Add pushover notifications, this should be a super basic MVP

vendor/github.com/dgraph-io/ristretto/z/README.md

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+## bbloom: a bitset Bloom filter for go/golang
+===
+
+package implements a fast bloom filter with real 'bitset' and JSONMarshal/JSONUnmarshal to store/reload the Bloom filter. 
+
+NOTE: the package uses unsafe.Pointer to set and read the bits from the bitset. If you're uncomfortable with using the unsafe package, please consider using my bloom filter package at github.com/AndreasBriese/bloom
+
+===
+
+changelog 11/2015: new thread safe methods AddTS(), HasTS(), AddIfNotHasTS() following a suggestion from Srdjan Marinovic (github @a-little-srdjan), who used this to code a bloomfilter cache.  
+
+This bloom filter was developed to strengthen a website-log database and was tested and optimized for this log-entry mask: "2014/%02i/%02i %02i:%02i:%02i /info.html". 
+Nonetheless bbloom should work with any other form of entries. 
+
+~~Hash function is a modified Berkeley DB sdbm hash (to optimize for smaller strings). sdbm  http://www.cse.yorku.ca/~oz/hash.html~~
+
+Found sipHash (SipHash-2-4, a fast short-input PRF created by Jean-Philippe Aumasson and Daniel J. Bernstein.) to be about as fast. sipHash had been ported by Dimtry Chestnyk to Go (github.com/dchest/siphash )
+
+Minimum hashset size is: 512 ([4]uint64; will be set automatically). 
+
+###install
+
+```sh
+go get github.com/AndreasBriese/bbloom
+```
+
+###test
++ change to folder ../bbloom 
++ create wordlist in file "words.txt" (you might use `python permut.py`)
++ run 'go test -bench=.' within the folder
+
+```go
+go test -bench=.
+```
+
+~~If you've installed the GOCONVEY TDD-framework http://goconvey.co/ you can run the tests automatically.~~
+
+using go's testing framework now (have in mind that the op timing is related to 65536 operations of Add, Has, AddIfNotHas respectively)
+
+### usage
+
+after installation add
+
+```go
+import (
+	...
+	"github.com/AndreasBriese/bbloom"
+	...
+	)
+```
+
+at your header. In the program use
+
+```go
+// create a bloom filter for 65536 items and 1 % wrong-positive ratio 
+bf := bbloom.New(float64(1<<16), float64(0.01))
+
+// or 
+// create a bloom filter with 650000 for 65536 items and 7 locs per hash explicitly
+// bf = bbloom.New(float64(650000), float64(7))
+// or
+bf = bbloom.New(650000.0, 7.0)
+
+// add one item
+bf.Add([]byte("butter"))
+
+// Number of elements added is exposed now 
+// Note: ElemNum will not be included in JSON export (for compatability to older version)
+nOfElementsInFilter := bf.ElemNum
+
+// check if item is in the filter
+isIn := bf.Has([]byte("butter"))    // should be true
+isNotIn := bf.Has([]byte("Butter")) // should be false
+
+// 'add only if item is new' to the bloomfilter
+added := bf.AddIfNotHas([]byte("butter"))    // should be false because 'butter' is already in the set
+added = bf.AddIfNotHas([]byte("buTTer"))    // should be true because 'buTTer' is new
+
+// thread safe versions for concurrent use: AddTS, HasTS, AddIfNotHasTS
+// add one item
+bf.AddTS([]byte("peanutbutter"))
+// check if item is in the filter
+isIn = bf.HasTS([]byte("peanutbutter"))    // should be true
+isNotIn = bf.HasTS([]byte("peanutButter")) // should be false
+// 'add only if item is new' to the bloomfilter
+added = bf.AddIfNotHasTS([]byte("butter"))    // should be false because 'peanutbutter' is already in the set
+added = bf.AddIfNotHasTS([]byte("peanutbuTTer"))    // should be true because 'penutbuTTer' is new
+
+// convert to JSON ([]byte) 
+Json := bf.JSONMarshal()
+
+// bloomfilters Mutex is exposed for external un-/locking
+// i.e. mutex lock while doing JSON conversion
+bf.Mtx.Lock()
+Json = bf.JSONMarshal()
+bf.Mtx.Unlock()
+
+// restore a bloom filter from storage 
+bfNew := bbloom.JSONUnmarshal(Json)
+
+isInNew := bfNew.Has([]byte("butter"))    // should be true
+isNotInNew := bfNew.Has([]byte("Butter")) // should be false
+
+```
+
+to work with the bloom filter.
+
+### why 'fast'? 
+
+It's about 3 times faster than William Fitzgeralds bitset bloom filter https://github.com/willf/bloom . And it is about so fast as my []bool set variant for Boom filters (see https://github.com/AndreasBriese/bloom ) but having a 8times smaller memory footprint: 
+
+	
+	Bloom filter (filter size 524288, 7 hashlocs)
+	github.com/AndreasBriese/bbloom 'Add' 65536 items (10 repetitions): 6595800 ns (100 ns/op)
+    github.com/AndreasBriese/bbloom 'Has' 65536 items (10 repetitions): 5986600 ns (91 ns/op)
+	github.com/AndreasBriese/bloom 'Add' 65536 items (10 repetitions): 6304684 ns (96 ns/op)
+	github.com/AndreasBriese/bloom 'Has' 65536 items (10 repetitions): 6568663 ns (100 ns/op)
+	
+	github.com/willf/bloom 'Add' 65536 items (10 repetitions): 24367224 ns (371 ns/op)
+	github.com/willf/bloom 'Test' 65536 items (10 repetitions): 21881142 ns (333 ns/op)
+	github.com/dataence/bloom/standard 'Add' 65536 items (10 repetitions): 23041644 ns (351 ns/op)
+	github.com/dataence/bloom/standard 'Check' 65536 items (10 repetitions): 19153133 ns (292 ns/op)
+	github.com/cabello/bloom 'Add' 65536 items (10 repetitions): 131921507 ns (2012 ns/op)
+	github.com/cabello/bloom 'Contains' 65536 items (10 repetitions): 131108962 ns (2000 ns/op)
+
+(on MBPro15 OSX10.8.5 i7 4Core 2.4Ghz)
+
+
+With 32bit bloom filters (bloom32) using modified sdbm, bloom32 does hashing with only 2 bit shifts, one xor and one substraction per byte. smdb is about as fast as fnv64a but gives less collisions with the dataset (see mask above). bloom.New(float64(10 * 1<<16),float64(7)) populated with 1<<16 random items from the dataset (see above) and tested against the rest results in less than 0.05% collisions.