Exploring Nginx workers load arbitration

May 20, 2019
By

(This article was first published on Тех-Детали, and kindly contributed to R-bloggers)

In the documentation of Haskell module NgxExport.Tool.Aggregate there is a small example of how to establish monitoring of Nginx worker’s load. In a few words, it is possible to set up an internal server that would sit on an arbitrary Nginx worker process and accumulate various data from all the workers. This data could be retrieved via specified interface configured in the Nginx configuration script. In the example, the internal server collects the number of requests and bytes that were sent back to clients from each worker process. This data is accessible in JSON format via a dedicated virtual server listening on port 8020. Say, to retrieve the current load, we can simply use curl and jq (to pretty-print JSON).


curl 'http://127.0.0.1:8020/' | jq
[
"2019-04-22T14:29:04Z",
{
"5910": [
"2019-04-22T14:31:34Z",
{
"bytesSent": 17751,
"requests": 97,
"meanBytesSent": 183
}
],
"5911": [
"2019-04-22T14:31:31Z",
{
"bytesSent": 549,
"requests": 3,
"meanBytesSent": 183
}
]
}
]

Now I want to show how to retrieve this data repeatedly and render it on an interactive dashboard online using R and a Shiny application with plotly.

Below is the annotated code saved in file load_monitoring.r.


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library(shiny)
library(plotly)
library(jsonlite)

ui <- fluidPage(
fluidRow(
headerPanel(h1("Nginx workers load arbitration", align = "center"),
"Nginx workers load arbitration")),
fluidRow(
wellPanel(div(align = "center",
div(style = "display: inline-block; margin-right: 20px",
textInput("i_url", NULL, "http://127.0.0.1:8020/",
width = "200px")), span(),
div(style = "display: inline-block; margin-right: 20px",
radioButtons("rb_mode", NULL,
c("Requests" = "reqs",
"Bytes_sent" = "bsent"),
selected = "reqs", inline = TRUE)),
div(style = "display: inline-block",
actionButton("b_reset", "Reset Traces"))))),
fluidRow(
div(plotlyOutput("plot"), id = "graph"))
)

server <- function(input, output, session) {
values <- reactiveValues()
values$init <- TRUE
values$load <- list("", list())
values$pids_prev <- list()

observe({
invalidateLater(5000, session)

if (class(values$load) != "try-error") {
values$pids_prev <- names(values$load[[2]])
}
values$load <- try(fromJSON(input$i_url))

m <- `if`(input$rb_mode == "reqs", 2, 1)

if (class(values$load) == "try-error" ||
length(values$load[[2]]) == 0) {
invalidateLater(1000, session)
} else {
pids <- names(values$load[[2]])

if (values$init) {
values$init <- FALSE
values$p <- plot_ly(type = "scatter",
mode = "lines",
colors = "YlOrRd")

for (i in 1:length(values$load[[2]])) {
xs <- as.POSIXct(values$load[[2]][[i]][[1]],
format = "%Y-%m-%dT%H:%M:%S")
values$p <- add_trace(values$p,
name = paste(pids[i],
names(values$load[[2]][[i]][[2]][m])),
x = xs,
y = values$load[[2]][[i]][[2]][[m]],
line = list(width = 2)) %>%
add_annotations(
x = xs,
y = values$load[[2]][[i]][[2]][[m]],
text = "",
showarrow = TRUE, arrowcolor = "#bbb")
}
values$p <- layout(values$p, yaxis = list(range = 0))

output$plot <- renderPlotly(values$p)
} else {
vs <- list()
xs <- list()
ts <- list()
len <- length(pids)

if (length(names(values$load[[2]])) !=
length(values$pids_prev) ||
length(setdiff(names(values$load[[2]]),
values$pids_prev)) > 0) {
invalidateLater(1000, session)
values$init <- TRUE
} else {
i <- 1
while (i <= len) {
vs[[i]] <- list(values$load[[2]][[i]][[2]][[m]])
xs[[i]] <- list(values$load[[2]][[i]][[1]])
ts[[i]] <- i
i <- i + 1
}

plotlyProxy("plot", session) %>%
plotlyProxyInvoke("extendTraces",
list(x = xs, y = vs), ts)
}
}
}
}
)

observeEvent(input$b_reset, {
values$init <- TRUE
}
)

observeEvent(input$rb_mode, {
values$init <- TRUE
}
)
}

In lines 1–3 all required libraries are loaded: shiny for UI, plotly for interactive plotting, and jsonlite for reading JSON data. The user interface is built in lines 5–23. It consists of the header (lines 6–8), a panel with control widgets (lines 9–20), and the plot area (lines 21–22).

In line 25–110 a Shiny server that would render data online, is defined. In lines 26–29 a number of reactive values are declared and initialized: they will be used in reactive observer observe defined in lines 31–99. The observer runs every 5 seconds (line 32) retrieving new data from Nginx (line 37) and plotting it on the dashboard in case of success (lines 44–97). If retrieval fails then observe re-runs in 1 second (lines 41–43).

There are two branches of execution on successful data retrieval: initialization of the plot (lines 47–70), and extending traces (lines 71–97). The initialization triggers when the reactive value of values$load is TRUE: in this phase the plotly object values$p gets initialized in calls to plot_ly, add_trace, and add_annotations. The plot type is set to scatter, its mode is lines, and the color palette for traces is YlOrRd (lines 49–51). The traces and annotations are added in a loop (lines 53–67), the number of iterations depends on JSON data saved in the value values$load and corresponds to the number of Nginx worker processes. The annotations are empty string (they have value ): they are only needed for drawing arrows at the beginnings of the traces.

Extending traces comes after the initialization phase, and only if the PIDs of the Nginx worker processes (found in line 45) did not change after the previous data retrieval (it gets checked in lines 77–82). If the PIDs have changed then the plot will be redrawn in 1 second (lines 81–82). A more graceful solution would be adding new traces dynamically, without redrawing of the whole plot, however this would be more challenging for such a simple example, and not very useful taking into account that workers’ PIDs shouldn’t change often in the normal case. So, when the PIDs do not change, the traces get extended with new values (lines 83–95).

In lines 101–109 actions for the Reset button and the Mode radio-button are defined: they simply reset the value of values$init to TRUE in order to redraw the plot.

Let’s start Nginx with this configuration.


nginx -c /path/to/nginx.conf
ps -ef | grep nginx
root 29516 1 0 13:01 ? 00:00:00 nginx: master process nginx -c /path/to/nginx.conf
nobody 29523 29516 1 13:01 ? 00:00:00 nginx: worker process
nobody 29524 29516 1 13:01 ? 00:00:00 nginx: worker process

The PIDs of the worker processes are 29523 and 29524. Later we should see them on the plot.

Now let’s start an R shell and run the server.


source("load_monitoring.r")
shinyApp(ui, server)

Listening on http://127.0.0.1:6678

The application will open in a browser. Now run a number of requests to the Nginx server from a shell.


for i in {1..100} ; do curl 'http://127.0.0.1:8010/' & done
...
for i in {1..100} ; do curl 'http://127.0.0.1:8010/' & done
...

The application in the bowser shall look like on the image below.

The application renders load of the Nginx workers in real-time. The address of the Nginx virtual stats server and types of traces can be altered using control widgets on the grayish panel above the plot.

To leave a comment for the author, please follow the link and comment on their blog: Тех-Детали.

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