Let's talk about the GEO geographical location module in Redis

GEO is a new geographical location module added to Redis after version 3.2. This article will take you through the GEO geographical location module. I hope it will be helpful to you!

GEO is a new geographical location module added to Redis after version 3.2, which means that Redis can be used to implement nearby location functions. [Related recommendations: Redis video tutorial]

Use database calculation

The general method is to pass A rectangular area is used to limit the number of elements, and then the full distance calculation is performed on the elements in the area and then sorted. This can significantly reduce the amount of calculation.

select id from positions where x0-r < x < x0+r and y0-r < y < y0+r

In order to increase performance, the above SQL needs to add a two-way composite index to the latitude and longitude coordinates. However, database query performance is limited after all. If it is used in high concurrency situations, this may not be a good solution.

GEO Algorithm

• The industry’s most common geographical distance sorting algorithm is the GeoHash algorithm, and Redis also uses GeoHash algorithm.
• GeoHash The algorithm maps two-dimensional longitude and latitude data to one-dimensional integers , so that all elements will be mounted on a line, close to each other The distance between points after mapping two-dimensional coordinates to one dimension will also be very close. When we want to calculate "people nearby", we first map the target position to this line, and then obtain nearby points on this one-dimensional line.
• Algorithm implementation, it treats the entire earth as a two-dimensional plane, and then divides it into a series of square grids, just like a Go board. All map element coordinates will be placed in unique squares. The smaller the square, the more precise the coordinates. Then perform integer coding on these squares, the closer the squares are, the closer the coding is.
• After encoding, the coordinates of each map element will become an integer. Through this integer, the coordinates of the element can be restored. The longer the integer, the smaller the loss of the restored coordinate value.
• GeoHash The algorithm will continue to do this integer oncebase32 Encoding(0-9,a-z remove the four letters a,i,l,o) becomes a string.
• In Redis, the latitude and longitude are encoded using 52-bit integers and put into zset. The value of zset is the # of the element. ##key, score is the 52-bit integer value of GeoHash.
When using Redis for Geo query, we must always remember that its internal structure is actually just a
• zset(skiplist). By sorting score of zset, you can get other elements near the coordinates (the actual situation is more complicated, but this is enough to understand), and by restoring score to The coordinate value can be used to obtain the original coordinates of the element.

Redis GEO command

##1. Add geoadd

geoadd key longitude latitude member [longitude latitude member ...]

127.0.0.1:6379> geoadd beijing 116.403856 39.924043 gugong
(integer) 1
127.0.0.1:6379> geoadd beijing 116.343620 39.947633 dongwuyuan
(integer) 1
127.0.0.1:6379> geoadd beijing 116.328643 39.900272 xizhan 116.415324 39.931231 meishuguan 116.416852 39.887607 tiantan
(integer) 3

using zset

2. Distance geodist

geodist key member1 member2 [unit]

127.0.0.1:6379> geodist beijing gugong xizhan km
"6.9402"
127.0.0.1:6379> geodist beijing gugong dongwuyuan   # 默认单位m
"5768.5737"
127.0.0.1:6379> geodist beijing xizhan xizhan
"0.0000"

3. Location geopos

geopos key member [member ...]

127.0.0.1:6379> geopos beijing gugong
1) 1) "116.4038559794426"
   2) "39.92404192186725"
127.0.0.1:6379> geopos beijing tiantan xizhan
1) 1) "116.41685396432877"
   2) "39.887607839922914"
2) 1) "116.32864147424698"
   2) "39.900271306834973"

4. Hash value geohash

geohash key member [member ...]

127.0.0.1:6379> geohash beijing gugong
1) "wx4g0gfwqk0"

encoding, you can directly find the latitude and longitude through http://geohash.org/wx4g0gfwqk0

1. Query ireader

20 The maximum number of elements within 3 kilometers are sorted by distance. , it will not exclude itself (use desc when using inversion)

127.0.0.1:6379> georadiusbymember company ireader 20 km count 3 asc
1) "ireader"
2) "juejin"
3) "meituan"

withdist is very useful, it can be used to display distance

georadiusbymember key member radius m|km|ft|mi [WITHCOORD] [WITHDIST] [WITHHASH] [COUNT count] [ASC|DES]

127.0.0.1:6379> georadiusbymember beijing gugong 5 km withcoord withdist withhash count 3 asc
1) 1) "gugong"
   2) "0.0000"
   3) (integer) 4069885568932443
   4) 1) "116.4038559794426"
      2) "39.92404192186725"
2) 1) "meishuguan"
   2) "1.2634"
   3) (integer) 4069885710390435
   4) 1) "116.41532510519028"
      2) "39.93123039107514"
3) 1) "tiantan"
   2) "4.2014"
   3) (integer) 4069885398502557
   4) 1) "116.41685396432877"
      2) "39.887607839922914"

 georadius key longitude latitude radius m|km|ft|mi [WITHCOORD] [WITHDIST] [WITHHASH] [COUNT count] [ASC|DES]

127.0.0.1:6379> georadius beijing 116.383882 39.922061 5 km withcoord withdist withhash count 3 asc
1) 1) "gugong"
   2) "1.7180"
   3) (integer) 4069885568932443
   4) 1) "116.4038559794426"
      2) "39.92404192186725"
2) 1) "meishuguan"
   2) "2.8693"
   3) (integer) 4069885710390435
   4) 1) "116.41532510519028"
      2) "39.93123039107514"
3) 1) "dongwuyuan"
   2) "4.4588"
   3) (integer) 4069879836419688
   4) 1) "116.34361892938614"
      2) "39.94763257169722"

Notes

In actual applications, there may be millions of pieces of data. We know that Redis Geo

zset collection. In the Redis cluster environment, the collection may be migrated from one node to another node. If the data of a single key is too large, it will have a greater impact on the migration work of the cluster. In a cluster environment, a single key The corresponding data volume should not exceed 1M, otherwise it will cause lags in cluster migration and affect the normal operation of online services.

So, it is recommended that Geo data be deployed using a separate Redis instance instead of a cluster environment.

If the amount of data exceeds 100 million or even larger, the Geo data needs to be split, split by country, province, city, even in very populous cities. Can be split by zone. This can significantly reduce the size of a single zset set.

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