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DOC-5227 added inline examples
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‎content/develop/clients/jedis/prob.md‎

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@@ -98,15 +98,50 @@ set's membership with a fixed memory size, regardless of how many items you
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add. The following example adds some names to a Bloom filter representing
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a list of users and checks for the presence or absence of users in the list.
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{{< clients-example home_prob_dts bloom Java-Sync >}}
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{{< /clients-example >}}
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```java
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List<Boolean> res1 = jedis.bfMAdd(
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"recorded_users",
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"andy", "cameron", "david", "michelle"
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);
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System.out.println(res1); // >>> [true, true, true, true]
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boolean res2 = jedis.bfExists("recorded_users", "cameron");
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System.out.println(res2); // >>> true
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boolean res3 = jedis.bfExists("recorded_users", "kaitlyn");
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System.out.println(res3); // >>> false
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```
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<!--< clients-example home_prob_dts bloom Java-Sync >}}
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< /clients-example >}}-->
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A Cuckoo filter has similar features to a Bloom filter, but also supports
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a deletion operation to remove hashes from a set, as shown in the example
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below.
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{{< clients-example home_prob_dts cuckoo Java-Sync >}}
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{{< /clients-example >}}
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<!--< clients-example home_prob_dts cuckoo Java-Sync >}}
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< /clients-example >}}-->
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```java
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boolean res4 = jedis.cfAdd("other_users", "paolo");
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System.out.println(res4); // >>> true
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boolean res5 = jedis.cfAdd("other_users", "kaitlyn");
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System.out.println(res5); // >>> true
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boolean res6 = jedis.cfAdd("other_users", "rachel");
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System.out.println(res6); // >>> true
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boolean[] res7 = jedis.cfMExists(
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"other_users",
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"paolo", "rachel", "andy"
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);
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System.out.println(res7); // >>> [true, true, false]
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boolean res8 = jedis.cfDel("other_users", "paolo");
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System.out.println(res8); // >>> true
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boolean res9 = jedis.cfExists("other_users", "paolo");
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System.out.println(res9); // >>> false
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```
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Which of these two data types you choose depends on your use case.
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Bloom filters are generally faster than Cuckoo filters when adding new items,
@@ -126,8 +161,30 @@ You can also merge two or more HyperLogLogs to find the cardinality of the
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[union](https://en.wikipedia.org/wiki/Union_(set_theory)) of the sets they
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represent.
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{{< clients-example home_prob_dts hyperloglog Java-Sync >}}
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{{< /clients-example >}}
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<!--< clients-example home_prob_dts hyperloglog Java-Sync >}}
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< /clients-example >}}-->
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```java
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long res10 = jedis.pfadd("group:1", "andy", "cameron", "david");
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System.out.println(res10); // >>> 1
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long res11 = jedis.pfcount("group:1");
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System.out.println(res11); // >>> 3
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long res12 = jedis.pfadd(
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"group:2",
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"kaitlyn", "michelle", "paolo", "rachel"
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);
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System.out.println(res12); // >>> 1
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long res13 = jedis.pfcount("group:2");
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System.out.println(res13); // >>> 4
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String res14 = jedis.pfmerge("both_groups", "group:1", "group:2");
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System.out.println(res14); // >>> OK
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long res15 = jedis.pfcount("both_groups");
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System.out.println(res15); // >>> 7
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```
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The main benefit that HyperLogLogs offer is their very low
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memory usage. They can count up to 2^64 items with less than
@@ -165,8 +222,44 @@ stay within 0.1% of the true value and have a 0.05% probability
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of going outside this limit. The example below shows how to create
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a Count-min sketch object, add data to it, and then query it.
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{{< clients-example home_prob_dts cms Java-Sync >}}
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{{< /clients-example >}}
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<!--< clients-example home_prob_dts cms Java-Sync >}}
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< /clients-example >}}-->
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```java
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// Specify that you want to keep the counts within 0.01
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// (0.1%) of the true value with a 0.005 (0.05%) chance
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// of going outside this limit.
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String res16 = jedis.cmsInitByProb("items_sold", 0.01, 0.005);
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System.out.println(res16); // >>> OK
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Map<String, Long> firstItemIncrements = new HashMap<>();
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firstItemIncrements.put("bread", 300L);
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firstItemIncrements.put("tea", 200L);
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firstItemIncrements.put("coffee", 200L);
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firstItemIncrements.put("beer", 100L);
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List<Long> res17 = jedis.cmsIncrBy("items_sold",
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firstItemIncrements
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);
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res17.sort(null);
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System.out.println(); // >>> [100, 200, 200, 300]
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Map<String, Long> secondItemIncrements = new HashMap<>();
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secondItemIncrements.put("bread", 100L);
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secondItemIncrements.put("coffee", 150L);
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List<Long> res18 = jedis.cmsIncrBy("items_sold",
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secondItemIncrements
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);
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res18.sort(null);
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System.out.println(res18); // >>> [350, 400]
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List<Long> res19 = jedis.cmsQuery(
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"items_sold",
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"bread", "tea", "coffee", "beer"
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);
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res19.sort(null);
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System.out.println(res19); // >>> [100, 200, 350, 400]
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```
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The advantage of using a CMS over keeping an exact count with a
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[sorted set]({{< relref "/develop/data-types/sorted-sets" >}})
@@ -197,8 +290,48 @@ maximum values, the quantile of 0.75, and the
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shows how to merge two or more t-digest objects to query the combined
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data set.
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{{< clients-example home_prob_dts tdigest Java-Sync >}}
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{{< /clients-example >}}
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<!--< clients-example home_prob_dts tdigest Java-Sync >}}
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< /clients-example >}}-->
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```java
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String res20 = jedis.tdigestCreate("male_heights");
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System.out.println(res20); // >>> OK
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String res21 = jedis.tdigestAdd("male_heights",
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175.5, 181, 160.8, 152, 177, 196, 164);
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System.out.println(res21); // >>> OK
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double res22 = jedis.tdigestMin("male_heights");
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System.out.println(res22); // >>> 152.0
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double res23 = jedis.tdigestMax("male_heights");
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System.out.println(res23); // >>> 196.0
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List<Double> res24 = jedis.tdigestQuantile("male_heights", 0.75);
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System.out.println(res24); // >>> [181.0]
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// Note that the CDF value for 181 is not exactly 0.75.
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// Both values are estimates.
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List<Double> res25 = jedis.tdigestCDF("male_heights", 181);
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System.out.println(res25); // >>> [0.7857142857142857]
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String res26 = jedis.tdigestCreate("female_heights");
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System.out.println(res26); // >>> OK
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String res27 = jedis.tdigestAdd("female_heights",
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155.5, 161, 168.5, 170, 157.5, 163, 171);
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System.out.println(res27); // >>> OK
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List<Double> res28 = jedis.tdigestQuantile("female_heights", 0.75);
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System.out.println(res28); // >>> [170.0]
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String res29 = jedis.tdigestMerge(
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"all_heights",
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"male_heights", "female_heights"
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);
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System.out.println(res29); // >>> OK
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List<Double> res30 = jedis.tdigestQuantile("all_heights", 0.75);
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System.out.println(res30); // >>> [175.5]
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```
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A t-digest object also supports several other related commands, such
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as querying by rank. See the
@@ -219,5 +352,33 @@ the `topkReserve()` method). It also shows how to list the
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top *k* items and query whether or not a given item is in the
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list.
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{{< clients-example home_prob_dts topk Java-Sync >}}
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{{< /clients-example >}}
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<!--< clients-example home_prob_dts topk Java-Sync >}}
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< /clients-example >}}-->
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```java
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String res31 = jedis.topkReserve("top_3_songs", 3L, 2000L, 7L, 0.925D);
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System.out.println(res31); // >>> OK
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Map<String, Long> songIncrements = new HashMap<>();
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songIncrements.put("Starfish Trooper", 3000L);
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songIncrements.put("Only one more time", 1850L);
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songIncrements.put("Rock me, Handel", 1325L);
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songIncrements.put("How will anyone know?", 3890L);
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songIncrements.put("Average lover", 4098L);
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songIncrements.put("Road to everywhere", 770L);
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List<String> res32 = jedis.topkIncrBy("top_3_songs",
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songIncrements
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);
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System.out.println(res32);
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// >>> [null, null, null, null, null, Rock me, Handel]
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List<String> res33 = jedis.topkList("top_3_songs");
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System.out.println(res33);
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// >>> [Average lover, How will anyone know?, Starfish Trooper]
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List<Boolean> res34 = jedis.topkQuery("top_3_songs",
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"Starfish Trooper", "Road to everywhere"
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);
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System.out.println(res34);
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// >>> [true, false]
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```

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