fix(lint): gofmt formatting across examples and packages

Author: vnykmshr

examples/gin-web-server/main.go

@@ -20,8 +20,8 @@ import (
 	"strconv"
 	"time"
 
-	"github.com/gin-gonic/gin"
 	"github.com/1mb-dev/obcache-go/v2/pkg/obcache"
+	"github.com/gin-gonic/gin"
 )
 
 // User represents a user profile

examples/prometheus/main.go

@@ -8,10 +8,10 @@ import (
 	"net/http"
 	"time"
 
+	"github.com/1mb-dev/obcache-go/v2/pkg/obcache"
 	"github.com/prometheus/client_golang/prometheus"
 	"github.com/prometheus/client_golang/prometheus/promauto"
 	"github.com/prometheus/client_golang/prometheus/promhttp"
-	"github.com/1mb-dev/obcache-go/v2/pkg/obcache"
 )
 
 // Prometheus metrics

examples/redis-cache/main.go

@@ -6,8 +6,8 @@ import (
 	"log"
 	"time"
 
-	"github.com/redis/go-redis/v9"
 	"github.com/1mb-dev/obcache-go/v2/pkg/obcache"
+	"github.com/redis/go-redis/v9"
 )
 
 // User represents a simple user struct

internal/eviction/lru.go

@@ -3,8 +3,8 @@ package eviction
 import (
 	"sync"
 
-	lru "github.com/hashicorp/golang-lru/v2"
 	"github.com/1mb-dev/obcache-go/v2/internal/entry"
+	lru "github.com/hashicorp/golang-lru/v2"
 )
 
 // LRUStrategy implements the LRU (Least Recently Used) eviction strategy

pkg/compression/compression_test.go

@@ -359,9 +359,9 @@ func TestDecompressAndDeserialize(t *testing.T) {
 
 func TestRoundTrip(t *testing.T) {
 	type ComplexData struct {
-		ID      int               `json:"id"`
-		Name    string            `json:"name"`
-		Tags    []string          `json:"tags"`
+		ID       int               `json:"id"`
+		Name     string            `json:"name"`
+		Tags     []string          `json:"tags"`
 		Metadata map[string]string `json:"metadata"`
 	}
 

pkg/obcache/doc.go

@@ -202,9 +202,9 @@
 //   - Use hooks judiciously to avoid performance overhead
 //   - Monitor hit rates and adjust cache policies accordingly
 //   - Choose eviction strategy based on access patterns:
-//     * LRU: Good for temporal locality (recently used data)
-//     * LFU: Good for frequency patterns (popular items)
-//     * FIFO: Simple, predictable, good for time-series data
+//   - LRU: Good for temporal locality (recently used data)
+//   - LFU: Good for frequency patterns (popular items)
+//   - FIFO: Simple, predictable, good for time-series data
 //
 // # Thread Safety
 //