Lista de todas las claves de Redis disponibles

Antes de comenzar a escribir código Java para explorar las colecciones, deberíamos tener una idea clara de cómo lo haremos usando la interfaz redis-cli . Supongamos que nuestra instancia de Redis está disponible en 127.0.0.1 en el puerto 6379 , para que exploremos cada tipo de colección con la interfaz de línea de comandos.

3.1. Lista enlazada

Primero, almacenemos nuestro conjunto de datos en una lista vinculada de Redis llamada bolas en el formato de nombre de deportes _ peso de bola con la ayuda del comando rpush :

% redis-cli -h 127.0.0.1 -p 6379 127.0.0.1:6379> RPUSH balls "cricket_160" (integer) 1 127.0.0.1:6379> RPUSH balls "football_450" (integer) 2 127.0.0.1:6379> RPUSH balls "volleyball_270" (integer) 3

Podemos notar que una inserción exitosa en la lista da como resultado la nueva longitud de la lista . Sin embargo, en la mayoría de los casos, no veremos la actividad de inserción de datos. Como resultado, podemos averiguar la longitud de la lista enlazada usando el comando llen :

127.0.0.1:6379> llen balls (integer) 3

Cuando ya conocemos la longitud de la lista, es conveniente usar el comando lrange para recuperar fácilmente el conjunto de datos completo:

127.0.0.1:6379> lrange balls 0 2 1) "cricket_160" 2) "football_450" 3) "volleyball_270"

3.2. Conjunto

A continuación, veamos cómo podemos explorar el conjunto de datos cuando decidimos almacenarlo en un conjunto de Redis. Para hacerlo, primero necesitamos completar nuestro conjunto de datos en un conjunto de Redis llamado bolas usando el comando sadd :

127.0.0.1:6379> sadd balls "cricket_160" "football_450" "volleyball_270" "cricket_160" (integer) 3

¡Ups! Teníamos un valor duplicado en nuestro comando. Pero, dado que estábamos agregando valores a un conjunto, no tenemos que preocuparnos por los duplicados. Por supuesto, podemos ver el número de elementos agregados del valor de respuesta de salida.

Ahora, podemos aprovechar el comando smembers para ver todos los miembros del conjunto :

127.0.0.1:6379> smembers balls 1) "volleyball_270" 2) "cricket_160" 3) "football_450"

3.3. Picadillo

Ahora, usemos la estructura de datos hash de Redis para almacenar nuestro conjunto de datos en una clave hash llamada bolas, de modo que el campo hash es el nombre del deporte y el valor del campo es el peso de la pelota. Podemos hacer esto con la ayuda del comando hmset :

127.0.0.1:6379> hmset balls cricket 160 football 450 volleyball 270 OK

Para ver la información almacenada en nuestro hash, podemos usar el comando hgetall :

127.0.0.1:6379> hgetall balls 1) "cricket" 2) "160" 3) "football" 4) "450" 5) "volleyball" 6) "270"

3.4. Conjunto ordenado

Además de un valor de miembro único, los conjuntos ordenados nos permiten mantener una puntuación junto a ellos. Bueno, en nuestro caso de uso, podemos mantener el nombre del deporte como valor del miembro y el peso de la pelota como puntuación. Usemos el Zadd comando para guardar nuestro conjunto de datos:

127.0.0.1:6379> zadd balls 160 cricket 450 football 270 volleyball (integer) 3

Ahora, primero podemos usar el comando zcard para encontrar la longitud del conjunto ordenado, seguido del comando zrange para explorar el conjunto completo :

127.0.0.1:6379> zcard balls (integer) 3 127.0.0.1:6379> zrange balls 0 2 1) "cricket" 2) "volleyball" 3) "football"

3.5. Instrumentos de cuerda

También podemos ver las cadenas de valores clave habituales como una colección superficial de elementos . Primero, completemos nuestro conjunto de datos usando el comando mset :

127.0.0.1:6379> mset balls:cricket 160 balls:football 450 balls:volleyball 270 OK

Debemos tener en cuenta que agregamos el prefijo "bolas: " para que podamos identificar estas claves del resto de las claves que pueden estar en nuestra base de datos de Redis. Además, esta estrategia de nomenclatura nos permite usar el comando keys para explorar nuestro conjunto de datos con la ayuda de la coincidencia de patrones de prefijo:

127.0.0.1:6379> keys balls* 1) "balls:cricket" 2) "balls:volleyball" 3) "balls:football"

4. Implementación ingenua de Java

Ahora que hemos desarrollado una idea básica de los comandos relevantes de Redis que podemos usar para explorar colecciones de diferentes tipos, es hora de que nos ensuciemos las manos con el código.

4.1. Dependencia de Maven

En esta sección, vamos a estar utilizando los Jedis biblioteca cliente para Redis en nuestra aplicación:

 redis.clients jedis 3.2.0 

4.2. Cliente Redis

The Jedis library comes with the Redis-CLI name-alike methods. However, it's recommended that we create a wrapper Redis client, which will internally invoke Jedis function calls.

Whenever we're working with Jedis library, we must keep in mind that a single Jedis instance is not thread-safe. Therefore, to get a Jedis resource in our application, we can make use of JedisPool, which is a threadsafe pool of network connections.

And, since we don't want multiple instances of Redis clients floating around at any given time during the life cycle of our application, we should create our RedisClient class on the principle of the singleton design pattern.

First, let's create a private constructor for our client that'll internally initialize the JedisPool when an instance of RedisClient class is created:

private static JedisPool jedisPool; private RedisClient(String ip, int port) { try { if (jedisPool == null) { jedisPool = new JedisPool(new URI("//" + ip + ":" + port)); } } catch (URISyntaxException e) { log.error("Malformed server address", e); } }

Next, we need a point of access to our singleton client. So, let's create a static method getInstance() for this purpose:

private static volatile RedisClient instance = null; public static RedisClient getInstance(String ip, final int port) { if (instance == null) { synchronized (RedisClient.class) { if (instance == null) { instance = new RedisClient(ip, port); } } } return instance; }

Finally, let's see how we can create a wrapper method on top of Jedis's lrange method:

public List lrange(final String key, final long start, final long stop) { try (Jedis jedis = jedisPool.getResource()) { return jedis.lrange(key, start, stop); } catch (Exception ex) { log.error("Exception caught in lrange", ex); } return new LinkedList(); }

Of course, we can follow the same strategy to create the rest of the wrapper methods such as lpush, hmset, hgetall, sadd, smembers, keys, zadd, and zrange.

4.3. Analysis

All the Redis commands that we can use to explore a collection in a single go will naturally have an O(n) time complexity in the best case.

We are perhaps a bit liberal, calling this approach as naive. In a real-life production instance of Redis, it's quite common to have thousands or millions of keys in a single collection. Further, Redis's single-threaded nature brings more misery, and our approach could catastrophically block other higher-priority operations.

So, we should make it a point that we're limiting our naive approach to be used only for debugging purposes.

5. Iterator Basics

The major flaw in our naive implementation is that we're requesting Redis to give us all of the results for our single fetch-query in one go. To overcome this issue, we can break our original fetch query into multiple sequential fetch queries that operate on smaller chunks of the entire dataset.

Let's assume that we have a 1,000-page book that we're supposed to read. If we follow our naive approach, we'll have to read this large book in a single sitting without any breaks. That'll be fatal to our well-being as it'll drain our energy and prevent us from doing any other higher-priority activity.

Of course, the right way is to finish the book over multiple reading sessions. In each session, we resume from where we left off in the previous session — we can track our progress by using a page bookmark.

Although the total reading time in both cases will be of comparable value, nonetheless, the second approach is better as it gives us room to breathe.

Let's see how we can use an iterator-based approach for exploring Redis collections.

6. Redis Scan

Redis offers several scanning strategies to read keys from collections using a cursor-based approach, which is, in principle, similar to a page bookmark.

6.1. Scan Strategies

We can scan through the entire key-value collection store using the Scan command. However, if we want to limit our dataset by collection types, then we can use one of the variants:

• Sscan can be used for iterating through sets
• Hscan helps us iterate through pairs of field-value in a hash
• Zscan allows an iteration through members stored in a sorted set

We must note that we don't really need a server-side scan strategy specifically designed for the linked lists. That's because we can access members of the linked list through indexes using the lindex or lrange command. Plus, we can find out the number of elements and use lrange in a simple loop to iterate the entire list in small chunks.

Let's use the SCAN command to scan over keys of string type. To start the scan, we need to use the cursor value as “0”, matching pattern string as “ball*”:

127.0.0.1:6379> mset balls:cricket 160 balls:football 450 balls:volleyball 270 OK 127.0.0.1:6379> SCAN 0 MATCH ball* COUNT 1 1) "2" 2) 1) "balls:cricket" 127.0.0.1:6379> SCAN 2 MATCH ball* COUNT 1 1) "3" 2) 1) "balls:volleyball" 127.0.0.1:6379> SCAN 3 MATCH ball* COUNT 1 1) "0" 2) 1) "balls:football"

With each completed scan, we get the next value of cursor to be used in the subsequent iteration. Eventually, we know that we've scanned through the entire collection when the next cursor value is “0”.

7. Scanning With Java

By now, we have enough understanding of our approach that we can start implementing it in Java.

7.1. Scanning Strategies

If we peek into the core scanning functionality offered by the Jedis class, we'll find strategies to scan different collection types:

public ScanResult scan(final String cursor, final ScanParams params); public ScanResult sscan(final String key, final String cursor, final ScanParams params); public ScanResult
     
       hscan(final String key, final String cursor, final ScanParams params); public ScanResult zscan(final String key, final String cursor, final ScanParams params);
     

Jedis requires two optional parameters, search-pattern and result-size, to effectively control the scanning – ScanParams makes this happen. For this purpose, it relies on the match() and count() methods, which are loosely based on the builder design pattern:

public ScanParams match(final String pattern); public ScanParams count(final Integer count);

Now that we've soaked in the basic knowledge about Jedis's scanning approach, let's model these strategies through a ScanStrategy interface:

public interface ScanStrategy { ScanResult scan(Jedis jedis, String cursor, ScanParams scanParams); }

First, let's work on the simplest scan strategy, which is independent of the collection-type and reads the keys, but not the value of the keys:

public class Scan implements ScanStrategy { public ScanResult scan(Jedis jedis, String cursor, ScanParams scanParams) { return jedis.scan(cursor, scanParams); } }

Next, let's pick up the hscan strategy, which is tailored to read all the field keys and field values of a particular hash key:

public class Hscan implements ScanStrategy
     
       { private String key; @Override public ScanResult
      
        scan(Jedis jedis, String cursor, ScanParams scanParams) { return jedis.hscan(key, cursor, scanParams); } }
      
     

Finally, let's build the strategies for sets and sorted sets. The sscan strategy can read all the members of a set, whereas the zscan strategy can read the members along with their scores in the form of Tuples:

public class Sscan implements ScanStrategy { private String key; public ScanResult scan(Jedis jedis, String cursor, ScanParams scanParams) { return jedis.sscan(key, cursor, scanParams); } } public class Zscan implements ScanStrategy { private String key; @Override public ScanResult scan(Jedis jedis, String cursor, ScanParams scanParams) { return jedis.zscan(key, cursor, scanParams); } }

7.2. Redis Iterator

Next, let's sketch out the building blocks needed to build our RedisIterator class:

• String-based cursor
• Scanning strategy such as scan, sscan, hscan, zscan
• Placeholder for scanning parameters
• Access to JedisPool to get a Jedis resource

We can now go ahead and define these members in our RedisIterator class:

private final JedisPool jedisPool; private ScanParams scanParams; private String cursor; private ScanStrategy strategy;

Our stage is all set to define the iterator-specific functionality for our iterator. For that, our RedisIterator class must implement the Iterator interface:

public class RedisIterator implements Iterator
     
       { }
     

Naturally, we are required to override the hasNext() and next() methods inherited from the Iterator interface.

First, let's pick the low-hanging fruit – the hasNext() method – as the underlying logic is straight-forward. As soon as the cursor value becomes “0”, we know that we're done with the scan. So, let's see how we can implement this in just one-line:

@Override public boolean hasNext() { return !"0".equals(cursor); }

Next, let's work on the next() method that does the heavy lifting of scanning:

@Override public List next() { if (cursor == null) { cursor = "0"; } try (Jedis jedis = jedisPool.getResource()) { ScanResult scanResult = strategy.scan(jedis, cursor, scanParams); cursor = scanResult.getCursor(); return scanResult.getResult(); } catch (Exception ex) { log.error("Exception caught in next()", ex); } return new LinkedList(); }

We must note that ScanResult not only gives the scanned results but also the next cursor-value needed for the subsequent scan.

Finally, we can enable the functionality to create our RedisIterator in the RedisClient class:

public RedisIterator iterator(int initialScanCount, String pattern, ScanStrategy strategy) { return new RedisIterator(jedisPool, initialScanCount, pattern, strategy); }

7.3. Read With Redis Iterator

As we've designed our Redis iterator with the help of the Iterator interface, it's quite intuitive to read the collection values with the help of the next() method as long as hasNext() returns true.

For the sake of completeness and simplicity, we'll first store the dataset related to the sports-balls in a Redis hash. After that, we'll use our RedisClient to create an iterator using Hscan scanning strategy. Let's test our implementation by seeing this in action:

@Test public void testHscanStrategy() { HashMap hash = new HashMap(); hash.put("cricket", "160"); hash.put("football", "450"); hash.put("volleyball", "270"); redisClient.hmset("balls", hash); Hscan scanStrategy = new Hscan("balls"); int iterationCount = 2; RedisIterator iterator = redisClient.iterator(iterationCount, "*", scanStrategy); List
     
       results = new LinkedList
      
       (); while (iterator.hasNext()) { results.addAll(iterator.next()); } Assert.assertEquals(hash.size(), results.size()); }
      
     

We can follow the same thought process with little modification to test and implement the remaining strategies to scan and read the keys available in different types of collections.

8. Conclusion

Comenzamos este tutorial con la intención de aprender cómo podemos leer todas las claves coincidentes en Redis.

Descubrimos que Redis ofrece una forma sencilla de leer las claves de una sola vez. Aunque simple, discutimos cómo esto ejerce presión sobre los recursos y, por lo tanto, no es adecuado para los sistemas de producción. Al profundizar, llegamos a saber que existe un enfoque basado en iteradores para escanear las claves de Redis coincidentes para nuestra consulta de lectura.

Como siempre, el código fuente completo para la implementación de Java utilizada en este artículo está disponible en GitHub.

Acabo de anunciar el nuevo curso Learn Spring , centrado en los fundamentos de Spring 5 y Spring Boot 2: