The caching pattern avoids expensive re-acquisition of resources by not releasing them immediately

after use. The resources retain their identity, are kept in some fast-access storage, and are

re-used to avoid having to acquire them again.

Real world example

In plain words

Wikipedia says:

**Programmatic Example**

Let's first look at the data layer of our application. The interesting classes are `UserAccount`

which is a simple Java object containing the user account details, and `DbManager` which handles

reading and writing of these objects to/from MongoDB database.

public class UserAccount {

private String userId;

private String userName;

private String additionalInfo;

}

public final class DbManager {

private static MongoClient mongoClient;

private static MongoDatabase db;

private DbManager() { /*...*/ }

public static void createVirtualDb() { /*...*/ }

public static void connect() throws ParseException { /*...*/ }

public static UserAccount readFromDb(String userId) { /*...*/ }

public static void writeToDb(UserAccount userAccount) { /*...*/ }

public static void updateDb(UserAccount userAccount) { /*...*/ }

public static void upsertDb(UserAccount userAccount) { /*...*/ }

}

In the example, we are demonstrating various different caching policies

* Write-through writes data to the cache and DB in a single transaction

* Write-around writes data immediately into the DB instead of the cache

* Write-behind writes data into the cache initially whilst the data is only written into the DB

when the cache is full

* Cache-aside pushes the responsibility of keeping the data synchronized in both data sources to

the application itself

* Read-through strategy is also included in the aforementioned strategies and it returns data from

the cache to the caller if it exists, otherwise queries from DB and stores it into the cache for

future use.

The cache implementation in `LruCache` is a hash table accompanied by a doubly

linked-list. The linked-list helps in capturing and maintaining the LRU data in the cache. When

data is queried (from the cache), added (to the cache), or updated, the data is moved to the front

of the list to depict itself as the most-recently-used data. The LRU data is always at the end of

the list.

public class LruCache {

static class Node {

String userId;

UserAccount userAccount;

Node previous;

Node next;

public Node(String userId, UserAccount userAccount) {

this.userId = userId;

this.userAccount = userAccount;

}

}

/* ... omitted details ... */

public LruCache(int capacity) {

this.capacity = capacity;

}

public UserAccount get(String userId) {

if (cache.containsKey(userId)) {

var node = cache.get(userId);

remove(node);

setHead(node);

return node.userAccount;

}

return null;

}

public void set(String userId, UserAccount userAccount) {

if (cache.containsKey(userId)) {

var old = cache.get(userId);

old.userAccount = userAccount;

remove(old);

setHead(old);

} else {

var newNode = new Node(userId, userAccount);

if (cache.size() >= capacity) {

LOGGER.info("# Cache is FULL! Removing {} from cache...", end.userId);

cache.remove(end.userId); // remove LRU data from cache.

remove(end);

setHead(newNode);

} else {

setHead(newNode);

}

cache.put(userId, newNode);

}

}

public boolean contains(String userId) {

return cache.containsKey(userId);

}

public void remove(Node node) { /* ... */ }

public void setHead(Node node) { /* ... */ }

public void invalidate(String userId) { /* ... */ }

public boolean isFull() { /* ... */ }

public UserAccount getLruData() { /* ... */ }

public void clear() { /* ... */ }

public List<UserAccount> getCacheDataInListForm() { /* ... */ }

public void setCapacity(int newCapacity) { /* ... */ }

}

The next layer we are going to look at is `CacheStore` which implements the different caching

strategies.

public class CacheStore {

private static LruCache cache;

/* ... details omitted ... */

public static UserAccount readThrough(String userId) {

if (cache.contains(userId)) {

LOGGER.info("# Cache Hit!");

return cache.get(userId);

}

LOGGER.info("# Cache Miss!");

UserAccount userAccount = DbManager.readFromDb(userId);

cache.set(userId, userAccount);

return userAccount;

}

public static void writeThrough(UserAccount userAccount) {

if (cache.contains(userAccount.getUserId())) {

DbManager.updateDb(userAccount);

} else {

DbManager.writeToDb(userAccount);

}

cache.set(userAccount.getUserId(), userAccount);

}

public static void clearCache() {

if (cache != null) {

cache.clear();

}

}

public static void flushCache() {

LOGGER.info("# flushCache...");

Optional.ofNullable(cache)

.map(LruCache::getCacheDataInListForm)

.orElse(List.of())

.forEach(DbManager::updateDb);

}

/* ... omitted the implementation of other caching strategies ... */

}

`AppManager` helps to bridge the gap in communication between the main class and the application's

back-end. DB connection is initialized through this class. The chosen caching strategy/policy is

also initialized here. Before the cache can be used, the size of the cache has to be set. Depending

on the chosen caching policy, `AppManager` will call the appropriate function in the `CacheStore`

class.

public final class AppManager {

private static CachingPolicy cachingPolicy;

private AppManager() {

}

public static void initDb(boolean useMongoDb) { /* ... */ }

public static void initCachingPolicy(CachingPolicy policy) { /* ... */ }

public static void initCacheCapacity(int capacity) { /* ... */ }

public static UserAccount find(String userId) {

if (cachingPolicy == CachingPolicy.THROUGH || cachingPolicy == CachingPolicy.AROUND) {

return CacheStore.readThrough(userId);

} else if (cachingPolicy == CachingPolicy.BEHIND) {

return CacheStore.readThroughWithWriteBackPolicy(userId);

} else if (cachingPolicy == CachingPolicy.ASIDE) {

return findAside(userId);

}

return null;

}

public static void save(UserAccount userAccount) {

if (cachingPolicy == CachingPolicy.THROUGH) {

CacheStore.writeThrough(userAccount);

} else if (cachingPolicy == CachingPolicy.AROUND) {

CacheStore.writeAround(userAccount);

} else if (cachingPolicy == CachingPolicy.BEHIND) {

CacheStore.writeBehind(userAccount);

} else if (cachingPolicy == CachingPolicy.ASIDE) {

saveAside(userAccount);

}

}

public static String printCacheContent() {

return CacheStore.print();

}

/* ... details omitted ... */

}

Here is what we do in the main class of the application.

public class App {

public static void main(String[] args) {

AppManager.initDb(false);

AppManager.initCacheCapacity(3);

var app = new App();

app.useReadAndWriteThroughStrategy();

app.useReadThroughAndWriteAroundStrategy();

app.useReadThroughAndWriteBehindStrategy();

app.useCacheAsideStategy();

}

public void useReadAndWriteThroughStrategy() {

LOGGER.info("# CachingPolicy.THROUGH");

AppManager.initCachingPolicy(CachingPolicy.THROUGH);

var userAccount1 = new UserAccount("001", "John", "He is a boy.");

AppManager.save(userAccount1);

LOGGER.info(AppManager.printCacheContent());

AppManager.find("001");

AppManager.find("001");

}

public void useReadThroughAndWriteAroundStrategy() { /* ... */ }

public void useReadThroughAndWriteBehindStrategy() { /* ... */ }

public void useCacheAsideStategy() { /* ... */ }

}

Finally, here is some of the console output from the program.

* Repetitious acquisition, initialization, and release of the same resource cause unnecessary

performance overhead.

* [Proxy](https://java-design-patterns.com/patterns/proxy/)

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import lombok.extern.slf4j.Slf4j;

LOGGER.error("Error connecting to MongoDB", e);