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¿RuTracker muere? Analizamos distribuciones

Cualquier actividad genera datos. Hagas lo que hagas, probablemente tengas en tus manos un almacén de información útil en bruto, o al menos acceso a su fuente.


Hoy el ganador es el que toma decisiones basadas en datos objetivos. Las habilidades del analista son más relevantes que nunca, y la disponibilidad de las herramientas necesarias a la mano le permite estar siempre un paso adelante. Esta es una ayuda en la aparición de este artículo.


¿Tienes tu propio negocio? O tal vez ... aunque, no importa. El proceso de minería de datos es interminable y emocionante. E incluso solo cavando bien en Internet, puede encontrar un campo para la actividad.


Esto es lo que tenemos hoy: una base de datos de distribución XML no oficial para RuTracker.ORG. La base de datos se actualiza cada seis meses y contiene información sobre todas las distribuciones para el historial de la existencia de este rastreador de torrents.


¿Qué puede decirle a los dueños del rutracker? ¿Y los cómplices directos de la piratería en Internet? ¿O un usuario común aficionado al anime, por ejemplo?


¿Entiendes lo que quiero decir?


Descargo de responsabilidad

. open source .


big data.


Pila - R, Clickhouse, Dataiku


Cualquier análisis pasa por varias etapas principales: extracción de datos, su preparación y estudio de datos (visualización). Cada etapa tiene su propia herramienta. Porque la pila de hoy:


  1. R. , Python. dplyr ggplot2. – .
  2. Clickhouse. . : “clickhouse ” “ ”. , . .
  3. Dataiku. , -.

: Dataiku . 3 . .


, , , . dataiku .


Big Data – big problems


xml– 5 . – rutracker.org, (2005 .) 2019 . 15 !


R Studio – ! . , .


, R. Big Data, Clickhouse … , xml–. . .


. Dataiku DSS . – 10 000 . . , . , 200 000 .


, . .


imagen


. : content — json.


content, . – .


recipe — . , . json .


imagen


. , , + dataiku.


recipe, — .


imagen


csv Clickhouse.



Clickhouse 15 rutracker-a.


?


SELECT ROUND(uniq(torrent_id) / 1000000, 2) AS Count_M
FROM rutracker

┌─Count_M─┐
│    1.46 │
└─────────┘
1 rows in set. Elapsed: 0.247 sec. Processed 25.51 million rows, 204.06 MB (103.47 million rows/s., 827.77 MB/s.)

1.5 25 . 0.3 ! .


, , .


SELECT COUNT(*) AS Count
FROM rutracker
WHERE (file_ext = 'epub') OR (file_ext = 'fb2') OR (file_ext = 'mobi')

┌──Count─┐
│ 333654 │
└────────┘
1 rows in set. Elapsed: 0.435 sec. Processed 25.51 million rows, 308.79 MB (58.64 million rows/s., 709.86 MB/s.)

300 — ! , . .


SELECT ROUND(SUM(file_size) / 1000000000, 2) AS Total_size_GB
FROM rutracker
WHERE (file_ext = 'epub') OR (file_ext = 'fb2') OR (file_ext = 'mobi')

┌─Total_size_GB─┐
│        625.75 │
└───────────────┘
1 rows in set. Elapsed: 0.296 sec. Processed 25.51 million rows, 344.32 MB (86.24 million rows/s., 1.16 GB/s.)

– 25 . , ?


R


R. , DBI ( ). Clickhouse.


R
library(DBI) #    , ... Clickhouse
library(dplyr) #   %>%

# 
library(ggplot2) 
library(ggrepel)
library(cowplot)
library(scales)
library(ggrepel)

#   localhost:9000 
connection <- dbConnect(RClickhouse::clickhouse(), host="localhost", port = 9000)

, . dplyr .


? rutracker.org .


R
years_stat <- dbGetQuery(connection,
                       "SELECT
                          round(COUNT(*)/1000000, 2) AS Files,
                          round(uniq(torrent_id)/1000, 2) AS Torrents,
                          toYear(torrent_registred_at) AS Year
                        FROM rutracker
                        GROUP BY Year")

ggplot(years_stat, aes(as.factor(Year), as.double(Files))) +
  geom_bar(stat = 'identity', fill = "darkblue", alpha = 0.8)+

  theme_minimal() +
  labs(title = "     RuTracker", subtitle = "  2005 - 2019\n")+

  theme(axis.text.x = element_text(angle=90, vjust = 0.5),
        axis.text.y = element_text(),

        axis.title.y = element_blank(),
        axis.title.x = element_blank(),

        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line(size = 0.9),
        panel.grid.minor.y = element_line(size = 0.4),

        plot.title = element_text(vjust = 3, hjust = 0, family = "sans", size = 16, color = "#101010", face = "bold"),
        plot.caption = element_text(vjust = 3, hjust = 0, family = "sans", size = 12, color = "#101010", face = "bold"),
        plot.margin = unit(c(1,0.5,1,0.5), "cm"))+

    scale_y_continuous(labels = number_format(accuracy = 1, suffix = " "))

ggplot(years_stat, aes(as.factor(Year), as.integer(Torrents))) +
  geom_bar(stat = 'identity', fill = "#008b8b", alpha = 0.8)+

   theme_minimal() +
   labs(title = "     RuTracker", subtitle = "  2005 - 2019\n", caption = "*  ")+

   theme(axis.text.x = element_text(angle=90, vjust = 0.5),
          axis.text.y = element_text(),

          axis.title.y = element_blank(),
          axis.title.x = element_blank(),

          panel.grid.major.x = element_blank(),
          panel.grid.major.y = element_line(size = 0.9),
          panel.grid.minor.y = element_line(size = 0.4),

          plot.title = element_text(vjust = 3, hjust = 0, family = "sans", size = 16, color = "#101010", face = "bold"),
          plot.caption = element_text(vjust = -3, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),
          plot.margin = unit(c(1,0.5,1,0.5), "cm")) +

     scale_y_continuous(labels = number_format(accuracy = 1, suffix = " "))

imagenimagen


2016 . , 2016 rutracker.org . , .


, . , .


.


R
extention_stat <- dbGetQuery(connection,
       "SELECT toYear(torrent_registred_at) AS Year,
              COUNT(tracker_id)/1000 AS Count,
              ROUND(SUM(file_size)/1000000000000, 2) AS Total_Size_TB,
              file_ext
         FROM rutracker
         GROUP BY Year, file_ext
         ORDER BY Year, Count")

#       
TopExt <- function(x, n) {
  res_tab <- NULL
  # 2005  2006, ..   
  for (i in (3:15)) {
    res_tab <-bind_rows(list(res_tab,
          extention_stat %>% filter(Year == x[i]) %>%
          arrange(desc(Count), desc(Total_Size_TB)) %>%
          head(n)
      ))
  }
  return(res_tab)
}

years_list <- unique(extention_stat$Year)
ext_data <- TopExt(years_list, 5)

ggplot(ext_data, aes(as.factor(Year), as.integer(Count),  fill = file_ext)) +
  geom_bar(stat = "identity",position="dodge2", alpha =0.8, width = 1)+

  theme_minimal() +
  labs(title = "     RuTracker", 
          subtitle = "  2005 - 2019\n", 
          caption = "* -5   ", fill = "") +

   theme(axis.text.x = element_text(angle=90, vjust = 0.5),
          axis.text.y = element_text(),

          axis.title.y = element_blank(),
          axis.title.x = element_blank(),

          panel.grid.major.x = element_blank(),
          panel.grid.major.y = element_line(size = 0.9),
          panel.grid.minor.y = element_line(size = 0.4),

          legend.title = element_text(vjust = 1, hjust = -1, family = "sans", size = 9, color = "#101010", face = "plain"),
          legend.position = "top",

          plot.title = element_text(vjust = 3, hjust = 0, family = "sans", size = 16, color = "#101010", face = "bold"),
          plot.caption = element_text(vjust = -4, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),
          plot.margin = unit(c(1,0.5,1,0.5), "cm")) +

     scale_y_continuous(labels = number_format(accuracy = 0.5, scale = (1/1000), suffix = " "))+guides(fill=guide_legend(nrow=1))

imagen


. . .


rutracker-a. .


R
chapter_stat <- dbGetQuery(connection, 
      "SELECT 
             substring(forum_name, 1, position(forum_name, ' -')) Chapter, 
             uniq(torrent_id) AS Count, 
             ROUND(median(file_size)/1000000, 2) AS Median_Size_MB, 
             ROUND(max(file_size)/1000000000) AS Max_Size_GB, 
             ROUND(SUM(file_size)/1000000000000) AS Total_Size_TB 
        FROM rutracker WHERE Chapter NOT LIKE('\"%') 
        GROUP BY Chapter 
        ORDER BY Count DESC")

  chapter_stat$Count <- as.integer(chapter_stat$Count)

#     
AggChapter2 <- function(Chapter){
  var_ch <- str(Chapter)
  res = NULL
  for(i in (1:22)){
    select_str <-paste0(
    "SELECT 
           toYear(torrent_registred_at) AS Year, 
           substring(forum_name, 1, position(forum_name, ' -')) Chapter, 
           uniq(torrent_id)/1000 AS Count, 
           ROUND(median(file_size)/1000000, 2) AS Median_Size_MB, 
           ROUND(max(file_size)/1000000000,2) AS Max_Size_GB, 
           ROUND(SUM(file_size)/1000000000000,2) AS Total_Size_TB 
      FROM rutracker 
      WHERE Chapter LIKE('", Chapter[i], "%') 
      GROUP BY Year, Chapter 
      ORDER BY Year")
    res <-bind_rows(list(res, dbGetQuery(connection, select_str)))
                  }
  return(res)
}

chapters_data <- AggChapter2(chapter_stat$Chapter)

chapters_data$Chapter <- as.factor(chapters_data$Chapter)
chapters_data$Count <- as.numeric(chapters_data$Count)

chapters_data %>% group_by(Chapter)%>% 

ggplot(mapping = aes(x = reorder(Chapter, Total_Size_TB), y = Total_Size_TB))+
geom_bar(stat = "identity", fill="darkblue", alpha =0.8)+

  theme(panel.grid.major.x = element_line(colour="grey60", linetype="dashed"))+
  xlab('\n') + theme_minimal() +

  labs(title = "C   RuTracker-", 
          subtitle = "  2019\n")+
  theme(axis.text.x = element_text(),
       axis.text.y = element_text(family = "sans", size = 9, color = "#101010", hjust = 1, vjust = 0.5),

       axis.title.y = element_text(vjust = 2.5, hjust = 0, family = "sans", size = 9, color = "grey40", face = "plain"),
       axis.title.x = element_blank(),

       axis.line.x  = element_line(color = "grey60", size = 0.1, linetype = "solid"),

       panel.grid.major.y = element_blank(),
       panel.grid.major.x = element_line(size = 0.7, linetype = "solid"),
       panel.grid.minor.x = element_line(size = 0.4, linetype = "solid"),

       plot.title = element_text(vjust = 3, hjust = 1, family = "sans", size = 16, color = "#101010", face = "bold"),
       plot.subtitle  = element_text(vjust = 2, hjust = 1, family = "sans", size = 12, color = "#101010", face = "plain"),
       plot.caption = element_text(vjust = -3, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),

       plot.margin = unit(c(1,0.5,1,0.5), "cm"))+
   scale_y_continuous(labels = number_format(accuracy = 1, suffix = " "))+
   coord_flip()

imagen


. — — . , . , Apple.


R
chapters_data %>% group_by(Chapter)%>% 

ggplot(mapping = aes(x = reorder(Chapter, Count), y = Count))+
   geom_bar(stat = "identity", fill="#008b8b", alpha =0.8)+

   theme(panel.grid.major.x = element_line(colour="grey60", linetype="dashed"))+
   xlab('') + theme_minimal() +
   labs(title = "    RuTracker-", 
           subtitle = "  2019\n")+
   theme(axis.text.x = element_text(),
       axis.text.y = element_text(family = "sans", size = 9, color = "#101010", hjust = 1, vjust = 0.5),

       axis.title.y = element_text(vjust = 3.5, hjust = 0, family = "sans", size = 9, color = "grey40", face = "plain"),
       axis.title.x = element_blank(),

       axis.line.x  = element_line(color = "grey60", size = 0.1, linetype = "solid"),

       panel.grid.major.y = element_blank(),
       panel.grid.major.x = element_line(size = 0.7, linetype = "solid"),
       panel.grid.minor.x = element_line(size = 0.4, linetype = "solid"),

       plot.title = element_text(vjust = 3, hjust = 1, family = "sans", size = 16, color = "#101010", face = "bold"),
       plot.subtitle  = element_text(vjust = 2, hjust = 1, family = "sans", size = 12, color = "#101010", face = "plain"),
       plot.caption = element_text(vjust = -3, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),

       plot.margin = unit(c(1,0.5,1,0.5), "cm"))+
    scale_y_continuous(limits = c(0, 300), labels = number_format(accuracy = 1, suffix = " "))+
    coord_flip()

imagen


, , : -.
~15 .


R
library("RColorBrewer")
getPalette = colorRampPalette(brewer.pal(19, "Spectral"))

chapters_data %>% #filter(Chapter %in% chapter_stat$Chapter[c(4,6,7,9:20)])%>%
  filter(!Chapter %in% chapter_stat$Chapter[c(16, 21, 22)])%>%
  filter(Year>=2007)%>%

ggplot(mapping = aes(x = Year, y = Count, fill = as.factor(Chapter)))+
   geom_area(alpha =0.8, position = "fill")+

   theme_minimal() +
   labs(title = "   -", 
           subtitle = " ~15 ", fill = "")+
   theme(axis.text.x = element_text(vjust = 0.5),
          axis.text.y = element_blank(),

          axis.title.y = element_blank(),
          axis.title.x = element_blank(),

          panel.grid.major.x = element_blank(),
          panel.grid.major.y = element_line(size = 0.9),
          panel.grid.minor.y = element_line(size = 0.4),

          plot.title = element_text(vjust = 3, hjust = 0, family = "sans", size = 16, color = "#101010", face = "bold"),
          plot.caption = element_text(vjust = -3, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),
          plot.margin = unit(c(1,1,1,1), "cm")) +

     scale_x_continuous(breaks = c(2008, 2010, 2012, 2014, 2016, 2018),expand=c(0,0)) +
     scale_fill_manual(values = getPalette(19))

imagen


- — . — Apple , .
. .


, Rutracker-a. - rutracker.org.


R
unique_torr_per_day <- dbGetQuery(connection, 
          "SELECT toDate(torrent_registred_at) AS date, 
                          uniq(torrent_id) AS count
           FROM rutracker 
           GROUP BY date
           ORDER BY date")

unique_torr_per_day %>% 
ggplot(aes(format(date, "%Y"), format(date, "%j"), fill = as.numeric(count)))+
  geom_tile() +

  theme_minimal() +
  labs(title = "   RuTracker-a", 
          subtitle = " ~15 \n\n", 
          fill = "-   \n")+
      theme(axis.text.x = element_text(vjust = 0.5),
          axis.text.y = element_text(),

          axis.title.y = element_blank(),
          axis.title.x = element_blank(),

          panel.grid.major.y = element_blank(),
          panel.grid.major.x = element_line(size = 0.9),
          panel.grid.minor.x = element_line(size = 0.4),

          legend.title = element_text(vjust = 0.7, hjust = -1, family = "sans", size = 10, color = "#101010", face = "plain"),
          legend.position = c(0.88, 1.30),
          legend.direction = "horizontal",

          plot.title = element_text(vjust = 3, hjust = 0, family = "sans", size = 16, color = "#101010", face = "bold"),
          plot.caption = element_text(vjust = -3, hjust = 1, family = "sans", size = 9, color = "grey60", face = "plain"),
          plot.margin = unit(c(1,1,1,1), "cm"))+ coord_flip(clip = "off") +
          scale_y_discrete(breaks = c(format(as.Date("2007-01-15"), "%j"), 
                                      format(as.Date("2007-02-15"), "%j"), 
                                      format(as.Date("2007-03-15"), "%j"), 
                                      format(as.Date("2007-04-15"), "%j"), 
                                      format(as.Date("2007-05-15"), "%j"), 
                                      format(as.Date("2007-06-15"), "%j"), 
                                      format(as.Date("2007-07-15"), "%j"),
                                      format(as.Date("2007-08-15"), "%j"),
                                      format(as.Date("2007-09-15"), "%j"),
                                      format(as.Date("2007-10-15"), "%j"),
                                      format(as.Date("2007-11-15"), "%j"),
                                      format(as.Date("2007-12-15"), "%j")), 
          labels = c("", "", "", "", "", "","", "", "", "","",""), position = 'right') +
          scale_fill_gradientn(colours = c("#155220", "#c6e48b"))  + 

       annotate(geom = "curve", x = 16.5, y = 119, xend = 13, yend = 135, 
                   curvature = .3, color = "grey15", arrow = arrow(length = unit(2, "mm"))) +
       annotate(geom = "text", x = 16, y = 45, 
label = "     « » \n", 
hjust = "left", vjust = -0.75, color = "grey25") + 

       guides(x.sec = guide_axis_label_trans(~.x)) + 
       annotate("rect", xmin = 11.5, xmax = 12.5, ymin = 1, ymax = 366,
                       alpha = .0, colour = "white", size = 0.1) + 
       geom_segment(aes(x = 11.5, y = 25, xend = 12.5, yend = 25, colour = "segment"), 
                                  show.legend = FALSE)

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2017 . (. GitHub ). 2016 , . .


. . – .
, content , , , 15 .


Dataiku


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: rutracker.org , , — 60. 2009 — 2014 .


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, dataiku — . , , (R, Python), . .


, RuTracker, : , . . , . .


UPD: , recipe dataiku.


Condicionalmente, la receta dada en este artículo se puede dividir en dos partes: preparación de datos para análisis en R y preparación de datos sobre anime para análisis directamente en la plataforma.


Etapa de preparación para el análisis en R

json- .


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. .


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timestamp .


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Etapa de preparación de datos de anime

, , . content — Descr_Data.


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content

regexp , , , . , regexp dataiku .


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