Slide 24
Slide 24 text
(c) 2020 Mercari, Inc.
Processing Time
24
Serving Kubernetes Cluster
Visual Search
Service
Object Detection
Service
Feature Extraction
Service
Monthly ANN
Service
Monthly ANN
Service
Monthly ANN
Service
Monthly ANN
Service
Monthly ANN
Service
Monthly ANN
Service
Kubernetes
Engine
168ms 62ms
255ms
Assuming that items in the past 1 year are searchable by
the system, the system will have 11 monthly ANN
services, 30 daily ANN services, and 24 hourly services.
(*) The number of the items of each ANN service is different from actual one
Monthly Similarity
Search Service
(30M items)
Daily Similarity
Search Service
(1M items)
Hourly Similarity
Search Service
(100K items)
Monthly Similarity
Search Service
(30M items)
Daily Similarity
Search Service
(1M items)
Hourly Similarity
Search Service
(100K items)
Visual Search
Service
20ms
13ms
12ms
11 Services 30 Services 24 Services
ANN (Similarity Search):
Library: Faiss
Index type: IVFADC (IndexIVFPQ)
Code length per vector: 64B
#cells visited for each query: 32,
#cells: 8,192
In this experiment, 4 CPU cores are allocated for
each service. Practically, resource allocation and
the parameters of ANN should be optimized for
each ANN service based on the number of
items/images for each service.
Docker allows us to allocate resources flexibly,
like 1.5 CPU cores.
Parallelly processed
(*) The actual system architecture is slightly different from this.
362.4M image
feature vectors
Monthly Similarity
Search Service
Daily Similarity
Search Service
Hourly Similarity
Search Service
12ms
13ms
20ms