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Benchmark of R's http framework
microbenchmark:::microbenchmark(system('curl http://127.0.0.1:9123/predict?val=190'))
microbenchmark:::microbenchmark(system('curl http://127.0.0.1:9124/predict?val=190'))
library(xgboost)
library(ElemStatLearn)
x <- as.matrix(spam[, -ncol(spam)])
y <- as.numeric(spam$spam) - 1
m <- xgboost(data = x, label = y, nrounds = 5, objective = 'binary:logistic')
saveRDS(m, file = "model.rds")
suppressPackageStartupMessages(library(rredis))
rredis::redisConnect(host = "localhost", port = 9736)
model <<- readRDS("model.rds")
message("Model loaded")
getdata <- function(id = '1'){
id <- as.character(id)
z <- numeric(57)
d <- as.numeric(unlist(rredis::redisHKeys(id)))
z[d] <- t(as.numeric(rredis::redisHVals(id)))
# rredis::redisClose()
return(as.matrix(t(z)))
}
#* @get /predict
web_console <- function(val){
res <- list()
res$v <- xgboost:::predict.xgb.Booster(object = model, newdata = getdata(val))
res$url <- paste("http://cc.bjt.name/data?v=", round(res$v, 5), "&id=", val, sep = '')
res
}
## 加载需要的扩展包,静默加载
suppressPackageStartupMessages(library(fiery))
suppressPackageStartupMessages(library(utils))
suppressPackageStartupMessages(library(jsonlite))
suppressPackageStartupMessages(library(shiny))
suppressPackageStartupMessages(library(xgboost))
suppressPackageStartupMessages(library(rredis))
app <- Fire$new() # 开启一个fiery实例
app$host <- "127.0.0.1"
app$port <- 9123 # 设置服务 ip 地址和端口号
rredis::redisConnect(host = "localhost", port = 9736)
model <- NULL
## 将预先训练好的模型加载到全局变量中
## 预训练模型通过 saveRDS 函数保存,此处略过
app$on("start", function(server, ...) {
message(sprintf("Running on %s:%s", app$host, app$port))
model <<- readRDS("model.rds")
message("Model loaded")
})
## 开启 request的监听
## 初始化定义 response 的 headers 和 body
app$on('request', function(server, id, request, ...) {
response <- list(
status = 200L,
headers = list('Content-Type'='text/html'),
body = ""
)
## 获取请求的 path,一旦判断为 /predict 则进行预测
path <- get("PATH_INFO", envir = request)
if (grepl("^/predict", path)) {
## 获取 query string,我们期待的结果是 val=##
query <- get("QUERY_STRING", envir = request)
## 解析query, 大概传递的是类似这个:parseQueryString("?foo=1&bar=b%20a%20r")
## 一般在前端需要 encoding,input 解析出来是 list 对象
input <- shiny::parseQueryString(query)
message(sprintf("Input: %s", input$val))
## 声明获取数据的函数
## 这里依旧模拟了从redis缓存取数的逻辑,但并未判断异常情况
## 读者可以在此做未获得数据的异常判断
getdata <- function(id = '1'){
id <- as.character(id)
z <- numeric(57)
d <- as.numeric(unlist(rredis::redisHKeys(id)))
z[d] <- t(as.numeric(rredis::redisHVals(id)))
return(as.matrix(t(z)))
}
## 进入模型预测环节
## 声明返回 res 是一个 list,传递参数为 input$val
res <- list()
res$v <- xgboost:::predict.xgb.Booster(object = model, newdata = getdata(input$val))
## 增加埋点信息
res$url <- paste("http://cc.bjt.name/data?v=", round(res$v, 5), "&id=", input$val, sep = '')
# 返回JSON
response$headers <- list("Content-Type"="application/json")
response$body <- jsonlite::toJSON(res, auto_unbox = TRUE, pretty = TRUE)
}
response
})
app$ignite(showcase=FALSE) # 启动服务
suppressPackageStartupMessages(library(xgboost))
library(plumber)
r <- plumb('plumber_app.R')
p = 9124
r$run(port=p, host='0.0.0.0')
Owner

badbye commented Jun 2, 2017 edited

Test commands:

Rscript model.R    # create a model first
$ redis-server --port 9736    # redis server

$ Rscript test_fiery.R             # fiery server

$ Rscript test_plumber.R      # plumber server

$ Rscript benchmark.R
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