.Sqrt(variance)
// Calculate z-score (how many standard deviations from mean)
zScore := 0.0
if stdDev > 0 {
zScore = math.Abs(dataPoint.Value - mean) / stdDev
}
// Output result
out <- ProcessedResult{
OriginalPoint: dataPoint,
AnomalyScore: zScore,
Prediction: mean, // Simple prediction is just the mean
ProcessedAt: time.Now(),
}
}
}
}
}
2.5 Creating a Data Sink
Finally, let's implement a simple sink that logs the results in sink.go:
package main
import (
"context"
"log"
)
// LoggingSink logs processed results to stdout
func LoggingSink() SinkFunc {
return func(ctx context.Context, in <-chan ProcessedResult) {
for {
select {
case <-ctx.Done():
return
case result, ok := <-in:
if !ok {
// Channel closed
return
}
// Log results
if result.AnomalyScore > 2.0 {
log.Printf("ANOMALY DETECTED: Source: %s, Value: %.2f, Score: %.2f",
result.OriginalPoint.SourceID, result.OriginalPoint.Value, result.AnomalyScore)
} else {
log.Printf("Data: Source: %s, Value: %.2f, Prediction: %.2f",
result.OriginalPoint.SourceID, result.OriginalPoint.Value, result.Prediction)
}
}
}
}
}
2.6 Putting It All Together
Now, update main.go to use our pipeline components:
package main
import (
"context"
"log"
"os"
"os/signal"
"syscall"
"time"
)
func main() {
// Setup signal handling for graceful shutdown
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
signalChan := make(chan os.Signal, 1)
signal.Notify(signalChan, syscall.SIGINT, syscall.SIGTERM)
// Create the pipeline
pipeline := NewPipeline(
RandomDataSource(100*time.Millisecond, "sensor-1"),
LoggingSink(),
100, // Buffer size
3, // Concurrency level
)
// Add processors
pipeline.AddProcessor(SimpleAnomalyDetector(50, 2.0))
// Run the pipeline in a goroutine
go func() {
if err := pipeline.Run(ctx); err != nil && err != context.Canceled {
log.Printf("Pipeline error: %v", err)
}
}()
log.Println("Stream processor started. Press Ctrl+C to exit.")
// Wait for termination signal
<-signalChan
log.Println("Shutdown signal received, initiating graceful shutdown...")
// Cancel the context to signal all components to shut down
cancel()
// Give components time to shut down gracefully
time.Sleep(2 * time.Second)
log.Println("Shutdown complete")
}
3. Implementing Key Stream Processing Patterns
3.1 Window Operations for Time-Based Analysis
Create a file called window.go to implement time-based windowing:
package main
import (
"context"
"time"
)
// Window represents a time window of data points
type Window struct {
StartTime time.Time
EndTime time.Time
Points []DataPoint
}
// WindowedProcessor buffers data into time windows and processes them as a batch
func WindowedProcessor(windowDuration time.Duration, processFn func(Window) []ProcessedResult) ProcessFunc {
return func(ctx context.Context, in <-chan DataPoint, out chan<- ProcessedResult) {
var currentWindow Window
currentWindow.StartTime = time.Now()
currentWindow.EndTime = currentWindow.StartTime.Add(windowDuration)
ticker := time.NewTicker(windowDuration)
defer ticker.Stop()
for {
select {
case <-ctx.Done():
// Process any remaining points before shutting down
results := processFn(currentWindow)
for _, result := range results {
out <- result
}
return
case <-ticker.C:
// Window time is up, process points and create new window
results := processFn(currentWindow)
for _, result := range results {
out <- result
}
now := time.Now()
currentWindow = Window{
StartTime: now,
EndTime: now.Add(windowDuration),
Points: make([]DataPoint, 0),
}
case point, ok := <-in:
if !ok {
return
}
// Add point to current window
currentWindow.Points = append(currentWindow.Points, point)
}
}
}
}
3.2 Implementing the Fan-Out/Fan-In Pattern
Create a file called fanout.go:
package main
import (
"context"
"sync"
)
// FanOutFanIn distributes work across multiple workers and combines results
func FanOutFanIn(workerFunc func(DataPoint) ProcessedResult, workerCount int) ProcessFunc {
return func(ctx context.Context, in <-chan DataPoint, out chan<- ProcessedResult) {
// Create a WaitGroup to track workers
var wg sync.WaitGroup
// Create a channel for collecting results from workers
results := make(chan ProcessedResult, workerCount)
// Start the result collector
go func() {
for result := range results {
select {
case <-ctx.Done():
return
case out <- result:
// Result sent downstream
}
}
}()
// Start workers
for i := 0; i < workerCount; i++ {
wg.Add(1)
go func(workerID int) {
defer wg.Done()
for {
select {
case <-ctx.Done():
return
case data, ok := <-in:
if !ok {
return
}
// Process the data and send result
result := workerFunc(data)
select {
case <-ctx.Done():
return
case results <- result:
// Result sent to collector
}
}
}
}(i)
}
// Wait for all workers to complete and close the results channel
go func() {
wg.Wait()
close(results)
}()
}
}
4. Integrating with Time-Series Databases
4.1 Setting Up InfluxDB
First, let's create a Docker Compose file for InfluxDB:
# docker-compose.yml
version: '3'
services:
influxdb:
image: influxdb:2.0
ports:
- "8086:8086"
volumes:
- influxdb-data:/var/lib/influxdb2
environment:
- DOCKER_INFLUXDB_INIT_MODE=setup
- DOCKER_INFLUXDB_INIT_USERNAME=admin
- DOCKER_INFLUXDB_INIT_PASSWORD=mypassword123
- DOCKER_INFLUXDB_INIT_ORG=myorg
- DOCKER_INFLUXDB_INIT_BUCKET=metrics
- DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=myadmintoken
volumes:
influxdb-data:
Start InfluxDB with:
docker-compose up -d
4.2 Creating an InfluxDB Sink
Now, let's create a sink that writes to InfluxDB in influxdb_sink.go:
package main
import (
"context"
"log"
"time"
influxdb2 "github.com/influxdata/influxdb-client-go/v2"
)
// InfluxDBSink writes processed results to InfluxDB
func InfluxDBSink(serverURL, token, org, bucket string, batchSize int) SinkFunc {
return func(ctx context.Context, in <-chan ProcessedResult) {
// Create InfluxDB client
client := influxdb2.NewClient(serverURL, token)
defer client.Close()
// Get non-blocking write API
writeAPI := client.WriteAPI(org, bucket)
// Listen for write errors
errorsCh := writeAPI.Errors()
go func() {
for err := range errorsCh {
log.Printf("InfluxDB write error: %s", err.Error())
}
}()
// Process incoming results
batch := make([]ProcessedResult, 0, batchSize)
ticker := time.NewTicker(1 * time.Second)
defer ticker.Stop()
flushBatch := func() {
for _, result := range batch {
// Create InfluxDB point
point := influxdb2.NewPoint(
"sensor_data",
map[string]string{
"source_id": result.OriginalPoint.SourceID,
},
map[string]interface{}{
"value": result.OriginalPoint.Value,
"anomaly_score": result.AnomalyScore,
"prediction": result.Prediction,
},
result.OriginalPoint.Timestamp,
)
// Write asynchronously
writeAPI.WritePoint(point)
}
// Clear the batch
batch = batch[:0]
}
for {
select {
case <-ctx.Done():
// Flush any remaining points
if len(batch) > 0 {
flushBatch()
}
// Ensure pending writes are flushed
writeAPI.Flush()
return
case result, ok := <-in:
if !ok {
// Channel closed
if len(batch) > 0 {
flushBatch()
}
writeAPI.Flush()
return
}
// Add to batch
batch = append(batch, result)
// Flush if batch is full
if len(batch) >= batchSize {
flushBatch()
}
case <-ticker.C:
// Time-based flush
if len(batch) > 0 {
flushBatch()
}
}
}
}
}
4.3 Querying Data from InfluxDB
Create a file called query.go with functions to retrieve analytics data:
package main
import (
"context"
"fmt"
"time"
influxdb2 "github.com/influxdata/influxdb-client-go/v2"
)
// TimeSeriesData represents a time series of values
type TimeSeriesData struct {
Times []time.Time
Values []float64
}
// QueryRecentData retrieves recent data from InfluxDB
func QueryRecentData(serverURL, token, org, bucket, measurement string, duration time.Duration) (*TimeSeriesData, error) {
// Create client
client := influxdb2.NewClient(serverURL, token)
defer client.Close()
// Get query client
queryAPI := client.QueryAPI(org)
// Create Flux query
query := fmt.Sprintf(`
from(bucket: "%s")
|> range(start: -%s)
|> filter(fn: (r) => r._measurement == "%s")
|> filter(fn: (r) => r._field == "value")
`, bucket, duration.String(), measurement)
// Run query
result, err := queryAPI.Query(context.Background(), query)
if err != nil {
return nil, fmt.Errorf("query error: %w", err)
}
defer result.Close()
// Process results
data := &TimeSeriesData{
Times: make([]time.Time, 0),
Values: make([]float64, 0),
}
for result.Next() {
data.Times = append(data.Times, result.Record().Time())
value := result.Record().Value()
if v, ok := value.(float64); ok {
data.Values = append(data.Values, v)
}
}
// Check for query errors
if result.Err() != nil {
return nil, fmt.Errorf("result error: %w", result.Err())
}
return data, nil
}
5. Implementing Memory-Efficient Algorithms for Online Learning
5.1 Creating an Online Learning Model
Create a file called online_learning.go for incremental model updates:
package main
import (
"math"
)
// OnlineStats keeps statistics that update incrementally
type OnlineStats struct {
Count int64
Mean float64
M2 float64 // Sum of squared differences from the mean
Min float64
Max float64
}
// NewOnlineStats creates a new online statistics tracker
func NewOnlineStats() *OnlineStats {
return &OnlineStats{
Min: math.Inf(1),
Max: math.Inf(-1),
}
}
// Update adds a value and updates statistics
func (s *OnlineStats) Update(value float64) {
// Update count
s.Count++
// Update min/max
if value < s.Min {
s.Min = value
}
if value > s.Max {
s.Max = value
}
// For the first value, just set the mean
if s.Count == 1 {
s.Mean = value
return
}
// Update mean and variance using Welford's algorithm
delta := value - s.Mean
s.Mean += delta / float64(s.Count)
delta2 := value - s.Mean
s.M2 += delta * delta2
}
// Variance returns the current variance
func (s *OnlineStats) Variance() float64 {
if s.Count < 2 {
return 0
}
return s.M2 / float64(s.Count)
}
// StdDev returns the current standard deviation
func (s *OnlineStats) StdDev() float64 {
return math.Sqrt(s.Variance())
}
// ZScore calculates how many standard deviations a value is from the mean
func (s *OnlineStats) ZScore(value float64) float64 {
stdDev := s.StdDev()
if stdDev == 0 {
return 0
}
return (value - s.Mean) / stdDev
}
// Normalize returns a value scaled to [0,1] range based on observed min/max
func (s *OnlineStats) Normalize(value float64) float64 {
if s.Max == s.Min {
return 0.5
}
return (value - s.Min) / (s.Max - s.Min)
}
5.2 Creating an Online Learning Processor
Now, let's use our online learning model in a processor:
package main
import (
"context"
"math"
"sync"
)
// OnlineLearningProcessor uses online statistics to detect anomalies
func OnlineLearningProcessor() ProcessFunc {
return func(ctx context.Context, in <-chan DataPoint, out chan<- ProcessedResult) {
// Map to track stats for each source
statsBySource := make(map[string]*OnlineStats)
var mu sync.RWMutex
for {
select {
case <-ctx.Done():
return
case point, ok := <-in:
if !ok {
return
}
// Get or create stats tracker for this source
mu.RLock()
stats, exists := statsBySource[point.SourceID]
mu.RUnlock()
if !exists {
stats = NewOnlineStats()
mu.Lock()
statsBySource[point.SourceID] = stats
mu.Unlock()
}
// Calculate anomaly score before updating model
zScore := stats.ZScore(point.Value)
anomalyScore := math.Abs(zScore)
// Update the model with the new data point
stats.Update(point.Value)
// Create result
result := ProcessedResult{
OriginalPoint: point,
AnomalyScore: anomalyScore,
Prediction: stats.Mean,
ProcessedAt: point.Timestamp,
}
out <- result
}
}
}
}
6. Optimizing Performance for Real-Time Analytics
6.1 Implementing a Memory Pool
Create a file called memory_pool.go:
package main
import "sync"
// DataBuffer represents a reusable buffer
type DataBuffer struct {
Data []byte
}
// BufferPool manages a pool of reusable buffers
type BufferPool struct {
pool sync.Pool
bufferSize int
}
// NewBufferPool creates a new buffer pool
func NewBufferPool(bufferSize int) *BufferPool {
return &BufferPool{
pool: sync.Pool{
New: func() interface{} {
return &DataBuffer{
Data: make([]byte, bufferSize),
}
},
},
bufferSize: bufferSize,
}
}
// Get retrieves a buffer from the pool
func (p *BufferPool) Get() *DataBuffer {
return p.pool.Get().(*DataBuffer)
}
// Put returns a buffer to the pool
func (p *BufferPool) Put(buffer *DataBuffer) {
// Clear for security (optional)
for i := range buffer.Data {
buffer.Data[i] = 0
}
p.pool.Put(buffer)
}
6.2 Implementing a Ring Buffer
Create a file called ring_buffer.go:
package main
import (
"errors"
"sync"
)
var (
ErrBufferFull = errors.New("ring buffer: full")
ErrBufferEmpty = errors.New("ring buffer: empty")
)
// RingBuffer is a thread-safe circular buffer
type RingBuffer struct {
buffer []interface{}
size int
mu sync.Mutex
read int // Read position
write int // Write position
count int // Number of elements in buffer
}
// NewRingBuffer creates a new ring buffer with the given size
func NewRingBuffer(size int) *RingBuffer {
return &RingBuffer{
buffer: make([]interface{}, size),
size: size,
}
}
// Push adds an item to the buffer
func (rb *RingBuffer) Push(item interface{}) error {
rb.mu.Lock()
defer rb.mu.Unlock()
if rb.count == rb.size {
return ErrBufferFull
}
rb.buffer[rb.write] = item
rb.write = (rb.write + 1) % rb.size
rb.count++
return nil
}
// Pop removes and returns an item from the buffer
func (rb *RingBuffer) Pop() (interface{}, error) {
rb.mu.Lock()
defer rb.mu.Unlock()
if rb.count == 0 {
return nil, ErrBufferEmpty
}
item := rb.buffer[rb.read]
rb.buffer[rb.read] = nil // Help GC
rb.read = (rb.read + 1) % rb.size
rb.count--
return item, nil
}
// Len returns the current number of items in the buffer
func (rb *RingBuffer) Len() int {
rb.mu.Lock()
defer rb.mu.Unlock()
return rb.count
}
// Cap returns the capacity of the buffer
func (rb *RingBuffer) Cap() int {
return rb.size
}
6.3 Setting Up Monitoring
Create a file called monitoring.go:
package main
import (
"net/http"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promhttp"
)
// Metrics holds Prometheus metrics for the application
type Metrics struct {
ProcessedCounter *prometheus.CounterVec
AnomalyCounter *prometheus.CounterVec
ProcessingDuration *prometheus.HistogramVec
QueueSize *prometheus.GaugeVec
}
// NewMetrics creates and registers metrics
func NewMetrics(registry *prometheus.Registry) *Metrics {
m := &Metrics{
ProcessedCounter: prometheus.NewCounterVec(
prometheus.CounterOpts{
Name: "stream_processed_total",
Help: "Total number of processed data points",
},
[]string{"source_id"},
),
AnomalyCounter: prometheus.NewCounterVec(
prometheus.CounterOpts{
Name: "stream_anomalies_total",
Help: "Total number of detected anomalies",
},
[]string{"source_id"},
),
ProcessingDuration: prometheus.NewHistogramVec(
prometheus.HistogramOpts{
Name: "stream_processing_duration_seconds",
Help: "Duration of processing operations",
Buckets: prometheus.ExponentialBuckets(0.001, 2, 10), // From 1ms to ~1s
},
[]string{"operation"},
),
QueueSize: prometheus.NewGaugeVec(
prometheus.GaugeOpts{
Name: "stream_queue_size",
Help: "Current number of items in processing queues",
},
[]string{"queue"},
),
}
registry.MustRegister(
m.ProcessedCounter,
m.AnomalyCounter,
m.ProcessingDuration,
m.QueueSize,
)
return m
}
// StartMetricsServer starts an HTTP server for Prometheus metrics
func StartMetricsServer(addr string, registry *prometheus.Registry) {
http.Handle("/metrics", promhttp.HandlerFor(registry, promhttp.HandlerOpts{}))
go func() {
http.ListenAndServe(addr, nil)
}()
}
7. Deploying Your System
7.1 Building the Application
Create a Dockerfile:
FROM golang:1.18-alpine AS builder
WORKDIR /app
# Copy go mod files
COPY go.mod go.sum ./
RUN go mod download
# Copy source code
COPY *.go ./
# Build the application
RUN CGO_ENABLED=0 GOOS=linux go build -o /go-streaming-analytics
# Create final minimal image
FROM alpine:latest
WORKDIR /app
COPY --from=builder /go-streaming-analytics /app/
CMD ["/app/go-streaming-analytics"]
Build and run with Docker:
docker build -t go-streaming-analytics .
docker run -p 8080:8080 -p 9090:9090 go-streaming-analytics
7.2 Configuration Management
Create a file called config.go:
package main
import (
"encoding/json"
"os"
"time"
)
// Config holds application configuration
type Config struct {
ServerPort string `json:"server_port"`
MetricsPort string `json:"metrics_port"`
BufferSize int `json:"buffer_size"`
ConcurrencyLevel int `json:"concurrency_level"`
WindowDuration time.Duration `json:"window_duration"`
InfluxDB struct {
URL string `json:"url"`
Token string `json:"token"`
Org string `json:"org"`
Bucket string `json:"bucket"`
} `json:"influxdb"`
}
// LoadConfig loads configuration from a JSON file
func LoadConfig(path string) (*Config, error) {
file, err := os.Open(path)
if err != nil {
return nil, err
}
defer file.Close()
config := &Config{}
decoder := json.NewDecoder(file)
if err := decoder.Decode(config); err != nil {
return nil, err
}
return config, nil
}
// DefaultConfig returns a default configuration
func DefaultConfig() *Config {
config := &Config{
ServerPort: "8080",
MetricsPort: "9090",
BufferSize: 1000,
ConcurrencyLevel: 4,
WindowDuration: 10 * time.Second,
}
config.InfluxDB.URL = "http://localhost:8086"
config.InfluxDB.Token = "myadmintoken"
config.InfluxDB.Org = "myorg"
config.InfluxDB.Bucket = "metrics"
return config
}
Conclusion and Next Steps
You've now built a complete real-time analytics pipeline in Go that can:
- Process streaming data concurrently
- Implement various stream processing patterns
- Connect to time-series databases
- Apply online learning algorithms
- Optimize for performance
To extend this system, consider:
- Adding more sophisticated ML models
- Implementing stream joins for correlating multiple data sources
- Adding a real-time visualization layer
- Implementing fault tolerance with save points
- Scaling out to a distributed architecture
How Berrijam AI Leverages These Principles
While Berrijam AI uses a different technology stack, the principles demonstrated in this guide are similar to those that power their real-time analytics platform. Berrijam's system incorporates:
- Efficient stream processing for immediate insights
- Memory-optimized algorithms for online learning and prediction
- Seamless integration with time-series data storage
- Highly concurrent processing architecture
These capabilities allow Berrijam AI to provide instantaneous insights from streaming data, enabling businesses to make data-driven decisions based on the most current information available.
To learn more about how Berrijam AI can help your organization implement real-time analytics solutions without the complexity of building them yourself, visit Berrijam or contact their solutions team for a personalized demonstration.
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