DOC-5227 added Jedis probabilistic data type examples

Author: andy-stark-redis

content/develop/clients/jedis/prob.md

@@ -0,0 +1,223 @@
+---
+categories:
+- docs
+- develop
+- stack
+- oss
+- rs
+- rc
+- oss
+- kubernetes
+- clients
+description: Learn how to use approximate calculations with Redis.
+linkTitle: Probabilistic data types
+title: Probabilistic data types
+weight: 5
+---
+
+Redis supports several
+[probabilistic data types]({{< relref "/develop/data-types/probabilistic" >}})
+that let you calculate values approximately rather than exactly.
+The types fall into two basic categories:
+
+-   [Set operations](#set-operations): These types let you calculate (approximately)
+    the number of items in a set of distinct values, and whether or not a given value is
+    a member of a set.
+-   [Statistics](#statistics): These types give you an approximation of
+    statistics such as the quantiles, ranks, and frequencies of numeric data points in
+    a list.
+
+To see why these approximate calculations would be useful, consider the task of
+counting the number of distinct IP addresses that access a website in one day.
+
+Assuming that you already have code that supplies you with each IP
+address as a string, you could record the addresses in Redis using
+a [set]({{< relref "/develop/data-types/sets" >}}):
+
+```java
+jedis.sadd("ip_tracker", new_ip_address)
+```
+
+The set can only contain each key once, so if the same address
+appears again during the day, the new instance will not change
+the set. At the end of the day, you could get the exact number of
+distinct addresses using the `scard()` function:
+
+```java
+long num_distinct_ips = jedis.scard("ip_tracker")
+```
+
+This approach is simple, effective, and precise but if your website
+is very busy, the `ip_tracker` set could become very large and consume
+a lot of memory.
+
+You would probably round the count of distinct IP addresses to the
+nearest thousand or more to deliver the usage statistics, so
+getting it exactly right is not important. It would be useful
+if you could trade off some accuracy in exchange for lower memory
+consumption. The probabilistic data types provide exactly this kind of
+trade-off. Specifically, you can count the approximate number of items in a
+set using the [HyperLogLog](#set-cardinality) data type, as described below.
+
+In general, the probabilistic data types let you perform approximations with a
+bounded degree of error that have much lower memory consumption or execution
+time than the equivalent precise calculations.
+
+## Set operations
+
+Redis supports the following approximate set operations:
+
+-   [Membership](#set-membership): The
+    [Bloom filter]({{< relref "/develop/data-types/probabilistic/bloom-filter" >}}) and
+    [Cuckoo filter]({{< relref "/develop/data-types/probabilistic/cuckoo-filter" >}})
+    data types let you track whether or not a given item is a member of a set.
+-   [Cardinality](#set-cardinality): The
+    [HyperLogLog]({{< relref "/develop/data-types/probabilistic/hyperloglogs" >}})
+    data type gives you an approximate value for the number of items in a set, also
+    known as the *cardinality* of the set.
+
+The sections below describe these operations in more detail.
+
+### Set membership
+
+[Bloom filter]({{< relref "/develop/data-types/probabilistic/bloom-filter" >}}) and
+[Cuckoo filter]({{< relref "/develop/data-types/probabilistic/cuckoo-filter" >}})
+objects provide a set membership operation that lets you track whether or not a
+particular item has been added to a set. These two types provide different
+trade-offs for memory usage and speed, so you can select the best one for your
+use case. Note that for both types, there is an asymmetry between presence and
+absence of items in the set. If an item is reported as absent, then it is definitely
+absent, but if it is reported as present, then there is a small chance it may really be
+absent.
+
+Instead of storing strings directly, like a [set]({{< relref "/develop/data-types/sets" >}}),
+a Bloom filter records the presence or absence of the
+[hash value](https://en.wikipedia.org/wiki/Hash_function) of a string.
+This gives a very compact representation of the
+set's membership with a fixed memory size, regardless of how many items you
+add. The following example adds some names to a Bloom filter representing
+a list of users and checks for the presence or absence of users in the list.
+
+{{< clients-example home_prob_dts bloom Java-Sync >}}
+{{< /clients-example >}}
+
+A Cuckoo filter has similar features to a Bloom filter, but also supports
+a deletion operation to remove hashes from a set, as shown in the example
+below.
+
+{{< clients-example home_prob_dts cuckoo Java-Sync >}}
+{{< /clients-example >}}
+
+Which of these two data types you choose depends on your use case.
+Bloom filters are generally faster than Cuckoo filters when adding new items,
+and also have better memory usage. Cuckoo filters are generally faster
+at checking membership and also support the delete operation. See the
+[Bloom filter]({{< relref "/develop/data-types/probabilistic/bloom-filter" >}}) and
+[Cuckoo filter]({{< relref "/develop/data-types/probabilistic/cuckoo-filter" >}})
+reference pages for more information and comparison between the two types.
+
+### Set cardinality
+
+A [HyperLogLog]({{< relref "/develop/data-types/probabilistic/hyperloglogs" >}})
+object calculates the cardinality of a set. As you add
+items, the HyperLogLog tracks the number of distinct set members but
+doesn't let you retrieve them or query which items have been added.
+You can also merge two or more HyperLogLogs to find the cardinality of the
+[union](https://en.wikipedia.org/wiki/Union_(set_theory)) of the sets they
+represent.
+
+{{< clients-example home_prob_dts hyperloglog Java-Sync >}}
+{{< /clients-example >}}
+
+The main benefit that HyperLogLogs offer is their very low
+memory usage. They can count up to 2^64 items with less than
+1% standard error using a maximum 12KB of memory. This makes
+them very useful for counting things like the total of distinct
+IP addresses that access a website or the total of distinct
+bank card numbers that make purchases within a day.
+
+## Statistics
+
+Redis supports several approximate statistical calculations
+on numeric data sets:
+
+-   [Frequency](#frequency): The
+    [Count-min sketch]({{< relref "/develop/data-types/probabilistic/count-min-sketch" >}})
+    data type lets you find the approximate frequency of a labeled item in a data stream.
+-   [Quantiles](#quantiles): The
+    [t-digest]({{< relref "/develop/data-types/probabilistic/t-digest" >}})
+    data type estimates the quantile of a query value in a data stream.
+-   [Ranking](#ranking): The
+    [Top-K]({{< relref "/develop/data-types/probabilistic/top-k" >}}) data type
+    estimates the ranking of labeled items by frequency in a data stream.
+
+The sections below describe these operations in more detail.
+
+### Frequency
+
+A [Count-min sketch]({{< relref "/develop/data-types/probabilistic/count-min-sketch" >}})
+(CMS) object keeps count of a set of related items represented by
+string labels. The count is approximate, but you can specify
+how close you want to keep the count to the true value (as a fraction)
+and the acceptable probability of failing to keep it in this
+desired range. For example, you can request that the count should
+stay within 0.1% of the true value and have a 0.05% probability
+of going outside this limit. The example below shows how to create
+a Count-min sketch object, add data to it, and then query it.
+
+{{< clients-example home_prob_dts cms Java-Sync >}}
+{{< /clients-example >}}
+
+The advantage of using a CMS over keeping an exact count with a
+[sorted set]({{< relref "/develop/data-types/sorted-sets" >}})
+is that that a CMS has very low and fixed memory usage, even for
+large numbers of items. Use CMS objects to keep daily counts of
+items sold, accesses to individual web pages on your site, and
+other similar statistics.
+
+### Quantiles
+
+A [quantile](https://en.wikipedia.org/wiki/Quantile) is the value
+below which a certain fraction of samples lie. For example, with
+a set of measurements of people's heights, the quantile of 0.75 is
+the value of height below which 75% of all people's heights lie.
+[Percentiles](https://en.wikipedia.org/wiki/Percentile) are equivalent
+to quantiles, except that the fraction is expressed as a percentage.
+
+A [t-digest]({{< relref "/develop/data-types/probabilistic/t-digest" >}})
+object can estimate quantiles from a set of values added to it
+without having to store each value in the set explicitly. This can
+save a lot of memory when you have a large number of samples.
+
+The example below shows how to add data samples to a t-digest
+object and obtain some basic statistics, such as the minimum and
+maximum values, the quantile of 0.75, and the 
+[cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function)
+(CDF), which is effectively the inverse of the quantile function. It also
+shows how to merge two or more t-digest objects to query the combined
+data set.
+
+{{< clients-example home_prob_dts tdigest Java-Sync >}}
+{{< /clients-example >}}
+
+A t-digest object also supports several other related commands, such
+as querying by rank. See the
+[t-digest]({{< relref "/develop/data-types/probabilistic/t-digest" >}})
+reference for more information.
+
+### Ranking
+
+A [Top-K]({{< relref "/develop/data-types/probabilistic/top-k" >}})
+object estimates the rankings of different labeled items in a data
+stream according to frequency. For example, you could use this to
+track the top ten most frequently-accessed pages on a website, or the
+top five most popular items sold.
+
+The example below adds several different items to a Top-K object
+that tracks the top three items (this is the second parameter to
+the `topkReserve()` method). It also shows how to list the
+top *k* items and query whether or not a given item is in the
+list.
+
+{{< clients-example home_prob_dts topk Java-Sync >}}
+{{< /clients-example >}}