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Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result = re.search(pattern, text) if result:     print("Pattern found:", result.group()) Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if re.search(pattern, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the

Hadoop HDFS Comics to Understand Quickly

HDFS file system in Hadoop helps to store data supplied as input. Its fault-tolerant nature avoids data loss. About HDFS, the real story of fault-tolerant  given in Comic book for you to understand in less time.

What is HDFS in Hadoop

HDFS is optimized to support high-streaming read performance, and this comes at the expense of random seek performance.
hdfs file system

This means that if an application is reading from HDFS, it should avoid (or at least minimize) the number of seeks. Sequential reads are the preferred way to access HDFS files.

HDFS supports only a limited set of operations on files — writes, deletes, appends, and reads, but not updates. It assumes that the data will be written to the HDFS once, and then read multiple times.

HDFS does not provide a mechanism for local caching of data. The overhead of caching is large enough that data should simply be re-read from the source, which is not a problem for applications that are mostly doing sequential reads of large-sized data files.

Hadoop Vs HDFS Comic Book


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