Alle verfügbaren Redis-Schlüssel auflisten

Java Top

Ich habe gerade den neuen Learn Spring- Kurs angekündigt , der sich auf die Grundlagen von Spring 5 und Spring Boot 2 konzentriert:

>> Überprüfen Sie den Kurs

1. Übersicht

Sammlungen sind ein wesentlicher Baustein, der normalerweise in fast allen modernen Anwendungen zu finden ist. Kein Wunder also, dass Redis eine Vielzahl gängiger Datenstrukturen wie Listen, Sets, Hashes und sortierte Sets zur Verwendung anbietet .

In diesem Tutorial erfahren Sie, wie Sie alle verfügbaren Redis-Schlüssel, die einem bestimmten Muster entsprechen, effektiv lesen können.

2. Sammlungen erkunden

Stellen wir uns vor, unsere Anwendung verwendet Redis, um Informationen über Bälle zu speichern, die in verschiedenen Sportarten verwendet werden. Wir sollten Informationen zu jedem Ball aus der Redis-Sammlung sehen können. Der Einfachheit halber beschränken wir unseren Datensatz auf nur drei Bälle:

  • Cricketball mit einem Gewicht von 160 g
  • Fußball mit einem Gewicht von 450 g
  • Volleyball mit einem Gewicht von 270 g

Lassen Sie uns wie üblich zunächst unsere Grundlagen klären, indem wir an einem naiven Ansatz zur Erkundung der Redis-Sammlungen arbeiten.

3. Naiver Ansatz mit redis-cli

Bevor wir anfangen, Java-Code zu schreiben, um die Sammlungen zu erkunden, sollten wir eine gute Vorstellung davon haben, wie wir dies mithilfe der redis-cli- Oberfläche tun . Nehmen wir an, dass unsere Redis-Instanz unter 127.0.0.1 an Port 6379 verfügbar ist , damit wir jeden Sammlungstyp mit der Befehlszeilenschnittstelle untersuchen können.

3.1. Verknüpfte Liste

Lassen Sie uns zunächst speichern unsere Datensatz in einer Redis Liste mit dem Namen verknüpft Bälle im Format von Sport-name _ Kugelgewicht mit Hilfe des rpush Befehl ein :

% 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

Wir können feststellen, dass ein erfolgreiches Einfügen in die Liste die neue Länge der Liste ausgibt . In den meisten Fällen sind wir jedoch blind für die Dateneinfügungsaktivität. Als Ergebnis können wir die Länge der verknüpften Liste mit dem Befehl llen ermitteln :

127.0.0.1:6379> llen balls (integer) 3

Wenn wir die Länge der Liste bereits kennen, können Sie mit dem Befehl lrange den gesamten Datensatz problemlos abrufen:

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

3.2. einstellen

Als nächstes wollen wir sehen, wie wir den Datensatz untersuchen können, wenn wir ihn in einem Redis-Satz speichern. Dazu müssen wir zuerst unseren Datensatz mit dem Befehl sadd in einen Redis-Satz mit dem Namen balls füllen :

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

Hoppla! Wir hatten einen doppelten Wert in unserem Befehl. Da wir einem Satz jedoch Werte hinzufügen, müssen wir uns keine Gedanken über Duplikate machen. Natürlich können wir die Anzahl der Elemente sehen, die aus dem Ausgabeantwortwert hinzugefügt wurden.

Jetzt können wir den Befehl smembers nutzen , um alle festgelegten Mitglieder anzuzeigen :

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

3.3. Hash

Verwenden wir nun die Hash-Datenstruktur von Redis, um unseren Datensatz in einem Hash-Schlüssel mit dem Namen "Bälle" zu speichern, sodass das Hash-Feld der Sportname und der Feldwert das Gewicht des Balls ist. Wir können dies mit Hilfe des Befehls hmset tun :

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

Um die in unserem Hash gespeicherten Informationen anzuzeigen , können wir den Befehl hgetall verwenden :

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

3.4. Sortiertes Set

In addition to a unique member-value, sorted-sets allows us to keep a score next to them. Well, in our use case, we can keep the name of the sport as the member value and the weight of the ball as the score. Let's use the zadd command to store our dataset:

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

Now, we can first use the zcard command to find the length of the sorted set, followed by the zrange command to explore the complete set:

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. Strings

We can also see the usual key-value strings as a superficial collection of items. Let's first populate our dataset using the mset command:

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

We must note that we added the prefix “balls:so that we can identify these keys from the rest of the keys that may be lying in our Redis database. Moreover, this naming strategy allows us to use the keys command to explore our dataset with the help of prefix pattern matching:

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

4. Naive Java Implementation

Now that we have developed a basic idea of the relevant Redis commands that we can use to explore collections of different types, it's time for us to get our hands dirty with code.

4.1. Maven Dependency

In this section, we'll be using the Jedis client library for Redis in our implementation:

 redis.clients jedis 3.2.0 

4.2. Redis Client

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

Wir haben dieses Tutorial mit der Absicht gestartet, zu erfahren, wie wir alle passenden Schlüssel in Redis lesen können.

Wir haben herausgefunden, dass Redis eine einfache Möglichkeit bietet, Schlüssel auf einmal zu lesen. Obwohl einfach, haben wir diskutiert, wie dies die Ressourcen belastet und daher nicht für Produktionssysteme geeignet ist. Beim tieferen Graben haben wir festgestellt, dass es einen iteratorbasierten Ansatz zum Scannen passender Redis-Schlüssel für unsere Leseabfrage gibt.

Wie immer ist der vollständige Quellcode für die in diesem Artikel verwendete Java-Implementierung über GitHub verfügbar.

Java unten

Ich habe gerade den neuen Learn Spring- Kurs angekündigt , der sich auf die Grundlagen von Spring 5 und Spring Boot 2 konzentriert:

>> Überprüfen Sie den Kurs