Senaraikan Semua Kekunci Redis Yang Ada

Java Teratas

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1. Gambaran keseluruhan

Koleksi adalah blok bangunan penting yang biasanya dilihat di hampir semua aplikasi moden. Oleh itu, tidak hairanlah Redis menawarkan pelbagai struktur data yang popular seperti senarai, set, hash, dan set yang disusun untuk kita gunakan.

Dalam tutorial ini, kita akan belajar bagaimana kita dapat membaca dengan berkesan semua kunci Redis yang ada yang sesuai dengan corak tertentu.

2. Terokai Koleksi

Mari kita bayangkan bahawa aplikasi kita menggunakan Redis untuk menyimpan maklumat mengenai bola yang digunakan dalam sukan yang berbeza. Kita seharusnya dapat melihat maklumat mengenai setiap bola yang tersedia dari koleksi Redis. Untuk kesederhanaan, kami akan menghadkan set data kami kepada tiga bola sahaja:

  • Bola kriket dengan berat 160 g
  • Bola sepak dengan berat 450 g
  • Bola tampar dengan berat 270 g

Seperti biasa, mari kita jelaskan asas-asas kita dengan mengusahakan pendekatan naif untuk meneroka koleksi Redis.

3. Pendekatan naif Menggunakan redis-cli

Sebelum kita mula menulis kod Java untuk meneroka koleksi, kita harus mempunyai idea yang baik tentang bagaimana kita akan melakukannya menggunakan antara muka redis-cli . Mari kita anggap bahawa contoh Redis kami tersedia di 127.0.0.1 di port 6379 , untuk kita meneroka setiap jenis koleksi dengan antara muka baris perintah.

3.1. Senarai Terpaut

Pertama, mari simpan set data kami dalam senarai yang dipautkan Redis bernama bola dalam format sport-name _ ball-weight dengan bantuan arahan 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

Kita dapat melihat bahawa penyisipan yang berjaya ke dalam senarai menghasilkan panjang senarai yang baru . Namun, dalam kebanyakan kes, kita akan buta dengan aktiviti memasukkan data. Hasilnya, kita dapat mengetahui panjang senarai yang dipautkan menggunakan perintah llen :

127.0.0.1:6379> llen balls (integer) 3

Apabila kita sudah mengetahui panjang senarai, lebih mudah menggunakan arahan lrange untuk mengambil keseluruhan set data dengan mudah:

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

3.2. Tetapkan

Seterusnya, mari kita lihat bagaimana kita dapat meneroka set data apabila kita memutuskan untuk menyimpannya dalam set Redis. Untuk melakukannya, pertama-tama kita perlu mengisi set data kita dalam set Redis bernama bola menggunakan perintah sadd :

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

Alamak! Kami mempunyai nilai pendua dalam perintah kami. Tetapi, kerana kami menambahkan nilai pada satu set, kami tidak perlu bimbang tentang pendua. Sudah tentu, kita dapat melihat jumlah item yang ditambahkan dari nilai tindak balas output.

Sekarang, kita dapat memanfaatkan perintah smembers untuk melihat semua ahli yang ditetapkan :

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

3.3. Hash

Sekarang, mari gunakan struktur data hash Redis untuk menyimpan set data kami dalam kunci hash bernama bola sehingga bidang hash adalah nama sukan dan nilai padang adalah berat bola. Kita boleh melakukan ini dengan bantuan arahan hmset :

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

Untuk melihat maklumat yang tersimpan di hash kami, kami dapat menggunakan perintah hgetall :

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

3.4. Set yang disusun

Sebagai tambahan kepada nilai ahli yang unik, set yang disusun membolehkan kami menyimpan skor di sebelahnya. Baiklah, dalam kes penggunaan kita, kita dapat mengekalkan nama sukan sebagai nilai anggota dan berat bola sebagai skor. Mari gunakan perintah zadd untuk menyimpan set data kami:

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

Sekarang, pertama kita dapat menggunakan perintah zcard untuk mencari panjang set yang disusun, diikuti dengan perintah zrange untuk meneroka set lengkap :

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. Rentetan

Kita juga dapat melihat rentetan nilai-kunci biasa sebagai koleksi item yang dangkal . Mari terlebih dahulu mengisi set data kami menggunakan arahan mset :

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

Kita mesti ambil perhatian bahawa kita telah menambahkan awalan “bola: supaya kita dapat mengenal pasti kunci ini dari kunci yang selebihnya mungkin terdapat dalam pangkalan data Redis kita. Selain itu, strategi penamaan ini membolehkan kita menggunakan perintah kunci untuk meneroka set data kami dengan bantuan padanan corak awalan:

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

4. Pelaksanaan Java Naif

Sekarang kita telah mengembangkan idea asas tentang perintah Redis yang relevan yang dapat kita gunakan untuk meneroka koleksi dari pelbagai jenis, sudah waktunya kita kotor tangan dengan kod.

4.1. Ketergantungan Maven

Di bahagian ini, kami akan menggunakan perpustakaan pelanggan Jedis untuk Redis dalam pelaksanaan kami:

 redis.clients jedis 3.2.0 

4.2. Pelanggan 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

Kami memulakan tutorial ini dengan tujuan untuk mengetahui bagaimana kami dapat membaca semua kunci yang sesuai di Redis.

Kami mendapat tahu bahawa ada cara mudah yang ditawarkan oleh Redis untuk membaca kunci dalam satu masa. Walaupun sederhana, kami membincangkan bagaimana ini membebankan sumber dan oleh itu tidak sesuai untuk sistem pengeluaran. Setelah menggali lebih mendalam, kami mengetahui bahawa ada pendekatan berasaskan iterator untuk mengimbas melalui kekunci Redis yang sepadan untuk pertanyaan baca kami.

Seperti biasa, kod sumber lengkap untuk implementasi Java yang digunakan dalam artikel ini tersedia di GitHub.

Bahagian bawah Java

Saya baru sahaja mengumumkan kursus Learn Spring yang baru , yang berfokus pada asas-asas Spring 5 dan Spring Boot 2:

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