|
Availability %
|
Downtime per year
|
Downtime per month
|
Downtime per week
|
Downtime per day
|
|
90% ("one
nine")
|
36.5 days
|
72 hours
|
16.8 hours
|
2.4 hours
|
|
95% ("one and a
half nines")
|
18.25 days
|
36 hours
|
8.4 hours
|
1.2 hours
|
|
97%
|
10.96 days
|
21.6 hours
|
5.04 hours
|
43.2 minutes
|
|
98%
|
7.30 days
|
14.4 hours
|
3.36 hours
|
28.8 minutes
|
|
99% ("two
nines")
|
3.65 days
|
7.20 hours
|
1.68 hours
|
14.4 minutes
|
|
99.5% ("two and
a half nines")
|
1.83 days
|
3.60 hours
|
50.4 minutes
|
7.2 minutes
|
|
99.80%
|
17.52 hours
|
86.23 minutes
|
20.16 minutes
|
2.88 minutes
|
|
99.9% ("three
nines")
|
8.76 hours
|
43.8 minutes
|
10.1 minutes
|
1.44 minutes
|
|
99.95% ("three
and a half nines")
|
4.38 hours
|
21.56 minutes
|
5.04 minutes
|
43.2 seconds
|
|
99.99% ("four
nines")
|
52.56 minutes
|
4.38 minutes
|
1.01 minutes
|
8.64 seconds
|
|
99.995% ("four
and a half nines")
|
26.28 minutes
|
2.16 minutes
|
30.24 seconds
|
4.32 seconds
|
|
99.999% ("five
nines")
|
5.26 minutes
|
25.9 seconds
|
6.05 seconds
|
864.3 milliseconds
|
|
99.9999% ("six
nines")
|
31.5 seconds
|
2.59 seconds
|
604.8 milliseconds
|
86.4 milliseconds
|
|
99.99999%
("seven nines")
|
3.15 seconds
|
262.97 milliseconds
|
60.48 milliseconds
|
8.64 milliseconds
|
|
99.999999%
("eight nines")
|
315.569 milliseconds
|
26.297 milliseconds
|
6.048 milliseconds
|
0.864 milliseconds
|
|
99.9999999%
("nine nines")
|
31.5569 milliseconds
|
2.6297 milliseconds
|
0.6048 milliseconds
|
0.0864 milliseconds
|
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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