Parquet Hadoop
Apache Parquet is an open-source columnar storage format that is designed for efficient storage and processing of large datasets in the Hadoop ecosystem. Parquet is particularly well-suited for use with Hadoop because of its columnar storage structure and compression capabilities. Here are some key points about using Parquet with Hadoop:
Columnar Storage: Parquet stores data in a columnar format rather than row-wise. This means that similar data types are stored together in contiguous memory, allowing for efficient compression and improved query performance. This is especially beneficial for analytical workloads where only specific columns need to be read.
Compression: Parquet supports various compression algorithms, such as Snappy, Gzip, and LZO. This can significantly reduce the storage footprint of your data while maintaining high query performance.
Schema Evolution: Parquet files contain a schema definition that describes the structure of the data. This schema evolution feature allows you to add, remove, or modify columns over time without breaking compatibility with existing data.
Predominantly Used with Hive and Spark: Parquet is commonly used in conjunction with Hive and Apache Spark for data processing in the Hadoop ecosystem. Both Hive and Spark have native support for reading and writing Parquet files.
Performance Benefits: Due to its columnar storage and compression, Parquet files are well-suited for analytical and reporting workloads, providing faster query execution compared to row-based formats like CSV or JSON.
Data Types: Parquet supports a wide range of data types, including primitive types (integers, floats, strings, etc.) and complex types (structs, arrays, maps, etc.), making it flexible for various data structures.
Partitioning: Parquet files can be partitioned based on one or more columns, which can significantly improve query performance when filtering data based on those columns.
Parquet-MR: The Parquet-MR project provides Hadoop MapReduce-based utilities for working with Parquet files. It includes tools for reading, writing, and converting data in Parquet format.
To work with Parquet files in Hadoop:
Writing Data to Parquet: You can use Hadoop MapReduce, Hive, or Apache Spark to write data to Parquet files. Each of these tools provides libraries or APIs for creating Parquet files.
Reading Data from Parquet: Similarly, you can use MapReduce, Hive, or Spark to read data from Parquet files. These tools offer native support for reading Parquet files, making it easy to integrate Parquet into your data processing pipelines.
Here’s a simple example of how to write data to a Parquet file using Apache Spark in Python:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ParquetExample").getOrCreate()
# Create a DataFrame
data = [("Alice", 34), ("Bob", 45), ("Charlie", 29)]
columns = ["Name", "Age"]
df = spark.createDataFrame(data, columns)
# Write DataFrame to Parquet file
df.write.parquet("/path/to/parquet/file")
# Stop the Spark session
spark.stop()Hadoop Training Demo Day 1 Video:
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