Understanding Timestamp Extraction in Hadoop using R

===========================================================

As data analysts and engineers, we often encounter file systems like HDFS (Hadoop Distributed File System) that store large amounts of data. One common task when working with these systems is extracting timestamp information from files. In this article, we will explore different methods for doing so, focusing on the use of R programming language.

Background

In Hadoop, timestamps are stored in a specific format within file metadata, such as the last modified date and time of the file. To extract these timestamps, we can use various commands like hadoop fs -ls or hdfs dfs (Data File System) command. The extracted timestamp is usually represented as a string in the format “YYYY-MM-DD HH:MM:SS” followed by a timezone identifier (e.g., UTC).

Extracting Timestamps using Hadoop Commands

Let’s start with the basic hadoop fs -ls command to extract timestamps from files.

Using `hadoop fs -ls`

hadoop fs -ls /hdfs/data/adhoc//InterfacePublique-Controle-PUB_1EPSE-201808-PR-20190110-183844-indicateurs-PUB_1EPSE/* | awk '{timestamp= $6 " " $7;print timestamp}'

This command lists the files under the specified directory and extracts the sixth and seventh fields ( indices 5 and 6, respectively) which contain the last modified date and time. The awk command formats these timestamps into a human-readable format.

Removing Quotes around `$6$7` in System Function

When using system functions like system() to execute Hadoop commands within R, we need to take care of quotes around variables. In this case, quotes are necessary for the $6$7 expression, but they seem to cause an error when trying to extract the timestamp.

x <- "/hdfs/data/adhoc//InterfacePublique-Controle-PUB_1EPSE-201808-PR-20190110-183844-indicateurs-PUB_1EPSE/*"

system(paste0("hadoop fs -ls ", x, " | awk '{timestamp= $6  $7;print timestamp}' "), intern = TRUE)

This approach may not work as expected due to the way quotes are handled by system().

Extracting Timestamps using R Libraries

A more robust approach involves utilizing dedicated libraries for data manipulation and date/time handling. Two popular libraries in this context are lubridate (for date/time operations) and stringr (for string manipulation).

Using `lubridate` Library

x <- "/hdfs/data/adhoc//InterfacePublique-Controle-PUB_1EPSE-201808-PR-20190110-183844-indicateurs-PUB_1EPSE/*"

library(lubridate)

ymd_hms(
  str_extract(x, "(\\d{8}-\\d{6})")
)

Here, we use lubridate to parse the extracted timestamp string into a proper date/time object using the ymd_hms() function. This is more efficient and accurate than manual manipulation.

Discussion

In summary, extracting timestamps from HDFS files can be achieved through various methods:

Using basic Hadoop commands like hadoop fs -ls.
Employing system functions with quotes around variables.
Leveraging dedicated R libraries such as lubridate and stringr.

While the first method is straightforward, its use within a system function may lead to issues due to quote handling.

The second approach using system functions might seem like an alternative but poses challenges due to how it handles quotes around variable expressions.

Using R libraries provides a powerful solution for extracting timestamps with accuracy. With lubridate, we can parse the extracted timestamp string into a proper date/time object, offering more flexibility and control over our data analysis workflow.

Example Use Cases

Timestamp Filtering: You need to filter your dataset based on specific time ranges.

Load required libraries

library(lubridate) library(stringr)

Extract timestamps from a directory

x <- “/hdfs/data/adhoc//InterfacePublique-Controle-PUB_1EPSE-201808-PR-20190110-183844-indicateurs-PUB_1EPSE/*”

Convert extracted timestamp strings to date/time objects

timestamps <- ymd_hms( str_extract(x, “(\d{8}-\d{6})”) )

Filter timestamps for a specific time range (e.g., between 2019-01-10 and 2019-01-15)

filtered_timestamps <- timestamps %>% filter(ymd >= ymd_hms(“2019-01-10”) & ymd <= ymd_hms(“2019-01-15”))

Perform analysis on filtered timestamps


2.  **Data Analysis**: You want to analyze data aggregated over specific time intervals.
    ```markdown
# Load required libraries

library(lubridate)
library(stringr)

# Extract timestamps from a directory

x <- "/hdfs/data/adhoc//InterfacePublique-Controle-PUB_1EPSE-201808-PR-20190110-183844-indicateurs-PUB_1EPSE/*"

# Convert extracted timestamp strings to date/time objects
timestamps <- ymd_hms(
  str_extract(x, "(\\d{8}-\\d{6})")
)

# Group data by time intervals (e.g., daily)
grouped_data <- timestamps %>%
  group_by(week_of_year) %>%
  summarise(avg_value = mean(value))

# Visualize the grouped data

In conclusion, extracting timestamps from HDFS files using R libraries like lubridate and stringr provides a robust solution with flexibility in handling various data analysis tasks.

Last modified on 2024-03-13