File formats in Hadoop Tutorial | A Concise Tutorial Just An Hour

Introduction:

Apache Parquet may be a columnar storage format on the market to any element within the Hadoop system, in spite of the information process framework, data model, or programing language. The Parquet file format incorporates many options that support knowledge warehouse-style operations:

Columnar storage layout:A question will examine and perform calculations on all values for a column whereas reading solely atiny low fraction of the information from a knowledge file or table.

Flexible compression choices:Knowledge will be compressed with any of many codecs. totally different knowledge files will be compressed otherwise.

Innovative cryptography schemes:Sequences of identical, similar, or connected knowledge values will be diagrammatical in ways in which save space and memory. The cryptography schemes offer an additional level of area savings on the far side overall compression for every record.

Large file size:The layout of Parquet knowledge files is optimized for queries that method giant volumes of information, with individual files within the multi-megabyte or maybe GB vary.

History:

Why Parquet was developed to be superior to Doug Cutting’s Trevni columnar storage format, the predecessor to Hadoop. Apache Parquet 1.0 was released in July of 2013. Apache Parquet has been an ASF flagship project since April 27, 2015.

Structure of Parquet:

Row groups, a header, and a footer make up a Parquet file. All of the columns in use are represented in each set of grouped rows. Each row cluster stores rows with the same column mapping. This layout is optimal in terms of both query speed and I/O overhead (minimizing the amount of data scanned).

Key options of parquet are:

• It’s cross platform.
• It’s a recognised file format employed by several systems.
• It stores knowledge in an exceedingly column layout.
• It stores data.

Feather vs Parquet

When talking about parquet, the Apache Arrow file format naturally comes up, as does the topic of how it will compare against feather.

Parquet vs RDS Formats

The RDS file format employed by readRDS()/saveRDS() and load()/save(). it’s file format native to R and may solely be scan by R. the most good thing about exploitation RDS is that it will store any R object – environments, lists, and functions.

If we tend to square measure exclusively curious about rectangular knowledge structures, e.g. knowledge frames, then reasons for exploitation RDS files square measure

• The file format has been around for a protracted time and isn’t doubtless to alter. this implies it’s backwards compatible.
• It doesn’t depend upon any external packages; simply base R.

What is Difference Between Parquet and CSV:

• Despite CSV’s popularity and ease of use (Excel, Google Sheets), Parquet has a number of advantages.
• Difference between Parquet and CSV is that the former is column-oriented while the latter is row-oriented. OLTP workloads fare best on row-oriented forms, while analytical workloads fare better on column-oriented formats.
• Costs for column-oriented databases like AWS Redshift Spectrum are based on the total number of bytes read during a query.
• Therefore, dividing and compressing the CSV file before converting it to Parquet reduces expenses and boosts performance.
• With Parquet, users have been able to cut their storage needs for huge datasets by at least a third, as well as significantly improve their scan and deserialization times and, in turn, their total expenses.

Parquet and Snowflake

Snowflake makes it easy to load Parquet, even semi-structured data, and to unload relational Snowflake table data into individual columns in a Parquet file.

The advantages of victimization parquet area unit

• The file size of parquet files area unit slightly smaller. If you wish to check file sizes, make certain you set compression = “gzip” in write_parquet() for a good comparison.
• Parquet files area unit cross platform
• In my experiments, parquet files, as you’d expect, area unit slightly smaller. for a few use cases, a further saving of fifty is also worthwhile. But, as always, it depends on your explicit use cases.

Benefits of Parquet

• Good for storing massive information of any kind (structured information tables, images, videos, documents).
• Saves on cloud cupboard space by victimization extremely economical column-wise compression, and versatile encryption schemes for columns with totally different information varieties.
• Increased information outturn and performance victimization techniques like information skipping, whereby queries that fetch specific column values needn’t browse the whole row of information.

Creation of Parquet:

Many data processing systems support Parquet, a columnar format. The benefits of a columnar storage system are as follows:

The number of IO operations can be kept to a minimum with columnar storage.Because of its columnar layout, columnar storage is ideal for retrieving only the columns you require.Columnar storage provides better summaries and adheres to type-specific encoding, and it uses less space than other storage methods.Spark SQL may be used to read and write parquet files, which automatically preserve the original data’s schema. Parquet files are structured similarly to JSON datasets. Let’s take a look once more at the same employee.parquet file that was dropped into the same folder as spark-shell.Consider the following information to be given: Do not bother transforming the personnel record input data into parquet format. The RDD data is transformed into a Parquet file using the following commands. Put in the employee.json file, which served as the input in the preceding samples.

The parquet file cannot be shared with you at this time. It’s a directory tree, and it can be found right here in your working directory. Apply this command to view the file and directory tree.

Using the following instructions, you can read data from a table, add it to a database, and perform various queries on it.

Creation of Parquet:

Follow this Spark shell starting procedure:

Generate a SQLContext Object

This command will generate a SQLContext.

Processing Text File As Input:The following statement will create an RDD DataFrame from the contents

Save the DataFrame as a Table:

To save the contents of the DataFrame into a table called “employee,” run the following command. Any other SQL statement can be applied after this command.

The employee table is ready. Let us now pass some SQL queries on the table using the method SQLContext.sql().

To run a query on a DataFrame,click the corresponding button.To retrieve all of the employees’ data, run this command. To do so, we use the allrecords variable, which stores all records. Use the show() function to display these records.scala> val allrecords = sqlContext.sql(“SELeCT * FROM employee”)To see the result data of allrecords DataFrame, use the following command.scala> allrecords.show()

Disadvantages

Not Column Based:No column-based approach is perfect, and Parquet is no exception. If you need to read whole records for processing or if you anticipate frequent schema alterations, you should give some thought to whether or not a column-based solution is the best fit for your needs.These are some of the problems that can arise when using column-based solutions:scanning the entire file (Parquet attempts to solve this issue with extensive metadata and clever row groups)

Simplicity of use:Nothing beats being able to import a CSV file into an Excel table.

Time-consuming difficulty in altering underlying data structures:Since keeping track of the physical locations of data during mutability is challenging, columnar forms require more storage than equivalent row formats.Spread out information necessitates extra effort to compile into a unified database.While Parquet shines with massive datasets, a small file containing only a few kilobytes of data will likely not provide any of the aforementioned benefits and may significantly raise disc space requirements in comparison to the CSV alternative.

Benchmarking:Different solutions, such as CSV and Avro, have been used to benchmark Parquet with various data sources.

Conclusion:

Parquet operates well with complex data in large volumes .It is known for its both performant data compression and its ability to handle a wide variety of encoding types.