---
title: "R Notebook"
output:
html_document:
df_print: paged
---
```{r include=FALSE}
library(tidyverse)
```
Using replicated KDBTrees for the spatial joins...
[Grid B1](Figures/KDBTree2/B1.png)
[Grid B2](Figures/KDBTree2/B2.png)
[Grid B4](Figures/KDBTree2/B4.png)
```{r echo=FALSE}
fields = c("Timestamp", "Tag", "appId", "Cores", "Executors", "Epsilon", "Mu", "Delta", "Time", "Load")
data_path = "~/Documents/PhD/Research/Scripts/R/R17/nohubKDBTree2.txt"
data = enframe(read_lines(data_path), name = "n", value = "line") %>% select(line) %>%
filter(grepl("PFLOCK", line)) %>% separate(line, into = fields, sep = "\\|") %>%
mutate(Time = as.numeric(Time)) %>%
group_by(Executors, Epsilon) %>% summarise(Time = mean(Time))
head(data)
```
```{r echo=FALSE}
data$Executors = factor(data$Executors, levels = c("3","6","12"))
data$Epsilon = factor(data$Epsilon, levels = c("100.0","110.0"))
p = ggplot(data = data, aes(x = Epsilon, y = Time, fill = Executors)) +
geom_bar(stat="identity", position=position_dodge(width = 0.75), width = 0.7) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="Epsilon(m)", y="Time(s)", title="Execution time after varying Epsilon parameter.")
plot(p)
```
* Dataset: Berlin (x1) with 3 executors, Berlin (x2) with 6 executors and Berlin (x4) with 12 executors.
* 4 cores per executor.
```{r echo=FALSE}
data = enframe(read_lines(data_path), name = "n", value = "line") %>% select(line) %>%
filter(grepl("PFLOCK", line)) %>% separate(line, into = fields, sep = "\\|") %>%
select(appId, Epsilon)
fields2 = c("Timestamp", "Tag", "appId", "Executors", "Cores", "Status", "Duration", "Stage", "Time", "Load", "Interval")
data1 = enframe(read_lines(data_path), name = "n", value = "line") %>% select(line) %>% filter(grepl("\\|FF\\|", line)) %>%
filter(grepl("END", line)) %>% separate(line, into = fields2, sep = "\\|") %>%
mutate(Time = as.numeric(Time))
data2 = data %>% inner_join(data1, by = "appId") %>% select(Epsilon, Executors, Stage, Time, Interval) %>%
filter(grepl("[1-6]\\.", Stage)) %>% filter(Epsilon == "110.0") %>% mutate(Stage = paste0(Interval,"-",str_trim(Stage))) %>%
group_by(Executors, Stage) %>% summarise(Time = mean(Time))
data2$Executors = factor(data2$Executors, levels = c("3","6","12"))
p = ggplot(data = data2, aes(x = Stage, y = Time, fill = Executors)) +
geom_bar(stat="identity", position=position_dodge(width = 0.75), width = 0.7) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="Stages", y="Time(s)", title="Execution time during the Flock Finding.")
plot(p)
```
```{r echo=FALSE}
data = enframe(read_lines(data_path), name = "n", value = "line") %>% select(line) %>%
filter(grepl("PFLOCK", line)) %>% separate(line, into = fields, sep = "\\|") %>%
select(appId, Epsilon)
fields3 = c("Timestamp", "Tag", "appId", "Executors", "Cores", "Status", "Duration", "Stage", "Time", "Load", "Interval")
data1 = enframe(read_lines(data_path), name = "n", value = "line") %>% select(line) %>% filter(grepl("\\|MF\\|", line)) %>%
filter(grepl("END", line)) %>% separate(line, into = fields3, sep = "\\|") %>%
mutate(Time = as.numeric(Time))
data2 = data %>% inner_join(data1, by = "appId") %>% select(Epsilon, Executors, Stage, Time, Interval) %>%
filter(grepl("[A-F]\\.", Stage)) %>% filter(Epsilon == "110.0") %>% mutate(Stage = paste0(Interval,"-",str_trim(Stage))) %>%
group_by(Executors, Stage) %>% summarise(Time = mean(Time))
data2$Executors = factor(data2$Executors, levels = c("3","6","12"))
p = ggplot(data = data2, aes(x = Stage, y = Time, fill = Executors)) +
geom_bar(stat="identity", position=position_dodge(width = 0.75), width = 0.7) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="Stages", y="Time(s)", title="Execution time during the Maximal Disks Finding.")
plot(p)
```