Redis learning and understanding of three special data types

Preface
 

Many blogs only talk about five basic types , exactly , Is the most commonly used , And 90% of programmers are interested in Redis Limited to understanding String This is the most commonly used . But I personally think , since Redis Other data types are officially provided , There must be corresponding considerations , In some special business scenarios , These special types can still give us a solution ！ that , This learning is worth it ！！！
 

1.Geospatial： Location

City longitude and latitude query ： Latitude and longitude query
Be careful 1： Two poles can't be added directly , We usually download city data , Directly through java The program is imported once ！
Be careful 2： Effective longitude from -180 C to 180 degree .
Be careful 3： Effective latitude from -85.05112878 C to 85.05112878 degree .
Be careful 4：m For meters .km In kilometers .mi For miles .ft Feet .
        ①geoadd（ add to ）、geopos（ see ）、geodist（ Calculated distance ） operation

127.0.0.1:6379> geoadd city 118.8921 31.32751 nanjing 197.30794 31.79322  
# When one or both of the longitude and latitude exceed the limit value , Report errors , The information is as follows ：
(error) ERR syntax error. Try GEOADD key [x1] [y1] [name1] [x2] [y2] [name2] ...
# Add city longitude and latitude   Grammar format ： geoadd key  longitude   latitude  name +++ You can add more than one 
# After adding successfully, return the quantity value of adding successfully 
127.0.0.1:6379> geoadd city 118.8921 31.32751 nanjing 117.30794 31.79322 hefei 102.82147 24.88554 kunming 91.13775 29.65262 lasa 116.23128 40.22077 beijing 106.54041 29.40268 chongqing  
(integer) 6
127.0.0.1:6379> ZRANGE city 0 -1  # Be careful ：geo How to view and zset The orders are consistent ,
# Thus we can see that ,geo It's essentially a collection , however Redis The official has re encapsulated it 
1) "lasa"
2) "kunming"
3) "chongqing"
4) "hefei"
5) "nanjing"
6) "beijing"
127.0.0.1:6379> geopos city nanjing  # View the longitude and latitude information of the specified city 
1) 1) "118.89209836721420288"
   2) "31.32750976275760735"
127.0.0.1:6379> geopos city nanjing beijing  # View the longitude and latitude information of multiple cities 
1) 1) "118.89209836721420288"
   2) "31.32750976275760735"
2) 1) "116.23128265142440796"
   2) "40.22076905438526495"
127.0.0.1:6379> geodist city nanjing beijing   # Calculate the distance between Nanjing and Beijing , The default return unit is m
"1017743.1413"
127.0.0.1:6379> geodist city nanjing beijing km  #km   km 
"1017.7431"
127.0.0.1:6379> geodist city nanjing beijing mi  #mi   miles 
"632.3978"
127.0.0.1:6379> geodist city nanjing beijing ft  #ft   feet 
"3339052.3010"

        ②georadius（ Query nearby locations ） operation

127.0.0.1:6379> ZRANGE city 0 -1  # View city 
1) "lasa"
2) "kunming"
3) "chongqing"
4) "hefei"
5) "nanjing"
6) "beijing"
# View the 1000 Which cities are within kilometers 
127.0.0.1:6379> georadius city 120 38 1000 km  
1) "beijing"
2) "hefei"
3) "nanjing"
127.0.0.1:6379> georadius city 120 38 400 km  # View the 400 Which cities are within kilometers 
(empty array)
127.0.0.1:6379> georadius city 120 38 550 km  # View the 550 Which cities are within kilometers 
1) "beijing"
# View the 550 Which cities are within kilometers ,withcoord Specifies the name of the city to return to name
127.0.0.1:6379> georadius city 120 38 1000 km withcoord
1) 1) "beijing"
   2) 1) "116.23128265142440796"
      2) "40.22076905438526495"
2) 1) "hefei"
   2) 1) "117.30793744325637817"
      2) "31.79321915080526395"
3) 1) "nanjing"
   2) 1) "118.89209836721420288"
      2) "31.32750976275760735"
# View the 550 Which cities are within kilometers ,withdist Specifies the name of the city to return to ’ Longitude and latitude ‘ value 
127.0.0.1:6379> georadius city 120 38 1000 km withcoord withdist
1) 1) "beijing"
   2) "408.3496"
   3) 1) "116.23128265142440796"
      2) "40.22076905438526495"
2) 1) "hefei"
   2) "732.6371"
   3) 1) "117.30793744325637817"
      2) "31.79321915080526395"
3) 1) "nanjing"
   2) "749.0265"
   3) 1) "118.89209836721420288"
      2) "31.32750976275760735"
# View the 550 Which cities are within kilometers ,withhash Specifies the name of the city to return to ’ Longitude and latitude ‘ Of hash value 
# If two cities hash Value more ’ image ‘, Prove that the closer the city is ！
127.0.0.1:6379> georadius city 120 38 1000 km withcoord withdist withhash
1) 1) "beijing"
   2) "408.3496"
   3) (integer) 4069896088584598
   4) 1) "116.23128265142440796"
      2) "40.22076905438526495"
2) 1) "hefei"
   2) "732.6371"
   3) (integer) 4052763834193093
   4) 1) "117.30793744325637817"
      2) "31.79321915080526395"
3) 1) "nanjing"
   2) "749.0265"
   3) (integer) 4054278565840695
   4) 1) "118.89209836721420288"
      2) "31.32750976275760735"
# View the 550 Which cities are within kilometers ,count num  Specify return ’num‘ Data volume of cities 
127.0.0.1:6379> georadius city 120 38 1000 km withcoord withdist withhash count 2
1) 1) "beijing"
   2) "408.3496"
   3) (integer) 4069896088584598
   4) 1) "116.23128265142440796"
      2) "40.22076905438526495"
2) 1) "hefei"
   2) "732.6371"
   3) (integer) 4052763834193093
   4) 1) "117.30793744325637817"
      2) "31.79321915080526395"

        ③ georadiusbymember （ Find the element within the specified range of the specified element ）、geohash （ Returns the latitude and longitude hash value ）、zrange、zrem（ Use zset The command operation geo）

# Query Nanjing  500 Which cities are within kilometers 
127.0.0.1:6379> georadiusbymember city nanjing 500 km
1) "hefei"
2) "nanjing"
# Query Chongqing  1500 Which cities are within kilometers 
127.0.0.1:6379> georadiusbymember city chongqing 1500 km
1) "lasa"
2) "kunming"
3) "chongqing"
4) "hefei"
5) "nanjing"
6) "beijing"
# Return to the longitude and latitude of Beijing and Nanjing  hash value 
127.0.0.1:6379> geohash city beijing nanjing
1) "wx4sucvncn0"
2) "wtsd1qyxfx0"
# See all cities name
127.0.0.1:6379> ZRANGE city 0 -1
1) "lasa"
2) "kunming"
3) "chongqing"
4) "hefei"
5) "nanjing"
6) "beijing"
# according to geo Medium name Delete g Elements 
127.0.0.1:6379> ZREM city lasa
(integer) 1
# Delete successful 
127.0.0.1:6379> ZRANGE city 0 -1
1) "kunming"
2) "chongqing"
3) "hefei"
4) "nanjing"
5) "beijing"

        ④ summary ： In actual demand , We can use it to query people nearby 、 Calculate the distance between two people, etc . Of course , We must combine the required longitude and latitude java Import the code once , Manual query and entry is too time-consuming ！

2.Hyperloglog：  base

The first thing to understand is base ？
On the mathematical level, it can be said to be ： Non repeating elements in two datasets ~
But again Redis in , There may be some error . The official error rate is 0.81%.
Hyperloglog Of advantage ：  The memory occupied is fixed ,2^64 Elements , Equivalent to just 12kb The memory of . Very efficient ！

        ①pfadd（ Add dataset ）、pfcount（ Statistical data sets ）、pfmegre（ Merged data set - Automatic weight removal

127.0.0.1:6379> pfadd dataList 1 2 3 4 5 6 7  # Add dataset 
(integer) 1
127.0.0.1:6379> pfcount dataList  # Elements in the statistical dataset 
(integer) 7
127.0.0.1:6379> pfadd dataList1 4 5 6 7 8 9 10  # Add dataset 
(integer) 1
127.0.0.1:6379> pfcount dataList1  # Elements in the statistical dataset 
(integer) 7
# take dataList  and dataList1   The two data sets are combined into a new  newdata Data sets , And automatically de duplication 
127.0.0.1:6379> pfmerge newdata dataList dataList1  
OK
127.0.0.1:6379> pfcount newdata
(integer) 10

        ② summary ： If in actual business , A certain error value is allowed , We can use cardinality statistics to calculate ~ Very efficient ！ such as ： Website visits , You can use it Hyperloglog To calculate statistics ！

3.Bitmap：  Bit storage

Bitmap Bitmap , data structure ！ It's all operating bits to record , Only 0 and 1 Two states ！

        ①setbit（ add to ）、getset（ obtain ）、bitcount（ Statistics ） operation

127.0.0.1:6379> setbit login 1 1   # Add Monday logged in   by 1
(integer) 0
127.0.0.1:6379> setbit login 2 1
(integer) 0
127.0.0.1:6379> setbit login 3 1
(integer) 0
127.0.0.1:6379> setbit login 4 0  # Added Thursday logged in   by 0
(integer) 0
127.0.0.1:6379> setbit login 5 0
(integer) 0
127.0.0.1:6379> setbit login 6 1
(integer) 0
127.0.0.1:6379> setbit login 7 0
(integer) 0
127.0.0.1:6379> getbit login 1  # Get whether to log in on Monday 
(integer) 1
127.0.0.1:6379> getbit login 4  # Get whether to log in on Thursday 
(integer) 0
127.0.0.1:6379> bitcount login  # Count the number of days logged in this week 
(integer) 4

        ② summary ： In actual demand , We may need to count the login information of users , Employees' punch in information, etc . As long as it is a business, only Two states Of , We can all use Bitmap To operate ！！！