In Go, in-memory cache is a common solution to improve application performance.
A good cache library can provide efficient storage mechanisms, support high concurrent access and ensure thread safety. There are some very efficient embedded cache libraries in the Go language, among which groupcache and bigcache are two very popular and high-performance libraries.
1. groupcache: high-performance cache library
groupcache is a high-performance cache library developed by Google. It is designed for data access in cache services. It has good scalability and has built-in some efficient concurrency control and cache failure strategies. groupcache is used in many large-scale distributed systems.
Install groupcache
go get /golang/groupcache
Basic usage examples
Here is a simple example of using groupcache to implement local caching. groupcache stores data in groups and can load data through getter functions.
package main
import (
"fmt"
"log"
"/golang/groupcache"
)
// Function to obtain data, simulate getting data from a database or remote servicefunc loadData(ctx , key string, dest ) error {
data := "value_for_" + key
(data)
return nil
}
func main() {
// Create a groupcache cache pool // The size parameter sets the cache size. Groupcache will automatically manage the cache size according to this parameter. cache := ("exampleCache", 64<<20, (loadData))
// Create a context and get the cached data var data string
err := (nil, "some_key", (&data))
if err != nil {
(err)
}
// Output cached data (data)
}
Key points:
groupcache uses group to manage a set of cached data, and access to each cache is loaded dynamically through the getter function.
Loading cached data is achieved through getters, which can load data from external data sources such as databases or APIs.
Supports concurrency security, multiple requests can concurrently access the cache and load data from the source.
2. bigcache: efficient memory cache library
bigcache is a high-performance cache library for Go. Its design focuses on optimizing concurrent access performance and is suitable for data caching in high concurrency scenarios. Compared with groupcache, bigcache focuses more on memory optimization and concurrency security, and supports caching of large data volumes.
Install bigcache
go get /allegro/bigcache/v3
Basic usage examples
bigcache is suitable for scenarios where large amounts of short life cycle data need to be stored, such as session caching, API caching, etc. in web applications.
package main
import (
"fmt"
"log"
"/allegro/bigcache/v3"
"time"
)
func main() {
// Create a bigcache instance, set the maximum cache size and expiration time cache, err := ((10 * ))
if err != nil {
(err)
}
//Save the data into the cache, the key is "user_123", and the value is "John Doe" ("user_123", []byte("John Doe"))
// Get data from cache entry, err := ("user_123")
if err != nil {
(err)
}
// Output cached data ("Cache entry:", string(entry))
}
Key points:
bigcache can set the expiration time of the cache and support automatic cleaning of expired caches.
Supports concurrency security, multiple Goroutines can be used to concurrent read and write caches.
The memory usage is optimized, especially suitable for storing large-scale cached data.
3. golang-lru: the easiest LRU cache
LRU (Least Recently Used, least recently used) cache is a cache phasing strategy based on access time. The golang-lru library can be used to implement memory-based LRU caching. It is suitable for caching when the data access volume is large. After exceeding the cache size, the system will automatically eliminate the least used data.
Install golang-lru
go get /golang/groupcache/lru
Basic usage examples
package main
import (
"fmt"
"/golang/groupcache/lru"
)
func main() {
// Create an LRU cache with capacity 3 cache := (3)
// Set cache ("a", 1)
("b", 2)
("c", 3)
// Print cached content ("Cache after adding 3 elements:", cache)
// Add new elements, exceeding cache capacity, the oldest elements will be removed ("d", 4)
("Cache after adding 4th element (eviction occurs):", cache)
// Check if there is an element if val, ok := ("b"); ok {
("Found 'b':", val)
} else {
("'b' not found")
}
}
Key points:
golang-lru manages caches using LRU policies.
When the amount of data exceeds the cache, the least used data will be automatically removed.
Suitable for scenarios where a certain amount of hot data is required to cache.
4. Cache Selection Guide
Using groupcache: groupcache is a good choice when you need an efficient cache and want the cache to be distributed and supports sharing data between multiple instances.
- Supports cache sharding and automatically manages cache distribution and access.
- Suitable for scenarios where high concurrent reading and infrequent updates.
Use bigcache: If your application has a large number of concurrent read and write requirements and the amount of cached data is large, bigcache is a more suitable choice.
- High concurrency, low latency.
- Suitable for caching of large amounts of short life cycle data, it can effectively avoid GC problems.
Use golang-lru: If you only need a simple LRU cache to manage data whose cache size does not exceed a certain threshold, and do not need to reload data from external sources when cache is lost, golang-lru is a simple and efficient choice.
Suitable for situations where cache storage is restricted and there is a need for phase-out.
5. Summary
In Go, there are many options for high-performance concurrent and secure embedded cache libraries. It is very important to choose a cache library that suits your business scenario:
groupcache: suitable for large-scale, distributed caching scenarios, especially when data from external services or databases is required.
bigcache: suitable for scenarios where large amounts of data are stored and high concurrent access is required, and is suitable for session caching, API caching, etc. in Web services.
golang-lru: Suitable for simple LRU cache management, especially when the amount of cached data is limited.
Each library has its own unique advantages, and choosing according to your needs can make your application perform better in concurrent access and data caching.
The above is a detailed explanation of the usage of embedded cache libraries in Go. For more information about Go embedded cache libraries, please pay attention to my other related articles!