RedisBloom quick start tutorial
For this quick start tutorial, you need:
- redis-cli with connectivity to a redis database
Trying it out
You can play with it a bit using redis-cli:
Connect to redis.
$ redis-cli -p 12543 127.0.0.1:12543>
Run these commands:
127.0.0.1:12543> BF.ADD bloom kirk 1) (integer) 1 127.0.0.1:12543> BF.ADD bloom redis 1) (integer) 1 127.0.0.1:12543> BF.EXISTS bloom kirk (integer) 1 127.0.0.1:12543> BF.EXISTS bloom redis (integer) 1 127.0.0.1:12543> BF.EXISTS bloom nonexist (integer) 0 127.0.0.1:12543> BF.EXISTS bloom que? (integer) 0 127.0.0.1:12543> 127.0.0.1:12543> BF.MADD bloom elem1 elem2 elem3 1) (integer) 1 2) (integer) 1 3) (integer) 1 127.0.0.1:12543> BF.MEXISTS bloom elem1 elem2 elem3 1) (integer) 1 2) (integer) 1 3) (integer) 1
You can also create a custom Bloom filter. The BF.ADD command creates a new Bloom filter suitable for a small-ish number of items. This consumes less memory but may not be ideal for large filters. In that case:
127.0.0.1:12543> BF.RESERVE largebloom 0.0001 1000000 OK 127.0.0.1:12543> BF.ADD largebloom kirk 1) (integer) 1
Using Cuckoo filters in Redis Enterprise Software
Cuckoo filters can also be used as part of the RedisBloom module. You can play with it using redis-cli:
127.0.0.1:12543> CF.ADD cuckoo redis (integer) 1 127.0.0.1:12543> CF.EXISTS newcuckoo redis (integer) 1 127.0.0.1:12543> CF.EXISTS newcuckoo notpresent (integer) 0 127.0.0.1:12543> CF.DEL newcuckoo redis (integer) 1
Debugging Bloom filters
Finally, I added a BF.DEBUG command, to see exactly how the filter is being utilized:
127.0.0.1:6379> BF.DEBUG test 1) "size:987949" 2) "bytes:239627 bits:1917011 hashes:14 capacity:100000 size:100000 ratio:0.0001" 3) "bytes:551388 bits:4411101 hashes:16 capacity:200000 size:200000 ratio:2.5e-05" 4) "bytes:1319180 bits:10553436 hashes:19 capacity:400000 size:400000 ratio:3.125e-06" 5) "bytes:3215438 bits:25723497 hashes:23 capacity:800000 size:287949 ratio:1.95313e-07"
This outputs the total number of elements as the first result, and then a list of details for each filter in the chain. As you can see, whenever a new filter is added, its capacity grows exponentially and the strictness for errors increases.
Note that this filter chain also uses a total of 5MB. This is still much more space efficient than alternative solutions, since we’re still at about 5 bytes per element, and the uppermost filter is only at about 12% utilization. Had the initial capacity been greater, more space would have been saved and lookups would have been quicker.
You can find more information in: